bluuebunny/crossref_metadata_2025 huggingface dataset:
Attention‐affect interactions and triarchic psychopathy: New electrophysiological insights from the late positive potential
Authors: Ribes‐Guardiola Pablo , Ventura‐Bort Carlos , Poy Rosario , Segarra Pilar , Branchadell Victoria , Moltó Javier
Published: November 2022

One of the most prominent characteristics of psychopathy is a reduced processing of emotionally relevant information. However, it is still unclear how attentional mechanisms may modulate this deficit. The current study aimed to examine the impact of attentional focus on emotion processing in relation to the triarchic constructs of boldness, meanness, and disinhibition. Participants performed two tasks in which pleasant, neutral, and unpleasant framed pictures were presented. In the first task, participants were required to indicate the color of the frame (alternative‐focus task), whereas in the second task they were instructed to indicate the emotional category of the image (affect‐focus task). The Late Positive Potential (LPP) was used as an index of sustained engagement of attention to affective material. Confirming a successful task manipulation, we observed reduced LPP amplitudes, particularly for affective relevant material, in the alternative‐focus task compared to the affect‐focus task. Most interestingly, our results evidenced that trait meanness scores were associated with blunted elaborative processing of affective material (both appetitive and aversive) when this information was task‐relevant (affect‐focus task), but not when it was task‐irrelevant (alternative‐focus task). These findings indicate that high mean individuals are characterized by blunted elaborative processing of affective stimuli when their motivational relevance is determined in a top‐down manner (i.e., when it is task‐relevant). Our results highlight the need for further studying of the bottom‐up and top‐down dynamics of emotional attention in psychopathy.
On the linearized system of equations for the condensate–normal fluid interaction at very low temperature
Authors: Escobedo M.
Published: November 2022

The linearization around one of its equilibrium of a system that describes the correlations between the superfluid component and the normal fluid part of a condensed Bose gas in the approximation of very low temperature and small condensate density is studied. A simple and transparent argument gives a necessary and sufficient condition for the existence of global solutions satisfying the conservation of the total number of particles and energy. The global solutions describe the evolution in time of the density of the thermal cloud and, unlike in previous work, that of the condensate's density. Their convergence to a suitable stationary state is also shown and rates of convergence for the normal fluid and superfluid components are obtained.
Characterization of the novel HLA‐DQA1*05:05:14 allele by sequencing‐based typing
Authors: Cargou Marine , Andreani Marco , Battarra Mariarosa , Ralazamahaleo Mamy , Visentin Jonathan
Published: November 2022

HLA‐DQA1*05:05:14 differs from HLA‐DQA1*05:05:01:04 by one nucleotide substitution in codon −8 in exon 1.
Detection and Characterization of Fatigue Cracks in Butt Welds of Offshore Structures Using the Eddy Current Method
Authors: Gansel René , Maier Hans Jürgen , Barton Sebastian
Published: November 2022

A reliable assessment of the actual condition of wind turbines is essential for their continuous operation, or if an extension of their service life is considered. To be able to make valid statements about the condition of the welded structure, an inspection concept for the reliable detection of fatigue cracks and damage on welds of the offshore tower and foundation structures is essential. In the present study, five different eddy current differential sensors were evaluated on cyclically fatigued DY butt welds under tensile load with a focus on the signal-to-noise ratio (SNR). Based on previous evaluations, two sensors were selected for semi-automatic weld testing. It is shown that due to the weld crossing necessary for the detection of fatigue cracks, air coils arranged parallel to the test surface have the highest SNR. This must be contrasted with the potential for crack depth determination. In this context, coils with different arrangements with respect to the test surface were analyzed. It is shown that groove depths can be differentiated based on the imaginary part of the measurement signal for groove depths of up to 8 mm, and actual fatigue cracks in welds with a crack depth of 0.5 mm were detected.
Machine Learning-Based Fault Detection and Diagnosis of Internal Combustion Engines Using an Optical Crank Angle Encoder
Authors: Geraei Hosna , Seddik Essam , Neame Ghabi , Huangfu Elliot (Yixin) , Habibi Saeid
Published: November 2022

