[Federal Register Volume 70, Number 200 (Tuesday, October 18, 2005)]
[Notices]
[Pages 60565-60575]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 05-20785]


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NUCLEAR REGULATORY COMMISSION


Notice of Availability of Interim Staff Guidance Document for 
Fuel Cycle Facilities

AGENCY: Nuclear Regulatory Commission.

ACTION: Notice of availability.

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FOR FURTHER INFORMATION CONTACT: James Smith, Project manager, 
Technical Support Group, Division of Fuel Cycle Safety and Safeguards, 
Office of Nuclear Material Safety and Safeguards, U.S. Nuclear 
Regulatory Commission, Washington, DC 20005-0001. Telephone: (301) 415-
6459; fax number: (301) 415-5370; e-mail: <a href="/cdn-cgi/l/email-protection#6309021057230d11004d040c15"><span class="__cf_email__" data-cfemail="7b111a084f3b150918551c140d">[email protected]</span></a>.

SUPPLEMENTARY INFORMATION:

I. Introduction

    The Nuclear Regulatory Commission (NRC) continues to issue Interim 
Staff Guidance (ISG) documents for fuel cycle facilities. These ISG 
documents provide clarifying guidance to the NRC staff when reviewing 
either a license application or a license amendment request for a fuel 
cycle facility under 10 CFR Part 70. The NRC is soliciting public 
comments on the attached draft ISG document, which will be considered 
in the final version or subsequent revisions.

II. Summary

    The purpose of this notice is to provide the public an opportunity 
to review and comment on a revised draft Interim Staff Guidance 
document for fuel cycle facilities. A previous version of this draft 
received substantive comments; therefore, to provide the public an 
opportunity to review and comment on the revised version, the document 
is being re-issued in draft. FCSS-Interim Staff Guidance-10 provides 
guidance to NRC staff relative to determining whether the minimum 
margin of subcriticality (MoS) is sufficient to provide an adequate 
assurance of subcriticality for safety to demonstrate compliance with 
the performance requirements of 10 CFR 70.61(d).

[[Page 60566]]

III. Further Information

    The document related to this action is available electronically at 
the NRC's Electronic Reading Room at http://www.nrc.gov/reading-rm/adams.html. From this site, you can access the NRC's Agencywide 
Documents Access and Management System (ADAMS), which provides text and 
image files of NRC's public documents. The ADAMS ascension number for 
the document related to this notice is ML052770515. If you do not have 
access to ADAMS or if there are problems in accessing the document 
located in ADAMS, contact the NRC Public Document Room (PDR) Reference 
staff at 1-800-397-4209, 301-415-4737, or by e-mail to <a href="/cdn-cgi/l/email-protection#62120610220c10014c050d14"><span class="__cf_email__" data-cfemail="1d6d796f5d736f7e337a726b">[email protected]</span></a>.
    This document may also be viewed electronically on the public 
computers located at the NRC's PDR, O 1 F21, One White Flint North, 
11555 Rockville Pike, Rockville, MD 20852. The PDR reproduction 
contractor will copy documents for a fee. Comments and questions should 
be directed to the NRC contact listed above by November 17, 2005. 
Comments received after this date will be considered if it is practical 
to do so, but assurance of consideration cannot be given to comments 
received after this date.

    Dated at Rockville, Maryland this 6th day of October 2005.

    For the Nuclear Regulatory Commission.
Melanie A. Galloway,
Chief, Technical Support Group, Division of Fuel Cycle Safety and 
Safeguards, Office of Nuclear Material Safety and Safeguards.

Attachment--Draft FCSS Interim Staff Guidance-10, Revision 1, 
``Justification for Minimum Margin of Subcriticality for Safety''

Prepared by Division of Fuel Cycle Safety and Safeguards, Office of 
Nuclear Material Safety and Safeguards

Issue

    Technical justification for the selection of the minimum margin of 
subcriticality for safety for fuel cycle facilities, as required by 10 
CFR 70.61(d)

Introduction

    10 CFR 70.61(d) requires, in part, that licensees or applicants 
(henceforth to be referred to as ``licensees'') demonstrate that 
``under normal and credible abnormal conditions, all nuclear processes 
are subcritical, including use of an approved margin of subcriticality 
for safety.'' There are a variety of methods that may be used to 
demonstrate subcriticality, including use of industry standards, 
handbooks, hand calculations, and computer methods. Subcriticality is 
assured, in part, by providing margin between actual and expected 
critical conditions. This interim staff guidance (ISG), however, only 
applies to margin used in those methods that rely on calculation of 
k<INF>eff</INF>, including deterministic and probabilistic computer 
methods. The use of other methods (e.g., use of endorsed industry 
standards, widely accepted handbooks, certain hand calculations), 
containing varying amounts of margin, is outside the scope of this ISG.
    For methods relying on calculation of K<INF>eff</INF>, margin may 
be provided either in terms of limits on physical parameters of the 
system (of which K<INF>eff</INF> is a function), or in terms of limits 
on K<INF>eff</INF> directly, or both. For the purposes of this ISG, the 
term margin of safety will be used to refer to the margin to 
criticality in terms of system parameters, and the term margin of 
subcriticality (MoS) will refer to the margin to criticality in terms 
of K<INF>eff</INF>. A common approach to ensuring subcriticality is to 
determine a maximum K<INF>eff</INF> limit below which the licensee's 
calculations must fall. This limit will be referred to in this ISG as 
the Upper Subcritical Limit (USL). Licensees using calculational 
methods perform validation studies, in which critical experiments 
similar to actual or anticipated facility calculations are chosen and 
then analyzed to determine the bias and uncertainty in the bias. The 
bias is a measure of the systematic differences between calculational 
method results and experimental data. The uncertainty in the bias is a 
measure of both the accuracy and precision of the calculations and the 
uncertainty in the experimental data. A USL is then established that 
includes allowances for bias and bias uncertainty as well as an 
additional margin, to be referred to in this ISG as the minimum margin 
of subcriticality (MMS). The MMS is variously referred to in the 
nuclear industry as minimum subcritical margin, administrative margin, 
and arbitrary margin, and the term MMS should be regarded as synonymous 
with those terms. The term MMS will be used throughout this ISG, and 
has been chosen for consistency with the rule. The MMS is an allowance 
for any unknown errors or uncertainties in the method of calculating 
K<INF>eff</INF> that may exist beyond those which have been accounted 
for explicitly in calculating the bias and its uncertainty.
    There is little guidance in the fuel facility Standard Review Plans 
(SRPs) as to what constitutes sufficient technical justification for 
the MMS. NUREG-1520, ``Standard Review Plan for the Review of a License 
Application for a Fuel Cycle Facility,'' Section 5.4.3.4.4, states that 
this margin must include, among other uncertainties, ``adequate 
allowance for uncertainty in the methodology, data, and bias to assure 
subcriticality.'' However, there has been almost no guidance on how to 
determine an appropriate MMS. Partly due to the lack of historical 
guidance, and partly due to differences between facilities' processes 
and methods of calculation, there have been significantly different MMS 
values approved for the various fuel cycle facilities over time. In 
addition, the different ways licensees have of defining margins and 
calculating K<INF>eff</INF> limits have made a consistent approach to 
reviewing K<INF>eff</INF> limits difficult. Recent licensing experience 
has highlighted the need for further guidance to clarify what 
constitutes an acceptable justification for the MMS.
    The MMS can have a substantial effect on facility operations (e.g., 
storage capacity, throughput) and there has, therefore, been 
considerable recent interest in decreasing margin in K<INF>eff</INF> 
below what has been licensed previously. In addition, the increasing 
sophistication of computer codes and the ready availability of 
computing resources means that there has been a gradual move towards 
more realistic (often resulting in less conservative) modeling of 
process systems. These two factors--the increasing interest in reducing 
the MMS and the reduction in modeling conservatism--make technical 
justification of the MMS more risk-significant than it has been in the 
past. In general, consistent with a risk-informed approach to 
regulation, a smaller MMS requires a more substantial technical 
justification.
    This ISG is only applicable to fuel enrichment and fabrication 
facilities licensed under 10 CFR part 70.

