NUREG/CR-6655,Vol. 1 

ORNL/TM-13692/V 1

Sensitivity and Uncertainty

Analyses Applied to 

Criticality Safety 

Validation 

Methods Development 

Oak Ridge National Laboratory 

U.S. Nuclear Regulatory Commission 

Office of Nuclear Regulatory Research 

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NUREG/CR-6655, Vol. 1 

ORNL/TM-13692/V1 

Sensitivity and Uncertainty 

Analyses Applied to 

Criticality Safety 

Validation 

Methods Development 

Manuscript Completed: October 1999 

Date Published: November 1999 

Prepared by 

B.L. Broadhead, C.M. Hopper, R.L. Childs, C.V. Parks 

Oak Ridge National Laboratory 

Managed by Lockheed Martin Energy Research Corporation 

Oak Ridge, TN 37831-6370 

C.W. Nilsen, NRC Project Manager 

Prepared for 

Division of Systems Analysis and Regulatory Effectiveness 

Office of Nuclear Regulatory Research 

U.S. Nuclear Regulatory Commission 

Washington, DC 20555-0001 

NRC Job Code W6479
ABSTRACT 

This report presents the application of sensitivity and uncertainty (S/U) analysis methodologies to the code/data 

validation tasks of a criticality safety computational study. Sensitivity and uncertainty analysis methods were first 

developed for application to fast reactor studies in the 1970s. This work has revitalized and updated the available 

S/U computational capabilities such that they can be used as prototypic modules of the SCALE code system, which 

contains criticality analysis tools currently used by criticality safety practitioners. After complete development, 

simplified tools are expected to be released for general use. 

The S/U methods that are presented in this volume are designed to provide a formal means of establishing the range 

(or area) of applicability for criticality safety data validation studies. The development of parameters that are 

analogous to the standard trending parameters forms the key to the technique. These parameters are the 

D parameters, which represent the differences by group of sensitivity profiles, and the ck parameters, which are the 

correlation coefficients for the calculational uncertainties between systems; each set of parameters gives 

information relative to the similarity between pairs of selected systems, e.g., a critical experiment and a specific 

real-world system (the application). 

The use of a generalized linear-least-squares methodology (GLLSM) tool is also described in this report. The 

application of the GLLSM tool in this work is largely to provide a preliminary understanding of the magnitude of 

the D and ck parameters and the number of experiments needed to rigorously define applicability and properly 

estimate the bias and uncertainty due to data. This work has determined that cý values of 0.80 and higher or D 

values of 0.40 and lower constitute systems that are similar to the extent that they are useful in the determination of 

bias and associated uncertainty for interpolation and extrapolation scenarios. Initial analyses have also shown that 

in order for the bias and associated uncertainty estimates to be meaningful, it is anticipated that about five or more 

very highly correlated systems (ck of 0.90 or higher) or more than about 10 moderately correlated systems (ck of 

0.80 or higher) should be included in the validation exercises. 

These methods and guidelines will be applied to a sample validation for uranium systems with enrichments greater 

than 5 wt % in Volume 2 of this document. This sample validation will compare these newly proposed methods 

with more traditional procedures. A side-by-side comparison of the results and procedures, along with guidance on 

their use, is also presented. 

iii NUREG/CR-6655, Vol. 1
CONTENTS 

A B STR A CT ............................................................ * .............. iii 

LIST OF FIGURES ..................................................................... vii 

LIST OF TABLES ...................................................................... xi 

EXECUTIVE SUMMARY ........................................................... xii 

ACKNOW LEDGMENTS ............................................................... xvii 

1 INTRODUCTION .................................................................. 1 

1.1 Purpose ...................................................................... 1 

1.2 B ackground .................................................................. 1 

1.3 Overview of Approach .......................................................... 1 

2 SENSITIVITY THEORY ............................................................ 5 

2.1 Sensitivity Sequence Development .................................................. 6 

2.2 Demonstration of Application ..................................................... 7 

2.2.1 25U-Fission Cross Section ................................................. 7 

2.2.2 238U-Capture Cross Section ................................................ 14 

2.2.3 Hydrogen Total Cross Section ............................................. 23 

2.2.4 Sum m ary ............................................................. 23 

3 UNCERTAINTY ANALYSIS THEORY ................................................ 31 

4 GENERALIZED LINEAR-LEAST-SQUARES METHODOLOGY ........................... 35 

4.1 Application of GLLSM to Validation .............................................. 37 

4.2 Testing of GLLSM with Low-Enriched U Systems .................................... 37 

4.2.1 Tests with Experimental Data .............................................. 38 

4.2.2 Lessons Learned for Use of Correlation Coefficients ............................. 49 

5 INTEGRAL PARAMETER APPLICATIONS ......................... : .................. 51 

6 SUM M ARY ...................................................................... 65 

7 REFERENCES ................................................................... 67 

APPENDIX A - Covariance Concepts ...................................................... 69 

APPENDIX B - Derivation of the Generalized Linear-Least-Squares Equations ....................... 75 

v NUREG/CR-6655, Vol. 1
LIST OF FIGURES 

Figure Page 

1. Total sensitivity trends with H/X for U(2)F4 and U(5)308 systems ............................... 8 

2. Total sensitivity trends with H/X for U(2)F4 and U(11)02 systems ............................... 9 

3. Comparison of 235U-fission sensitivity profiles for U(2)F4 systems 

with H/X values of 195, 614, and 972 .............. .................................... 10 

4. Comparison of 235U-fission sensitivity profiles for U(2)F4 H/X = 195, 

and U(5) 308 H /X = 147 and 757 ....................................................... 11 

5 Comparison of 235U-fission sensitivity profiles for U(1 1)02 systems 

with H/X values of 0, 3, and 5 ........................................................ 12 

6. Comparison of 235U-fission sensitivity profiles for U(11)02 systems 

with H/X values of 10, 20, and 40 ..................................................... 13 

7. Comparison of 235U-fission sensitivity profiles for U1(11)02 systems 

with HlX values of 80, 200, and 300 .................................................... 15 

8. Comparison of 1 5U-fission sensitivity profiles for U(11)02 systems 

with H/X values of 400, 600, and 1000 .................................................. 16 

9. Comparison of 281U-capture sensitivity profiles for U(2)F4 systems 

with H/X values of 195, 614, and 972 ................................................... 17 

10. Comparison of 238U-capture sensitivity profiles for U(2)F4 H/X = 195, 

and U(5)308 H !X = 147 and 757 ....................................................... 18 

11. Comparison of 238U-capture sensitivity profiles for U(1 1)02 systems 

with HlX values of 0, 3, and 5 ........................................................ 19 

12. Comparison of 238U-capture sensitivity profiles for U(11)02 systems 

with H/X values of 10, 20, and 40 ..................................................... 20 

13. Comparison of 23sU-capture sensitivity profiles for U(1 1)02 systems 

with H/X values of 80, 200, and 300 .................................................... 21 

14. Comparison of 2 8U-capture sensitivity profiles for U1(11)02 systems 

with H/X values of 400, 600, and 1000 ....... ........................................... 22 

15. Comparison of H total cross-section sensitivity profiles for U(2)F4 systems 

with H/X values of 195, 614, and 972 ................................................... 24 

16. Comparison of H total cross-section sensitivity profiles for U(2)F4 H/X = 195, 

and U(5)308 H/X = 147 and 757 ....................................................... 25 

vii NUREG/CR-6655, Vol. 1
LIST OF FIGURES (continued) 

Figure Page 

17. Comparison of H total cross-section sensitivity profiles for U(11)0 2 systems 

with H/X values of 0, 3, and 5 ........................................................ 26 

18. Comparison of H total cross-section sensitivity profiles for U(1 1)02 systems 

with H/X values of 10, 20, and 40 ..................................................... 27 

19. Comparison of H total cross-section sensitivity profiles for U(1 1)02 systems 

with H/X values of 80, 200, and 300 .................................................... 28 

20. Comparison of H total cross-section sensitivity profiles for U(1)0 2 systems 

with HIX values of 400, 600, and 1000 .................................................. 29 

21. Predicted values of (e-a)/c as a function of the number of experiments included 

for 2-wt % UF4 systems (0.4% experimental uncertainty) ............... ..................... 40 

22. Predicted values of (e-a)/c as a function of the number of experiments included 

for 2-wt % UF4 systems (0.1% experimental uncertainty) .................................... 41 

23. Predicted values of (e-a)/c as a function of the number of experiments included 

for 2-wt % UF4 systems (H/X = 294 system omitted from analysis) ............................. 42 

24. Predicted values of (e-a)/c as a function of the number of experiments included 

for 2-wt % UF4 systems. The 2% experiments were added in the following H/X sequence: 

195,406, 496, 614, 294, 972 . ........................................................ 44 

25. Predicted values of (e-a)/c as a function of the number of experiments included 

for 5-wt % U30 8 systems (0.4% experimental uncertainty) ................................... 45 

26. Predicted values of (e-a)/c as a function of the number of experiments included 

for 5-wt % U308 systems (0.1% experimental uncertainty) ................................... 46 

27. Predicted values of (e-a)/c as a function of the number of experiments included 

for 5-wt % U30 8 systems (0.1% experimental uncertainty, H/X = 245 system omitted) .............. 47 

28. Predicted values of (e-a)/c as a function of the number of experiments included 

for 5-wt % U308 systems (0.4% experimental uncertainty) ................................... 48 

29. Dependence of the predicted (e-a)/c values on the inclusion of experiments in various ranges 

of correlation coeffi cients ............................................................ 50 

30. D, profile for U(2)F4 H/X = 195 and U(11)0 2 H/X = 0 systems; Total D, = 1.7986 ................ 53 

31. D, profile for U(2)F4 H/X = 195 and U(l1)0 2 H/X = 200 systems; Total D = 0.1261 .............. 54 

32. D. profile for U(2)F4 HIX = 195 and U(1 1)02 H/X = 1000 systems; Total D, = 0.4271 ........... .. 55

NUREG/CR-6655, Vol. 1
LIST OF FIGURES (continued) 

Figure Page 

33. D,. profile for U(2)F4 H/X = 195 and U(1 1)02 H/X = 0 systems; Total D, = 0.7938 ................. 56 

34. D. profile for U(2)F4 H/X = 195 and U(1 1)02 HI/X = 200 systems; Total D. = 0.3086 ............. 57 

35. D, profile for U(2)F4 H/X = 195 and U(1 1)02 H/X = 1000 systems; Total D, = 0.6201 .............. 58 

36. D, profile for U(2)F4 H/X = 195 and U(1 1)02 H/X = 0 systems; Total D, = 0.7381 ................. 59 

37. D, profile for U(2)F4 H/X = 195 and U(l 1)02 H/X = 200 systems; Total D, = 0.6085 ............... 60 

38. D, profile for U(2)F4 H/X = 195 and U( 1)02 H/X = 1000 systems; Total D, = 0.5198 .............. 61 

39. Comparison of Dn, Ds, D, values with Ck values for benchmark database with respect 

to UI(11)0 2 H/X = 500 system ......................................................... 63 

40. Comparison of Dm values with c, values for benchmark database with respect 

to U(11)0 2 H/X = 500 system ......................................................... 64

ix NUREG/CR-6655, Vol. 1
LIST OF TABLES 

Table Page 

1 Cross-correlation coefficients (due to cross sections) for k. of 2% and 5 % systems ................. 32 

2 Cross-correlation coefficients (due to cross sections) for kf of 11% systems ...................... 33 

3 Comparison of correlation coefficients (due to cross sections) for kff of 2%, 3 %, 5 %, and 11% systems.. 34 

4 Properties of 2-wt % UF4 experiments ................................................... 39 

5 Properties of 5-wt % U308 experiments .................................................. 39 

6 D parameters for H/X = 195 U(2)F4 + CH2 as compared with U(1 1)02 + H20 .................... 52

xi NUREG/CR-6655, Vol. 1
EXECUTIVE SUMMARY 

This report develops the formalisms necessary for the application of sensitivity and uncertainty (S/U) analysis 

methodologies to the code/data validation tasks of a criticality safety study. The specific investigation tasks 

involved the formal determination of the range (or area) of applicability for a set of critical benchmarks and the 

means of using that benchmark set for applications requiring interpolation and extrapolation of that range. 

The validation requirements in ANSI/ANS-8.1-1998 define the area(s) of applicability as follows: the limiting 

ranges of material compositions, geometric arrangements, neutron energy spectra, and other relevant 

parameters (such as heterogeneity, leakage, interaction, absorption, etc.) within which the bias of a calculational 

method is established. 

The standard also notes that the area(s) of applicability of a calculational method may be extended beyond the 

range of experimental conditions over which the bias is established by making use of trends in the bias. Where 

the extension is large, the method should be supplemented by other calculational methods to provide a better 

estimate of the bias, and especially of its uncertainty in the extended area (or areas), and to demonstrate 

consistency of computed results. 

The standard is vague on how to establish areas of applicability. No guidance is given with respect to determining 

what constitutes a valid range or under what conditions the range is breached. The second statement does little to 

clarify the situation because the methods used to extend the areas of applicability are not stated. 

A great deal of judgement is often needed in the current validation techniques in order to establish the area of 

applicability. A number of parameters are currently used in establishing bounds for the application of the 

supporting critical benchmarks. These parameters include hydrogen-to-fissile nuclide ratio (H/X), average energy 

group causing fission (AEG), energy of average lethargy causing fission (EALF) and others. Each of these 

parameters can be useful in establishing possible areas of applicability; however, most systems have multiple 

variables and their simultaneous variation can make a definite determination of applicability difficult. The 

combined variations in H/X, soluble poison concentrations, reflected/unreflected geometry, enrichment, and 

impurity concentrations may be treated poorly by using single- or even multiple-parameter trend curves. Similarly, 

no method is available to determine if there is sufficient coverage for the entire range of the parameter in ill-defined 

trend curves. For example, given systems with H/X values of 200, 500, and 1500: is there coverage of the entire 

range from 200-1500? If experiments with H/X of 0 and 50 are added, is there adequate coverage from 0-1500? 

If two of these systems have soluble poison levels of 200 and 500 ppm, is there sufficient coverage for 0-500 ppm 

levels of poison over the full range of H/X? These are difficult questions, with answers typically derived from 

expert judgement. 

A useful tool in establishing similarities between systems is the use of sensitivity coefficients. Physically sensitivity 

coefficients are defined such that they represent the percentage effect on some response due to a 1% change in an 

input parameter. For fissionable material systems, an appropriate response is the effective neutron multiplication of 

the system (k,), with the current input parameters of interest being the nuclear reaction probabilities or cross 

sections. These sensitivities can be presented either as "total" sensitivities, where the relative cross-section change 

is uniform over all energies, or as a "profile," where the change in k, due to cross sections is given as a function of 

the energy of the cross section. The total sensitivity, while interesting, is not unique and therefore is unable to 

completely characterize similarities between systems. For example, two very different systems could have the same 

total sensitivities because they represent the integral over all energies of the sensitivity profiles. However, it was 

shown in this work that the plot of total sensitivities as a function of a key parameter, like H/X, does give insight 

into the possible ranges of these parameters that could be considered as defining similar systems. For regions of the 

xiii NUREG/CR-6655, Vol. 1
Executive Summary

parameter where the total sensitivities are constant or have a linear slope with respect to the key parameter, the full

sensitivity profiles clearly indicate that the systems are very similar to each other, since the shape and magnitude of 

the sensitivity profiles are very nearly the same. 

The fMll-sensitivity profiles can be generated in the selected problem neutron-energy-group structure for each 

material and may include reaction types (e.g., fission, capture, scatter) or other parameters such as X (fission 

spectrum) or V' (neutrons per fission). For a typical critical system (comprising a few materials, i.e., 235

U, 13U, H, 

0, etc.), some 20 such profiles are usually generated. Sensitivity profiles were generated for six critical systems 

with U(2)F4 fuel and HJX values of 195, 294, 406, 496, 614, and 972; two critical systems of U(5) 30 8 stereotex 

blocks with H/X values of 147 and 757; and 14 hypothetically critical systems of U( 1)0 2 with H/X values of 0, 3, 

5, 10, 20, 40, 80, 200, 300, 400, 500, 600, 800, and 1000. These profiles give considerable information about the 

particular systems; however, the amount of information is too large to be of general use (20 profiles with about 

40 values each, i.e., one for each energy group). Therefore, a method of obtaining the differences between the 

sensitivity profiles for pairs of systems was devised to reduce the amount of needed information to only a few 

parameters, while maintaining the uniqueness of the information present in the full sensitivity profiles. 

Tests of different potential integral parameters derived from the sensitivity profiles were carried out in order to 

determine a norm that would best satisfy the needed similarity determination. The most promising set of parameters 

is a family of "D" value norms as defined below: 

g 

g 

D. S.. - . 

D = Si- Si 

i=1 

where S is the relative sensitivity of kff for the safety application, a, or experimental configuration, e, to V (n) for 

energy group i, or to the capture (c) or to scattering (s) cross sections. 

These coefficients are useful in making a quick determination of the similarity between pairs of systems. Since the 

range of D values is from 0-2, values of D that are greater than 1 are clearly indicative of dissimilar systems. 

Based on a comparison of the respective sensitivity profiles and expert judgement, it appears that values of D that 

are about 0.40 or less indicate similar systems. Additionally, a D, parameter, which consists of a sum of the D., 

D, and D, values, was shown to be a good indicator of similarity if its value is less than 1.2. These D values are 

quite useful because they give an indication of similarity with respect to individual materials and reaction processes. 

Besides determination of D., D,, and D, as an integral system quantity, these values can also be tabulated for each 

material in the problem, (i.e., D, values are tabulated for both 2 5U and 23U for low-enriched systems). As a result, 

the D parameters can be used to imply that certain systems are similar with respect to a given isotope and possibly 

not similar with respect to another isotope. This information can be extremely useful in determining which systems 

should be included in a criticality safety validation, and the range over which those systems can be used for 

validation. 

An alternative approach to exploring the similarity of systems is using uncertainty analyses. This procedure 

involves the propagation of estimated cross-section uncertainty information to the calculated kf value of a given

NUREG/CR-6655, Vol. 1 xiv
Executive Summary

system via the sensitivity coefficients. Mathematically, this is accomplished by a quadratic product of the 

sensitivity profile vectors by material and reaction type with the cross-section uncertainty matrices by material and 

reaction type. The result of this procedure is not only an estimate of the uncertainty in the system k•, for a given 

system, but also an estimate of the correlated uncertainty between systems. These correlated uncertainties can be 

represented by correlation coefficients, which effectively represent the degree of correlation (0 = no correlation, 

1 = full correlation, - 1 = full anticorrelation) in the uncertainties between the two systems. This parameter, 

denoted as ck, has not only the desirability of a single quantity relating the two systems, but the similarity of the 

systems is measured in terms of uncertainty, not just sensitivity. As a result of this work, it appears that a ck value 

of about 0.8 or higher is indicative of similar systems. These correlation coefficients are felt to be particularly 

useful when used in traditional trending analyses for criticality safety validation. When used as a trending 

parameter in these analyses, the correlation coefficient should relate to the degree in which the uncertainties in the 

critical benchmarks are coupled with the uncertainties in the application of interest. This coupling with the 

common uncertainties in the various systems is expected to closely mimic the coupling in predicted biases and 

associated uncertainties between the various systems, since they should both be related to the cross-section 

uncertainties. The underlying assumption in this approach is that the cross-section processing biases and modeling 

approximations are either small or they are identified and included in the analysis. 