Fault Detection and Diagnosis (FDD) in internal combustion engines is an important tool for better performance, safety, reliability, and instrument to reduce maintenance costs. Early detection of engine faults can help avoid abnormal event progression to failure. This study is carried out to develop two FDD algorithms to detect and diagnose internal combustion engine faults using an optical crank angle encoder. Experiments were carried out on a 2018 Ford Gen 3, 5.0L, V8, Coyote engine to achieve these goals. The engine head was modified to access the combustion chamber of specific cylinders for in-cylinder pressure measurement and, subsequently, combustion analysis. During this project, three engine faults were introduced: EGR valve failure, cylinder leakage, and spark plug degradation. In the first method, Fast Fourier Transform (FFT) is applied to the data collected using the optical crank angle encoder. FFT converts the crank angle domain data to the frequency domain. Then, the data dimension is reduced using Principal Component Analysis (PCA). The dataset with reduced dimensions is used as Multi-layer Perceptron (MLP) inputs. 10-fold cross-validation is used to determine the number of hidden layers in the MLP. The MLP model detects and diagnoses severities of cylinder leaks and EGR faults with a relatively high success rate (92%). The second method developed a classification model using the Random Forest (RF) classifier and Curve Descriptive (CD) Features. The performance of the MLP model and the Curve Descriptive features with Random Forest (CD-RF) models for detecting and diagnosing misfire faults are compared. Results show that the MLP model and CD-RF model accuracy for classifying misfire faults are 86.67% and 88,89%, respectively.
Exploring the Oxy-Fuel Combustion in Spark-Ignition Engines for Future Clean Powerplants
Authors: Serrano José Ramón , Martín Díaz Jaime , Gomez-Soriano Josep , Raggi Rodrigo
Published: November 2022

In the context of the CO2 challenge, oxy-fuel combustion in internal combustion engines (OFC-ICE) arises as a promising technology for carbon capture and almost zero-NOx solutions. Although the literature shows some experimental and theoretical works on OFC-ICE, there is a lack of systematic studies dealing with dilution strategies or where non-synthetic EGR is used. Using a combination of 0D-1D and CFD modeling and experimental measurements, dilution with oxygen (λ > 1) and real EGR in a single-cylinder spark-ignition OFC-ICE is here assessed, considering thermo-mechanical limitations and knocking. Results show that an EGR strategy is more appropriate than O2 dilution. A slightly poor mixture near stoichiometric conditions, with EGR rates around 70%, reduces NOx more than 99%, and CO and uHC up to 90% with respect to the conventional ICE. It is concluded that OFC-ICE has a lower knocking propensity, thus allowing to increase the compression ratio to partially compensate for the expected efficiency diminution to about 4 percentage points.
Application of Modal Decomposition Techniques to Characterize the Internal Nozzle Flow of a Medium-Duty Diesel Injector Operating With Gasoline-Like Fuels
Authors: Asztalos Katherine J. , Torelli Roberto , Pei Yuanjiang , Zhang Yu , Tao Feng , Garg Rajesh , Langenderfer David , Moon Chi Young , Sforzo Brandon A. , Powell Christopher F.
Published: November 2022

Improving the efficiency of internal combustion engines can result in lower operating costs and enable reduced total cost of vehicle ownership. On the other hand, it is of utmost importance that the development of engine technologies is also directed toward curbing climate-altering and harmful emissions. One such technology is gasoline compression ignition, which can offer increased fuel efficiency and reduced emissions and criteria pollutants. By means of modal analysis methods, this study focuses on characterizing the internal flow behavior of a production, eight-hole, medium-duty diesel injector operating with gasoline-like fuels under typical diesel injection conditions. High-resolution tomographic reconstructions of the injector tip geometry, along with needle motion measurements, were obtained via X-ray measurements. This information was combined with a previously validated computational setup to perform a series of in-nozzle flow computational fluid dynamics (CFD) simulations. The CFD results were analyzed to identify the connection between the injector’s global behavior and relevant flowfields such as velocity and vorticity. The results obtained with the X-ray geometry were then compared against those obtained with a nominal geometry to investigate the effect of the injector’s geometric features on its behavior. Variations to the K-factor and inlet ellipticity of the orifices were explored, and these parameters’ ability to reduce in-orifice cavitation was assessed by analyzing the predicted in-orifice fuel vapor volume fraction. Relatively large and persistent flow structures arising within the injector sac were identified as the main driver for orifice-to-orifice variability, as their location and size impacted the amount of fuel delivered by each orifice. Assessing the extent to which these energetic structures impact the injector performance is of interest; thus, it is desirable to investigate the most energetically significant flowfield structures as a function of modifications to specific features of the injector geometry. Modal decomposition tools, such as space-only proper orthogonal decomposition (POD) and spectral proper orthogonal decomposition (SPOD) were applied to gain insight into dominant coherent structures that persist over the early phase of the needle opening event. It was found that the geometrical modifications applied resulted in variations of the modal energy content, as well as in the leading dominant spatial mode features. Additionally, it was found that the dominant frequency associated with the most significant energetic content was largely invariant to the geometrical modifications, with the difference between realistic and modified geometries being mostly found in the modal energy content.
Accelerating Chemical Kinetics Calculations With Physics Informed Neural Networks
Authors: Almeldein Ahmed , Van Dam Noah
Published: November 2022