Discussion

    This guidance is applicable to evaluating the MMS in methods of 
evaluation that rely on calculation of K<INF>eff</INF>. The 
K<INF>eff</INF> value of a fissionable system depends, in general, on a 
large number of physical variables. The factors that can affect the 
calculated value of K<INF>eff</INF> may be broadly divided into the 
following categories: (1) The geometric configuration; (2) the material 
composition; and (3) the neutron distribution. The geometric form and 
material composition of the system determine--together with the 
underlying nuclear data (e.g., v,<INF>X</INF>(E), cross-section data)--
the spatial and energy distribution of neutrons in the system (flux and 
energy spectrum). An error in the nuclear data or the

[[Page 60567]]

geometric or material modeling of these systems can produce an error in 
the neutron flux and energy spectrum, and thus in the calculated value 
of K<INF>eff</INF>. The bias associated with a single system is defined 
as the difference between the calculated and physical values of 
K<INF>eff</INF>, by the following equation:
    [bgr] = k<INF>calc</INF> - k<INF>physical</INF>
    Thus, determining the bias requires knowing both the calculated and 
physical K<INF>eff</INF> values of the system. The bias associated with 
a single critical experiment can be known with a high degree of 
confidence, because the physical (experimental) value is known a priori 
(k<INF>physical</INF> [ap] 1). However, for calculations performed to 
demonstrate subcriticality of facility processes (to be referred to as 
``applications''), this is not generally the case. The bias associated 
with such an application (i.e., not a known critical configuration) is 
not typically known with this same high degree of confidence, because 
the actual physical K<INF>eff</INF> of the system is usually not known. 
In practice, the bias is determined as the average calculated 
K<INF>eff</INF> for a set of experiments that cover different aspects 
of the licensee's applications. The bias and its uncertainty must be 
estimated by calculating the bias associated with a set of critical 
experiments having geometric forms, material compositions, and neutron 
spectra similar to those of the application. Because of the large 
number of factors that can affect the bias, and the finite number of 
critical experiments available, staff should recognize that this is 
only an estimate of the true bias of the system. The experiments 
analyzed cannot cover all possible combinations of conditions or 
sources of error that may be present in the applications to be 
evaluated. The effect on K<INF>eff</INF> of geometric, material, or 
spectral differences between critical experiments and applications 
cannot be known with precision. Therefore, an additional margin (MMS) 
must be applied to allow for the effects of any unknown uncertainties 
that may exist in the calculated value of K<INF>eff</INF> beyond those 
accounted for in the calculation of the bias and its uncertainty. As 
the MMS decreases, there needs to be a greater level of assurance that 
the various sources of bias and uncertainty have been taken into 
account, and that the bias and uncertainty are known with a high degree 
of accuracy. In general, the more similar the critical experiments are 
to the applications, the more confidence there is in the estimate of 
the bias and the less MMS is needed.
    In determining an appropriate MMS, the reviewer should consider the 
specific conditions and process characteristics present at the facility 
in question. However, the MMS should not be reduced below 0.02. The 
nuclear cross sections are not generally known to better than ~1-2%, 
and thus it is not possible to have a greater level of assurance in the 
calculated results than this. Moreover, errors in the criticality codes 
have been discovered over time that have produced K<INF>eff</INF> 
differences of roughly this same magnitude of 1-2% (e.g., Information 
Notice 2005-13, ``Potential Non-Conservative Error in Modeling 
Geometric Regions in the KENO-V.a Criticality Code'').
    Staff should recognize the important distinction between ensuring 
that processes are safe and ensuring that they are adequately 
subcritical. The value of K<INF>eff</INF> is a direct indication of the 
degree of subcriticality of the system, but is not fully indicative of 
the degree of safety. A system that is very subcritical (i.e., with 
K<INF>eff</INF> [Lt]1) may have a small margin of safety if a small 
change in a process parameter can result in criticality. An example of 
this would be a UO2 powder storage vessel, which is subcritical when 
dry, but may require only the addition of water for criticality. 
Similarly, a system with a small MoS (i.e., with K<INF>eff</INF> [Lt]1) 
may have a very large margin of safety if it cannot credibly become 
critical. An example of this would be a natural uranium system in light 
water, which may have a K<INF>eff</INF> value close to 1 but will never 
exceed 1.
    Because of this, a distinction should be made between the margin of 
subcriticality and the margin of safety. Although a variety of terms 
are in use in the nuclear industry, the term margin of subcriticality 
will be taken to mean the difference between the actual (physical) 
value of k<INF>eff</INF> and the value of k<INF>eff</INF> at which the 
system is expected to be critical. The term margin of safety will be 
taken to mean the difference between the actual value of a parameter 
and the value of the parameter at which the system is expected to be 
critical. The appropriate MMS depends only on the confidence that 
applications calculated to be subcritical will be subcritical. It does 
not depend on other aspects of the process (e.g., safety of the process 
or the ability to control parameters within certain bounds) that may 
need to be reviewed as part of an overall licensing review.
    There are a variety of different approaches that a licensee could 
choose in justifying the MMS. Some of these approaches and means of 
reviewing them are described in the following sections, in no 
particular preferential order. Many of these approaches consist of 
qualitative arguments, and therefore there will be some degree of 
subjectivity in determining the adequacy of the MMS. Because the MMS is 
an allowance for unknown (or difficult to identify or quantify) errors, 
the reviewer must ultimately exercise his or her best judgement in 
determining whether a specific MMS is justified. Thus, the topics 
listed below should be regarded as factors the reviewer should take 
into consideration in exercising that judgement, rather than any kind 
of prescriptive checklist.
    The reviewer should also bear in mind that the licensee is not 
required to use any or all of these approaches, but may choose an 
approach that is applicable to its facility or a particular process 
within its facility. While it may be desirable and convenient to have a 
single k<INF>eff</INF> limit or MMS value (and single corresponding 
justification) across an entire facility, it is not necessary for this 
to be the case. The MMS may be easier to justify for one process than 
for another, or for a limited application versus generically for the 
entire facility. The reviewer should expect to see various combinations 
of these approaches, or entirely different approaches, used, depending 
on the nature of the licensee's processes and methods of calculation. 
Any approach used must ultimately lead to a determination that there is 
adequate assurance of subcriticality.
(1) Conservatism in the Calculational Models
    The margin in k<INF>eff</INF> produced by the licensee's modeling 
practices, together with the MMS, provide the margin between actual 
conditions and expected critical conditions. In terms of the 
subcriticality criterion taken from ANSI/ANS-8.17-1984 (R1997), 
``Criticality Safety Criteria for the Handling, Storage, and 
Transportation of LWR Fuel Outside Reactors'' (as explained in Appendix 
A): MoS >= [Delta]K<INF>m</INF> + [Delta]K<INF>sa</INF>
where [Delta]k<INF>m</INF> is the MMS and [Delta]k<INF>sa</INF> is the 
margin in k<INF>eff</INF> due to conservative modeling of the system 
(i.e., conservative values of system parameters).
    Two different applications for which the sums on the right hand 
side of the equation above are equal to each other are equally 
subcritical. Assurance of subcriticality may thus be provided by 
specifying a margin in k<INF>eff</INF> ([Delta]k<INF>m</INF>), or 
specifying conservative modeling practices ([Delta]k<INF>sa</INF>), or 
some combination thereof. This principle will be particularly useful to 
the reviewer evaluating a proposed reduction in the currently approved 
MMS; the review of such a reduction should prove straightforward in 
cases in which the overall combination of modeling