The uncertainties in the calculated values of kff for each of the 2-11 wt % systems described above were generated, 

along with their D values and correlation coefficients, the c, values. The standard deviation values for the 

uncertainty in the calculated system kf value range from 0.87 to 1.91%. The highest uncertainties correspond to 

the lowest H/X values due to the fact that a harder spectrum enhances the sensitivity to the higher-energy cross 

sections, which are usually less well known than the thermal values. The correlation coefficients among the 

2-5 wt % systems are all 0.80 or higher (with the exception of two systems with H/X values over 700), indicating 

that most of these systems are similar to each other. Another way of looking at the 0.80 correlation coefficient is 

that 80% of the variance is common to all these systems. Thus, these systems are expected to behave in a very 

similar manner with respect to bias determinations for the SCALE 44-group cross-section library on which these 

results are based. 

By comparing ck values among the 14 systems with 11 wt % U0 2, (using a criterion of 0.8 or greater to indicate 

similar systems) conclusions regarding similar systems are nearly identical to those based on a comparison of 

detailed sensitivity profiles. Comparison of the detailed sensitivity profiles allow visual confirmation of the degree 

of similarity between two systems. The ck results indicate that the H/X of 0 system is only similar to the H/X of 3 

system and then only marginally so (ck is 0.8328). For HIX values between 5 and 1000, similar systems are 

indicated if the H/X values are within a factor of about 5. 

The final issue addressed in this work is an interpretation of the D and c, parameters with respect to how they relate 

to interpolation and extrapolation conditions of a criticality safety validation. The key issues are the magnitudes of 

the ck and D parameters and the number of systems needed for a meaningful estimate of bias and associated 

uncertainty. Once these quantities are determined, a number of options are available for determination of bias and 

associated uncertainty. A standard trending approach can be performed with only those experiments deemed to be 

similar. As mentioned previously, these D and c, parameters can be used in a traditional trending analysis for the 

determination of the predicted bias and associated uncertainty. An alternative approach to the traditional trending 

analysis for the determination of biases and associated uncertainty is the use of the generalized linear-least-squares 

methodology (GLLSM). 

Physically the GLLSM is designed to predict a revised set of data such that differences between the measured and 

calculated values of kff are minimized for the entire set of criticals used in the data validation process. The inputs

XV NUREG/CR-6655, Vol. 1
Executive Summary

needed for such an analysis are almost identical to the concepts presented thus far - the sensitivity coefficients, the 

cross-section uncertainties, the actual calculated and measured ke- values - but with the addition of an estimate of 

the uncertainty in the measured kff value for each critical experiment. Mathematically, the GLLSM represents a 

combination of measurements. These measurements include the experimental values of k. for each critical 

benchmark as well as the calculated value of kff obtained by utilizing evaluated nuclear data from differential cross

section measurements. This calculation is simply the transport analysis determination of the system multiplication 

factor, kff. The identification of a calculated value for kff as an integral "measurement" with uncertainties 

consisting of the evaluated cross-section uncertainty contribution and method uncertainty contributions 

(uncertainties in the transport method determination of kff and data processing methods) is the key to understanding 

the GLLSM approach. The "data changes" that result from the application of the GLLSM can then be used to 

predict (via interpolation or extrapolation) the bias and associated uncertainty for any application determined to be 

relevant to the benchmark area of applicability. 

One of the benefits of the GLLSM approach is that not only can the bias and associated uncertainty for a given 

application be estimated, the cumulative "combination" of critical benchmarks can be used to determine the 

convergence of the procedure. Questions that can be addressed include: how many experiments are needed to 

verify an application, and how much correlation to the application is necessary in order to validate the application 

area? 

This work applied a GLLSM tool to the set of U(2)F4 and U(5)30 8 systems. From an analysis of the results, it 

appears that for correlation coefficients equal to 0.9 or higher, about 5-10 experiments are needed For correlation 

coefficients between 0.80 and 0.89 approximately 10-20 experiments are needed (although these rules of thumb 

were not sufficient for some systems). The primary factor in whether systems with correlation coefficients between 

0.80 and 0.89 produce convergence is believed to be the "completeness" of the experiments (i.e., do the other 

experiments with lower ck values provide validation of the application system for all important cross sections and 

energy ranges?). Further development of the concept of completeness is currently in progress.

NUREG/CR-6655, Vol. 1 xvi
ACKNOWLEDGMENTS 

This work was supported by the U.S. Nuclear Regulatory Commission under Project JCN W6479, "Development 

and Applicability of Criticality Safety Software for Licensing Review." The direction of C. W. Nilsen, the NRC 

project manager, along with D. R. Damon, S. A. Parra and K- J. Hardin, the NRC technical monitors, is 

acknowledged. 

The technical work described in the report has been presented to several audiences and the reports have been 

reviewed by individuals with a wide spectrum of interests and experience. This process has been valuable to the 

authors and appreciation to those willing to provide opinions and feedback is gratefully appreciated. In particular 

the authors would like to recognize the efforts of D. R. Damon and S. A. Parra who provided review comments for 

the NRC. Useful discussions and technical reviews were also received from R. M. Westfall, W. C. Jordan, and 

J. F. Mincey of ORNL. The initial suggestions from J. J. Wagschal of the Hebrew University, Jerusalem, relative to 

presenting the methodology as well as his careful review of the final report were of great benefit to the authors. 

Similarly the comments and suggestions provided by R. L. Murray of N. C. State University, M. L. Williams of 

Louisiana State University, R. E. Wilson of Safe Sites of Colorado, Keyes Niemer of Duke Engineering and 

Services, and F. Trumble of Westinghouse Safety Management Systems helped the authors identify areas where 

improved clarity and explanation were needed.

xvii NUREG/CR-6655, Vol. 1
1 INTRODUCTION 

1.1 Purpose 

The purpose of this report is to present research performed to demonstrate that sensitivity and uncertainty (S/U) 

analysis methodologies can be used to help establish areas of applicability related to the validation of computational 

codes and data for nuclear criticality safety. In addition, rigorous, quantitative methods that can estimate the bias 

and associated uncertainty due to nuclear cross-section data are explored. This estimated bias and uncertainty can 

be utilized in defining adequate margins of subcriticality. 

1.2 Background 

The validation requirements in ANSIIANS-8.1-19981 define the area(s) of applicability as follows: the limiting 

ranges of material compositions, geometric arrangements, neutron energy spectra, and other relevant 

parameters (such as heterogeneity, leakage, interaction, absorption, etc.,) within which the bias of a 

calculational method is established. 

The standard also notes that the area(s) of applicability of a calculational method may be extended beyond the 

range of experimental conditions over which the bias is established by making use of trends in the bias. Where 

the extension is large, the method should be supplemented by other calculational methods to provide a better 

estimate of the bias, and especially of its uncertainty in the extended area (or areas), and to demonstrate 

consistency of computed results. 

The standard is vague on how to establish areas of applicability. No guidance is given with respect to determining 

what constitutes a valid range or under what conditions the range is breached. The second statement does little to 

clarify the situation in that the methods used to extend the areas of applicability are not stated. 

A more formal procedure for defining the area(s) of applicability is needed, so that systems that appear to be 

similar can indeed be shown to be (or not to be) in the same area of applicability. Systems can indeed appear to be 

similar, but in actuality be very different. That is, systems with small-versus-medium concentrations of poison 

material behave very differently, even though they may "look" the same. Another problem can be an 

"interpolation" within ranges that are very broad. It is difficult to determine if the entire range is covered by its 

endpoint conditions without some formal process to verify similarity. 

1.3 Overview of Approach 

The concept of similarity is somewhat vague itself. How similar is similar? It is proposed that a useful gauge of 

system similarity would be sensitivity coefficients. Sensitivity coefficients are defined physically such that they 

represent the percentage effect on some response due to a fractional (e.g., typically a 1%) change in an input 

parameter. For fissionable material systems, one of the appropriate responses is the effective neutron multiplication 

of the system (kg) relative to input parameters of interest (e.g., the nuclear reaction probabilities or cross sections). 

These sensitivities can be presented either as "t6tal" sensitivities, where the cross-section change is uniform over all 

energies, or as a "profile," where the change in kf due to cross sections is given as a function of the energy of the 

cross section. The total sensitivity, while interesting, is not unique and, therefore, is lacking in its ability to 

characterize the similarities between systems. For example, two very different systems could have the same total 

sensitivities since they represent the integral over all energies of the sensitivity profiles. These sensitivity profiles 

can be generated for each material and may include various reaction rates (e.g., scatter, absorption, fission) as well 

as X (fission spectrum) and9 (neutrons per fission). For a typical system (comprising a few materials, i.e., 2

"U,

I NUREG/CR-6655, Vol. 1
238

U, H, 0, etc), some 20 such profiles are usually generated. These profiles give a great deal of information about 

the particular system, because they provide the effect on keg for differential changes in the cross-section information 

or other system parameters. However, the amount of information is too large to be of general use (20 profiles with 

about 40 values each, i.e., one for each energy group). Some type of norm for measuring these differences in 

sensitivities would be useful to reduce the amount of information to a few parameters. The sensitivity theory 

utilized in this work will be given in Section 2. The results of various norms to represent sensitivity differences will 

be discussed in Section 5. 

Another method for exploring the similarity of systems is to use uncertainty analyses. This method involves the 

propagation of estimated cross-section uncertainty information to the calculated kff value of a given system using 

the sensitivity coefficients. Mathematically, this is accomplished by a quadratic product of the sensitivity profile 

vectors by nuclide and reaction type, with the cross-section uncertainty matrices determined by nuclide and reaction 

type. The result of this method is not only an estimate of the uncertainty in the system k, for a given system, but 

the procedure also provides estimates of the correlated uncertainty between systems. These correlated uncertainties 

can be represented by correlation coefficients, which effectively represent the degree of correlation (0 = no 

correlation, 1 = full correlation, - 1 = full anticorrelation) in the uncertainties between the two systems. This 

parameter is desirable, not only because it provides a single quantity relating the two systems, but because the 

similarities of the systems are measured in terms of uncertainty, not just sensitivity. Therefore, two systems with 

identical sensitivities with respect to a well-known nuclide of medium importance could be determined to be 

dissimilar if they have differing sensitivities to a nuclide of high importance, and with large uncertainties. 

Similarly, two systems that may appear different, could be deemed similar if they have similar large sensitivities to 

a nuclide with large uncertainties, but very different sensitivities to a well-known nuclide. These correlation 

coefficients are felt to be particularly useful when used in traditional trending analyses for criticality safety data 

validation. When used as a parameter in the trending analyses, the correlation coefficient should relate to the 

degree in which the uncertainties in the critical benchmarks are coupled to the uncertainties in the application of 

interest. This coupling to the common uncertainties in the various systems is expected to very closely mimic the 

coupling in predicted bias and associated uncertainty between the various systems, since they should both be related 

to the cross-section uncertainties. Details of the uncertainty analysis theory are presented in Section 3. 

The use of the previously described sensitivity and uncertainty analysis techniques are intended to help provide a 

formal and rigorous approach to the determination of area(s) of applicability for criticality safety validation per 

ANSI/ANS-8.1. As mentioned earlier, the correlation coefficient can be used as the parameter in a traditional 

trending analysis for the determination of expected bias and associated uncertainty for specific applications in 

criticality safety. An alternative approach to the traditional trending analysis for the determination of bias and 

uncertainty is the use of the so-called generalized linear-least-squares methodology (GLLSM). Physically the 

GLLSM is designed to predict adjustments in the underlying data such that differences among the measured and 

calculated values of kff are minimized for the entire set of criticals used in the data validation process. The inputs 

needed for such an analysis are almost identical to the concepts presented thus far - the sensitivity coefficients, the 

cross-section uncertainties, the actual calculated and measured kf values - but with the addition of an estimate of 

the uncertainty in the measured kf values. Mathematically, the GLLSM represents a combination of 

measurements. These measurements include the experimental values of ke for each critical benchmark and the 

calculated value of kff obtained by utilizing evaluated nuclear data determined from differential cross-sections 

measurements. This calculation is simply the transport analysis determination of k, The identification of a 

calculated value of k. as an integral "measurement" with uncertainties consisting of the evaluated cross-section 

uncertainty contribution and method uncertainty contributions (uncertainties in the transport method determination 

of kf and data processing methods) is the key to understanding the GLLSM approach. The "data changes" that 

result from the application of the GLLSM can then be used to predict the bias and associated uncertainty for any

NUREG/CR-6655, Vol. 1

Introduction Section 1

2
similar application where the area of application corresponds to an interpolation or extrapolation scenario. The 

GLLSM approach is described in detail in Section 4. 

One of the benefits of the GLLSM approach is that not only can the bias and associated uncertainty for a given 

application be estimated, the cumulative "combination" of critical benchmarks can be used to determine the 

convergence of the procedure. For example, questions that need to be addressed include: how many experiments 

are needed to validate computations for a safety application, and how much correlation to the application is 

necessary in order to validate the area of applicability? A separate question is: can the validation be performed 

collectively with a set of benchmarks, none of which are considered explicitly to be in the area of applicability for 

the application? The last subject is especially provocative in that experiments that are not even critical experiments 

could be used to validate a criticality safety application. The first two topics will be discussed in this report, while 

the last is the topic of ongoing research. 

Currently, these techniques are felt to be valuable in many areas where the set of available, "relevant" experiments 

seems to be limited with respect to current applications of interest. These areas include, but are not limited to, the 

extension of criticality data validation for U0 2 fuel enrichments greater than 5 wt %, low-moderation applications 

where H/X values approach zero, mixed-oxide systems with the introduction of weapons-grade plutonium, 

incorporation of burnup credit (use of spent fuel nuclides), and fissile material waste applications where the matrix 

includes nuclides not available in the type and quantity found in critical experiments.

NUREG/CR-6655, Vol. 1

Section 1 Introduction

3
2 SENSITIVITY THEORY 

The techniques used in this work to generate sensitivity information for the various critical benchmarks are based 

on the familiar perturbation theory of reactor analysis. Textbooks provide a progression from a one-group 

neutron diffusion model2 to a two-group model3 to a multigroup transport version. Research papers' examine 

the higher-order perturbation effects and show relationships between various formulations. From those 

generalized approaches, we extract the specific theory for the generation of kff sensitivities. For a full derivation 

of the general sensitivity equations, the reader is referred to Refs. 9 and 10. 

Considering the Boltzmann transport equation written in the form: 

[A - XB] =0 (1) 

whereX = 1/ke,, and a perturbed system 

[A' - X'B']4b' = 0, (2) 

the adjoint equation to Eq. (1) is 

[A* - XB]* =0. (3) 

Multiplying Eq. (2) by 4)*, and integrating over all phase space (indicated by the bracket notation < >), yields 

K(4' (A' - V'B')(4') = 0. (4) 

Defining: 

A' = A + dA 

B' = B + dB (5) 

'= X + dX 

and inserting in Eq. (4) leads to 

(4ý- (dA - XdB - BdX - dXdB)4ý') = 0. (6) 

Ignoring second-order terms (dXdB), assuming use of the unperturbed flux, and solving for the reactivity 

perturbation yields 

dX/X = (*(dA - XdB))/(ý * (XB),ý) . (7) 

Hence, the sensitivity of X, with respect to the multigroup reaction x cross section, _, becomes 

dVX Ex (q*(dA/dEx-_XdS/dEx)() 

dT,/Ex X ((q * B(ý)(8

5 NUREG/CR-6655, Vol. 1
Sensitivity Theory

Note that since X = llkf, then d&/A = - dkjc/kf such that the above equation is essentially the defining equation for 

the kf relative sensitivity, S•, where 

S.= dlkeff-kff -dX/p. d,•XIY-x dEx/Ex 

In practice, the dA and dB terms in Eq. (8) are simple functions of the scattering, capture, fission cross sections 

represented symbolically as Z. The evaluation of Eq. (8) then becomes an integration of the forward and adjoint 

fluxes and the cross sections over the entire system. 

Typically, the energy dependence of the cross sections is represented by averaging the 1 quantities over 

an energy group i, represented as I.nsertion of these group quantities into Eq. (8) yields the definition of a 

tdkekexý sensitivity profile, S_ - , where group index i is varied to obtain the sensitivity for all groups. " d rXex, i 

2.1 Sensitivity Sequence Development 

The kff sensitivity calculation, as described above, has previously been implemented in the FORSS 10 (Fantastic Oak 

Ridge Sensitivity System) package. The FORSS system was developed in the late 1970s primarily for use in the 

development of fast reactor systems. The FORSS system makes use of the evaluated nuclear data files (ENDF/B), 

covariance information, and results of integral experiments. Sensitivity formulas for multigroup discrete ordinates 

were developed and applied successfully to Argonne ZPR critical assemblies.9 Extensive tests were made to assure 

internal consistency and achieve precision in sensitivity profiles. A version of the system is available from the 

Radiation Shielding Information and Computational Center (RSICC) as CCC-334. The FORSS system package 

placed into RSICC, while operational, is not complete (e.g., thermal upscatter is not operational). More complete 

versions of FORSS, while internally available in the 1980s at ORNL, were not maintained and documented as fast 

reactor funding dwindled. 

At the beginning of this project, it was decided that the most appropriate procedure was to start with the RSICC 

version of FORSS and reactivate and enhance the individual modules with the goal of putting portions of the 

original system into the SCALE" system. The "SCALE philosophy" is to include standard well-known computer 

codes into an application-specific computational sequence with a single integrated input file. Using this philosophy 

as a guide, a one-dimensional (1-D) sensitivity sequence, SEN1,1'2 was produced for use in this project and for 

subsequent release in future versions of SCALE. This sequence performs standard resonance processing tasks 

(BONAMI and NITAWL modules), then determines, using 1-D transport theory, the forward and adjoint angular 

fluxes needed for sensitivity coefficient generation. The XSDRNPM radiation transport code within the SCALE 

system is used to calculate the forward and adjoint angular fluxes. The sequence then calls modules VIPlD, 

LAKE, and PLOT, which computes the sensitivity coefficients, estimates the uncertainty in the system ke value, 

and plots the sensitivity profiles, respectively. The user input to SEN1 is very similar to the user input of the 

SCALE shielding module, SAS 1, except that since kff sensitivities do not require a source input, a fixed source is 

not required. 