Detailed chemical kinetics calculations can be very computationally expensive, and so various approaches have been used to speed up combustion calculations. Deep neural networks (DNNs) are one promising approach that has seen significant development recently. Standard DNNs, however, do not necessarily follow physical constraints such as conservation of mass. Physics Informed Neural Networks (PINNs) are a class of neural networks that have physical laws embedded within the training process to create networks that follow those physical laws. A new PINN-based DNN approach to chemical kinetics modeling has been developed to make sure mass fraction predictions adhere to the conservation of atomic species. The approach also utilizes a mixture-of-experts (MOE) architecture where the data is distributed on multiple sub-networks followed by a softmax selective layer. The MOE architecture allows the different sub-networks to specialize in different thermochemical regimes, such as early stage ignition reactions or post-flame equilibrium chemistry, then the softmax layer smoothly transitions between the sub-network predictions. This modeling approach was applied to the prediction of methane-air combustion using the GRI-Mech 3.0 as the reference mechanism. The training database was composed of data from 0D ignition delay simulations under initial conditions of 0.2–50 bar pressure, 500–2000 K temperature, an equivalence ratio between 0 and 2, and an N2-dilution percentage of up to 50%. A wide variety of network sizes and architectures of between 3 and 20 sub-networks and 6,600 to 77,000 neurons were tested. The resulting networks were able to predict 0D combustion simulations with similar accuracy and atomic mass conservation as standard kinetics solvers while having a 10–50× speedup in online evaluation time using CPUs, and on average over 200× when using a GPU.
Investigation on the Effects of Passive Pre-Chamber Ignition System and Geometry on Engine Knock Intensity
Authors: Di Sabatino Francesco , Martinez-Hernandiz Pablo Jose , Novella Rosa Ricardo , Ekoto Isaac
Published: November 2022

The effects of passive pre-chamber (PC) geometry and nozzle pattern as well as the use of either conventional spark or non-equilibrium plasma PC ignition system on knocking events were studied in an optically-accessible single-cylinder gasoline research engine. The equivalence ratio of the charge in the main chamber (MC) was maintained equal to 0.94 at a constant engine speed of 1300 rpm, and at constant engine load of 3.5 bar indicated mean effective pressure for all operating conditions. MC pressure profiles were collected and analyzed to infer the amplitude and the frequency of pressure oscillations that resulted in knocking events. The combustion process in the MC was investigated utilizing high-speed excited methylidyne radical (CH*) chemiluminescence images. The collected results highlighted that PC volume and nozzle pattern substantially affected the knock intensity (KI), while the use of the non-equilibrium plasma ignition system exhibited lower KI compared to PC equipped with a conventional inductive ignition system. It was also identified that knocking events were likely not generated by conventional end gas auto-ignition, but by jet-related phenomena, as well as jet-flame wall quenching. The relation between these phenomena and PC geometry, nozzle pattern, as well as ignition system has been also highlighted and discussed.
A Study on the Oil Film Thickness Between the Lower Rail of Oil Control Ring and Lower Flank of Oil Control Ring Groove of an Engine
Authors: Miura Ken , Ito Akemi , Nakamura Yuta , Yamada Rina , Nishibe Koichi , Usui Miyuki , Iijima Naoki
Published: November 2022

As friction reduction of an engine sometimes causes an increase in lubricating oil consumption, the requirement of reducing oil consumption has become crucial recently. The oil used for lubrication around a piston travels into the combustion chamber via piston ring’ gap, side and back, and sliding surface. This study focused on the oil flow via the side of the lower rail of the three-piece type oil control ring (OCR). Oil film thickness was measured by the laser induced fluorescence method. Oil film thickness between the lower rail and the lower flank of the ring groove at the outer and the inner side was successfully measured. The waveform of the oil film thickness was compared to the wave forms of friction force, inertial force, pressure under the OCR and pressure at the third land. The waveform of oil film thickness was well explained by those forces. Furthermore, it was also found that the shape of the lower rail remarkably affected the oil film thickness.
Predicting Combustion Variability Using Machine Learning From the Flow Field Data at Spark Timing for a Gasoline Direct Injection Engine
Authors: Probst Daniel , Attal Nitesh , Mandhapati Raju , Avanessian Oshin
Published: November 2022