[[Page 60568]]

conservatism and MMS has not changed. Because of this straightforward 
quantitative relationship, any modeling conservatism that has not been 
previously credited should be considered before examining other 
factors. Cases in which the overall MoS has decreased may still be 
acceptable, but would have to be justified by other means.
    In evaluating justification for the MMS relying on conservatism in 
the model, the reviewer should consider only that conservatism in 
excess of any manufacturing tolerances, uncertainties in system 
parameters, or credible process variations. That is, the conservatism 
should consist of conservatism beyond the worst-case normal or abnormal 
conditions, as appropriate, including allowance for any tolerances. 
Examples of this added conservatism may include assuming optimum 
concentration in solution processes, neglecting neutron absorbers in 
structural materials, or requiring minimum reflector conditions (e.g., 
at least a 1-inch, tight-fitting reflector around process equipment). 
These technical practices used to perform criticality calculations 
generally result in conservatism of at least several percent in 
k<INF>eff</INF>. To credit this as part of the justification for the 
MMS, the reviewer should have assurance that the modeling practices 
described will result in a predictable and dependable amount of 
conservatism in keff. In some cases, the conservatism may be process-
dependent, in which case it may be relied on as justification for the 
MMS for a particular process. However, only modeling practices that 
result in a global conservatism across the entire facility should be 
relied on as justification for a site-wide MMS. Ensuring predictable 
and dependable conservatism includes verifying that this conservatism 
will be maintained over the facility lifetime, such as through the use 
of license commitments or conditions.
    If the licensee has a program that establishes operating limits (to 
ensure that subcritical limits are not exceeded) below subcritical 
limits determined in nuclear criticality safety evaluations, the margin 
provided by this (optional) practice may be credited as part of the 
conservatism. In such cases, the reviewer should credit only the 
difference between operating and subcritical limits that exceeds any 
tolerances or process variation, and should ensure that operating 
limits will be maintained over the facility lifetime, through the use 
of license commitments or conditions.
    Some questions that the reviewer may ask in evaluating the use of 
modeling conservatism as justification for the MMS include:
    <bullet> How much margin in k<INF>eff</INF> is provided due to 
conservatism in modeling practices?
    <bullet> How much of this margin exceeds allowance for tolerances 
and process variations?
    <bullet> Is this margin specific to a particular process or does it 
apply to all facility processes?
    <bullet> What provides assurance that this margin will be 
maintained over the facility lifetime?
(2) Validation Methodology and Results
    Assurance of subcriticality for methods that rely on the 
calculation of k<INF>eff</INF> requires that those methods be 
appropriately validated. One of the goals of validation is to determine 
the method's bias and the uncertainty in the bias. After this has been 
done, an additional margin (MMS) is specified to account for any 
additional uncertainties that may exist. The appropriate MMS depends, 
in part, on the degree of confidence in the validation results. Having 
a high degree of confidence in the bias and bias uncertainty requires 
both that there be sufficient (for the statistical method used) 
applicable benchmark-quality experiments and that there be a rigorous 
validation methodology. If either the data or the methodology is 
deficient, a high degree of confidence in the results cannot be 
attained, and a larger MMS may need to be employed than would otherwise 
be acceptable. Therefore, although validation and determining the MMS 
are separate exercises, they are related. The more confidence one has 
in the validation results, the less additional margin (MMS) is needed. 
The less confidence one has in the validation results, the more MMS is 
needed.
    Any review of a licensing action involving the MMS should involve 
examination of the licensee's validation methodology and results. While 
there is no clear quantifiable relationship between the validation and 
MMS (as exists with modeling conservatism), several aspects of 
validation should be considered before making a qualitative 
determination of the adequacy of the MMS.
    There are four factors that the reviewer should consider in 
evaluating the validation: (1) The similarity of benchmark experiments 
to actual applications; (2) sufficiency of the data (including the 
number and quality of experiments); (3) adequacy of the validation 
methodology; and (4) conservatism in the calculation of the bias and 
its uncertainty. These factors are discussed in more detail below.

Similarity of Benchmark Experiments

    Because the bias and its uncertainty must be estimated based on 
critical experiments having similar geometric form, material 
composition, and neutronic behavior to specific applications, the 
degree of similarity between the critical experiments and applications 
is a key consideration in determining the appropriateness of the MMS. 
The more closely critical experiments represent the characteristics of 
applications being validated, the more confidence the reviewer has in 
the estimate of the bias and the bias uncertainty for those 
applications.
    The reviewer must understand both the critical experiments and 
applications in sufficient detail to ascertain the degree of similarity 
between them. Validation reports generally contain a description of 
critical experiments (including source references). The reviewer may 
need to consult these references to understand the physical 
characteristics of the experiments. In addition, the reviewer may need 
to consult process descriptions, nuclear criticality safety 
evaluations, drawings, tables, input files, or other information to 
understand the physical characteristics of applications. The reviewer 
must consider the full spectrum of normal and abnormal conditions that 
may have to be modeled when evaluating the similarity of the benchmarks 
to applications.
    In evaluating the similarity of experiments to applications, the 
reviewer must recognize that some parameters are more significant than 
others to accurately calculate k<INF>eff</INF>. The parameters that 
have the greatest effect on the calculated k<INF>eff</INF> of the 
system are those that are most important to match when choosing 
critical experiments. Because of this, there is a close relationship 
between similarity of benchmarks to applications and system 
sensitivity. Historically, certain parameters have been used to trend 
the bias because these are the parameters that have been found to have 
the greatest effect on the bias. These parameters include the 
moderator-to-fuel ratio (e.g., H/U, H/X, v<SUP>m</SUP>/v<SUP>f</SUP>), 
isotopic abundance (e.g., uranium-235 (<SUP>235</SUP>U), plutonium-239 
(<SUP>239</SUP>Pu), or overall Pu-to-uranium ratio), and parameters 
that characterize the neutron energy spectrum (e.g., energy of average 
lethargy causing fission (EALF), average energy group (AEG)). Other 
parameters, such as material density or overall

[[Page 60569]]

geometric shape, are generally considered to be of less importance. The 
reviewer should consider all important system characteristics that can 
reasonably be supposed to affect the bias. For example, the critical 
experiments should include any materials that can have an appreciable 
effect on the calculated k<INF>eff</INF>, so that the effect due to the 
cross sections of those materials is included in the bias. Furthermore, 
these materials should have at least the same reactivity worth in the 
experiments (which may be evidenced by having similar number densities) 
as in the applications. Otherwise, the effect of any bias from the 
underlying cross sections or the assumed material composition may be 
masked in the applications. It is also important that the materials be 
present in a statistically significant number of experiments having 
similar neutron spectra to the application. Conversely, materials that 
do not have an appreciable effect on the bias may be neglected and 
would not have to be represented in the critical experiments.
    Merely having critical experiments that are representative of 
applications is the minimum acceptance criterion, and does not alone 
justify having any particular value of the MMS. There are some 
situations, however, in which there is an unusually high degree of 
similarity between the critical experiments and applications, and in 
these cases, this fact may be credited as justification for having a 
smaller MMS than would otherwise be acceptable. If the critical 
experiments have geometric forms, material compositions, and neutron 
spectra that are nearly indistinguishable from those of the 
applications, this may be justification for reducing the MMS. For 
example, justification for having a small MMS for finished fuel 
assemblies could include selecting critical experiments consisting of 
fuel assemblies in water, where the fuel has nearly the same pellet 
diameter, pellet density, cladding materials, pitch, absorber content, 
enrichment, and neutron energy spectrum as the licensee's fuel. In this 
case, the validation should be very specific to this type of system, 
because including other types of benchmark experiments could mask 
variations in the bias. Therefore, this type of justification is 
generally easiest when the area of applicability (AOA) is very narrowly 
defined. The reviewer should pay particular attention to abnormal 
conditions. In this example, damage to the fuel or partial flooding may 
significantly affect the applicability of the critical experiments.
    There are several tools available to the reviewer to ascertain the 
degree of similarity between critical experiments and applications. 
Some of these are listed below:

    1. NUREG/CR-6698, ``Guide to Validation of Nuclear Criticality 
Safety Calculational Method,'' Table 2.3, contains a set of 
screening criteria for determining benchmark applicability. As is 
stated in the NUREG, these criteria were arrived at by consensus 
among experienced nuclear criticality safety specialists and may be 
considered to be conservative. The reviewer should consider 
agreement on all screening criteria to be justification for 
demonstrating a very high degree of benchmark similarity. (Agreement 
on the most significant screening criteria for a particular system 
should be considered as demonstration of an acceptable degree of 
benchmark similarity.) Less conservative (i.e., broader) screening 
criteria may also be acceptable, if appropriately justified.
    2. Analytical methods that systematically quantify the degree of 
similarity between a set of critical experiments and applications in 
pair-wise fashion may be used. One example of this is the TSUNAMI 
code in the SCALE 5 code package. One strength of TSUNAMI is that it 
calculates an overall correlation that is a quantitative measure of 
the degree of similarity between an experiment and an application. 
Another strength is that this code considers all the nuclear 
phenomena and underlying cross sections and weighs them by their 
importance to the calculated k<INF>eff</INF> (i.e., sensitivity of 
k<INF>eff</INF> to the data). The NRC staff currently considers a 
correlation coefficient of c<INF>k</INF> >=0.95 to be indicative of 
a very high degree of similarity. This is based on the staff's 
experience comparing the results from TSUNAMI to those from a more 
traditional screening criterion approach. Conversely, a correlation 
coefficient less than 0.90 should not be used as a demonstration of 
a high degree of benchmark similarity. Because of limited use of the 
code to date, these observations should be considered tentative and 
thus the reviewer should not use TSUNAMI as a ``black box,'' or base 
conclusions of adequacy solely on its use. However, it may be used 
to test a licensee's statement that there is a high degree of 
similarity between experiments and applications.
    3. Traditional parametric sensitivity studies may be employed to 
demonstrate that k<INF>eff</INF> is highly sensitive or insensitive 
to a particular parameter. For example, if a 50% reduction in the 
<SUP>10</SUP>B cross section is needed to produce a 1% change in the 
system k<INF>eff</INF>, then it can be concluded that the system is 
highly insensitive to the boron content, in the amount present. This 
is because a credible error in the <SUP>10</SUP>B cross section of a 
few percent will have a statistically insignificant effect on the 
bias. Therefore, in the amount present, the boron content is not a 
parameter that is important to match in order to conclude that there 
is a high degree of similarity between benchmarks and applications.
    4. Physical arguments may demonstrate that k<INF>eff</INF> is 
highly sensitive or insensitive to a particular parameter. For 
example, the fact that oxygen and fluorine are almost transparent to 
thermal neutrons (i.e., cross sections are very low) may justify why 
experiments consisting of UO<INF>2</INF>F<INF>2</INF> may be 
considered similar to UO<INF>2</INF> or UF<INF>4</INF> applications, 
provided that both experiments and applications occur in the thermal 
energy range.

    The reviewer should ensure that all parameters which can measurably 
affect the bias are considered when assessing benchmark similarity. For 
example, comparison should not be based solely on agreement in the 
<SUP>235</SUP>U fission spectrum for systems in which the system 
k<INF>eff</INF> is highly sensitive to <SUP>238</SUP>U fission, 
<SUP>10</SUP>B absorption, or <SUP>1</SUP>H scattering. A method such 
as TSUNAMI that considers the complete set of reactions and nuclides 
present can be used to rank the various system sensitivities, and to 
thus determine whether it is reasonable to rely on the fission spectrum 
alone in assessing the similarity of benchmarks to applications.
    Some questions that the reviewer may ask in evaluating reliance on 
benchmark similarity as justification for the MMS include:
    <bullet> Do the benchmarks cover all geometric forms, material 
compositions, and neutron energy spectra expected in applications?
    <bullet> Are the materials present with at least the same 
reactivity worth as in applications?
    <bullet> Do the licensee's criteria for determining whether 
experiments are sufficiently similar to applications consider all 
nuclear reactions and nuclides that could affect the bias?

Sufficiency of the Data

    Another aspect of evaluating the selected benchmarks for a specific 
MMS is ensuring that there is a sufficient number of benchmark quality 
experiments to determine the bias across the entire AOA. Having a 
sufficient number of benchmark-quality experiments means that: (1) 
There are enough (applicable) experiments to make a statistically 
meaningful calculation of the bias and its uncertainty; (2) the 
experiments somewhat evenly span the entire range of all the important 
parameters, without gaps requiring extrapolation or wide interpolation; 
and (3) the experiments are all benchmark-quality experiments. The 
number of benchmarks needed is dependent on the statistical method used 
to analyze the data. For example, some methods require a minimum number 
of data points to reliably determine whether the data are normally 
distributed. Merely having a large number of experiments is not

[[Page 60570]]

sufficient to provide confidence in the validation result, if the 
experiments are not applicable to the application. The reviewer should 
particularly examine whether consideration of only the most applicable 
experiments would result in a larger negative bias (and thus a lower 
USL) than that determined based on the full set of experiments. The 
experiments should also be sufficiently well-characterized (including 
experimental parameters and their uncertainties) to be considered 
benchmark experiments. They should be drawn from established sources 
(such as from the International Handbook of Evaluated Criticality 
Safety Benchmark Experiments (IHECSBE), laboratory reports, or peer-
reviewed journals).
    Some questions that the reviewer may ask in evaluating the number 
and quality of benchmark experiments as justification for the MMS 
include:
    <bullet> Are the critical experiments chosen all high-quality 
benchmarks from reliable (e.g., peer-reviewed and widely-accepted) 
sources?
    <bullet> Are the critical experiments chosen taken from multiple 
independent sources, to minimize the possibility of systematic errors?
    <bullet> Have the experimental uncertainties associated with the 
critical experiments been provided and used in calculating the bias and 
bias uncertainty?
    <bullet> Is the number and distribution of critical experiments 
sufficient to establish trends in the bias across the entire range of 
parameters?
    <bullet> Is the number of critical experiments commensurate with 
the statistical methodology being used?