A prototypic sequence to generate two-dimensional (2-D) sensitivities has also been developed for use in the current 

work This capability is not as fully integrated into the SCALE system as the 1-D sensitivity module. The 2-D 

sensitivity package is based on 2-D discrete-ordinates code DORT,`3 which is not contained in the SCALE system. 

The documentation of the 2-D sensitivity module is included in the SEN1 documentation."2

NUREG/CR-6655, Vol. 1

Section 2

6
Sensitivity Theory

The feasibility of generating three-dimensional (3-D) sensitivity coefficients, using Monte Carlo methods, was 

investigated as a separate task under this project. The results of this work have been documented.14 This 

development of 3-D sensitivity methods is continuing. 

2.2 Demonstration of Application 

2.2.1 235

U-Fission Cross Section 

Sensitivity analyses can be excellent tools for understanding the underlying characteristics of systems. As an 

example of the information that can be gleaned from sensitivity plots, eight systems were analyzed using the 1-D 

and 2-D sensitivity tools. These systems include six systems'5 with U(2)F4 fuel and H/X values of 195, 294, 406, 

496, 614, and 972; and two systems"6 of U(5) 30, stereotex blocks with H/X values of 147 and 757. Total 

sensitivity trends of kff for each of these systems to the "U fission, 23'U capture, and H total cross sections are 

plotted versus H/X in Figure 1. This curve gives a visual representation of the similarities between the various 

systems. It is clear from these curves that all of these system are "similar" with respect to ' 5U-fission cross 

sections because the magnitude and slope of the sensitivities remain nearly constant over a large range of H/X 

values. It is less apparent that the systems are similar with respect to "U capture and H total cross sections, 

because the respective curves are further apart. However, the slopes of the curves are very nearly the same and 

constant and, therefore, provide an additional indicator that the systems are similar. To illustrate this point, 

Figure 2 gives plots of total sensitivity trends for U(2)F4 versus U(1 1)02 systems. The sensitivity trends for the 11

wt % UO2 systems look very similar to those of the 2-wt % UF 4 systems above an H/X of 200. The UF 4 curves are 

given for actual systems that had H/X values in the range 200 to 1000. The U(1 1)02 systems are "artificial" 

systems, that is to say that no such measurements exist, and they were generated for calculational comparison 

purposes. Each of these systems is a critical bare sphere with H/X values corresponding to 0, 3, 5, 10, 20, 40, 80, 

500, 600, 800, and 1000. The curves below an H/X of 200 exhibit large changes in slope along with maxima and 

minima. Clearly the curves indicate that the systems are changing rapidly at low values of HIX. Although the total 

sensitivity of kffto the '-5U fission cross section (e.g., for H/X = 1000 and for H/X = 3) can be almost equal it does 

not always mean that the systems are similar. These curves are useful for determining trends within a family of 

systems; however, by themselves they should not be used as a formal basis for establishing similar systems. 

In order to understand the details behind the total sensitivity trends shown in the previous two plots, it is necessary 

to look at the individual sensitivity profiles. Note that Figures 3-20 contain plots of the relative sensitivity per unit 

lethargy. The relative sensitivities are the energy-dependent quantities, S defined in Section 2. To smooth out the 

effects of variable energy group widths, these quantities have been divided by the group lethargy width, defined as 

4ni/E-0) - ýn(Ei+ I/Eo) , where E0 = 107 eV. This renormalization is not expected to alter the conclusions of this 

section since the procedure only smooths the plots. Shown in Figure 3 are the sensitivity profiles of three selected 

systems (i.e., U(2)F4 with H/X of 195, 614, and 972) to the "U fission cross section. Even though there are slight 

spectrum variations in the three systems, it is clear that all three are most sensitive to the '-U thermal (i.e., 0.02 to 

0.1 eV) fission cross section, only to differing magnitudes. Figure 4 gives a similar comparison for systems of 

different compositions and different enrichments, namely to U(2)F4 with H/X of 195, and U(5) 30 8 with H/X of 147 

and 757. These plots are interesting in that they show the near equivalence with respect to '5U fission between 2 

and 5 wt % systems. For H/X values within 30 to 50% of each other, the sensitivity plots are nearly identical. 

Looking further at the sensitivity information for the 11 wt % systems, the differences between the H/X values of 0, 

3, and 5 are clearly seen in Figure 5. The 23U fission cross section of importance is very different for the system 

with H/X = 0. However, the systems with H/X values of 3 and 5 appear quite similar. In Figure 6, the 25U fission 

sensitivities are given for systems with H/X values of 10, 20, and 40. It appears from these plots that

7NUREG/CR-6655, Vol. 1

Section 2

7
cdp z CD 

STotal &II Sensitivity Trends With H/X U(2)F4 and U(5)308 Results 

0.6 

0.4 -- _ _". 

0.2 "- -__ -__ -_-_---:- --

0.2 

C(0 

00 40_-0.2_ MU 

-0.4__ - --------- _ 

-0.6

0 200 400 600 800 1000 

H/X values 

- U-235 Fis (2) -4- U-238 Capt (2) -- H Total (2) 

-9 U-235 Fis (5) -X U-238 Capt (5) -- H Total (5)

Figure 1 Total sensitivity trends with HIX for U(2)F4 and U(5) 308 systems
Total Sensitivity Trends With H/X 

.U(2)F4 and U(11)02 Results

0 200 400 600 800 1000 

H/X values

-- U-235 Fis (2) 

E U-235 Fis (11)

-4- U-238 Capt (2) 

X U-238 Capt (11)

-*-- H Total (2) 

• H Total (11)

Figure 2 Total sensitivity trends with H/X for U(2)F4 and U(1 1)02systems

.>_ 

0) 

Cl) 

I--

-0.6

CN 

0\q

1200
u(2)f4 h/x=195 u(2)f4 h/x=614

..... 1 I'll I I I I .

u(2)f4 h/x=972

.II . .. . .. .Il111 . 1 '.. .I IlIl I I I 1 11 1 M I I I11111l'"

I I I I 11111 I I I I liii I

07\ 

LA

-5 -4 -3 -2 -1 0

10-5 10-4 10-3 10-2 10- 100 101 

Energy

102 

(eV)

103 104 105 106 10

Figure 3 Comparison of 235

U-fission sensitivity profiles for U(2)F4 systems with H/X values of 195, 614, and 972

if" ir' 

-j-I 

IL . . 

I-,_ .I-I~~ 

i.: 

I ...... ~ I..l.... .. I .. I i i *i IaIII I I111 I111 111 I[

0 

0

ctu

I0 

0 

0

ri

0 (

-

4 --j 

0) 

:

c 

CL 

0) 

U) 

c 

Q) 

L_

0

0 

X%-"

Ci2 

t'J

_ I I I IIIIII I I l itllii

------------------

I llll!l I I I I IIIII I I I IIIlll I I I IIIIII D I I IIIIII I I I llllll . . . . ..... . . . ...... . . . ...... .
u(2)f4 h/x=195

0 0 I 11111 P IIPIIU

u(5).3o8 h/x=147 u(5)3o8 h/x=757

..... ..... I I . .... I..... . . . ..... I 1

Symbols for positive values 

---------------------. U(2)F4 H/X=195 

U(5)308 H/X=147 

U(5)308 H/X=757

-- - - -l-li I - - - iu- I I

I ........ I

10 10 10 10 10 10 10 10 10 10 10 10 10 

Energy (eV) 

Figure 4 Comparison of 235U-fission sensitivity profiles for U(2)F4 H/X = 195, and U(5) 308 HIX = 147 and 757

ri-L "" 

I I. 

r L 

LL ii I . 

I I Ik -" <- - IL ,. ,-I L 

hr~ i i - L_, 

L ___I

I -

tQ

0 

C%4 

0 

0

>. 

0 

L- 4

-J 

>3 

0) 

ai 

E~. 

U) 

cD 

U)

0

0) 

(-'

I I DIlll I

S...... I ........ I
u(1 1)o2 h/x=0

I I 1111111

L 

J 

r 

L ;II ~ ~ j Lj 1~ $

10 - 5 10- 4 10- 3 10- 2 10- 1 10 0 10 1 10 2 10 3 10 4 10 5 

Energy (eV)

.-M i i W ;

(n 

CD

>1 

CY) 

0 

c

4-J 

c: 

4-j 

c: 

Q) 

V)

u(1 I )o2 h/x=3 h/x=5

0 0 

TI I i ! MI

0 T

0 

0

r 

r 

d

0 

T10 6 10 7

Figure 5 Comparison of "5U-fission sensitivity profiles for UO 1)02 Systems with H/X values of 0, 3, and 5

u(I I )o2
u(1 1)o2 h/x= 10 u(11)o2 h/x=20 u(11)o2 h/x=40

. . . . . . . . . .. . . . I .I I. . .. . I . I I . 222l 1 1 1111 1 11 1 1 1 1 I 11 I M I - I IIIII I I IIII1II I 11111 I I 111111 1 I I 111111

r L - [ ---- ,I \I-- -,-•' I- -- . . . 

r '* 'ý 

L ---- ,-- ', 

-- L"j -- -

r ' 

I r. i i L . . 

r- - I l| rI I l II I l-• 

I I 1- . . .I I• . . 

I ' i' I I 

' ... - " . .. ' ' 

I 'I 

---- J1I

I I -. ° L--.

I I .. I....I........ I......... I......... I........ I 1.1,1.1 *IIIIIVII I I 1111111 -

I I IIIiii l I i IIIIII I Ilil11 l I I ! I I I IIIIIll I II Iif I I I I III VIII II III 1 II 1 1 1 1 1 1 1 1 11 1 1 1 11 ] 1 1 1 II10- 510-4 10- 310- 210-1 10° 101 102 103 104 

Energy (eV)

Figure 6 Comparison of 235U-fission sensitivity profiles for U(1 1)02 systems with H/X values of 10, 20, and 40

0 

0

Co

c

Q) 

>1, 

4-j 

(I)

0 

0 

0

0

:z 

c7

CIO 100 100 10, r= •,

1 "1 1.11 1 . 1' . 1. 1 . '1.. .I . . . . 1 . . . .I 1.. . . . .
Sensitivity Theory

none of these systems is similar to the other. Systems with an H/X of 80, 200, and 300, shown in Figure 7, and 

systems with H/X of 400, 600, and 1000, shown in Figure 8, have ' 5U fission cross-section sensitivities that are 

very similar to each other. This region corresponds to the approximately linear region in the sensitivity trend plots 

shown in Figure 2. Thus, these results confirm that for linear portions of the trend plots, the systems are indeed 

very similar with respect to that material. The underlying physical phenomenon appears to be the similarity in the 

system spectra. Thus far, only the detailed sensitivity plots for ' 5U-fission cross sections have been discussed. 

The behavior of the same systems with respect to the "

8U-capture cross-section sensitivities is discussed below. 

2.2.2 238

U-Capture Cross Section 

A review of the detailed 238

U-capture total sensitivity trend plots for the U(2)F4 systems shown in Figure 2 is useful 

for understanding the following discussion. The region from an HIX of 200 to 400 appears to be slightly non-linear 

with respect to the remaining portions of the trend plot. Observation of the full sensitivity profiles in Figure 9 for 

the U(2)F4 systems with H/X values of 195, 614, and 972 indicates that in general the 238U capture cross sections of 

most importance are the resonance region, especially at the lowest energy resonance of 6.67 eV and the thermal 

energy range of 0.02 to 0.1 eV. However, for the system with H/X = 195 the resonance cross sections just above 

the 6.67 eV resonance are also marginally important. This region, from 10 to 100 eV, is represented in the SCALE 

44-group structure as two groups, when in actuality-it consists of a number of resonances. The influence of these 

resonances appears to be the cause of the slight nonlinear response seen in the sensitivity trend curves for 23'U 

capture in systems with H/X values between 200 and 400. The decreased nonlinearity for the 11-wt % cases is 

discussed later. 

In Figure 10, a comparison is made of the "

8U-capture sensitivity profiles for a U(2)F4 system with H/X = 195 

versus two U(5) 30 8 systems with H/X values of 147 and 757. Here the 238U-capture sensitivities are not as closely 

related as those for 235 U fission. However, it is still clear that all systems are still most sensitive to the 238

U-capture 

cross section in the same energy ranges, 0.02 to 0.1 eV and the 6.67 eV resonance. 

The 238U-capture sensitivity profiles are presented in Figure 11 for the U(1 1)02 systems with H/X values of 0, 3, 

and 5 for comparison purposes. The system with H/X of 0 is very "fast" with the highest sensitivity to the 

238U-capture cross section being in the 10 to 100 keV range. Interestingly, the sensitivity profiles for the two 

U(l 1)02 systems with H/X of 3 and 5 are "nearly identical." This situation is also true in Figure 12, where the 

sensitivities of kf to '8U capture for U(l 1)02 systems with H/X values of 10, 20, and 40 are shown. Comparison 

of Figures 12 and 6 indicates that for `

5U fission, the region of H/X values between 10 and 40 is a transition 

region, while a lesser effect is seen for '8U capture. This seeming contradiction is explained by noting that the '8U 

thermal capture cross section is much smaller than the 2 5

U thermal fission cross section, negating the importance of 

23'U thermal capture and making the transition much less severe. This observation is confirmed by noting the shape 

of the 238

U-capture total sensitivity trend curve in Figure 2, where the sensitivity changes very little through this 

transition region. 

The comparison of the sensitivity profiles for 238U capture for U(1 1)02 systems with H/X values of 80, 200 and 

300 (Figure 13), and 400, 600, and 1000 (Figure 14) indicate again the similarities of these respective systems. 

Again noting the total sensitivity trends for `U capture seen in Figure 2, the nonlinear shape for the U(2)F4 system 

is not as severe as that in the U(11)0 2 systems for H/X>200. Careful study of the detailed sensitivity profiles 

shows that for the low-enrichment (2-wt %) systems, the thermal (0.02-0.1 eV) '8U-capture cross sections appear 

to be competing with those in the resonance range (10-100 eV). For the 11-wt % systems, the increased amount of 

23 5U combined with the corresponding decreased amounts of 238U causes the 28U capture to become less important 

in the thermal range.

NUREG/CR-6655, Vol. 1

Section 2

14
u(11)o2 h/x=80 u(11)o2 h/x=200 u(11)o2 h/x=300

- I 1"11"1 I I I I I I T I 1 "T 111111 1 1 1111111 I 1 1"111111 1 I " I '"I 1111 1""" [ I I III I I

Symbols for positive values ---.--.----.-.-------. U(11)02 H/X=80 

U(11)02 H/X=200 

U(11)02H/X=300

r~ 

I III 

, r----,.j, 

-I L1 .... 

o-- I-I lI.-- --- i J° 

, I ' r ,rI 

- I • ti i 

I ,'f l... I- L 

" I .. . .

0 

(D 

0 

0 

0

10-5 10-4 10-3 10-2 10-1 100 101 

Energy

10,.I 2 ,,,,.,I 0 3 1 I 1, 4 ,,,,,,, 10 5 1 ,I 6 ,10 

102 103 104 105 106 10/

(eV)

Figure 7 Comparison of 235

U-fission sensitivity profiles for U(1 1)02 systems with H/X values of 80, 200, and 300

0 0

toi

L 

0

U

U) 

>.,

- SI I I 1111 I I I 11111 I IiI 11111 I I I 11111 I I 1111111

z 

c) 

0\ 

0

(tl Cb .111 . . I I IMI I I 1 111111 1 1 1111111 1 1 1111111 1 1 1 111111 T I !

-- ---- - - - - -
u(11)o2 h/x=400 u(11)o2 h/x=600 u(11)o2 h/x=1000

z 

C 

I-.

I I

I""l111111 ' "I ' 1 1"I 1 1111 l 1 I I ..... I 

Symbols for positive values 

-...-.-.-------------. U(11)02 H/X=400 

U(11)02 H/X=600 

I I(t1i'NC9 bI-/Y-IAAA

0 

0 

0 

0 

0 

I10-5 10 -410- 10-2 10-1

S -I IIIII I I HI MI I f I l I I0 I I I ll Ifi I I I I lll I I 1 1 I 1 1 11114 1 1 1I fl1

10o 101 102 103 104 

Energy (eV)

105 106 107

Figure 8 Comparison of 235U-fission sensitivity profiles for U(1 1)02 systems with HIX values of 400, 600, and 1000

t7Q

- II I IIIIII I I IIIIII ,

-- [V 

"1z....-' il 

I ] 

I------I.. ........ I ... ....... I ......... I ........ I........ I .. ,...... I .. .. ,I ,, ,,L

W

ON

L., 

0 

(--

:3 

c-4 

(I_ 

C 

L.4

0

111111 ..........
u(2)f4 h/x=195 u(2)f4 h/x=614 u(2)f4 h/x=972

cEo

Energy (eV)

Figure 9 Comparison of 238

U-capture sensitivity profiles for U(2)F4 systems with H/X values of 195, 614, and 972

0 

0

>1, 

4 -J 

Q) 

c(I)

0 

0 

0

:z 

C, 

0x 

ii~

0

1

co
u(2)f4 h/x=195

z 

Lu 

0•

- - - - - - - I

I I II 111"1 II ' ' 1 .I . , IIII I 1 11 1 1

u(5)3o8 h/x=147 u(5)3o8 h/x=757

0 0 

0 

C4 

0 

r~) 

0

10-5 10-4 10-`3 10-2 10- 100 101 

Energy

IL 

3 I II I 2 11-111 I

102 103 104 105 106 

(eV)

Figure 10 Comparison of 238U-capture sensitivity profiles for U(2)F4 HIX=195, and U(5) 308 H/X=147 and 757

Ii

S11111] I I I I I I II I I I I t 1111111 I I I Illl f I I I I I III I I IIIl [l I . . ..I l . . ... . I I . 1.. . .i

-- i 1 L 

-- I L• 

L . 

L.I. 

00

>-I 

L. 

Q 

c

c

(3) 

_) ci)

0

I
u(11 )o2 h/x=O u(11)o2 h/x=3 u(11 )o2 h/x=5

SI I 1111111 I 1 1111111 I I II IIII I U 111111I I I IIIIIM

0 

0 

0 

T

0

10-5 10-4 10-3 10-2 10-1 100

I 1111111 I I 1111111 I I I 111111 I I 1111111 II I I 11111 I Iii 11111 I I 1111111 0 

0

Er

101 102 

lergy (eV)

104 105 106 10

Figure 11 Comparison of 238

U-capture sensitivity profiles for U(1 1)02 systems with H/X values of 0, 3, and 5

I , iillliI 'l'i1111i1 1 i i Miit l lII l I I i i iilii I 1 1 11lii1 1 i i 1 

Symbols for negative values 

U(11)02 H/X=O 

U(11)02 H/X=3 

U(11)02 H/X=5 

Fi . . .•--= 2 

I ' , I - I 

'~r I L s -I •- l - ' 

L[ 

I

1 2

r.,f

c

Ci 

>1 

4-1 

(D 

U) 

0) 

o3 

N F iFr'i 

I i 

,II 

I . ...... I i i i i ll i i iil - i i it l l i ii itnI

C) 

('I 

0
I 10A '999-IID/D9HNfI

rel. sensitivity per unit lethargy

10 0

SI I I I I II I I I I I I 1 I I I I I I I I .