Machine learning was used to predict three combustion metrics based on the flow field data at spark angle (SA), obtained from large eddy simulations (LES). These metrics were peak cylinder pressure (PCP), crank angle of PCP (CA of PCP), and indicated mean effective pressure (IMEP). The ML model was evaluated for use as a surrogate for detailed combustion model in studies of cycle-to-cycle variation (CCV). The computational time for combustion predictions (from SA to exhaust valve opening (EVO)) was 15 hours on 24 cores while the trained ML model was able to make predictions in 3 seconds (a speed up factor of 7200). The ML model was trained on a single core in 45 minutes using 88 LES simulation cycles. As the LES simulation data at SA has high fidelity (in this case approximately 260,000 computational cells) a workflow to filter the data to a reasonably sized feature set for use in ML was developed. Subvolumes were defined and the Pearson Correlation Coefficient (PCC) was used to evaluate the features. An ensemble ML approach was employed, which combines the predictions of multiple base learners to improve the final prediction. The ML model predictions for the three combustion metrics showed good correlation with the CFD simulations.
Development of a Synthetic Surrogate for F-24 From Blends of Iso-Paraffinic Kerosene (IPK) and Fischer-Tropsch Synthetic Kerosene (S8) in a Constant Volume Combustion Chamber (CVCC)
Authors: Soloiu Valentin , Parker Lily , Smith Richard , Weaver Amanda , Brant Austin , Rowell Aidan , Ilie Marcel
Published: November 2022

Investigations were conducted using mass blends of Iso-Paraffinic Kerosene (IPK) and Fischer-Tropsch Synthetic Kerosene (S8) to produce a synthetic surrogate for aerospace F-24. Due to the fossil fuel origin of F-24, the introduction of a synthetic surrogate would create a sustainable aviation fuel (SAF) with sources obtained from within the United States. An analysis of ignition delay (ID), combustion delay (CD), derived cetane number (DCN), negative temperature coefficient (NTC) region, Low-Temperature Heat Release region (LTHR) and High-Temperature Heat Release (HTHR) was conducted using a PAC CID 510 Constant Volume Combustion Chamber (CVCC). The fuels examined in this study are neat IPK, neat S8, neat F-24, and by mass percentages, as follows: 75IPK 25S8, 52IPK 48S8, 51IPK 49S8, 50IPK 50S8 and 25IPK 75S8.

The DCN values determined for IPK, S8, and F-24 were 26.92, 59.56 and 44.35 respectively. The influence of IPK present in the blends increases CD, thus reducing the DCN significantly. The fuel blend of 50IPK 50S8 was observed to be the closest match to F-24 when comparing DCN, ID and CD.

The surrogate blends were determined to have a lower magnitude of peak pressure ringing compared to that of the neat S8 and F-24, this is due to the extended NTC region caused by the IPK present in the blend. During further refinement of the surrogate blend, the Apparent Heat Release Rate (AHRR) curve for the 51IPK 49S8 fuel blend was found to have the closest match to the AHRR of F24. The surrogate blend 50IPK 50S8 was shown to have the smallest percent difference and best match during the LTHR stage, compared to F-24, while 52IPK 48S8 had the smallest percent difference for the energy released during LTHR. The ID and CD of the 25/75% blends were too dissimilar from the F-24 target to be considered as a surrogate.

A Noise Vibration Harshness (NVH) analysis was also conducted during the combustion of the three neat fuels in the CVCC. This analysis was conducted to relate the ID, CD, HTHR and ringing to the vibrations that occur during combustion. Neat S8 was observed to have the most vibrations occurring during the combustion process. Additionally, the HTHR was observed to have a distinct pattern for the three neat fuels and the combustion of these fuels was quieter overall.
Machine Learning and Genetic Algorithm Method for Powertrain Development: Rapid Generation of Engine Calibration Maps
Authors: Williams Zachary L. , Meruva Prathik , Bitsis Daniel Christopher
Published: November 2022

Meeting regulatory and customer demands requires detailed powertrain calibration which can be expensive and time-consuming. There is often a reliance on mathematical optimization tools to convert experimental learnings into a final calibration. This work focuses on developing multiple neural network machine learning (ML) models which were trained on different test-train data splits of test-cell recorded steady-state medium-duty (MD) diesel engine data. The output data was used to develop engine actuator maps by utilizing a genetic algorithm (GA). The genetic algorithm contains a fitness function which was varied to target different combinations of low NOx and CO2 emissions. The input variables used for the ML model were engine speed, engine torque, fuel rail pressure, exhaust gas recirculation (EGR) valve command, main injection timing, and wastegate valve command. The output variables predicted were NOx mass flow rate, exhaust temperature, fuel flow rate, and dry intake mass flow rate. The ML models were used to predict cycle-averaged engine-out emissions and time-series predictions of all output variables for different transient drive cycles. The drive cycles used for this case were the Heavy-Duty Federal Test Procedure (HDFTP) transient cycle, the Non-Road Transient Cycle (NRTC), the Ramped Mode Cycle (RMC) and the newly proposed on-road Low-Load Cycle (LLC).
In Situ Local Void Fraction Measurement in the Vertical Air-Gas Two-Phase Flow by the Combination of Multiple Current-Voltage (MCV) and Plural Long Short-Term Memory Lasso (MCV-pLSTM-L)
Authors: Saito Daisuke , Prayitno Yosephus Ardean Kurnianto , Sejati Prima Asmara , Takei Masahiro
Published: November 2022