Validation Methodological Rigor

    Having a sufficiently rigorous validation methodology means having 
a methodology that is appropriate for the number and distribution of 
critical experiments, that calculates the bias and its uncertainty 
using an established statistical methodology, that accounts for any 
trends in the bias, and that accounts for all apparent sources of 
uncertainty in the bias (e.g., the increase in uncertainty due to 
extrapolating the bias beyond the range covered by the benchmark data.) 
Examples of deficiencies in the validation methodology may include: (1) 
Using a statistical methodology relying on the data being normally 
distributed about the mean k<INF>eff</INF> to analyze data that are not 
normally distributed; (2) using a linear regression fit on data that 
has a non-linear dependence on a trending parameter; (3) use of a 
single pooled bias when very different types of critical experiments 
are being evaluated in the same validation. These deficiencies serve to 
decrease confidence in the validation results and may warrant 
additional margin (i.e., a larger MMS). Additional guidance on some of 
the more commonly observed deficiencies is provided below.
    The assumption that data is normally distributed is generally 
valid, unless there is a strong trend in the data or different types of 
critical experiments with different mean calculated k<INF>eff</INF> 
values are being combined. Tests for normality require a minimum number 
of critical experiments to attain a specified confidence level 
(generally 95%). If there is insufficient data to verify that the data 
are normally distributed, or the data are shown to be not normally 
distributed, a non-parametric technique should be used to analyze the 
data.
    The critical experiments chosen should provide a continuum of data 
across the entire validated range, so that any variation in the bias as 
a function of important system parameters may be observed. The presence 
of discrete clusters of experiments having a lower calculated 
k<INF>eff</INF> than the set of critical experiments as a whole should 
be examined closely, to determine if there is some systematic effect 
common to a particular type of calculation that makes use of the 
overall bias non-conservative. Because the bias can vary with system 
parameters, if the licensee has combined different subsets of data 
(e.g., solutions and powders, low- and high-enriched, homogeneous and 
heterogeneous), the bias for the different subsets should be analyzed. 
In addition, the goodness-of-fit for any function used to trend the 
bias should be examined to ensure it is appropriate to the data being 
analyzed.
    If critical experiments do not cover the entire range of parameters 
needed to cover anticipated applications, it may be necessary to extend 
the AOA by making use of trends in the bias. Any extrapolation (or wide 
interpolation) of the data should be done by means of an established 
mathematical methodology that takes into account the functional form of 
both the bias and its uncertainty. The extrapolation should not be 
based on judgement alone, such as by observing that the bias is 
increasing in the extrapolated range, because this may not account for 
the increase in the bias uncertainty that will occur with increasing 
extrapolation. The reviewer should independently confirm that the 
derived bias is still valid in the extrapolated range and should ensure 
that the extrapolation is not large. NUREG/CR-6698 states that critical 
experiments should be added if the data must be extrapolated more than 
10%. If the extrapolation is too large, new factors that could affect 
the bias may be introduced as the physical phenomena in the system 
change. The reviewer should not view validation as a purely 
mathematical exercise, but should bear in mind the neutron physics and 
underlying physical phenomena when interpreting the results.
    Discarding an unusually large number of critical experiments as 
outliers (i.e., more than 1-2%) should also be viewed with some 
concern. Apparent outliers should not be discarded based purely upon 
judgement or statistical grounds (such as causing the data to fail 
tests for normality), because they could be providing valuable 
information on the method's validity for a particular application. The 
reviewer should verify that there are specific defensible reasons, such 
as reported inconsistencies in the experimental data, for discarding 
any outliers. If any of the critical experiments from a particular data 
set are discarded, the reviewer should examine other experiments 
included to determine whether they may be subject to the same 
systematic errors. Outliers should be examined carefully especially 
when they have a lower calculated k<INF>eff</INF> than the other 
experiments included.
    NUREG-1520 states that the MoS should be large compared to the 
uncertainty in the bias. The observed spread of the data about the mean 
k<INF>eff</INF> should be examined as an indicator of the overall 
precision of the calculational method. The reviewer should ascertain 
whether the statistical method of validation considers both the 
observed spread in the data and the experimental and calculational 
uncertainty in determining the USL. The reviewer should also evaluate 
whether the observed spread in the data is consistent with the reported 
uncertainty (e.g., whether <INF>X</INF><SUP>2</SUP>/N [ap] 1). If the 
spread in the data is larger than or comparable to the MMS, then the 
reviewer should consider whether additional margin (i.e., a larger MMS) 
is needed.
    As a final test of the code's accuracy, the bias should be 
relatively small (i.e., bias [ap] 2 percent), or else the reason for 
the bias should be determined. No credit should be taken for positive 
bias, because this would result in making changes in a non-conservative 
direction without having a clear understanding of those changes. If the 
absolute value of the bias is very large--and especially if the reason 
for the large bias cannot be determined--this may indicate that the 
calculational method is not very accurate, and a larger MMS may be 
appropriate.
    Some questions that the reviewer may ask in evaluating the rigor of 
the

[[Page 60571]]

validation methodology as justification for the MMS include:
    <bullet> Are the results from use of the methodology consistent 
with the data (e.g., normally distributed)?
    <bullet> Is the normality of the data confirmed prior to performing 
statistical calculations? If the data does not pass the tests for 
normality, is a non-parametric method used?
    <bullet> Does the assumed functional form of the bias represent a 
good fit to the critical experiments? Is a goodness-of-fit test 
performed?
    <bullet> Does the method determine a pooled bias across disparate 
types of critical experiments, or does it consider variations in the 
bias for different types of experiments? Are there discrete clusters of 
experiments for which the bias appears to be non-conservative?
    <bullet> Has additional margin been applied to account for 
extrapolation or wide interpolation? Is this done based on an 
established mathematical methodology?
    <bullet> Have critical experiments been discarded as apparent 
outliers? Is there a valid reason for doing so?
    Performing an adequate code validation is not by itself sufficient 
justification for any specific MMS. The reason for this is that the 
validation analysis determines the bias and its uncertainty, but not 
the MMS. The MMS is added after the validation has been performed to 
provide added assurance of subcriticality. However, having a validation 
methodology that either exceeds or falls short of accepted standards 
for validation may be a basis for either reducing or increasing the 
MMS.

Statistical Conservatism

    In addition to having conservatism in k<INF>eff</INF> due to 
modeling practices, licensees may also provide conservatism in the 
statistical methods used to calculate the USL. For example, NUREG/CR-
6698 states that an acceptable method for calculating the bias is to 
use the single-sided tolerance limit approach with a 95/95 confidence 
(i.e., 95% confidence that 95% of all future critical calculations will 
lie above the USL). If the licensee decides to use the single-sided 
tolerance limit approach with a 95/99.9 confidence, this would result 
in a more conservative USL than with a 95/95 confidence. This would be 
true of other methods for which the licensee's confidence criteria 
exceeds the minimum accepted criteria. Generally, the NRC has accepted 
95% confidence levels for validation results, so using more stringent 
confidence levels may provide conservatism. In addition, there may be 
other reasons a larger bias and/or bias uncertainty than necessary has 
been used (e.g., because of the inclusion of inapplicable benchmark 
experiments that have a lower calculated k<INF>eff</INF>).
    The reviewer may credit this conservatism towards having an 
adequate MoS if: (1) The licensee demonstrates that this translates 
into a specific [Delta]k<INF>eff</INF>; and (2) the licensee 
demonstrates that the margin will be dependably present, based on 
license or other commitments.
(3) Additional Risk-Informed Considerations
    Besides modeling conservatism and the validation results, other 
factors may provide added assurance of subcriticality. These factors 
should be considered in evaluating whether there is adequate MoS and 
are discussed below.

System Sensitivity and Uncertainty

    The sensitivity of k<INF>eff</INF> to changes in system parameters 
can be used to assess the potential effect of errors on the calculation 
of k<INF>eff</INF>. If the calculated k<INF>eff</INF> is especially 
sensitive to a given parameter, an error in that parameter could have a 
correspondingly large contribution to the bias. Conversely, if 
k<INF>eff</INF> is very insensitive to a given parameter, then an error 
may have a negligible effect on the bias. This is of particular 
importance when assessing whether the chosen critical experiments are 
sufficiently similar to applications to justify a small MMS.
    The reviewer should not consider the sensitivity in isolation, but 
should also consider the magnitude of uncertainties in the parameters. 
If k<INF>eff</INF> is very sensitive to a given parameter, but the 
value of that parameter is known with very high accuracy (and its 
variations are well-controlled), the potential contribution to the bias 
may still be very small. Thus, the contribution to the bias is a 
function of the product of the k<INF>eff</INF> sensitivity with the 
uncertainty. To illustrate this, suppose that k<INF>eff</INF> is a 
function of a large number of variables, <INF>x1</INF>, 
<INF>x2</INF>,..., <INF>xN</INF>. Then the uncertainty in 
k<INF>eff</INF> may be expressed as follows, if all the individual 
terms are independent:
[GRAPHIC] [TIFF OMITTED] TN18OC05.014

where the partial derivatives [part]k/[part]x<INF>i</INF> are 
proportional to the sensitivity and the terms [delta]<INF>i</INF> 
represent the uncertainties, or likely variations, in the parameters. 
Each term in this equation then represents the contribution to the 
overall uncertainty in k<INF>eff</INF>.
    There are several tools available to the reviewer to ascertain the 
sensitivity of k<INF>eff</INF> to changes in the underlying parameters. 
Some of these are listed below:
    1. Analytical tools that calculate the sensitivity for each 
nuclide-reaction pair present in the problem may be used. One example 
of this is the TSUNAMI code in the SCALE 5 code package. TSUNAMI 
calculates both an integral sensitivity coefficient (i.e., summed over 
all energy groups) and a sensitivity profile as a function of energy 
group. The reviewer should recognize that TSUNAMI only calculates the 
k<INF>eff</INF> sensitivity to changes in the underlying nuclear data, 
and not to other parameters that could affect the bias and should be 
considered. (See section on Benchmark Similarity for caveats about 
using TSUNAMI.)
    2. Direct sensitivity calculations may be used, in which system 
parameters are perturbed and the resulting impact on k<INF>eff</INF> 
determined. Perturbation of atomic number densities can also be used to 
confirm the sensitivity calculated by other methods (e.g., TSUNAMI). 
Such techniques are not limited to considering the effect of the 
nuclear data.
    There are also several sources available to the reviewer to 
ascertain the uncertainty associated with the underlying parameters. 
For process parameters, these sources of uncertainty may include 
manufacturing tolerances, quality assurance records, and experimental 
and/or measurement results. For nuclear data parameters, these sources 
of uncertainty may include published data, uncertainty data distributed 
with the cross section libraries, or the covariance data used in 
methods such as TSUNAMI.
    Some systems are inherently more sensitive to changes in the 
underlying parameters than others. For example, high-enriched uranium 
systems typically exhibit a greater sensitivity to changes in system 
parameters (e.g., mass, moderation) than low-enriched systems. This has 
been the reason that HEU (i.e., >20wt% <INF>235</INF>U) facilities have 
been licensed with larger MMS values than LEU (<=10wt% <INF>235</INF>U) 
facilities. This greater sensitivity would also be true of weapons-
grade Pu compared to low-assay mixed oxides (i.e., with a few percent 
Pu/U). However, it is also true that the uncertainties associated with 
measurement of the <INF>235</INF>U cross sections are much smaller than 
those associated with measurement of the <INF>238</INF>U cross 
sections. Both the greater sensitivity and smaller uncertainty would 
need to be considered in evaluating whether a larger MMS is needed for 
high-enriched systems.