(*a 

CD, 

C,, 

CD, 

R

I I I .I . I I1 I I I It33 t I I I111

zuor~s /JOQtL XIAllISUOS

oz

I 1131

i0-2

1 10

(-PJ 

0D 

0D 

-s

-i 

1 -' 

F --- 4--

CD 

IT -- % 

1-0 

(D 

0

'I I .

10- 4

0 

PIJ 

Z57 

0 

0 

x 

NJ 

CD 

0 

x 

11 

-Ph

I

Z UOTWS
u(1 1)o2 h/x=80

. I I 111111 I 1 1 11111

u(11)o2 h/x=200 u(11 )o2 h/x=300

i I l 1 I I I I1111 i I Il I llllI I I lllll I I . . .. I III I I II I II I I IIIIII I I I I . . . . 

Symbols for negative value 

U(1 1)02 H/X=80 

U(11)02 H/X=200 

U(11)02 H/X=300

0 

CN

0 

0

0

r .... I I 1111111 I 11111111 I IIIIIIIIIIiIIIIII

0 

(D :3f 

ci) D 

C/) 

4-j 

U)

-L

-5 -3 . 14 ....... -1 1 1..... -.... 1 1 1........

10 -510- 410 -310 -210- 100 101 

Energy

L r 

L 

.. . . .__

102 

(eV)

103 104 105 106 107

Figure 13 Comparison of 238

U-capture sensitivity profiles for U(1 1)02 systems with H/X values of 80, 200, and 300

0 

0

(12

U

z 

nI . . ,. ,,,I

I ! I I IIIIII I I I IIIIII I I I IIIIII I I I I IIIII . . . ...... .
u(11)o2 h/x=400 u(11)o2 h/x=600 u(11)o2 h/x=1000

z 

CN 

(-I 

0l

-5 - 4 -3fII -2 -1 011

10-5 10-4 10-3 10-2 10- 100 101 

Energy

102 

(eV)

0 

0 

0 

0 

0 

r•3

103 104 105 106 107

Figure 14 Comparison of 238

U-capture sensitivity profiles for U(1 1)02 systems with HIX values of 400, 600, and 1000

>,, 

c

4-j 

U. 

V-, 

> 

C

O0

C/) 

8

0

09 

RO

i i i ii{III I

1 " "1"' I I ... I"I II .I I 

L I 

L. L 

I_.Ii 

... . .I . .......1 , , I ,, I 111,,,., I, ,, ,, I , ,,,,.I~ 1[,,,,,I.I .

'II'I "l I I II" "'l 1 1 1" 1"'I . . 1 .. 111 . 1 . ...... 1 

Symbols for negative values 

U(1 1)02 H/X=400 

------- U(11)02 H/X=600 

U(11)02 H/X=IO00 

I--. ..-- 

r

L I 

S....... , .. ... ..... M 1, I ...... 'i l .., I . ...... . . . ...... . . . ...... . . . ...... . .. . ......

tO I",3
Sensitivity Theory

2.2.3 Hydrogen Total Cross Section 

Lastly, the hydrogen sensitivity plots and their trends with respect to the systems H/X values were examined. If the 

previously mentioned "rules-of-thumb" hold, the prediction can be made that for H/X values of about 100 to 1000, 

the systems should largely be very similar with respect to hydrogen cross sections due to the linear behavior of the 

total sensitivity trends shown in Figures 1 and 2. In Figure 15, the hydrogen sensitivity profiles are given for H/X 

values of 195, 614, and 972 for the U(2)F4 systems. At first glance these systems would appear to have large 

spectral changes. However, these plots are constructed differently from the previously reported plots and therefore 

require a slightly different interpretation. These plots give the sensitivity of the system kf to the hydrogen total 

cross section. In these cases the sensitivity to the total cross section is essentially the sum of the sensitivities of the 

scattering and capture cross sections. The scattering sensitivities are generally positive, whereas the capture 

sensitivities are always negative. Hence, the "spectral" changes are due to changes in the relative magnitudes of the 

scattering and capture components as the system goes from under-moderated to over-moderated. The "spectra" for 

individual scattering and capture components remain essentially unchanged as a function of H/X. The same 

conclusions can be reached for the comparison of hydrogen sensitivity profiles for the systems U(2)F4 with H/X of 

195 and U(5) 30 8 with HIX values of 147 and 757 shown in Figure 16. 

The hydrogen sensitivity comparisons for the U(1 1)02 systems are shown in the following figures: H/X values of 3 

and 5 (Figure 17); H/X values of 10, 20, and 40 (Figure 18); H/X values of 80, 200, and 300 (Figure 19); and H/X 

values of 400, 600, and 1000 (Figure 20). For each figure, the individual scattering and capture sensitivity profiles 

appear to have relatively constant spectral shapes, giving rise to the conclusion of very similar systems with respect 

to the hydrogen cross sections. Indeed, for the individual scattering and capture portions, the spectral shapes of 

these sensitivity profiles are relatively constant over most of the H/X range from about 10 to 1000. 

2.2.4 Summary 

Analyses such as the preceding studies of Sections 2.2.1-2.2.3 are essential for understanding interaction 

mechanisms in the systems and are also valuable in determining the similarities between systems. However, for 

general validation studies, it is hoped that more concise methods could be developed to convey the same information 

in a more compact manner. These studies for more concise methods are described in Section 5.

NUREG/CR-6655, Vol. 1

Section 2

23
u(2)f4 h/x=195 u(2)f4 h/x=614 u(2)f4 h/x=972

.. I I I 1 1I I | l l | I I ll ill I I 111111 1 1111111 I 1 I 11lli

i-i

7rq 

------------- -- - -K-I-- - t 

Ii LI 

i.I r1 IL I 1 

I I 

rIJ Ii ' 

I 1 

i. 

Ii 

I, 

iinln , n i I~ l- I I I IIII11 I I 11n111 I i li 11ll I i i,,,11l I , ,, 111

z 

('I 

C

10-5 10-4 10-5 10-2 10- 100 101 102 103 104

Energy (eV)

Figure 15 Comparison of H total cross-section sensitivity profiles for U(2)F4 systems with HIX values of 195, 

614, and 972

>N, C71 

L 

Q_ V

CL 

09 

I.

0 

0 

T---

0

0I 

0 

0 

T,_..

L 

L

0

105 106 107

Co

I - I I I I I I I I " " I I I '"'"I

I-I 

L. . . . . . ..- - --1 , * '

i I ,- -,' 

r--.J - L.. ,I . , I I oj 

I'

|1 

I 1111 I I II )111 I I1111 I I Il~ n
u(2)f4 h/x=195 u(5)3o8 h/x=147 u(5)3o8 h/x=757

- 11111111I I 111111 Ii 1111'i Ii l 111ii I i iil I 1111 I ""111 I ..... 1 ...... I 'I 1111"1"'I i lli inI1 I '•"I 1111 " i 1111 

Symbols for negative values Symbols for positive values 

U(2)F4 H/X=195 U(2)F4 H/X=195 , •,•I ,..^ • ,,. I I/ afOt 0 UIV __4 A -7

u (bJ-UU H/X=147 -- - U(5)308 H/X=757

U 5)3•0 8h1= I75l U (5)308 H/X=757

I I 

r" ---- ,--t_ 

.. I . . . I ',1 

f U 

I-- r l- - - J' -- I 

:.]1 

':,1 LjH 

r; ire 

II 

Negative values 4- - Positive values t

, ,,,,1, ,I 1 111i 1 1 1 [11 U IL 1 ,,,l .1 ,,.,,JI 1 1 JJ , 1 1

101 

lergy

I 1111111 I I I 111111 I I I llIIIt I I

103 104

>., 

4_j JC 

A: 

U) 

C 

(3 

0)

105 106 10/ 10( 2 

(eV)

Figure 16 Comparison of H total cross-section sensitivity profiles for U(2)F4 H/X = 195, and U(5) 30 8 H/X = 147 and 757

0 

0

(A

0 

(N 

0 

0 

Tr'-

k 

I 

ri

-- - - - -

z 0

L's 

0

10-5 10-4 10- 10-2 10-1 100

Er

(A

I 1 1111111 1 1 Imill I I I
I *10A '999-WID/D0I1MN

rel. sensitivity per unit lethargy

10-2 10- 1

II''' I I liii,;

C" 

0 

0 

0 

CD

CI 

C4 

0 

M 

0 

(b

10~ 4 . . I . . I I I

z uomos i(ioqj MWUS

-I_ 

r 

I I -1 

I I 

I; 

II 

1 

I. 

r 

I'-

- ---- I----

-- I 

111111 I I I 111111

I I I a I I I I I

m :3 

CD 

(0 

CD

--- 3 

0 

0 

0 

N)

-S 

0 

(JD 

-S) 

0

10- 3

I

I I I I I 11111

C 

-S 

0 

K) 

x 

II 

0 

c 

0 

NJ 

x 

It 

c 

-S 

-S 

0 

NJ 

x 

II 

01

z Mims

10- 

4
I "IA 'ES99-)AID/ dCIN L/ 

rel. sensitivity per unit lethargy 

00 _ 04 

2 10 10- 10.12 10 

- --- --- ---I i. 

( - ... -- ...

CDC 

0~0 

0- -'- 0 

-~ ICD o I I I I, 

-- '-_• 2 -" 1 -----------

.L- .

- D 

-oo< ,h - r-r 0 

r~ r 

* C -- xC D 

,P 0 -i--- - , 

--- --- -- ----- 0 

. . . . . ' I-I 

iZ 

.. . -- - Ir . .I 0 S• .. .. _K)T -. . . . - -- _ 

0 l I I I i el II I I I I I 1 l 

AM~so (I• E uIoo
u(1 1)o2 h/x=80

10-5 10-4 10-3 10-2 10-1

u(11)o2 h/x=200 u(11)o2 h/x=300

z 

C) 

0\ 0 

0 

0 

0 

04 

0

>,, CD 

0 

4

I.. 

>1 

4_j 

C3 

(

U) 

Qi)

"---------- I , , ,,,,. I , , , ,,,, , , ,I . , , I , , , , ,,,,,. I , , ,,. ,,I

103 104 105 0 1 2

10 101 102 

Energy (eV)

Figure 19 Comparison of H total cross-section sensitivity profiles for U(1 1)02 systems with H/X values of 80, 

200, and 300

I.. I I . ..... I I l I I I 1 1 1 1 1 1 111 1 1 1 II I I I I II IIIII 111111 I I I 1I 111111 I 1 I I 111111 I I I I I Ii

--------------

8

00

t.. -

0

106 107

Cl'

I I I 111111 I I I llllll

S..... ! . ..... I

r-i 

Negative Positive J 

values values 

--- L:r_] .~ 

J.- ., If r:-9 

[I L 

Li 

I-- . , I-
u(11)o2 h/x=400 u(11)o2 h/x=600 u(11)o2 h/x=1000

Symbols for negative values 

U(11)02 H/) 

U(11)02 H/X 

U(11)02 H/> 

r-,-L 

r--I 

F--

0 

0

0 

V_ 

N 

0 

0 

0

10-5 10-4 10-3 10-2 10-1 10° 101 

Energy

102 

(eV)

103 104 105 106

Figure 20 Comparison of H total cross-section sensitivity profiles for U(1 1)02 systems with H/X values of 400, 

600, and 1000

C_ 

4-J 

Q._ 

C" 

Q) 

U) 

_.2 

0)

- I I I I 1111111 I I 1111 I I

C.A 

LAI

I . I

I ! ....................................................

' I" ' ' I I ' I I . ...... I . . . ... . . . i 

Symbols for positive values 

=400 --------------------- U(11)02 H/X=400 

=600 U(11)02H/X=600 

<=1000 U(11)02 H/X=1I000 

r*'-~ ,-- •. . ~ ----- ....--

r

,U

il il"z:I I;i- -

<2 - - - - - - - - - -
3 UNCERTAINTY ANALYSIS THEORY 

The determination of uncertainties in the calculated values of the system multiplication factor is accomplished by 

two steps: the estimation/processing of uncertainties in the underlying cross-section data and the propagation of 

those uncertainties to the system kff value. The techniques for processing cross-section uncertainty data are 

well-known 17, 8 and will be discussed here only briefly. 

The two primary cross-section covariance processing codes, PUFF-217 and NJOY,18 use very similar techniques. 

These codes are designed to read the covariance information from the ENDF/B files (typically files 31 and 33) and 

process it into a user-defined group structure. The codes generate an energy "supergrid," which is the combination 

of the energy group structures of the ENDF/B data, the processed cross-section data, and the user-defined energy 

grid. The covariance information contained in the ENDF/B data is then expanded onto the energy supergrid by 

assuming the relative uncertainty is constant over each ENDF/B group. This allows the relative variances to be 

generated onto the supergrid. The final step is to collapse the supergrid to the user-defined grid via the same 

weight functions that are used in the cross-section collapse. These techniques are appropriate for the so-called 

smooth cross sections in files 1 and 3. However, for the resonance region an alternative approach is used. 

In the resonance region, the resonance parameter uncertainties (given in file 32) are propagated to the groupwise 

resonance self-shielded cross sections using an approximate technique suggested in Ref. 17. This technique is 

limited to the single-level Breit-Wigner formulation of the resonance cross sections. The procedure uses simple 

formulae to predict the area under each resonance. By differentiating these formulae, the sensitivity coefficients of 

the resonance area to the individual resonances parameters are obtained. These sensitivity coefficients are then 

used to propagate the resonance parameter uncertainties to the resonance area, hence to the groupwise cross 

section. The entire resonance area, and hence its uncertainty, is assumed to be contained within a single group. 

The correlations between groups are assumed to be zero using this technique. A previous study' 9 compared this 

approximate technique with direct perturbations. In that work, general agreement is seen for many cases. In some 

cases there are large differences seen in the predicted and actual sensitivities, even opposite signs. Improvements 

need to be made in the existing procedures in order to correctly treat the impact of changes in self-shielding. 

Additional work is currently under way to remedy some of the limitations with respect to the resonance parameter 

uncertainty treatment. Some of these studies have been reported2

" and will be incorporated into the current analyses 

when the decision to use later cross-section libraries is made. The cross-section libraries used in this study are 

based on ENDF/B-V since an ENDF/B-VI multigroup cross-section library pertinent to criticality safety 

applications is not available, therefore the use of the data covariances based on later versions of ENDF/B are 

judged to be inappropriate. 

Using uncertainty information for the cross sections for the nuclides and reaction processes that are available, it is 

possible to estimate the uncertainty in the system multiplication factor due to these data uncertainties. Denoting the 

matrix of uncertainty information for all of the cross sections as C. and the sensitivity matrix relating changes in 

each constituent material and process to the system kf as Sk, the uncertainty matrix for the system kff values, Ck, 

is given as: 

= -kk S C. Sk . (9) 

The Sk matrix is I x N, where I is the number of critical systems being considered, N is the number of nuclear data 

parameters in the problem. Typically, N is the number of nuclide/reaction processes times the number of energy 

groups. The CQ. matrix is an N x N matrix, with the resulting symmetrical Ckk matrix, which is I x I. The Ck 

matrix consists of variance values, (i.e., the standard deviation squared), a,, for each of the critical systems under 

consideration (the diagonal elements), as well as the so-called "covariance" between systems ai'j 2 (the off-diagonal

31 NUREG/CR-6655, Vol. 1
Uncertainty Analysis Theory

elements). See Appendix A for a discussion of these variance and covariance definitions. These off-diagonal 

elements represent the shared or common variance, hence the term covariance, between any two of the various 

systems. For presentation, these off-diagonal elements are typically divided by the square root of the corresponding 

diagonal elements (i.e., the respective standard deviations) to generate a correlation coefficient matrix. Thus, the Ck 

coefficients are defined as, k = a a a., such that each c value represents the correlation coefficient between 

uncertainties in system i and system j. Note that the ij notation is omitted in the remainder of this report. These 

correlations arise due to the fact that the kff values for two different systems are both functions of the same 

underlying random variables. The underlying random variables are the evaluated nuclear data for each nuclide. 

Systems with the same materials and similar spectra would be strongly correlated, while systems with different 

materials or differing spectra would be weakly correlated. The physical interpretation of the correlation matrix is 

the following: a value of zero represents no correlation between the systems, a value of unity represents full 

correlation between the systems, and a value of - 1 represents a full anticorrelation. A kf correlation matrix for 

eight of the critical benchmarks described earlier (i.e., 6-U(2)F4, 2-U(5) 30 8) is given in Table 1. Since the diagonal 

elements are unity, each diagonal element is replaced by the corresponding fractional standard deviation 

representing the uncertainty in the calculated k. value. 

Table 1 Cross-correlation coefficientsa (due to cross sections) for k. of 2% and 5% systems 

Critical system 2%(195) 2%(294) 2%(406) 2%(496) 2%(614) 2%(972) 5%(147) 5%(757) 

2% H/X = 195 0.0158 

2% HIX = 294 0.9884 0.0139 

2% HIX = 406 0.9729 0.9896 0.0129 

2% H/X = 496 0.9556 0.9801 0.9908 0.0122 

2% H/X = 614 0.9269 0.9605 0.9782 0.9880 0.0116 

2% H/X = 972 0.8019 0.8560 0.8886 0.9177 0.9499 0.0110 

5% H/X = 147 0.8604 0.8624 0.8467 0.8383 0.8242 0.7521 0.0128 

5% H/X = 757 0.7276 0.7713 0.7961 0.8153 0.8335 0.8373 0.7795 0.0093 

aNote the diagonal elements give the fraction standard deviation since the diagonal correlation coefficient is defined as 

unity. 