In situ local void fraction in the vertical air-gas two-phase flow has been detected by the combination of multiple current-voltage (MCV) and plural long short term memory lasso (MCV-pLSTM-L) for the flow status monitoring in cooling equipment of nuclear reactor. The combination of MCV and machine learning has been applied to void fraction measurement of vertical air-gas two-phase flow. However, in our previous research Plural Long Short Term Memory Lasso, the machine learning employed drift flux model to calculate the true void fraction. There was a possibility of disagreement between true in situ void fraction and calculated void fraction. MCV-pLSTM-L has five steps, 1) true local void fraction measurement by electrical probe sensor, 2) voltage measurement by the MCV system, 3) voltage extraction by Lasso, 4) flow regime identification by 1st LSTM, and 5) void fraction α estimation by 2nd LSTM. Experiments were conducted in a vertical pipe with an inner diameter of 25mm and a length oh 80mm from the inlet. Two vertical flow regimes which were liquid single-phase flow and bubbly flow were used. As a result, the local void fraction is successfully estimated and applicability of MCV-pLSTM-L is confirmed.
Evolution of the Evening Method of Coolant Flow in the Large Core Based on Patent History
Authors: Yu Wenchi , Fu Hao
Published: November 2022

Generally, the reactor vessel is cylindrical having a hemispherical lower end. The core barrel is connected to the interior walls of the reactor vessel at or adjacent to the area where the cylindrical and hemispherical portions of the reactor vessel meet. Below the main core support, i.e. the core barrel that is capped at its lower end with the lower core support, the hemispherical vessel defines a lower head or lower plenum. A generally annular downcomer surrounds the reactor core barrel between the core barrel and an inner wall of the reactor vessel. Cooling fluid, typically water, is pumped into this annular downcomer. The coolant fluid circulates downward into the lower plenum. The hemispherical shape of the lower plenum assists in evenly circulating the coolant fluid therein. A plurality of reactor core coolant inlet openings are located on the underside of the lower core support plate. Coolant flows from the lower plenum, into the core coolant inlet openings and upwardly into the core to cool the fuel assemblies.

With the advent of larger plants with larger cores it became evident that further means were necessary to improve the distribution of coolant flow in the lower plenum to assure uniform coolant flow and pressure were maintained across all of the reactor core coolant inlet openings in the lower core support plate.

Non-uniform coolant pressure or flow causes uneven coolant flow into the core, which results in uneven cooling of the fuel assemblies of the core. Uneven fuel assembly cooling may force the entire core to be derated to accommodate “hot assembly” locations. Non-uniform coolant flow and pressure may result in vortices or other flow disruptions to form in the coolant fluid circulating in the lower plenum. According to patent filing history, dozens of devices have been proposed to uniform the distribution of coolant flow. Those device could be categorized several kinds. As coolant flow in the fringe of low core plate, firstly the cylinder shape has been applied to prevent the vortex. This biggest vortex is formed by that the downflow from core barrel has 180° direction diverting with the upflow into LCP, diverting between the high speed flows. In order to maintain adequate and uniform cooling throughout the core, it is important that a uniform coolant flow and pressure be maintained across all of the reactor core coolant inlet openings in the lower core support plate. The second way to is put some device in the bottom of lower plenum to prevent vortex. The third is changing flow distribution of LCP by setting some flow limiting devices.
Application of First-Order Method to Estimate Structural Integrity in a Probabilistic Form of Component Subjected to Thermal Transient for Optimization of Design Parameter
Authors: Okajima Satoshi , Mori Takero , Kikuchi Norihiro , Tanaka Masaaki , Miyazaki Masashi
Published: November 2022

Japan Atomic Energy Agency has been developing “Advanced Reactor Knowledge- and AI-aided Design Integration Approach through the whole plant lifecycle (ARKADIA)”, which aims to offer best solution for challenges on the design and operation of a nuclear plant. In a part of ARKADIA for design study including design optimization of components named ARKADIA-Design, we have been developing a process to automatically optimize design parameter of structural components subjected to various kinds of loads, including thermal transient.