[[Page 60572]]

    Frequently, operating limits that are more conservative than safety 
limits determined using k<INF>eff</INF> calculations are established to 
prevent those safety limits from being exceeded. For systems in which 
k<INF>eff</INF> is very sensitive to the system parameters, more margin 
between the operating and safety limits may be needed. Systems in which 
k<INF>eff</INF> is very sensitive to the process parameters may need 
both a larger margin between operating and safety limits and a larger 
MMS. This is because the system is sensitive to any change, whether it 
be caused by normal process variations or caused by unknown errors. 
Because of this, the assumption is often made that the MMS is meant to 
account for variations in the process or the ability to control the 
process parameters. However, the MMS is meant only to allow for unknown 
(or difficult to quantify) uncertainties in the calculation of 
k<INF>eff</INF>. The reviewer should recognize that determination of an 
appropriate MMS is not dependent on the ability to control process 
parameters within safety limits (although both may depend on the system 
sensitivity).
    Some questions that the reviewer may ask in evaluating the system 
sensitivity as justification for the MMS include:
    <bullet> How sensitive is k<INF>eff</INF> to changes in the 
underlying nuclear data (e.g., cross sections)?
    <bullet> How sensitive is k<INF>eff</INF> to changes in the 
geometric form and material composition?
    <bullet> Are the uncertainties associated with these underlying 
parameters well-known?
    <bullet> How does the MMS compare to the expected magnitude of 
changes in k<INF>eff</INF> resulting from uncertainties in these 
underlying parameters?

Knowledge of the Neutron Physics

    Another important consideration that may affect the appropriate MMS 
is the extent to which the physical behavior of the system is known. 
Fissile systems which are known to be subcritical with a high degree of 
confidence do not require as much MMS as systems where subcriticality 
is less certain. An example of a system known to be subcritical with 
high confidence is a light-water reactor fuel assembly. The design of 
these systems is such that they can only be made critical when highly 
thermalized. Due to extensive analysis and reactor experience, the 
flooded isolated assembly is known to be subcritical. In addition, the 
thermal neutron cross sections for materials in finished reactor fuel 
have been measured with a very high degree of accuracy (as opposed to 
cross sections in the resonance region). Other examples of systems in 
which there is independent corroborating evidence of subcriticality may 
include systems consisting of very simple geometric shapes, or other 
idealized situations, in which there is strong evidence that the system 
is subcritical based on comparison with highly similar systems in 
published sources (e.g., standards and handbooks). In these cases, the 
MMS may be significantly reduced due to the fact that the calculation 
of k<INF>eff</INF> is not relied on alone to provide assurance of 
subcriticality.
    Reliance on independent knowledge that a given system is 
subcritical necessarily requires that the configuration of the system 
be fixed. If the configuration can change from the reference case, 
there will be less knowledge about the behavior of the changed system. 
For example, a finished fuel assembly is subject to strict quality 
assurance checks and would not reach final processing if it were 
outside of specifications. In addition, it has a form that has both 
been extensively studied and is highly stable. For these reasons, there 
is a great deal of certainty that this system is well-characterized and 
is not subject to change. A typical solution or powder system (other 
than one with a simple geometric arrangement) would not have been 
studied with the same level of rigor as a finished fuel assembly. Even 
if they were studied with the same level of rigor, these systems have 
forms that are subject to change into forms whose neutron physics has 
not been as extensively studied.
    Some questions that the reviewer may ask in evaluating the 
knowledge of the neutron physics as justification for the MMS include:
    <bullet> Is the geometric form and material composition of the 
system rigid and unchanging?
    <bullet> Is the geometric form and material composition of the 
system subject to strict quality assurance, such that tolerances have 
been bounded?
    <bullet> Has the system been extensively studied in the nuclear 
industry and shown to be subcritical (e.g., in reactor fuel studies)?
    <bullet> Are there other reasons besides criticality calculations 
to conclude that the system will be subcritical (e.g., handbooks, 
standards, published data)?
    <bullet> How well-known is the nuclear data (e.g., cross sections) 
in the energy range of interest?

Likelihood of the Abnormal Condition

    Some facilities been licensed with different sets of 
k<INF>eff</INF> limits for normal and abnormal conditions. Separate 
k<INF>eff</INF> limits for normal and abnormal conditions are 
permissible but are not required. There is some likelihood that 
processes calculated to be subcritical will in fact be critical, and 
this likelihood increases as the MMS is reduced (though it cannot in 
general be quantified). NUREG-1718, ``Standard Review Plan for the 
Review of an Application for a Mixed Oxide (MOX) Fuel Fabrication 
Facility,'' states that abnormal conditions should be at least unlikely 
from the standpoint of the double contingency principle. Then, a 
somewhat higher likelihood that a system calculated to be subcritical 
is in fact critical is more permissible for abnormal conditions than 
for normal conditions, because of the low likelihood of the abnormal 
condition being realized. The reviewer should verify that the licensee 
has defined abnormal conditions such that achieving the abnormal 
condition requires at least one contingency to have occurred, that the 
system will be closely monitored so that it is promptly detected, and 
that it will be promptly corrected upon detection. It is also true that 
there is generally more conservatism present in the abnormal case, 
because the parameters that are assumed to have failed are analyzed at 
their worst-case credible condition.
    The increased risk associated with having a smaller MMS for 
abnormal conditions should be commensurate with and offset by the low 
likelihood of achieving the abnormal condition. That is, if the normal 
case k<INF>eff</INF> limit is judged to be acceptable, then the 
abnormal case limit will also be acceptable, provided the increased 
likelihood (that a system calculated to be subcritical will be 
critical) is offset by the reduced likelihood of realizing the abnormal 
condition because of the controls that have been established. Note that 
if two or more contingencies must occur to reach a given condition, 
there is no requirement to ensure that the resulting condition is 
subcritical. If a single k<INF>eff</INF> limit is used (i.e., no credit 
for unlikelihood of the abnormal condition), then the limit must be 
found acceptable to cover both normal and credible abnormal conditions. 
The reviewer should always make this finding considering specific 
conditions and controls in the process(es) being evaluated.
(4) Statistical Justification for the MMS
    The NRC does not consider statistical justification an appropriate 
basis for a specific MMS. Previously, some licensees have attempted to 
justify specific MMS values based on a comparison of two statistical 
methods. For example, the USLSTATS code issued with the SCALE code 
package