The standard deviation values shown in Table 1 range from 0.93 to 1.58%. The highest uncertainties correspond to 

the lowest H/X values due to the fact that a harder spectrum enhances the sensitivity to the higher-energy cross 

sections, which are usually less well known than the thermal values. Note that the correlation coefficients, denoted 

as ck, are all 0.71 or higher, indicating that most of these systems are similar to each other. Another way of looking 

at the 0.71 coefficient is that 71% or more of the variance is common to all these systems. Thus, these systems are 

expected to behave in a very similar manner with respect to bias and uncertainty determinations for the SCALE 44

group cross-section library on which these results are based. Shown in Table 2 is a correlation matrix for the 

14 - U(1 1)02 artificial systems discussed earlier. The trends in standard deviation are replicated here with a peak 

uncertainty of 1.92% for an HIX of 0, going down to 0.87 for an H/X of 1000. Looking at these values with a ck 

criterion of 0.8 or greater indicating similar systems leads to conclusions nearly identical to those based on a 

comparison of the sensitivity profiles. For example, the H/X of 0 system is only similar to the HIX of 3 system and 

then only marginally, so ck is 0.8328. We see that for H/X values between 5 and 40, the similar systems include

NUREG/CR-6655, Vol. 1

Section 3

32
Uncertainty Analysis Theory

Table 2 Cross-correlation coefficientsa (due to cross sections) for kf of 11% systems

11%-0 11%-3 11%-5 11%0-10 11%-20 11%-40 11%-80 11%-200 11%-300 11%-400 11%-50011%-60011%.C80011%-1000

0.4887 0.8409 0.9161 0.9784 0.0176 

0.4067 0.7562 0.8403 0.9253 0.9763 0.0151 

0.3428 0.6585 0.7392 0.8327 0.9094 0.9698 

0.2800 0.5240 0.5888 0.6760 0.7696 0.8705 

0.2633 0.4751 0.5315 0.6115 0.7058 0.8157 

0.2557 0.4452 0.4953 0.5687 0.6604 0.7727 

0.2517 0.4329 0.4688 0.5359 0.6235 0.7349 

0.2490 0.4076 0.4482 0.5097 0.5927 0.7014 

0.2432 0.3755 0.4076 0.4567 0.5278 0.6265

ll%H/X= 1000 0.2353 0.3452 0.3697 0.4071 0.4652 0.5509

0.0128

0.9526 0.0106 

0.9148 0.9832 0.0099 

0.8798 0.9668 0.9846 

0.8453 0.9448 0.9717 

0.8123 0.9200 0.9543 

0.7331 0.8514 0.8991 

0.6484 0.7702 0.8277

0.0095

0.9845 0.0091 

0.9742 0.9847 0.0089 

0.9330 0.9576 0.9734 0.0087 

0.8731 0.9097 0.9367 0.9752 0.0087

aNote the diagonal elements give the fraction standard deviation since the diagonal correlation coefficient is defined as unity. 

only the two or three neighboring systems with either higher or lower HIX values. For systems with H/X values of 

80 to 1000, the systems are typically similar to the nearest three or four neighboring systems. Based on these 

observations, these systems appear to be similar if the H/X values are within about a factor 5 of each other. 

The explicit comparison of sensitivity profiles performed earlier did not examine quantitatively the similarities of 

low-enriched systems (2-5 wt %) with the U(1 1)02 system. Table 3 gives a comparison of ck values for these 

systems for comparison. These results give indications that the 2- and 3-wt % systems are only marginally similar 

to the 11 -wt % systems. For systems with similar HIX values, the 2- and 3-wt % systems typically have a ck value 

of only 0.75 with respect to the 11-wt % systems. For the 5-wt % systems and HIX values near to those of the 

11 -wt % systems, ck values are already above 0.80, indicating similarity. Of course such a comparison is not 

possible for H/X values below about 100, where criticality is not possible for enrichments of 2-5 wt %. 

These ck values are judged to be most appropriate for correlation with error trends in a criticality safety validation 

analysis because they are essentially the sensitivities to the individual cross sections weighted by their uncertainties. 

Thus, the ck values represent the systems similarity with respect to materials with highest sensitivity/uncertainty 

combination. For determination of the similarity of systems with respect to each material/reaction process it would 

be useful to have additional parameters that are more differential in nature. These additional parameters will be 

discussed in Section 5.

NUREG/CR-6655, Vol. 1

Section 3

Critical system

11%N/X=0 0.0191 

11%H/X=3 0.8328 0.0185 

11%H/X=5 0.7379 0.9818 0.0188 

11 TG Rr= 10 0.6011 0.9205 0.9725 0.0165

11% H/X= 20 

11%H/X=40 

11%H/X=80 

11% H/X=200 

ll%H/X =300 

11% H/X = 400 

11% I-X = 500 

11%H/X= 600 

11%t/X=800

33
Uncertainty Analysis Theory Section 3

Table 3 Comparison of correlation coefficientsa (due to cross sections) for kff of 2%, 3%, 5%, and 11% systems

Cntical system 

2% H/X = 294 

2% H/X = 406 

2% H/X = 496 

2% H/X = 614 

2% H/X = 972 

3% H/X = 133 

3% H/X = 277 

5% H/X = 757 

11% HiX = 200 

11%H/X=300 

11% HIX = 400 

11% H/X= 500 

11%H/X= 600 

11% H/X= 800 

11%H/X= 1000

2%-294 

0.0139 

0.9896 

0.9801 

0.9605 

0.8560 

0.8752 

0.8564 

0.7713 

0.7575 

0.7391 

0.7248 

0.7116 

0.6992 

0.6667 

0.6282

20-406 2%-496 2%-614 2%-972 3%-133 3%o-277 5%-757 llo%-200 11l%-300 11%-400 11%-500 11l%-600 11%o-800

0.0129 

0.9908 

0.9782 

0.8886 

0.8489 

0.8453 

0.7961 

0.7596 

0.7504 

0.7429 

0.7354 

0.7274 

0.7027 

0.6699

0.0122 

0.9880 

0.9177 

0.8341 

0.8400 

0.8153 

0.7621 

0.7589 

0.7561 

0.7526 

0.7478 

0.7290 

0.7004

0.0116 

0.9499 

0.8133 

0.8294 

0.8335 

0.7599 

0.7634 

0.7663 

0.7675 

0.7667 

0.7554 

0.7326

0.0110 

0.7297 

0.7666 

0.8373 

0.7142 

0.7330 

0.7491 

0.7622 

0.7716 

0.7805 

0.7747

0.0157 

0.8396 

0.7137 

0.7440 

0.7114 

0.6870 

0.6661 

0.6478 

0.6052 

0.5601

0.0129 

0.7733 0.0093 

0.7633 0.7892 

0.7496 0.8170 

0.7383 0.8383 

0.7273 0.8544 

0.7164 0.8652 

0.6858 0.8736 

0.6481 0.8639

0.0106 

0.9832 

0.9668 

0.9448 

0.9200 

0.8514 

0.7702

0.0099 

0.9846 

0.9717 

0.9543 

0.8991 

0.8277

0.0095 

0.9845 0.0091 

0.9742 0.9847 0.0089 

0.9330 0.9576 0.9734 0.0087 

0.8731 0.9097 0.9367 0.9752

aNote the diagonal elements give the fraction standard deviation since the diagonal correlation coefficient is defined as unity.

NUREG/CR-6655, Vol. 1 34
4 GENERALIZED LINEAR-LEAST-SQUARES METHODOLOGY 

The GLLSM has been referred to as a data adjustment procedure, a data consistency analysis, and even a data 

evaluation technique. The most appropriate description of GLLSM for this particular application would be that of a 

generalized trending analysis tool. Physically the GLLSM is designed to minimize differences between the measured 

and calculated values of kf for the entire set of experiments used in the data validation process. The "data changes" 

that result from the application of the GLLSM can then be used to predict the biases and associated uncertainties for 

any application determined to be similar to the benchmark area of applicability. Functionally, the GLLSM can be 

thought of as a trending of a suite of critical benchmarks with respect to the cross-section correlation coefficient 

between the various systems. The GLLSM has the capability to identify experiments that contain inconsistencies 

(i.e., the magnitude of the measured-to-calculated kff difference is larger than their combined uncertainties). A)? 

consistency indicator is used to directly predict the overall consistency of the suite of benchmarks, a value of )? for 

each experiment is also available from the GLLSM tool. 

This present work utilizes GLLSM procedures based on those introduced by Gandini,21 Dragt et al.,2 and Barhen, 

Wagschal, and Yeivimn. The derivation of the GLLSM equations in this work follows the general notation from 

Ref. 25, however, the fall derivations are included in Appendix B for completeness. The vector m (mD, i = 1, 2, 

I, represents a series of kff measurements on critical benchmark experiments that are to be used in the validation of a 

dataset for criticality safety computations. This vector m has a corresponding symmetric I x I uncertainty matrix 

associated with it, which is denoted as C.a = cov(mnjn.) - <5mn•5mr>. Further, the vector k - (k) is denoted as the 

corresponding series of calculated values of kf for each of these experiments. The vector a E (aj, n = 1, 2, ... N, 

with its corresponding symmetric N x N uncertainty matrix Co, E cov(cxa,) - <8a8a >, represents the differential 

data used in the calculations (i.e., nuclear data such as fission, capture, and scattering cross sections, the fission 

spectrum and other problem parameter data) and, additionally, the material densities used in the problem description. 

This procedure also allows for the possibility of correlations between the integral and differential quantities, which 

may be present at times in the analysis. These correlations are denoted by the N x I covariance matrix Cm

The sensitivities of the calculated kffto the a parameters are given as Sk ak-lIa, with Sk being an I x N matrix. 

Representing perturbation of the a parameters as linear changes in the calculated kff value, yields the following: 

k(a') = k(a + 5a) = k(a) + 5k - k(a) + SkSa , (10) 

with the corresponding uncertainty matrix of the calculated values of 

Ci -= k<Skj-k> = Sk<Sa >S = Sk CS(11) 

If the deviations of the calculated values from their corresponding measured responses is denoted by the vector 

d (d,) = k(a) - m, then the uncertainty matrix for the deviation vector d, denoted by Cd, is 

Cdd =Ckk +C -C SC - CmSS m k S (12) 

SkCaa k + C. - SkCm - CSk. 

Denoting x = a' - a, and y = k(ca') - m, we can rewrite Eq. (10) as 

y = d + Sk X. (13) 

The measured kf values, m, and the measured (or evaluated from measurements) parameter values, a, both have 

their corresponding uncertainties. The best evaluated parameters a' and the best evaluated values of the 

measurement m' will be those values that are consistent with each other, namely m' = k(a'), and are consistent

35 NUREG/CR-6655, Vol. 1
Generalized Linear-Least-Squares Methodology

with their estimated values and uncertainties (i.e., in general they do not deviate more than a standard deveiation 

from their current best estimates m and a, respectively). 

The GLLSM procedure involves minimizing the quadratic loss function 

Q(x,y) = (y,x)t C.C= (y'X))- (14) 

where (y,x)t - (Y1, Y2 .... I Y, X1, x2 , ... , xN), subject to the constraint expressed by Eq. (13). Adopting the procedure 

of Refs. 23-25, the above conditional minimum formulation is equivalent to unconditionally minimizing the function 

R(x,y), where 

R(x,y) = Q(x,y) + 21t(SkX - y), (15) 

and 2k is an I-dimensional vector of Lagrange multipliers. Thus x and y satisfy the equations 

aR(x,y)/ax = aR(x,y)/ay = 0. (16) 

Solving the resulting equations for x and y, we obtain 

a' = a + (C. - C.S 1)Cd~'d, and 

m' = m + (C,. - CLSk) (17) 

where Cd- 1 is the inverse of the I x I matrix Cd Eq. (12) and a' and m' are the best estimate of the parameters and 

measurement within their uncertainty range. 

A few observations are due here: 

1. If the a' values obtained in Eq. (17) are substituted in k(a'), using the linearity assumption of Eq. (10), 

the m' value of Eq. (17) should be obtained, thus m' = k(a') is satisfied. 

2. Moreover, not only are the new/best estimates of the cross sections and of the key values consistent, but 

their uncertainties are reduced as well. 

3. The minimum value of Q(x,y) is also known as ?, which is a measure of the consistency of the various 

calculated and measured responses. The value of )/I should be near unity for a particular study to 

indicate consistency for the entire set of data. 

The reduced uncertainty matrices are given by 

Cm,= C. - Cyy and Cc,., = C= -CX, (18) 

where 

Cy= (C" - Cm=SJ)Cd-1(C= - SkC.m) 

and (19) 

= (Ca- CSkk)Cdd -(C. - SkC.).

NUREG/CR-6655, Vol. 1

Section 4

36
Generalized Linear-Least-Squares Methodology

This could of course suggest that any criticality application that is similar to the selected benchmarks should be 

calculated using the modified cross sections and thus have a reduced uncertainty. However, even when maintaining "conventional" criticality estimates using "established" cross sections and trend curves, the GLLSM approach can 

be beneficial, as will be demonstrated in the next section. 

In summary, the GLLSM procedure as applied to the validation of cross-section libraries for criticality safety 

applications is designed to predict the data changes, x, such that the differences between measured and calculated kff 

values (i.e., the quantity, y) are minimized. These kff differences are the trends observed in the traditional criticality 

safety trending analyses. Removal of these trends and the identification of the data responsible for them are keys to 

the application of GLLSM techniques to criticality safety data validation. 

4.1 Application of GLLSM to Validation 

For a criticality safety scenario of interest (denoted the "application"), the "measurement," mr, associated with the 

predicted kff values, k(a), does not exist. The quality of interest is the bias, which is defined as the difference 

between the predicted value k(a) and the best estimate "measurement" for the application, m.'. Now consider 

rewriting Eq. (10) as: 

k(a') - ma' = [k(a) - ma'] + Sa(a' - c), (20) 

where Sa are the calculated sensitivities for the application. The solution of Eq. (17) via the GLLSM procedures 

allows evaluation of a'. The GLLSM theory predicts that if a sufficient number of experiments are similar to the 

application of interest, the calculated value of kff using the "best" cross sections, a', will indeed approach the value 

of the best estimate measurement for the application. Thus, k(a') - ma' and the bias for the application can be 

determined to be 

ba = k(a)- m =- S.(a' - a), (21) 

where a' - a was obtained in Eq. (17) using all similar benchmark criticality measurements. 

The definitions of "similar" and "sufficient" number of experiments can now be determined by tests using actual 

benchmark experiments. 

4.2 Testing of GLLSM with Low-Enriched U Systems 

The general approach of GLLSM is the combination of information obtained in a series of integral experiments, with 

the aim of predicting changes in the underlying nuclear data such that differences in the calculated and measured k4 

values are minimized. The GLLSM techniques are useful for understanding the relative consistency of experiments 

in that this combination of integral experiments can be in a graded manner. An overall measure of the consistency of 

the GLLSM application is obtained via the use of a )? analysis, thus inconsistencies between groups of experiments 

can be identified. Experiments in the series can also be added cumulatively in order to see the "convergence" of the 

technique. Using actual experiments as example "applications," trial analyses can be performed to test for 

convergence, where the measure of convergence is that the quantity in the left-hand side of Eq. (20) asymptotically 

approaches zero within the bounds of the measurement uncertainty as the number of experiments used in the 

GLLSM process is increased. For consistency with later applications, this absolute quantity is recast into the 

relative quantity (e-a)/c where e is the experimental value, rna'; a is the adjusted calculated value, kl(ax'); and c is the 

original calculated value ka(a). These tests are used to help determine a minimum magnitude of ck that represents

NUREG/CR-6655, Vol. 1

Section 4

37
Generalized Linear-Least-Squares Methodology

"similarity" or "applicability." In addition these tests help indicate the sufficient number of experiments needed for 

convergence by the GLLSM procedure. 

4.2.1 Tests with Experimental Data 

The 2 wt % and 5 wt % systems discussed earlier were chosen for further study as a demonstration application of 

the GLLSM procedures. For this phase of the study, the measured results from selected experiments are ignored to 

see if GLLSM can accurately predict observed biases, i.e., individual experiments are assumed to be the application 

and the process tested using an increasing number of the remaining experiments. These systems are excellent 

candidates since they are highly correlated with each other and consist of multiple enrichments and a fairly broad 

range of H/X values from 147 to 972. These systems are documented in Refs. 15 and 16 with the properties given 

in Tables 4 and 5. (These 3-D systems were modeled as 1-D spheres; the stated references give both the actual 

system dimensions and spherical model dimensions.) The uncertainties in the number densities noted in the tables 

were included in the analysis, along with a uniform uncertainty in the measured system multiplication factor that 

varied between 0.1 and 0.4% for each system. 

Correlations in the material density uncertainties were established, but the measurement uncertainties of 0.1 to 0.4% 

were assumed to be uncorrelated between systems. The tightening of the system uncertainties (0.1%), and the 

loosening of the system uncertainties (0.4%) were used to test the variability of the solutions with assumptions in 

these quantities. 

Unlike the real applications where measured values are not available, the 2% and 5% systems can serve as a good 

testing arena for the GLLSM predicted kff values. The convergence of the 2 wt % systems is shown in Figure 21. 

Here the (e-a)/c values are given as a function of the number of experiments "added" together in the GLLSM 

procedure. For convenience, the "0 experiments included" case assigns the calculated value to the adjusted value 

such that this data point gives the actual calculated-versus-experimental discrepancy. The order of adding systems 

into the GLLSM procedure is from low to high H/X values: the first system added has an HIX of 195, the second 

has an H/X of 294, and so on. The convergence toward an (e-a)/c value of 0 is seen in Figure 21, even though the 

convergence is incomplete. The identical situation is shown in Figure 22, except for an experimental uncertainty of 

0.1%, instead of 0.4% in the results for Figure 21. The convergence is closer to 0 in Figure 22 due to the decreased 

flexibility in movement of the measured values, and hence greater movement of the calculated values. With the 

exception of the system with H/X of 972, the systems converge to approximately the same values in both Figures 21 

and 22. Note that in Figures 21 and 22 the two systems with H/X values of 972 and 294 seem to have some 

difficulty converging. The system with an H/X value of 972 does indeed "converge" for experimental uncertainty of 

0.1%, but only after the system itself is included in the adjustment. This behavior is due to the fact that this system 

is less correlated to the remaining systems. In Table 1, the correlation coefficients for the system with an H/X of 

972 range from 0.80 - 0.95, with only two systems that have correlation coefficients greater than 0.90. While 

certainly these correlation coefficients should indicate similarity between these systems, it appears that they are only 

marginally able to produce convergence in the GLLSM scheme. The system with an HIX value of 294 is quite 

different, in that four of the other systems have correlation coefficients values of 0.96 or higher. The system seems 

to be converged, only it doesn't converge to 0. This behavior appears to be due to a slight inconsistency in this 

experiment (based on its ) value) in relation to its companion experiments. The experiment is still valid; however, 

for this limited set of experiments, the system with an H/X of 294 is somewhat inconsistent with the other five 

systems. In Figure 23, the effect of omitting this experiment is seen, where the results are largely unchanged, thus 

this experiment doesn't add anything or take away any information from the combined systems.

NUREG/CR-6655, Vol. 1

Section 4

38
Section 4 Generalized Linear-Least-Squares Methodology 

Table 4 Properties of 2-wt % UF4 experimentsa

U(2)F4-1 U(2)F4-2 U(2)F4-3 U(2)F4-4 U(2)F4-5 U(2)F4-6 

Atom densities 

231U (1020 atoms/cm3 1.5811 1.3303 1.1191 0.9924 0.8667 0.6232 

± 1.2%) 

2 8

U (1021 atoms/cm3 7.6467 6.4370 5.4152 4.7998 4.1941 3.0150 

± 0.7% 

H (102 atoms/cm3 3.0864 3.9097 4.5472 4.9212 5.3187 6.0557 

± 1.2%) 

C (1022 atoms/cm3 1.4839 1.8797 2.1861 2.3660 2.5570 2.9114 

± 1.2%) 

F (102 atoms/cm3 3.1219 2.6280 2.2109 1.9596 1.7123 1.2309 

± 0.7% 

Critical radius (cm) 44.91 38.50 36.38 36.36 37.67 49.65 

.Source: Ref. 15. 