In this paper, we propose the simplified procedure to estimate failure probability of components subjected to thermal transient for the design optimization. An objective function of this optimization based on failure probability of the components is assumed, because failure probability can be commonly used as an indicator of component integrity for various failure mechanisms. In order to enable necessary number of the estimations for the design optimization with practical calculation cost, we have aimed to reduce number of analyses required for one estimation. For this purpose, we have adopted the First Order Second Moment (FOSM) method as the estimation method of failure probability on the process of the optimization and an orthogonal table in experiment design method is utilized to define conditions of the analyses for the evaluation of mean and variance of thermal transient stress, which are used as inputs for the FOSM method. The proposed procedure has been applied to the demonstration study to optimize thickness of cylindrical vessel subjected to thermal transient derived from shutdown. We confirmed that the procedure can evaluate the failure probability depending on the cylinder thickness with practical calculation cost.
Research on Generalization of Typical Data-Driven Fault Diagnosis Methods for Nuclear Power Plants
Authors: Li Jiangkuan , Lin Meng
Published: November 2022

With the development of artificial intelligence technology, various achievements have been realized in data-driven nuclear power plant fault diagnosis. Even endowed with high flexibility and practicability, most of the proposed data-driven methods are based on the same assumptions that the test data is in the same distribution as the training data. In practice, nuclear power plants may be in variable operating conditions, which brings challenges to the generalization of the diagnosis model trained by finite data. In this paper, the widely used data-driven models in nuclear power plant fault diagnosis: Random Forest (RF), K-Nearest Neighbor algorithm (KNN), Fully Connected Neural Network (FCNN) and Convolutional Neural Network (CNN) are taken as examples to study the influence of the distribution discrepancy between training data (source domain) and test data (target domain) on their generalization. The results show that the distribution discrepancy exert serious adverse effects on the diagnostic performance of the data-driven models. At the same time, to improve the generalization of data-driven models, a nuclear power plant fault diagnosis transfer learning method based on pre-trained model is proposed, which can utilize the fault diagnosis knowledge from the source domain task to accelerate the model training in the target domain task, so that the model can achieve better diagnosis performance with limited labeled data in target domain.
Creep Performance and Microstructure Evolution of IN617 at 950 °C for VHTR Applications
Authors: Wang Yue , Wang Haitao , Li Kejian
Published: November 2022

IN617 was considered the primary material candidate for the IHX in the VHTR. Researching microstructure evolution during high-temperature creep of IN617 helped understand its fracture laws and guide IHX operation under creep loading. Creep tests of IN617 were conducted under 19 MPa, 24 MPa, 27 MPa, and 38 MPa at 950 °C. Creep rupture mechanisms of IN617 were discussed by correlating creep performance, microstructure characteristics and fracture-surface morphology. The results indicated that DRX, creep voids and brittle-phase precipitation were found under different stresses during microstructure observation, which would cause the specimen ductile rupture, intergranular rupture and brittle rupture, respectively. Specifically, under the highest stress 38 MPa, DRX occurred and grain size was decreased greatly to 11.1 μm from 99.7 μm. Fine grains were easy to migrate, causing significant plastic deformation and ductile rupture of specimens. With stress decreased to 27 MPa, grain boundaries became vulnerable and intergranular rupture occurred because intergranular carbides dissolved and their pinning effect was weakened. As stresses were lowered to 24 MPa and 19 MPa, nitrogen was diffused into specimens and brittle nitrides precipitated into continuous networks along GBs. The internal cracking of nitride networks caused brittle rupture. Meanwhile, steady creep rates were increased, and creep rupture lives were shortened greatly, especially under 19 MPa.
Preliminary Study on Corrosion Behavior and Mechanical Properties Of Inconel 617 in Impure Helium Environment of VHTR
Authors: Du Bin , Yin Huaqiang , Ma Tao , Li Haoxiang , Zheng Wei , He Xuedong
Published: November 2022

Helium is generally used as coolant in Very-High-Temperature Reactor (VHTR), but a small amount of impurity gas will inevitably be mixed in the primary coolant during construction, operation and maintenance. At high temperature, these impurity gases will corrode with the alloy materials of the intermediate heat exchanger, resulting in the decline of the properties of the superalloy materials. This paper mainly studied the changes of microstructure and mechanical properties of Inconel 617, a candidate material for high temperature reactor intermediate heat exchanger, after aging for 50 hours in impure helium at 950 °C. The microstructure of the alloy was characterized by weighing, scanning electron microscope and energy dispersive X-ray spectroscopy. After corrosion, Cr oxide layer was formed on the alloy surface, and Al internal oxidation appeared below the oxide layer. The mechanical test results showed that the strength and plasticity of Inconel 617 decreased significantly, which was related to the intergranular oxides and carbides. The fracture morphology of the alloy is mainly brittle fracture.
A Bi-Layered Three-Dimensional Mechanical Modeling of the Cladding and Its Creep Deformation Analysis
Authors: Zhang Ming , Li Jinggang , Liu Xiaohan , Lu Yong , Zhu Yanan
Published: November 2022