[[Page 60573]]

contains two methods for calculating the USL: (1) the Confidence Band 
with Administrative Margin approach (calculating USL-1), and (2) the 
Lower Tolerance Band approach (calculating USL-2). The value of the MMS 
is an input parameter to the Confidence Band approach, but is not 
included explicitly in the Lower Tolerance Band approach. In this 
particular justification, adequacy of the MMS is based on a comparison 
of USL-1 and USL-2 (i.e., the condition that USL-1, including the 
chosen MMS, is less than USL-2). However, the reviewer should not 
accept this justification.
    The condition that USL-1 (with the chosen MMS) is less than USL-2 
is necessary, but is not sufficient, to show that an adequate MMS has 
been used. These methods are both statistical methods, and a comparison 
can only demonstrate whether the MMS is sufficient to bound any 
statistical uncertainties included in the Lower Tolerance Band approach 
but not included in the Confidence Band approach. There may be other 
statistical or systematic errors in calculating k<INF>eff</INF> that 
are not included in either statistical treatment. Because of this, an 
MMS value should be specified regardless of the statistical method 
used. Therefore, the reviewer should not consider such a statistical 
approach an acceptable justification for any specific value of the MMS.
(5) Summary
    Based on a review of the licensee's justification for its chosen 
MMS, taking into consideration the aforementioned factors, the staff 
should make a determination as to whether the chosen MMS provides 
reasonable assurance of subcriticality under normal and credible 
abnormal conditions. The staff's review should be risk-informed, in 
that the review should be commensurate with the MoS and should consider 
the specific facility and process characteristics, as well as the 
specific modeling practices used. As an example, approving an MMS value 
greater than 0.05 for processes typically encountered in enrichment and 
fuel fabrication facilities should require only a cursory review, 
provided that an acceptable validation has been performed and modeling 
practices at least as conservative as those in NUREG-1520 have been 
utilized. The approval of a smaller MMS will require a somewhat more 
detailed review, commensurate with the MMS that is requested. However, 
the MMS should not be reduced below 0.02 due to inherent uncertainties 
in the cross section data and the magnitude of code errors that have 
been discovered. Quantitative arguments (such as modeling conservatism) 
should be used to the extent practical. However, in many instances, the 
reviewer will need to make a judgement based at least partly on 
qualitative arguments. The staff should document the basis for finding 
the chosen MMS value to be acceptable or unacceptable in the SER, and 
should ensure that any factors upon which this determination rests are 
ensured to be present over the facility lifetime (e.g., through license 
commitment or condition).

Regulatory Basis

    In addition to complying with paragraphs (b) and (c) of this 
section, the risk of nuclear criticality accidents must be limited by 
assuring that under normal and credible abnormal conditions, all 
nuclear processes are subcritical, including use of an approved margin 
of subcriticality for safety. [10 CFR 70.61(d)]

Technical Review Guidance

    Determination of an adequate MMS is strongly dependent upon 
specific processes, conditions, and calculational practices at the 
facility being licensed. Judgement and experience must be employed in 
evaluating the adequacy of the proposed MMS. In the past, an MMS of 
0.05 has generally been found acceptable for most typical low-enriched 
fuel cycle facilities without a detailed technical justification. A 
smaller MMS may be acceptable but will require some level of technical 
review. However, for reasons stated previously, the MMS should not be 
reduced below 0.02.
    An MMS of 0.05 should be found acceptable for low-enriched fuel 
cycle processes and facilities if:
    1. A validation has been performed that meets accepted industry 
guidelines (e.g., meets the requirements of ANSI/ANS-8.1-1998, NUREG/
CR-6361, and/or NUREG/CR-6698).
    2. There is an acceptable number of benchmark experiments with 
similar geometric forms, material compositions, and neutron energy 
spectra to applications. These experiments cover the range of 
parameters of applications, or else margin is provided to account for 
extensions to the AOA.
    3. The processes to be evaluated include materials and process 
conditions similar to those that occur in low-enriched fuel cycle 
applications (i.e., no new fissile materials, unusual moderators or 
absorbers, or technologies new to the industry that can affect the 
types of systems to be modeled).
    The reviewer should consider any factors, including those 
enumerated in the discussion above, that could result in applying 
additional margin (i.e., a larger MMS) or may justify reducing the MMS. 
The reviewer must then exercise judgement in arriving at an MMS that 
provides for adequate assurance of subcriticality.
    Some of the factors that may serve to justify reducing the MMS 
include:
    1. There is a predictable and dependable amount of conservatism in 
modeling practices, in terms of k<INF>eff</INF>, that is assured to be 
maintained (in both normal and abnormal conditions) over the facility 
lifetime.
    2. Benchmark experiments have nearly identical geometric forms, 
material compositions, and neutron energy spectra to applications, and 
the validation is specific to this type of application.
    3. The validation methodology substantially exceeds accepted 
industry guidelines (e.g., it uses a very conservative statistical 
approach, considers an unusually large number of trending parameters, 
or analyzes the bias for a large number of subgroups of critical 
experiments).
    4. The system k<INF>eff</INF> is demonstrably much less sensitive 
to uncertainties in cross sections or variations in other system 
parameters than typical low-enriched fuel cycle processes.
    5. There is reliable information besides results of calculations 
that provides assurance that the evaluated applications will be 
subcritical (e.g., experimental data, historical evidence, industry 
standards or widely-accepted handbooks).
    6. The MMS is only applied to abnormal conditions, which are at 
least unlikely to be achieved, based on credited controls.
    Some of the factors that may necessitate increasing (or not 
approving) the MMS include:
    1. The technical practices employed by the licensee are less 
conservative than standard industry modeling practices (e.g., do not 
adequately bound reflection or the full range of credible moderation, 
do not take geometric tolerances into account).
    2. There are few similar critical experiments of benchmark quality 
that cover the range of parameters of applications.
    3. The validation methodology substantially falls below accepted 
industry guidelines (e.g., it uses less than a 95% confidence in the 
statistical approach, fails to consider trends in the bias, fails to 
account for extensions to the AOA).

[[Page 60574]]

    4. The validation results otherwise tend to cast doubt on the 
accuracy of the bias and its uncertainty (i.e., the critical 
experiments are not normally distributed, there is a large number of 
outliers discarded (>=2%), there are distinct subgroups of experiments 
with lower k<INF>eff</INF> than the experiments as a whole, trending 
fits do not pass goodness-of-fit tests, etc.).
    5. The system k<INF>eff</INF> is demonstrably much more sensitive 
to uncertainties in cross sections or other system parameters than 
typical low-enriched fuel cycle processes.
    6. There is reliable information that casts doubt on the results of 
the calculational method or the subcriticality of evaluated 
applications (e.g., experimental data, reported concerns with the 
nuclear data).
    The purpose of asking the questions in the individual discussion 
sections is to ascertain the degree to which these factors either 
provide justification for reducing the MMS or necessitate increasing 
the MMS. These lists are not all-inclusive, and any other technical 
information that demonstrates the degree of confidence in the 
calculational method should be considered.

Recommendation

    The guidance in this ISG should supplement the current guidance in 
the nuclear criticality safety chapters of the fuel facility SRPs 
(NUREG-1520 and -1718). However, NUREG-1718, Section 6.4.3.3.4, states 
that the licensee should submit justification for the MMS, but then 
states that an MMS of 0.05 is ``generally considered to be acceptable 
without additional justification when both the bias and its uncertainty 
are determined to be negligible.'' These two statements are 
inconsistent. Therefore, NUREG-1718, Section 6.4.3.3.4, should be 
revised to remove the following sentence:

    ``A minimum subcritical margin of 0.05 is generally considered 
to be acceptable without additional justification when both the bias 
and its uncertainty are determined to be negligible.''