Table 5 Properties of 5-wt % U308 experiments" 

U(5) 308 U(5) 30 8 U(5) 308 U(5) 3 0 8 U(5) 308 U(5) 308 

H/X = 147 HIX = 245 H/X = 320 H/X = 396 HIX = 503 H/X = 757 

Atom densities 

234U (1017 atoms/cm3 8.7460 5.8410 5.1680 4.2730 3.5050 2.3260 

± 0.71% 

23SU (1020 atoms/cm3 2.1290 1.4220 1.2580 1.0400 0.8532 0.5663 

± 0.71%) 

231U (1021 atoms/cm3 4.0880 2.7300 2.4160 1.9970 1.6380 1.0870 

_ 0.5% 

H (102 atoms/cm3 3.1220 3.4780 4.0280 4.1090 4.2960 4.2870 

± 0.5%) 

C (1022 atoms/cm3 1.5660 1.7440 2.0200 2.0610 2.1550 2.1500 

± 0.5%) 

0 (102 atoms/cm3 1.3410 0.9698 0.9086 0.7926 0.6993 0.5440 

± 0.5% 

Critical radius (cm) 35.563 34.106 32.383 33.158 37.67 41.214 

.Source: Ref. 16.

39 NUREG/CR-6655, Vol. 1
z 

C.) 

0 

0

1 2 3 4 5 

experiments included

-- H/X=195 

-- H/X=294 

-- H/X=406 

--- H/X=496 

--- H/X=614 

-- H/X=972

6

Figure 21 Predicted values of (e-a)/c as a function of the number of experiments included for 2-wt % UF4 systems 

(0.4% experimental uncertainty)

2% SYSTEMS"' ADJUSTMENT

1.5 

1 

0.5 

0

#OWN 

(U 

%&o

C) (L

'1 

a 

(t

0

-0.5 

-1

0
2% SYSTEMS ADJUSTMENT (0.1%)

1.5 

1 

0.5 

0

-0.5 

-1

0 1 2 3 4 5

-- H/X=195 

-- H/X=294 

SH/X=406 

--- H/X=496 

-*- H/X=614 

-- H/X=972

6

experiments included

Figure 22 Predicted values of (e-a)/c as a function of the number of experiments included for 2-wt % UF4 systems 

(0.1% experimental uncertainty)

C')

c

z7 

Qj

(t

0 

'1 

tL4 

0 

C')
z 

C) 

0•

H/X=195 

H/X=294 

H/X=406 

H/X=496 

H/X=614 

H/X=972

5

N 

ct 

tL

Figure 23 Predicted values of (e-a)/c as a function of the number of experiments included for 2-wt % UF4 systems 

(H/X = 294 system omitted from analysis)

2% SYSTEMS (NO HIX=294, 0.1%)

0 

I

1.5 

1 

0.5 

0 

-0.5 

-1

0 1 2 3 4

experiments included
Generalized Linear-Least-Squares Methodology

The examples given in Figures 21-23 for the application of GLLSM procedure to a series of 2-wt % systems are not 

typical for a standard analysis. They are given only to show that for very highly correlated systems, convergence 

can be quite rapid. The more general situation is shown in Figure 24, which shows the combined effect of the same 

6 experiments, only the values to the far left of the plot correspond to the "learned" effects of some 62 other 

experiments. These experiments correspond to experiments 7-66, 91, 94 summarized in Table 1 of Volume 2 of 

this report. These 62 experiments consist of numerous low-enriched U systems, several systems with enrichments in 

the 10-20-wt % range and quite a few high-enriched U systems. Although this collection of critical experiments is 

incomplete, it is designed to demonstrate the use of GLLSM techniques on a wide variety of typical application 

scenarios, which will be the subject of Volume 2. 

Comparison of the (e-a)/c predictions in Figure 24 between those on the far left to those of the far right reveal that 

for all cases, except the H/X of 972 system, the 62 other experiments are sufficient to converge the GLLSM 

procedure (i.e., the predicted values on the left- and right-hand sides of the figure are approximately equal to each 

other). In essence, there is little to no difference between the predicted differences in the 2-wt % systems, whether or 

not they are included in the GLLSM procedure. The HIX = 972 case does not converge.* Interestingly, the 62 other 

experiments do not contain a single additional system with a correlation coefficient greater than 0.9. Hence, this 

system still does not converge even though there are 62 systems in the procedure instead of only 6. 

The key question for the HIX = 972 system is whether its failure to converge under these circumstances is due to an 

inconsistency or due to the lack of sufficient similar experiments in the GLLSM procedure. Even though the 

definitive answer to this question requires more study, the apparent answer is that with additional similar 

experiments included in the GLLSM procedure it would converge. This scenario is believed to be plausible since 

when the experiment itself is added to the GLLSM procedure, as shown in Figures 21 and 22, this case either begins 

to converge, or does indeed converge. 

The application of the GLLSM procedure to the 5-wt % systems was also examined in a similar manner. In 

Figure 25, the (e-a)/c values are given for each of the 6 systems consisting of 5-wt % U30 8 material. Again, there is 

rapid convergence of the GLLSM procedure for these very highly correlated systems. In this case, 2 systems appear 

to be unconverged after the sequential combination of these experiments. These systems correspond to H/X values 

of 245 and 320. The first 3 systems (H/X of 147, 245 and 320) are all very highly correlated with correlation 

coefficients greater than 0.95. Thus, these 2 systems would appear to simply have inconsistencies with the 

remaining systems. In Figure 26, the tightening of the experimental uncertainties from 0.4% to 0.1% has little or no 

effect on the convergence of these experiments. Interestingly, if one assumes that the system with an HIX of 245 is 

inconsistent and removes it from the GLLSM procedure, the effect is shown in Figure 27, where the other 5 

remaining systems converge while no effect is seen for the H/X = 245 system. Thus, it would appear from this 

analysis that the system with an H/X of 245 is inconsistent with the remaining systems and should be removed from 

the analysis if only a few experiments are available for consideration. This observation is confirmed by noting that 

the ? value for the experiment with an H/X = 245 (its ) value is much greater than unity) indicates the least 

consistency as compared with the remaining experiments. 

The behavior of the 5-wt % systems with the inclusion of the entire 62 experimental systems was also examined. 

In Figure 28, the convergence of the (e-a)/c values is shown, first considering the 62-system database, followed by 

the addition of the 6 systems with 5-wt % enrichments. The addition of these 5-wt % systems is performed using an 

"optimal" procedure. The optimal sequence is defined such that the most consistent experiments are added first, 

followed by those which have less and less consistency. The order of inclusion from left to right is H/X 

*Convergence is not seen for this experiment in Figure 24; however, when the experimental uncertainty is reduced to 0.1%, all of the 2% 

systems converge to better than 0.1% with the exception of the "troublesome" II/X = 294 which converges to only 0.15%.

NUREGICR-6655, Vol. 1

Section 4

43
Cz 

0. 

0& 

U' 

U'

0.8 

0.6

a 

a 

(U, 

(U 

I 

0

0.4 

0.2 

0 

-0.2 

-0.4 -4

62 63 64 65 66 67 68

H/X=195 

-- H/X=294 

H/X=406 

-- H/X=496 

-- H/X=614 

H/X=972

experiments included

Figure 24 Predicted values of (e-a)/c as a function of the number of experiments included for 2-wt % UF 4 systems. The 

2% experiments were added in the following H/X sequence: 195, 406, 496, 614, 294, 972.

2% OPTIMAL SEQUENCE

0.

7m
5% SYSTEMS ADJUSTMENT (0.4%)

1 2 3 4 5

H/X=147 

H/X=245 

H/X=320 

H/X=396 

-,--H/X=503 

H/X=757

6

experiments included 

Figure 25 Predicted values of (e-a)/c as a function of the number of experiments included for 5-wt % U30 8 systems 

.(0.4% experimental uncertainty)

0

2 

1.5 

1 

0.5 

0 

-0.5 

-1 

-1.5 

-2

A 

C-) 

0\ 

0\ 

0

0

0• 

A3
C-) 

ON 

O--.

H/X=147 

H/X=245 

H/X=320 

H/X=396 

-,-H/X=503 

H/X=757

6

CD 

(T2 

-i

Figure 26 Predicted values of (e-a)/c as a function of the number of experiments included for 5-wt % U30 8 systems 

(0.1% experimental uncertainty) 50

5% SYSTEMS ADJUSTMENT (0.1%)

2 

1 

0 

-1

(U, 

(U 

I 

C,

-2 

-3

0 I 2 3 4 5

experiments included
5% SYSTEMS (NO H/X=245, 0.1%)

*-ý0

2.5 

2 

1.5 

1 

0.5 

0 

-0.5 

-1 

-1.5

0

H/X=147 

H/X=245 

H/X=320 

H/X=396 

H/X=503 

H/X=757

1 2 3 4 5

experiments included 

Figure 27 Predicted values of (e-a)/c as a function of the number of experiments included for 5-wt % U30 8 systems 

(0.1% experimental uncertainty, H/X = 245 system omitted)

4Ž•

z 

C) 

Ci

0 

f-" 

a 

rA
z 

ý0 

6-It 

(3' 

0 

00l

64 65 66

experiments included

67

-.- H/X=147 

-- H/X=245 

-*- H/X=320 

-*- H/X=396 

-*- H/X=503 

--- H/X=757

68

Figure 28 Predicted values of (e-a)/c as a function of the number of experiments included for 5-wt % U308 systems

Figure 28 Predicted values of (e-a)/c as a function of the number of experiments included for 5-wt % U308 Systems 

(0.4% experimental uncertainty) CA

5% OPTIMAL SERIES

0 

U 

(U 

I 

0)

0 CD 

CD 

0

2.5 

2 

1.5 

1 

0.5 

0 

-0.5 

-1 

-1.5

62 63
Generalized Linear-Least-Squares Methodology

values of 757, 396, 503, 320, 147, and 245. The divergence of the solutions near the right-hand side of the graph 

was expected, since the analysis of the six 5-wt % experiments alone concluded that the H/X = 245 system is 

inconsistent with the remaining systems. However, it appears from this analysis that the system with an H/X value 

of 147 is also inconsistent with the remaining systems. This finding is curious since the calculated value for this 

system agrees well with its measured value; however, it appears to be somewhat inconsistent with the general trends 

seen in the other data. For the remaining 4 systems (H/X of 320, 396, 503 and 757), it is clear that, not only are 

these systems converged, but the database of 62 experiments very accurately predicts the (e-a)/c values for these 

experiments. 

4.2.2 Lessons Learned for Use of Correlation Coefficients 

As stated in the introduction to this section, two of the goals of this GLLSM analysis were to predict the number of 

experiments and the magnitude of the correlation coefficient necessary for convergence, i.e., the similarity criteria. 

Obviously these two quantities are connected. It would be expected that fewer experiments are needed to ensure 

convergence if the correlation coefficients are larger, than if they were smaller. With this in mind, a numerical 

exercise was carried out using the 2-wt % experiments. In Figure 29, the values of (e-a)/c are plotted versus the 

correlation coefficient range for the 62 plus experiments in the benchmark database. The values plotted to the far 

right correspond to the inclusion of all of the experiments (i.e., correlation coefficients 0.9-1.0 and lower), followed 

by inclusion of experiments with correlation coefficients of 0.8-0.89 and lower, and so on. This curve is designed to 

estimate the correlation coefficient by which convergence is typically obtained. It appears from these results that in 

general correlation coefficients of 0.80 and higher can be utilized to obtain convergence. The exception is again the 

system with an H/X of 972, which was shown previously to be quite difficult to converge. Even though these results 

only correspond to the group of 2-wt % systems, there appears to be a clear break in the behaviour of the 

convergence for systems with correlation coefficients below 0.80. Although the actual number of experiments 

needed to ensure convergence is quite difficult to conclude precisely, it appears that for correlation coefficients equal 

to 0.9 or higher, about 5-10 experiments are needed. These recommendations are based on the 2 wt % and 5 wt % 

cases shown in the previous section which seem to indicate that convergence is seen when about 4 other highly

correlated (ck > 0.9) experiments are included in a GLLSM analysis. For correlation coefficients value between 0.80 

and 0.89 approximately 10-20 experiments are needed, although these are not sufficient in all cases, as seen for the 

system with an H/X of 972. The recommendations for less-highly correlated systems (0.8 _< ck • 0.89) are loosely 

based on the number of experiments in that range for the exercise shown in Figure 29. The primary factor in 

whether systems with correlation coefficients between 0.80 and 0.89 produce convergence is believed to be the 

"completeness" of the experiments, that is, do the other experiments provide validation of the application system for 

all important cross sections and energy ranges. Further development of the concept of completeness is currently 

ongoing.

NUREG/CR-6655, Vol. 1

S ection 4

49
z 

tJI ct" 

a=--

A-H/X=94 

6-- X,406 

K-%-HX=496 

S-M.--HiX=614 

--- H/X=972

0.7 0.8 0.9 1

Ck Coefficient 

Values plotted represent lower end of range

Figure 29 Dependence of the predicted (e-a)/c values on the inclusion of experiments in various ranges of correlation coefficients

2% Systems Behavior with Ck

1.5

I

0

S0.5 

a,

0 

-0.5

0.6
5 INTEGRAL PARAMETER APPLICATIONS 

Two integral parameter quantities have been discussed thus far in this report: the total sensitivities and the 

correlation coefficients. The total sensitivities are quite useful in identifying trends in the various systems; however, 

they can be quite confusing when applied to only a few disconnected systems because their uniqueness cannot be 

guaranteed. The correlation coefficients, ck, on the other hand, give simple indications of the degree of commonality 

between the various uncertainty contributors for each system, regardless of their origin. In order to generate the 

correlation coefficients, cross-section uncertainty information must be available for all important systems 

components. In some cases, system similarity may need to be determined based solely on their respective 

sensitivities. A measure of similarity consisting of a single parameter or perhaps a few parameters is needed for 

these situations. 

Tests of many different parameter types were carried out in order to determine a set of values that would best satisfy 

the needed similarity determination. The most promising set of parameters are a family of "D" values as defined 

below: 

g 

D = = Snaj - Sneil 

i=l 

D. = jI Si - S~i (22) 

Ds = S - SseiI 

i'~l 

where S is the relative sensitivity of kf for the application, a, or experimental configuration, e, toyT (n) or to the 

capture (c) and scattering (s) cross sections for energy group i. See Eq. (9) for the defining relation for the S values. 

These parameters can be presented as a single quantity as defined above or as an energy-dependent profile, 

corresponding to the absolute value of the differences in the sensitivities by energy group. Values of Dn, D,, and D, 

are given in Table 6 for a U(2)F4, HIX = 195 system versus the family of U(l 1)02 "artificial" systems described 

earlier in this report.

51 NUREG/CR-6655, Vol. 1
Integral Parameter Applications 

Table 6 D parameters for H/X = 195 U(2)F4 + CH 2 as compared with U(11)0 2 + H20

Section 5

H/X D. D, D, 

0 1.7986 0.7938 0.7381 

3 1.6084 0.6641 0.6030 

5 1.5215 0.6125 0.5497 

10 1.3241 0.5230 0.4781 

20 1.0329 0.4220 0.4681 

40 0.6748 0.3593 0.5140 

80 0.3430 0.3220 0.5615 

200 0.1261 0.3086 0.6085 

300 0.2355 0.3751 0.6164 

400 0.2982 0.4270 0.6123 

500 0.3373 0.4694 0.6013 

600 0.3633 0.5042 0.5867 

800 0.4025 0.5701 0.5539 

1000 0.4271 0.6201 0.5198 

These coefficients are useful in making a quick determination of the similarity between pairs of systems. Since the 

range of D values is 0 to 2, values above 1 are certainly indicative of systems that are dissimilar. This dissimilarity 

is clear from Figure 30, where a D, value of 1.7986 is represented by an energy-dependent profile. The spectra of 

the two systems appear to be quite different, one thermal and the other fast. Contrast Figure 30 with the Figure 31, 

which compares the sensitivities by group for systems of 2- and 1 1-wt % U with nearly identical H/X values. The 

D, value in this case is 0.1261. In Figure 32, a Dn value of 0.4271 seems to indicate similar systems for an H/X 

value of 195 as compared with HIX of 1000. Figures 33-35 give a comparison of the same systems, but for the D, 

parameter. In these figures, the D, value of 0.3086 shown in Figure 34 certainly indicates similar systems, while the 

profiles representing D. values of 0.7938 and 0.6201 (Figures 33 and 35) indicate that sizeable differences exist in 

these systems. The energy-dependent values for D. corresponding to these same systems are given in Figures 36-38. 

These values are interesting in that none of the three are below 0.4. The D profiles indicate that while the differences 

in sensitivities are largely confined to two energy regions, the relative magnitudes of the sensitivities between these 

energy regions changes; hence the systems should not be considered similar.

NUREG/CR-6655, Vol. 1 52
nubar total 1.7986 u(2)f4 h/x=195 -

10 104 105 106 107

Energy (eV)

Figure 30 Dn profile for U(2)F4 H/X = 195 and U(l 1)02 H/X = 0 systems; Total D. = 1.7986

Co

u(11)o2 h/x=O

0.4 

0.3

0.2

>1, 

~4-j 

U) 

('9 

<:]

0.1

(A

0.01 

@2 10

C 

ON 

ON Un 

(A 

ci 

0

0 

q9 

0 

-t 

C 

p 

0
nubar total .1261 u(2)f4 h/x=195 -

z 

(3• 

(i 

07

u(1 1)o2 h/x=200

I I IIIII1 I I I I II I 1 9 11 11

0.0 -5 I 1 lill 

10 10

I 111111 iii I I I 1.111 L...4...JtIIIII I I II IIrTt

-4 10-3 1 -2 1 -1 nr

0" 10 

Energ

I1 

2

1 102 

/ (eV)

10 3104 105 106 107

Figure 31 D, profile for U(2)F4 H/X = 195 and U(11)0 2 H/X = 200 systems; Total D, = 0.1261

0.4 I I I If"I'II I I I I 1111111 1"I"II 111"11 " I 1 1 1 111 1 1 1 1 11 1"1"1 ' " f ' II 1 " 1'" 1 111111

0.3

<3 

4 -J 

(I) 

(I-) 0) 

'-I

0.2

0.1

2 

0 

(-I'

I I I I ! 