The cladding of the fuel rod is a cylinder-shaped structure made of Zirconium alloy, which will collapse due to structural creep under the extremely service conditions such as high temperature, high pressure, and high irradiation. The collapse of the cladding results in losing its structure function and threatening the safety of the reactor. Based on the commercial finite element software ABAQUS with its user subroutine CREEP, a bi-layered (coating and matrix material) three-dimensional (3D) cylindrical cladding model is established with thermal and irradiation finite creep behavior. The external pressure is assumed to be constant acting on the outer surface. The deformation and the rate of deformation increase with the increasing of the irradiation time in the reactor, which leads to the collapse of the cladding eventually. The initial ovality has a positive effect on the creep deformation. Compared with the single-layered model, the coating of the bi-layered cladding can prevent the Zirconium alloy matrix from excessive creep deformation and thus can protect the cladding. The thicker the coating, the stronger the protective effect from the mechanical point of view. A qualitative case of the cladding creep burst was simulated, and the behavior of the creep burst and creep collapse is similar. The corrosion and oxidation behavior are not considered herein for simplicity. The current bi-layered 3d model can be extended to the structural design, safety analysis, as well as life evaluation of some multi-layered cladding of the accident tolerant fuel (ATF).
Research on Risk Assessment of Heavy Load Drops in Nuclear Power Plants
Authors: Ma Chao , Wang Yiming , Feng Churan , Yang Jian
Published: November 2022

After years of development, probabilistic safety assessment has become a tool for the evaluation and optimization of the design and operation of nuclear power plants, and the analysis scope has also developed from internal events to internal hazards and external hazards. In the assessment of internal and external hazards, the probabilistic safety assessment of internal fire, internal flooding and seismic has been fully studied, and a systematic analysis method has been formed, which is used to support the design and operation of nuclear power plants. The drop of heavy load is one of the internal hazards of nuclear power plants. At present, there is no systematic method how to quantitatively evaluate the risks to nuclear power plants. This paper refers to the analysis requirements of IAEA, NEI and other institutions on the analysis of heavy load drop, and combines the design characteristics and operating experience of the power plant to carry out a study on the risk assessment of heavy load drop, including: analysis of the possibility of heavy load drop, analysis of the impact of heavy load drop, CDF levels caused by heavy load drop, etc. Through the research in this paper, it is expected to provide insights into the risk of heavy load drop and provide support for subsequent risk assessment and optimization of nuclear power plants.
Probabilistic Safety Assessment of Newly Added Auxiliary Transformers In Fangjiashan Nuclear Power Plant
Authors: Zou Shengjia , Wu Jian , Cao Yong , Wang Zilong , Luo Yang , Zhao Ming , Liu Deyi , Chen Honghao
Published: November 2022

In order to further improve the safety of the nuclear power plant, China nuclear power Fangjiashan nuclear power plant plans to add an auxiliary standby transformer, which is used for the redundancy of the original auxiliary transformer. This permanent change will improve the reliability of external power supply of nuclear power plant and reduce the frequency of loss of external power supply, so as to improve the safety level of Fangjiashan nuclear power plant. In this paper, the probabilistic safety assessment method is applied to analyze Fangjiashan unit, and the level-1 PSA model of internal events under power operation conditions, level-1 PSA model of internal events under low power and shutdown conditions and spent fuel pool model are modeled respectively. After the permanent change of auxiliary standby transformer is added during Fangjiashan power operation, low power and shutdown, The quantitative safety analysis results are given for the improvement of safety and reliability of Fangjiashan nuclear power plant. This evaluation provides an idea for the application of PSA in the permanent change of nuclear power plant. Before the permanent change of power plant plan, through the analysis of probabilistic safety evaluation, we can get how much benefit this change brings to the nuclear safety of power plant.
Research on Deposition Performance of Fine Particles of Heat Pipe in Small Reactor
Authors: Liu Xiaofang , Zhou Tao , Mao Shang , Wei Dong
Published: November 2022