References

    ANSI/ANS-8.1-1998, ``Nuclear Criticality Safety in Operations with 
Fissionable Materials Outside Reactors,'' American Nuclear Society.
    ANSI/ANS-8.17-1984 (R1997), ``Criticality Safety Criteria for the 
Handling, Storage, and Transportation of LWR [Light Water Reactor] Fuel 
Outside Reactors,'' American Nuclear Society.
    IN 2005-13, ``Potential Non-Conservative Error in Modeling 
Geometric Regions in the KENO-V.a Criticality Code,'' May 17, 2005.
    U.S. Nuclear Regulatory Commission (U.S.) (NRC). NUREG-1520, 
``Standard Review Plan for the Review of a License Application for a 
Fuel Cycle Facility.'' NRC: Washington, DC March 2002.
    U.S. Nuclear Regulatory Commission (U.S.) (NRC). NUREG-1718, 
``Standard Review Plan for the Review of an Application for a Mixed 
Oxide (MOX) Fuel Fabrication Facility.'' NRC: Washington, DC August 
2000.
    U.S. Nuclear Regulatory Commission (U.S.) (NRC). NUREG/CR-6698, 
``Guide for Validation of Nuclear Criticality Safety Calculational 
Methodology.'' NRC: Washington, DC January 2001.
    U.S. Nuclear Regulatory Commission (U.S.) (NRC). NUREG/CR-6361, 
``Criticality Benchmark Guide for Light-Water-Reactor Fuel in 
Transportation and Storage Packages.'' NRC: Washington, DC March 1997.

Approved: -------- Date: ----
Director, Division of Fuel Cycle Safety and Safeguards, NMSS

Appendix A--ANSI/ANS-8.17 Calculation of Maximum k<INF>eff</INF>

    ANSI/ANS-8.17-1984 (R1997), ``Criticality Safety Criteria for 
the Handling, Storage, and Transportation of LWR Fuel Outside 
Reactors,'' contains a detailed discussion of the various factors 
that should be considered in setting k<INF>eff</INF> limits. This is 
consistent with, but more detailed than, the discussion in ANSI/ANS-
8.1-1998.
    The subcriticality criterion from Section 5.1 of ANSI/ANS-8.17-
1984 (R1997) is:

k<INF>s</INF> <= k<INF>c</INF> - [Delta]k<INF>s</INF> - 
[Delta]k<INF>c</INF> - [Delta]k<INF>m</INF>

where k<INF>s</INF> is the calculated k<INF>eff</INF> corresponding 
to the application, [Delta]k<INF>s</INF> is its uncertainty, 
k<INF>c</INF> is the mean k<INF>eff</INF> resulting from the 
calculation of critical experiments, [Delta]k<INF>c</INF> is its 
uncertainty, and [Delta]k<INF>m</INF> is the MMS. The types of 
uncertainties included in each of these ``delta'' terms is provided, 
and includes the following:
    [Delta]k<INF>s</INF> = (1) Statistical uncertainties in 
computing k<INF>s</INF>; (2) convergence uncertainties in computing 
k<INF>s</INF>, (3) material tolerances; (4) fabrication tolerances; 
(5) uncertainties due to limitations in the geometric representation 
used in the method; and (6) uncertainties due to limitations in the 
material representations used in the method.
    [Delta]k<INF>c</INF> = (7) uncertainties in the critical 
experiments; (8) statistical uncertainties in computing 
k<INF>c</INF>; (9) convergence uncertainties in computing 
k<INF>c</INF>; (10) uncertainties due to extrapolating k<INF>c</INF> 
outside the range of experimental data; (11) uncertainties due to 
limitations in the geometric representations used in the method; and 
(12) uncertainties due to limitations in the material 
representations used in the method.
    [Delta]k<INF>m</INF> = an allowance for any additional 
uncertainties (MMS).
    To the extent that not all 12 sources of uncertainty listed 
above have been explicitly taken into account, they may be allowed 
for by increasing the value of [Delta]k<INF>m</INF>. The more of 
these sources of uncertainty that have been taken into account, the 
smaller the necessary additional margin [Delta]k<INF>m</INF>. As a 
general principle, however, the MMS should be large compared to 
known uncertainties in the nuclear data and limitations of the 
methodology. However, a value of the MMS below 0.02 should not be 
used.
    Frequently, the terms in the above equation relating to the 
application are grouped on the left-hand side of the equation, so 
that the equation is rewritten as follows:

k<INF>s</INF> + [Delta]k<INF>s</INF> <= k<INF>c</INF> - 
[Delta]k<INF>c</INF> - [Delta]k<INF>m</INF>

where the terms on the right-hand side of the equation are often 
lumped together and termed the Upper Subcritical Limit (USL), so 
that the USL = k<INF>c</INF> - [Delta]k<INF>c</INF>- 
[Delta]k<INF>m</INF>.

Relation to the Minimum Subcritical Margin (MMS)

    The MoS has been defined as the difference between the actual 
value of k<INF>eff</INF> and the value of k<INF>eff</INF> at which 
the system is expected to be critical. The expected (best estimate) 
critical value of k<INF>eff</INF> is the mean k<INF>eff</INF> value 
of all critical experiments analyzed (bias), including consideration 
of the uncertainty in the bias (i.e., k<INF>c</INF>-
[Delta]k<INF>c</INF>). The calculated value of k<INF>eff</INF> for 
an application generally exceeds the actual (physical) 
k<INF>eff</INF> value due to conservative assumptions in modeling 
the system. In terms of the above USL equation, the MoS may be 
expressed mathematically as:

MoS = k<INF>c</INF> - [Delta]k<INF>c</INF> - (k<INF>s</INF> - 
[Delta]k<INF>sa</INF>) - [Delta]k<INF>s</INF>

where the term in parentheses is equal to the actual (physical) 
k<INF>eff</INF> of the application, k<INF>sa</INF>. A term, 
[Delta]k<INF>sa</INF>, has been added to represent the difference 
between the actual and calculated value of k<INF>eff</INF> for the 
application (i.e., [Delta]k<INF>sa</INF> = change in k<INF>eff</INF> 
resulting from modeling conservatism). In terms of the USL:

MoS = USL + [Delta]k<INF>m</INF> -k<INF>s</INF> + 
[Delta]k<INF>sa</INF> - [Delta]k<INF>s</INF>

    The minimum allowed value of the MoS is reached when the 
calculated k<INF>eff</INF> for the application, k<INF>s</INF> + 
[Delta]k<INF>s</INF>, is equal to the USL. When this occurs, the 
minimum value of the MoS is:

MoS >= [Delta]k<INF>m</INF> + [Delta]k<INF>sa</INF>

    Thus, adequate margin (MoS) may be assured either by 
conservatism in modeling practices or in the explicit specification 
of [Delta]k<INF>m</INF> (MMS). This is discussed in the ISG section 
on modeling conservatism.

Glossary

    Application: calculation of a fissionable system in the facility 
performed to demonstrate subcriticality under normal or credible 
abnormal conditions.
    Area of applicability: the ranges of material compositions and 
geometric arrangements within which the bias of a calculational 
method is established.
    Benchmark experiment: a critical experiment that has been peer-
reviewed and published and is sufficiently well-defined to be used 
for validation of calculational methods.
    Bias: a measure of the systematic differences between 
calculational method results and experimental data.

[[Page 60575]]

    Bias uncertainty: a measure of both the accuracy and precision 
of the calculations and the uncertainty in the experimental data.
    Calculational method: includes the hardware platform, operating 
system, computer algorithms and methods, nuclear reaction data, and 
methods used to construct computer models.
    Critical experiment: a fissionable system that has been 
experimentally determined to be critical (with k<INF>eff</INF> [ap] 
1).
    Margin of safety: the difference between the actual value of a 
parameter and the value of the parameter at which the system is 
expected to be critical with critical defined as k<INF>eff</INF> = 1 
= bias = bias uncertainty.
    Margin of subcriticality (MoS): the difference between the 
actual value of k<INF>eff</INF> and the value of k<INF>eff</INF> at 
which the system is expected to be critical with critical defined as 
k<INF>eff</INF> = 1 = bias = bias uncertainty.
    Minimum margin of subcriticality (MMS): a minimum allowed margin 
of subcriticality, which is an allowance for any unknown 
uncertainties in calculating k<INF>eff</INF>.
    Subcritical limit: the bounding value of a controlled parameter 
under normal case conditions.
    Upper subcritical limit (USL): the maximum allowed value of 
k<INF>eff</INF> (including uncertainty in k<INF>eff</INF>), under 
both normal and credible abnormal conditions, including allowance 
for the bias, the bias uncertainty, and a minimum margin of 
subcriticality.

[FR Doc. 05-20785 Filed 10-17-05; 8:45 am]
BILLING CODE 7590-01-P


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