I

1(
nubar total .4271

0.4 

0.3

0.2

0.1

u(2)f4 h/x=195 -

CO 

0 

C/i

u(11)o2 h/x=1000

Energy (eV)

Figure 32 D. profile for U(2)F4 HIX = 195 and U(11)O 2 H/X = 1000 systems; Total D. = 0.4271

(Ai 

CAi

<3 

I., 

Cl

z 

(3 

C/i 

C/i

(El 

I-i 

0 

p 

0
capture total .7938 u(2)f4 h/x=195

Energy (eV)

Figure 33 D, profile for U(2)F4 H/X = 195 and U( 1)02 HIX = 0 systems; Total D, = 0.7938

z 

C-) 

ON 

ON 0.20

- u(11)o2 h/x=O

(tr 

CD 

0q

(-'I 

ON

0.15

K

>., 

"50.10 

U) 

<: a)j 

0)

0.05 

0.00 

1c
capture total .3086 u(2)f4 h/x=195 - u(11)o2 h/x=200

0 .2 0 , I 11111 1 " 1 '"i"i m 1 .'""'"'" 1 11 1 11 ... I "I 1 111 ,M 

0.15 

:3 

4-, 

"* 0.10 

cn 

(n Q) 

0.05 

~~~~~~~~~ .... I , I ,,,,, J1.......1 ,, I ,.,1 ,,11l ,1,d 

0.00-5 -4 -3 2 1 0 1 2 3 4 5 

10 10 10 10 10 10 10 10 10 10 10 10 

Energy (eV) 

Figure 34 D, profile for U(2)F4 HYX = 195 and U(1 1)02 H/X = 200 systems; Total D, = 0.3086

nZ 

ON

II 

C) 

0
capture total .6201 u(2)f4 h/x=195 

0 .2 0 , -- 1 .-, . I I I I 1,,,i 1 I , 1 I , 11111 , ,,,,' 1

- u(11)o2 h/x= 1000

z 

C) 

LA

I111 I i111 111111l I 1 111111 1 1 1111111 I1.I 1.1111 111 111m

10 410- 310- 210- 100 101 102 10 104 105 106 107 

Energy (eV)

Figure 35 D, profile for U(2)F4 H/X = 195 and U(1 1)02 H/X = 1000 systems; Total D, = 0.6201

I I 111111 I I I 111111 I I I I 11111 I I I 11111

(A 

00

0.15 

• 0.10 

Cn 0 . 

0.05

0 

0

0.00 

10

C12 

LA

I 1 11 11 1 • 1 1 1111 "1 - 1 1 1"1"1 1 1.ll I . ...... II II
scatter total .7381 u(2)f4 h/x=195

C/2 

01o 

-u(11)o2 h/x=O

Energy (eV)

Figure 36 D, profile for U(2)F4 H/X = 195 and U(11)0 2 HIX = 0 systems; Total D, = 0.7381

0.20 

0.15 

D 

>,, 

4 --J >50.10 

V) 

0r) 

0.05 

0.00 1 (

LAi

0

z 

C.) 

ON 

ON 

0
scatter total .6085 u(2)f4 h/x=195

Energy (eV)

Figure 37 D. profile for U(2)F4 HI/X = 195 and U(11)0 2 H/X = 200 systems; Total D, = 0.6085

:z 

0d3

0.20

- u(11)o2 h/x=200

C

(t' 

01

0.15

:]

:>. 

i-

(13

0.05 

0.00 

1(

c~i:
scatter total .5198 u(2)f4 h/x=195 

0 .2 0 I 1 1 1111111 ,11111 , I., , I 1,, 1...,. u ,,,. 1 111111 1111 ,,,,,,,, ,

0.15 

:3 

>., 

"30.10 

C

0.05

0 0 , I I I I , 1 ni- q m ml 1 11111111I I 1I Irlll1l 1111111 I I I 1I1II 

10- 510 410- 310- 210- 100 101 102 103 1 

Energy (eV)

Figure 38 D, profile for U(2)F4 H/X = 195 and U(1 1)02 H/X = 1000 systems; Total D, = 0.5198

- u(11)o2 h/x=1000

Co)

ON

z 

C) 

0• 

ON

(-4 0 

-4 cr

1 

0 

C-) 

0
Integral Parameter Applications

The precise numerical value of these D's that determine system similarity is difficult to define, requiring some degree 

of expert judgement. For comparison with the ck values presented previously, values of D., D,, and D, below about 

0.4 generally correspond to systems with ck values above about 0.8. In order to show the explicit relationship 

between the various ck and D values, Figure 39 contains a plot of the values of Da, D•, and D. versus ck, 

corresponding to the U(l 1)02 H/X = 500 system with respect to the experimental benchmark data base used in Vol. 

2. Linear fits to these data points are shown as a solid line for D, vs ck, a dotted line for D. vs ck, and a dashed line 

for D, vs ck. Even though the fits are somewhat fuzzy, clearly the ck values greater than 0.8 correspond very closely 

to Dn, D0, and D, values below 0.4-0.5. 

The individual values of D , D, and D, give specific similarity information relating to fission, capture and scattering. 

However, to be compared with the single value of cý, an additional parameter was postulated. The parameter Dm is 

defined as the sum of D., Dc and D,. A plot of D,,m versus ck is shown in Figure 40. There the ck value of 0.8 

clearly corresponds to a Du value of about 1.2. Thus, an additional indicator of system similarity would be a Dum 

value of less than 1.2.

NUREG/CR-6655, Vol. 1

Section 5

62
2.5

.2 

~1.5 

m Dcl 

E 

L.CA 

. ,-- UI U 

0.5 _m - - ; N 

0

z 

0 0.2 0.4 0.6 0.8 1 

n Ck 

61 Figure 39 Comparison of D., D,, D. values with ck values for benchmark database with respect to U(I 1)02 H/X 500 system 

M 0
4i

L~h3.5• 

3 

2.5 

E 

2 

1.5 

0.5 

0 0.2 0.4 0.6 0.8 

Ck 

Figure 40 Comparison of Ds., values with ck values for benchmark database with respect to U(1 1)02 I/X 500 system 

Li
6 SUMMARY 

This report has presented the theoretical basis for the application of the sensitivity and uncertainty analysis methods 

to the validation of data sets for use in criticality safety applications. These procedures involve standard sensitivity 

and uncertainty analyses that were developed in the late 1970s primarily for use in the development of fast reactor 

systems in the United States and abroad. The sensitivity analyses produce energy-dependent sensitivity values 

(sensitivity profiles) which give the change in the system ke value as a function of changes in the cross sections by 

energy. These analyses provide the basic understanding of the physics of each critical benchmark system in order to 

properly characterize similarities between systems in a consistent manner. The uncertainty analyses provide an 

estimate of the uncertainties in the calculated values of the system k,,, as well as correlations in these uncertainties 

between systems. The use of both sensitivity and uncertainty analyses in the formal determination of areas (or 

ranges) of applicability has been developed in this work. These determinations of applicability can be accomplished 

via parameters, which represent the differences in V', capture, and scattering sensitivity profile values (i.e., D•, Dr, 

and D,, respectively) and the correlation coefficients, ck. Ranges of these parameters, proposed to formally define 

the applicability of a series of critical benchmark experiments to a particular application area, are ck values that are 

0.80 or higher, D,,m values that 1.2 or less and values of D., Dr, and D, that are 0.40 or less. These parameters 

relate directly to the applicability or similarity between pairs of systems - one of which is assumed to be a 

benchmark; the other, an application. 

The sensitivity and uncertainty analysis results, along with the calculated and measured kO values and estimates of 

uncertainties in the measurements, were used in this work to demonstrate application of the full GLLSM procedure 

to data validation for criticality safety studies. The primary goal of the GLLSM analysis is the prediction of the 

calculated-versus-measured differences for systems that have not been measured. These calculated-versus-measured 

differences are the so-called calculational biases. Application of the GLLSM procedures to a series of critical 

experiments is designed to identify "changes" in the underlying nuclear data such that the calculated-versus

measured differences are minimal. This work has identified the relationship between these predicted data changes 

and the calculational bias for systems that have not been measured, and hence correspond to interpolations or 

extrapolations in the vector space corresponding to the original set of benchmark experiments. Uncertainties in the 

bias can be estimated based on the standard deviation provided by the GLLSM procedure. 

To demonstrate the basic concepts and usefulness of the GLLSM procedure and to estimate the required magnitude 

of the ck and D parameters and number of systems needed for a meaningful estimate of bias, the GLLSM procedure 

was applied to a set of 2-wt % and 5-wt % uranium oxide and uranium fluoride critical benchmarks, which consisted 

of 6 experiments each. Initial studies were performed on the 2-wt % and 5-wt % systems in isolation to demonstrate 

that for highly correlated systems, the GLLSM procedure can produce rapid convergence on the predicted bias for 

such systems. These studies were unique in that omission of one of the experiments from the GLLSM procedure 

allowed simulation of the prediction of bias for that experiment as if it had never been performed. These predictions 

can then be compared directly to the actual biases, because the measured and calculated values of ke are known. 

Results using this limited number of highly correlated experiments show that convergence can be quite rapid. 

However, the use of a large number of experiments in the GLLSM procedure is recommended since they place 

additional constraints on the cross-section data changes that are not present in a case with few experiments. The use 

of a large number of experiments in the GLLSM procedure was also demonstrated in this work. The results show 

that the large database is quite effective in producing convergence for the 2-wt % and 5-wt % sets without including 

any of the 2-wt % and 5-wt % data directly in the GLLSM combination procedure. This method is the desired 

operation of the GLLSM for applications in which no direct measurements are available. 

This work also demonstrates approximate values of the ck parameter and the associated number of experiments for 

which convergence of the GLLSM procedure can be expected. These results are expected to be useful for non

GLLSM analyses as well, in that these guidelines should also apply to more traditional trending procedures as well.

65 NUREG/CR-6655, Vol. 1
Summary

An estimated 5-10 benchmarks with values of ck equal to 0.9 or higher are needed to ensure convergence in a 

GLLSM procedure. Therefore, a corresponding number and type of systems should be considered a minimum for 

standard validation analyses. Under certain conditions, convergence can also be expected for 10-20 systems with ck 

values between 0.8 and 0.9. The type of conditions under which convergence can be produced with systems with a 

ck value less than 0.8 (even much less than 0.8) is the subject of current research. 

Further comparison of the use of ck and D values indicate that D values less than 0.4 (or sum of the D values less 

than 1.2) match the area of applicability criteria associated with cý > 0.8.

NUREG/CR-6655, Vol. 1

Section 6

66
7 REFERENCES 

1. American National Standard for Nuclear Criticality Safety in Operations with Fissionable Material Outside 

Reactors, ANSIIANS-8.1-1998, American Nuclear Society (1998). 

2. R. L. Murray, Nuclear Reactor Physics, Prentice-Hall, Englewood Cliffs, NJ, 1957. 

3. J. R. Lamarsh, Nuclear Reactor Theory, Addison-Wesley, Reading, MA, 1966. 

4. A. Henry, Nuclear-Reactor Analysis, MIT Press, Cambridge, MA, 1975. 

5. E. M. Oblow, "Sensitivity Theory from a Differential Viewpoint," Nucl. Sci. Eng. 59, 187 (1976). 

6. W. M. Stacey, Jr., "Variational Estimates and generalized Perturbation Theory for the Ratios of Linear and 

Bilinear Functionals," J. Math. Phys., 13, 1119 (1972); see also W. M. Stacey, Jr., "Variational Estimates of 

Reactivity Worths and Reaction Rate Ratios in Critical Nuclear Reactors," Nucl. Sci. Eng. 48, 444 (1972). 

7. L. N. Usachev, "Perturbation Theory for the Breeding Ratio and for Other Number Ratios Pertaining to 

Various Reactor Processes," J. Nucl. Energy Pts. A/B, 18, 571 (1964). 

8. A. Gandini, "A Generalized Perturbation Method for Bilinear Functionals of the Real and Adjoint Neutron 

Fluxes," I Nucl. Energy 21, 755 (1967). 

9. C. R. Weisbin, J. H. Marable, J. L. Lucius, E. M. Oblow, F. R. Mynatt, R. W. Peelle, and F. G. Perey, 

Application of FORSS Sensitivity and Uncertainty Methodology to Fast Reactor Benchmark Analysis, 

ORNL/TM-5563, Union Carbide Corp., Oak Ridge National Lab., December 1976. See also, Nucl. Sci. Eng., 

66, 307 (1978). 

10. J. L. Lucius, C. R. Weisbin, J. H. Marable, J. D. Drischler, R. Q. Wright, and J. E. White, A Users Manual 

for the FORSS Sensitivity and Uncertainty Analysis Code System, ORNL-5316, Martin Marietta Energy 

Systems, Oak Ridge National Lab., January 1981. 

11. SCALE: A Modular Code System for Performing Standardized Computer Analyses for Licensing 

Evaluations, NUREG/CR-0200, Rev. 5 (ORNL/NUREG/CSD-2/R5), Vols. I-III, March 1997. Available 

from Radiation Safety Information ComputationalCenter at Oak Ridge National Laboratory as CCC-545. 

12. R. L. Childs, SENI: A One-Dimensional Cross-Section Sensitivity and Uncertainty Module for Criticality 

Safety Analysis, NUREG/CR-5719 (ORNL/TM-13738), U.S. Nuclear Regulatory Commission, Oak Ridge 

National Lab., July 1999. 

13. W. A Rhodes, and R. L. Childs, An Updated Version of the DOT 4 One- and Two-Dimensional 

Neutron/Photon Transport Code, ORNL-5851, Union Carbide Corp., Oak Ridge National Lab., 1982. 

14. B. L. Broadhead, and B. T. Rearden, Exploratory Studies for Three-Dimensional Sensitivity Methods, 

ORNL/M-6583, Lockheed Martin Energy Research Corp., Oak Ridge National Lab., 1998. 

15. S. J. Raffety and J. T. Mihalczo, Homogenized Critical Assemblies of 2 and 3% Enriched Uranium in 

Paraffin, Y-DR-14, Union Carbide Corp., Nuclear Division, Oak Ridge Y-12 Plant, 1969.

67 NUREG/CR-6655, Vol. 1
16. D. F. Cronin, Critical Mass Studies, PartX, ORNL-2968, Union Carbide Nuclear Corp., Oak Ridge National 

Lab., 1960. 

17. J. D. Smith, III and B. L. Broadhead, Multigroup Covariance Matrices for Fast Reactor Studies, ORNL/TM

7389, Martin Marietta Energy Systems, Oak Ridge National Lab., 1981. 

18. R. E. McFarlane, R. J. Barrett, D. W. Muir, and R. M. Boicourt, The NJOY Nuclear Data Processing System: 

User's Manual, LA-7584-M, Los Alamos National Lab., 1978. 

19. B. L. Broadhead, A Nuclear Data Adjustment Methodology Utilizing Resonance Parameter Sensitivities and 

Uncertainties, ORNL/CSDITM-212, Martin Marietta Energy Systems, Oak Ridge National Lab., 1984. 

20. H. Derrien, N. M. Larson, and L. C. Leal, Covariance Matrices for Use in Criticality Safety Predictability 

Studies, ORNL/TM-13492, Lockheed Martin Energy Research Corp., Oak Ridge National Lab., 1997. 

21. A. Gandini, Nuclear Data and Integral Measurements Correlation for Fast Reactors, Part 1: Statistical 

Formulation, RT/FI(73)5, Comitato Nazionale Energia Nucleare, 1973. 

22. J. B. Dragt, J. W. M. Dekker, H. Gruppelaar, and A. J. Janssen, "Methods of Adjustment and Error Evaluation 

of Neutron Capture Cross Sections; Application to Fission Product Nuclides," Nucl. Sci. Eng. 62, 117 (1977). 

23. J. J. Wagschal, and Y. Yeivin, "The Significance of Lagrange Multipliers in Cross-Section Adjustment," Trans. 

Am. Nucl. Soc. 34, 776 (1980). 

24. J. Barhen, J. J. Wagschal, and Y. Yeivin, "Response-Parameter Correlations in Uncertainty Analysis," Trans. Am. 

Nucl. Soc. 35 (1980). 

25. R. E. Maerker, B. L. Broadhead, and J. J. Wagschal, "Theory of an New Unfolding Procedure in Pressurized Water 

Reactor Pressure Vessel Dosimetry and Development of an Associated Benchmark Data Base," Nucl. Sci. Eng. 

91, 369 (1985). See also Development and Demonstration of an Advanced Methodology for LWR Dosimetry 

Applications, EPRI NP-2188, Electric Power Research Institute, Palo Alto, Calif., December 1981.

NUREG/CR-6655, Vol. 1

References Section 7

68
APPENDIX A 

Covariance Concepts

69 NUREG/CR-6655, Vol. 1
APPENDIX A 

Covariance Conceptst 

Let the variable y represent measurements taken with some instrument or device that will not yield an exact result, 

but will give a variety of numbers with some spread. If a very large number of measurements were made, a 

probability distribution such as a Gaussian could be developed. A related expression is the probability density, 

labeled f(y), having the property that its integral over all values of y is 1, i.e, 

ff(y)dy = 1. 

The average value of y is 

y = fy f(y) dy 

For a finite number of measurements, yi, where i = 1, 2, 3, ...,N, 

y -- (1/N)F- yj f(y)-. 

These are examples of an expectation value (or expected value), written either as E(y) or as <y>. More generally, 

we can find the expectation value of a function of y, e.g., g(y), by forming 

E[g(y)] = fg(y)f(y)dy. 

A measure of the width of the distribution is sought. We take note of the differences between each observation and 

the average, 

8y=y- y. 

Form the square of that difference and evaluate the expectation value 

E[(5y)21 = E[(y - 7) 2] = f (y - -)2 f(y)dy. 

This is called the variance, often symbolized by var(y) or simply v(y). The square root of the variance is the 

standard deviation, 

a(y) = [var(y)]1⁄2 

having the same units as y and a measure of the width of the distribution. Related parameters often used are the 

relative variance, var(y)/[fy] 2 and the relative standard deviation, a(y)/[y]. All of these quantities may be found for 

other variables such as u, v, w, z, etc. 

There are several distributions that are appropriate for certain processes, but the standard distribution (or 

Gaussian) is most often applied. One calculates the average of the measured data, y, and also the standard 

deviation, c, and inserts them in the theoretical expression, 

tUsed with permission from Dr. R. L. Murray, North Carolina State University, Raleigh, NC.

71 NUREG/CR-6655, Vol. 1
Covariance Concepts

f(y) = 1/[o(2rr)/] exp{-1⁄22 [(y Y)/0]21, 

which is used for evaluation of expectation values. 

When a measured variable depends on two or more parameters, for example, r(xy), each of the variables x and y 

has its own expectation value 

E(x) = E(x + bx) = x 

E(y) = E(y + by) = y. 

A joint probability density function is needed, f(x,y). To obtain the f(x) needed to find E(x), it is required to 

integrate f(x,y) over all y, and similarly for f(y). 

It is possible that there is some relationship between x and y. To check that, a new quantity is introduced, the 

covariance. For the two-variable case, it is written as 

cov(x,y) = oxy2 = E(8x by) = f (x -x)(y - y) f(x,y) dx dy 

It is seen to be the weighted product of the error in x and the error in y. A related quantity is the correlation 

coefficient, defined by 

p (x,y) = cov(xy)/oY oy 

with p (y,y) = 1. It can be shown that if there is direct dependence of x on y the correlation coefficient p is +1, if 

there is no dependence, p is zero. 

To be more specific, let us consider a critical assembly similar to the Los Alamos Godiva, but composed of pure 

U-235 as metal. To a rough approximation the neutron flux can be described by a one-group diffusion model, 

DV2(ý - yZ,+ X) Zv=0 

where X = l/ke,,. Ignoring variations in the diffusion coefficient D and the number density N for uranium, the 

value of keff depends on v, oa, and of. The method of measuring the number of neutrons per fission is generally 

different from that used to measure cross sections. However, the methods of measuring the two cross sections 

may be similar so that 0a and of are correlated, and there is a non-zero value of the covariance cov(oa, of). 

Let us derive a formula for the variance of a function of two variables, assuming a linear relationship 

r(x,y) = ax + by. 

where a and b are constants. The expectation value of r is clearly 

r =a x + b y.

NUREG/CR-6655, Vol. 1

Appendix A

72
The variance of r is

v(r) = E[(r - -)2] 

Form the quantity 

r - r-= a(x - i) + b(y- y) 

= a 5x + b Sy.

Thus

(r - T)-2 = a2 (Sx)2 + b2 (5y)2 + 2 a b 5x 5y.

Form the expectation value to obtain the variance 

v(r) = a

2 v(x) + b2 v(y) + 2 a b cov(x,y), 

which says in words that the variance of r as a linear function of x and y is composed of the variances of x and y 

plus a covariance term. If x and y are not related, the last term is zero, and the variance is merely the sum of those 

for x and y. 

A more general case is where r depends on a number of variables, i.e., 

r(x1 , x2, X3,...,XN). 

It is very convenient to turn to a matrix representation of r, 

r =x 

X2 

Xs 

XN 

There may be relationships between pairs of variables xi and xj, leading to a set of variances vij = E(3xi, 5x) that 

form a matrix array as follows: 

v(r) = v1 1 v 12 v 13 ... 

v2 1 v22 v23 ... 

v31 v32 v33... 

This matrix is called the covariance matrix, even though the main diagonal consists of variances. It is symmetric 

about that diagonal, because vj is the same as vj~i. It is also known as the error matrix.

73 NUTREG/CR-6655, Vol. 1
Covariance Concepts

To illustrate the benefits of the matrix method, we now derive a compact relation involving the way the variable r 

changes with the various x,. Propose that the variations are small, so that the first two terms of a Taylor series are 

sufficient. Thus write 

r(x) = r(x) + (dr/dxl)dx1 + (dr/dx2)dx2 + (dr/dx3)dx 3 +... + (dr/dxN)dxN 

where, strictly speaking, partial derivative symbols should be used. Let r'i stand for the rate of change or slope 

dr/dxi, directly related to sensitivity. Then a compact expression for 8r = r - r can be written, 

8r=r', 6xl + r' 2 6x 2 + r' 3 6X3 +... 

Write out the square of 5r for a case of three variables, 

(5r)2 = (r'1 Sxl + r' 2 5x2 + r' 3 5x3)(r', 6xj + r' 2 5x2 + r' 3 6x3) 

= r'I(6x,)2 r' 1 + r'1(8xI)(5x)r'2 + r',(8x1)(5x3) r' 3 

"+ r,2 (x2)(6x 1 )2 r', + r' 2(5x2) 2 r'2 + r' 2(5x2)(5x3)r'3 

"+ r' 3(6x3)(6x 1) r', + r'3(5x,)(5x2)r' 2 + r'3(5x3)

2r' 3. 

Take the expectation value of the sum to produce variances and covariances. The expression can be written in 

matrix notation as 

v(r) = E[(5r)2] = r' v (r')T 

where T stands for transposed, i.e. interchanged rows and columns. The two r matrices are 

r'= rII (r')T r'1 r' 2 r' 3 

r/2 

rt 3 

The compact formula expresses the total variance due to the variations Sxj, 8x2, and 5x3 including all possible 

interactions, weighted by the sensitivities of the function r to changes. The virtue of the matrix method is the ability 

to perform algebraic operations conveniently.

NUREG/CR-6655, Vol. 1

Appendix A

74
APPENDIX B 

Derivation of the Generalized Linear-Least-Squares Equations

75 NUREG/CR-6655, Vol. 1
APPENDIX B 

Derivation of the Generalized Linear-Least-Squares Equations 

The derivation of the generalized linear least squares methods is taken from ref. 25 with only minor variations. 

Let the vector m = (m-, i = 1,2.... I represent a series of critical benchmark measurements (i.e., responses) for all 

of which calculations are available. This vector m has a corresponding symmetric I x I uncertainty matrix 

associated with it which we denote as C•,, - cov(mrr, rn) = <5mrr 6m1> = <(5m)(6m)t>.* Let the vector k = (t,) 

represent a calculation of the kf values for each experiment. Further, let the vector a - (a,), i = 1,2.... N, with its 

corresponding symmetric N x N uncertainty matrix C. =- <65a 5c6>, represent the differential data used in the 

calculations ( i.e., nuclear data such as fission cross sections, partial cross sections used in the transport calculation 

and the fission spectrum, and nonnuclear data such as corrections (biases) arising from methods approximations). 

We shall also allow for the possibility of correlations between the integral and differential data, which although may 

often be neglected in a first iteration of the adjustment procedure, are present in any subsequent iteration because of 

the nature of the combining process. These covariances are denoted by the N x I matrix C. - <5a 5mr->. 

If we define the sensitivities of the calculated responses to each of the parameters a as Sk (ak/acx), with Sk being 

an I x N matrix, and if we impose the linearity restriction, 

k(a + 6a) = k(a) + 6k - k(a) + SkSa , (1) 

Ckk - <(6k) (5k)1> = Sk <(5a) (8a)t> Skt = SkC.CSkt (2) 

then the uncertainty matrix corresponding to the calculated responses becomes 

which reflects the propagation of the uncertainties in a through the calculation of k. 

If we denote the deviations of the measured responses from their corresponding calculated values by the vector d 

Cdd = <(k - m) 5(k - m)ý> = <5k~kt> + <5m~mt> - <5k~mt> - <Sm~kt> = Ckk + C. - SkC. -CmSk 

= s kc..st + c. - s c. - c."st 3 S kCS+ k kC,5~ (3) 

(d) - k - m, then the uncertainty matrix for d, denoted by Cd,, is 

Let the (unknown) adjusted parameter vector be a' =- (ai'), and the adjusted calculated response vector be k'(a'). 

By imposing the condition m' = k'(a'), we are forcing the adjusted values a' and m' to be consistent within the 

limits of linearity of the theory. If we further denote the actual adjustments by x = a' - a and y = m' - m, then the 

linear approximation expressed by Eq. (1) becomes 

*The symbol '"T' is used to denote the transpose, and the brackets "< >" are used to designate an expectation value, 

i.e., an average over a probability distribution, of possible deviations (6) from the mean value.

77 NUREG/CR-6655, Vol. 1
Derivation of Generalized Linear-Least-Squares Equations

m'= k + Skx

or

m' - m= k- m+ Skx

thus leading to

y=d+Skx. (4)

The generalized linear least squares procedure involves minimizing the quadratic loss function

Q(x,y) = (y,x) C( CM :)1 (y,x) I (5)*

where (y,x) =- (yl,... ,yi, xl,.... xI),subject to the constraint expressed by Eq. (4). Adopting the procedure of 

Refs. 23-24, the above conditional minimum formulation is equivalent to unconditionally minimizing the function 

R(x,y), where

R(x,y) = Q(x,y) + 2Xt(Skx -y), (6)

and 21 is an I-dimensional vector of Lagrange multipliers. Thus, x and y satisfy the equations

aR(x,y)/ax = aR(x,y)/ay = 0. (7)

The matrix in Eq. (5) can be expressed as the following identity

A -'

C C( m , C.mM.) (8)

-1 in which A = C. - C CI Cm. .Thus, Eq. (6) becomes 

R(x,y)= yt(C1 + C•CmCA-1C C- y - xt(A-'C.ýCb)y 

- yt(C C MA-1) x + xtA-A x + 2Xt(Skx - y) , 

*The symbol "-1" refers to the inverse of a matrix.

(9)

NUREG/CR-6655, Vol. 1

Appendix B

c.Z ÷ C.A -'CC -' 

-A-I C= C78
Derivation of Generalized Linear-Least-Squares Equations

and the Eqs. (7) become

aR(x,y)/ax = 0 = -It(A-'C.C,ý) y - y (CxiCmciA- ) 1 

- I 1 x + x t A- 1 l + 2X1 SkI , 

aR(x,y)/ay = 0 = It(C1 + CmmCm A-'C.CI) y 

+ yt(C + C .,C.mA A'C.C -) 1 

xt(A-1CmC.) 1 - it (C-1 C A-l) x - •21 ,

(10a) 

(lOb)

where we have introduced the symbols "1" and "0" to represent unit and zero vectors of dimensions 

N x 1 in Eq. (10a) and of dimensions I x 1 in Eq. (IOb). The symbols "I"' and "0" denote the corresponding row 

vectors. Since A71 is symmetric and the. transpose of a scalar and the scalar are the same, Eqs. (10) may be 

simplified to

kS2. + A-'x - A-'C.Cýy = 0

-- -)- (CIC+A -)I+ (C, ..... C, m,,,A 1C=C~m,) y - (-CA ' = 0

(10'a)

(10'b)

Solving for x and y yields 

and

x = (C. - C.S2) X 

y = (C. - Cm=SIk) X,

(11a) 

(lIb)

which reflect the driving influence of the vector I• and the independence of the solution on A71. Substituting the 

values of x and y into Eq. (4) yields

S=Cdld d,

with

c) = <(RX) (6X)t> = C ' <(5d) (1)'> C -d1 = C1

(12)

(13)

The adjusted values a' and m'are found from the adjustments in Eqs. (11) to be

a' = a + (C= - cS') C -'d 

r' = r + (C- CmSk) C dd,

and

(14a) 

(14b)

NUREG/CR-6655, Vol. 1

and

Appendix B

79
Derivation of Generalized Linear-Least-Squares Equations

where C-1 is obtained by taking the inverse of Eq. (3) and is a matrix of dimensions I x I. It should be noticed that 

in order to obtain a' and m' !Lif Cd has to be inverted, although AK1 appears in Eqs. (8)-(10). 

The covariances of the adjusted differential values are readily obtained from the first of Eqs. (14): 

Ca', - <(a') (6a')I> = C. + (C= - C.S') C-'<(3d) (Od)t> C-'(Cm - S C.) 

+ (C• - CSk') Cd <(5d) (6a)t> + <(6a) (5d)t> C -(C., - SkC,) 

and since 

<(5d) (5a)t> = <(6k - 5m) ()*yt> = Sk<(6a) (3a)ý> - <(6m) (5a)t> = SkCaa - Cma

then

Ca, ==C.+(C. - CaS t)C -I(C., k - SkC) k.•c•( - 2(C amck --d C.S )C-I(C. m S C.) ),

or

Coý(a' = C.- (15) (C. - C.•S ) C -I(C." - S C.) 

Similarly, 

Cm'm, = C. + (Cr - C.Sk) Cd <(8d) (5d)t> Cdd(C - SkCa) 

CmaSk) Cd-d1 <(5d) (5m)t> + <(6m) (5d) > C '(Ca - SkC.n)

with

<(3d) (6m)t> = <(5k - 6m) (6m)t> = Sk <(8Ct) (5m)t> - <(6m) (3m)t> = SkCc - C.

leads to

era' = C"-- (Cm.- CmaSt) C-dQ(C. - SkCom) (16)

Finally, the cross covariances of the adjusted integral and differential data become 

Ccem, = <(5a '

) (m'),> =Cmn + (C'M - C St) Cd-'<(5d) (5d)t> C d'(C - Sk C) 

+ (Co - C=S ) Cd' <(3d) (5m)t> + <(5a) (5d)t> C -J(C. - SkCam) 

which reduces to

Cam, = C. - (C. - C..SI) Cd-7(Cm - SkC). (17)

Thus, from Eq. (17), even if no a priori correlation between the differential and integral data existed, by virtue of 

the combination (adjustment) procedure an a posteriori correlation does exist and must be considered in any further 

iteration or in the covariance representation of the adjusted data for future use.

NUREG/CR-6655, Vol. 1

Appendix B

80
Derivation of Generalized Linear-Least-Squares Equations

The minimum value of Q in Eq. (5), found by substituting the expressions for x and y from Eqs. (11), is denoted by 

the symbol ): 

Q•(x,y) = dtC C d = d t (Cni + SkCSkt - SkC.- Cm.S')-' d. (18) 

The quantity )? is a measure of the adjustments made in units of (combined) standard deviations summed over all 

participating integral experiments. 

Thus e/I should be of the order of 1 or less (i.e., on the average, the adjustment in each integral experiment should 

be within about one combined standard deviation). This quantity, which is a figure of merit for the proposed 

adjustment, can be calculated without actually performing the adjustment, since all the ingredients of Eq. (18) are 

known d priori and just reflect the consistency status of the available data considered. 

For the particular case of a single measurement in which there is no correlation between the integral and differential 

data, Eq. (18) reduces to 

? = (k- m)2 /(C. + SkCS)(') 

which is just the square of the discrepancy between the calculated quantity k (using the available differential data a) 

and the corresponding measured quantity m expressed in units of the combined variance of m (i.e., C.) and k (i.e., 

Skc SI). 

The Translation or Extrapolation Problem 

The calculation of k. for an criticality safety scenario, denoted the "application," involves the use of cross sections. 

The calculated value of kf, k1, will generally deviate from the value that would have been measured. Our purpose 

is to calculate an adjusted k,0

f value in such a way that its deviation from a projected measured value will be 

minimal and that the uncertainty in this calculated value be as small as possible. This adjusted calculated kff value, 

ka' can be recomputed based on the suggested data adjustments, (x' - a, or a Ak bias can simply be estimated using 

the benchmark data. The value of ka' can be computed as: 

k' = k + S.(a'-a) = ka +Sa(C.- C.S) Cd'd, (19) 

or alternatively the adjusted Kl value can be written in terms of a bias, b.; 

ka = k. + ba. (20) 

from this comparison, it is clear that 

b. = - sW(a' - a) 

Taking the covariance of both sides of Eq. (19) and noting that the uncertainty in Kl denoted C, is: 

C <(5k.) (8ka•t> = S,<(5a) (8a)l> St = SaC.St , (21)

NUREG/CR-6655, Vol. I

Appendix B

81
Derivation of Generalized Linear-Least-Squares Equations

we obtain for the covariance matrix of the ka values as

C.'-- <(kýk) (6ka)t> SaC Sa + Sa(Co -CaS ) C-, <(5d) (5d)t> Cg,' ( - SkC_)S 

+ 2Sa (CCM - C.St) Ca- <(Sd) (Ska)t>

(22)

Since 

<(5d) (Sk)t> = <('k) (Sk)ý> - <(Sm) (Sk,)t> Sk<(6() ('a)t> Sat 

<(8m) (8a)t> SI = Sc= St -C c.StI 

Ekqa a ma 

Equation (21) becomes

Ca,., = S ac S - Sa(C. - C.St) C-,(Cm. - skC) st (23)

Equations (19) and (23) represent the adjusted ka value and its uncertainty. Again, the calculations involve only the 

inversion of a small I x I matrix together with some matrix multiplication. 

*Equation (23) can be alternatively derived by substituting C ,(, directly from Eq. (15) into the following equation: 

Ca,a= SC,,S = Sa[C - (C. - C S) 1

OdCI (Gra.C - SkC.)ISt

NUREG/CR-6655, Vol. 1

Appendix B

82
NRC FORM 335 U.S. NUCLEAR REGULATORY COMMISSION 1. REPORT NUMBER 

(2-89) (Assigned by NRC. Add Vol., Supp., Rev., 

NRCM 1102, and Addendum Numbers, if any.) 

3201,3202 BIBUOGRAPHIC DATA SHEET 

(See insftrzicons on fle rerse) NUREG/CR-6655, Vol. 1 

ORNLJTM-1 3692N1 

2. TITLE AND SUBTITLE 

Sensitivity and Uncertainty Analyses Applied to Criticality 3. DATE REPORT PUBLISHED 

Safety Validation I 

Methods Development November 1999 

4. FIN OR GRANT NUMBER 

W6479 

5. AUTHOR(S) 6. TYPE OF REPORT 

B.L. Broadhead, C.M. Hopper, R.L. Childs, C.V. Parks Technical 

7. PERIOD COVERED (inclusive Detes) 

8. PERFORMING ORGANIZATION - NAME AND ADDRESS (If NRC, provide Division, Ofrce or Region, U.S. Nuclear Regulefory Commission, and naeting address, f contector, 

pr•vide neme and mailing address.) 

Oak Ridge National Laboratory 

Managed by Lockheed Martin Energy Resarch Corporation 

Oak Ridge, TN 37831-6370

9. SPONSORING ORGANIZATION - NAME AND ADDRESS (/f NRC, typ "Samneas aboveý dfconftacto, provide NRC Division, Ofice orRegion, U.S. NucleerRegulatory Commission, 

and railing address) 

Division of Systems Analysis and Regulatory Effectiveness 

Office of Nuclear Regulatory Research 

U.S. Nuclear Regulatory Commission 

Washington, DC 20555-0001

10. SUPPLEMENTARY NOTES

C.W. Nilsen, NRC Project Manager

11. ABSTRACT r2" words or less) 

This report develops the methodology for application of Sensitivt and Uncertainty (S/U) Analysis Techniques to the data validation 

tasks of a criticality safety computational study. The S/U methods which are presented in this volume are designed to provide a 

formal means of establishing the range or area of applicability for criticality safety data validation studies. The development of two 

parameters that are analogous to the standard trending parameters form the key to the technique. These parameters represent 

the differences by group of S/U-generated sensitivity profiles and traditional correlation coefficients, each of which give information 

relative to the similarity between pairs of selected systems. 

A Generalized Linear Least Squares Methodology (GLLSM) tool is also described, which is used largely to provide an 

understanding of the magnitude and quantity (i.e., number of systems) of these new parameters that can be used in a formal 

definition of applicability and bias estimation. 

These methods and guidelines will be applied to a sample validation for uranium systems with enrichments greater then 5 wt % in 

Volume 2 of this document

12. KEY WORDS/DESCRIPTORS (List words orphrases diet will assist remewches in locating bhe report.) 13. AVAILABILITY STATEMENT 

unlimited 

criticality safety, data validation, sensitivity analysis, uncertainty analysis unlimited 

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