Small modular reactor (SMR) adopts heat pipe as the heat exchanger, which can improve its inherent safety. In the process of heat exchange, the deposition of fine particles caused by fluid flowing through the surface of heat pipe will cause heat transfer deterioration and the corrosion of pipe wall, which will adversely affect the safe operation of the reactor. The law of fine particle deposition on the outer wall of the simplified heat pipe model was numerically simulated, and the particle deposition in the fluid field outside the pipe was obtained under different temperature difference and flow velocity. The results showed that in the process of axial flow along the pipe wall, the velocity near the wall decreases gradually along the radial direction, and decreases to a fairly small value at the pipe wall; due to the heating effect of the heat pipe, the temperature of the fluid near the wall increases gradually along the axis, and there is a heat affected zone in the fluid temperature on the cross section. With the increase of working fluid flow rate and inlet temperature, the particle deposition rate increased first and then decreased.
On Line Monitoring of Sensor Failure in Nuclear Power Plant Based on Sequential Probability Ratio Test
Authors: Yu Fangxiaozhi , Ma Xintong , Miao Zhuang , Zhang Min
Published: November 2022

The safe and reliable operation of nuclear power plant is inseparable from the integrity of the control system function, and the integrity of the sensor function is the basis for the I&C system to monitor the operation status of nuclear power plant and trigger automatic action. However, there are some shortcomings in the traditional threshold alarm method of nuclear power plant: for example, it is difficult to identify the tiny sensor failure (such as tiny drift) earlier by this method. The threshold of this method is fixed and cannot be adjusted according to the actual situation (such as transient change). This paper introduces the on-line monitoring technology of sensor failure, which plays an important role in reducing the probability of unplanned shutdown and unit design and operation cost. In the future, the on-line monitoring technology of sensor failure has a good application prospect, and the relevant research is of great significance.

This paper mainly studies an overall technical scheme of sensor failure monitoring in nuclear power plant. In this paper, the data of the PWR nuclear power plant simulation simulator are used, and the sequential probability ratio test (SPRT) model, a typical failure diagnosis algorithm, is used to establish the sensor condition monitoring model, and the performance of the monitoring model are tested. The results show that the SPRT sensor monitoring model established in this paper has high accuracy, and can effectively monitor the occurrence of tiny drift phenomenon of sensors, and preliminarily verify the feasibility of on-line monitoring technology of sensor failure in PWR nuclear power plant.
Research and Application of Remote Intelligent Operation and Maintenance System for Digital Instrument and Control Equipment in Nuclear Power Plant
Authors: Guilian Shi , Xiaojin Huang , Xiaopeng Zhao , Lei Wang
Published: November 2022

In view of the problems of poor maintenance timeliness of nuclear power digital instrument and control system (I&C system) and the inability to evaluate the health condition of the I&C system in real time, a remote intelligent operation and maintenance strategy based on virtual DCS and Prognostic and Health Management (PHM) technology framework was proposed. The key technical information of virtual DCS and PHM is introduced. Combining with the technical characteristics of digital I&C system of nuclear power plant (NPP), the remote intelligent operation and maintenance framework combining virtual and actual object is constructed. With respect to the actual I&C system, virtual DCS was established to realize condition based maintenance for its flexibility and predictive characteristics. At the same time, the actual system data can be collected and stored through the I&C system data center, which will be the data basis for the remote monitoring of the site. Then, five functions of remote monitoring, fault diagnosis, condition prediction, intelligent maintenance and resource allocation and management are described. The key technologies and data resources needed to realize the scheme are analyzed. Finally, according to the strategy, a prototype with remote intelligent operation and maintenance function is built on the basis of a nuclear power platform, which realizes the strategy and provides feasibility guarantee for remote intelligent maintenance of I&C equipment.
Validation of NUIT for Decay Heat Predictions of PWR Spent Fuel Assemblies
Authors: Li Jian , Li Ruihan , She Ding , Liang Jin'gang , Shi Lei
Published: November 2022

Decay heat prediction is essential for nuclear reactor safety, waste management and spent fuel disposal. The NUIT code is dedicated for nuclide inventory calculations, which was developed by Institute of Nuclear and New Energy Technology (INET), Tsinghua University. NUIT is able to compute isotope density, radioactivity, decay heat, photon spectrum and other source term quantities. The problem-specific neutron spectrum and tabular effective one-group cross-section library produced by neutron transport codes were utilized by NUIT. Therefore, the NUIT code could be applied to many types of fission reactors, such as HTGR, PWR, BWR, etc. In this paper, validation of the NUIT’s capabilities in predicting decay heat for spent fuel assemblies was conducted with measured decay heat data from PWRs. The test cases for validation include measurements of 52 fuel assemblies (14 × 14, 15 × 15 and 17 × 17 fuel designs) that were irradiated in 5 PWR plants, with burnup values ranging from 19.7 to 51 GWd/tU. The cooling periods for the measured samples vary from 2.4 to 26.7 years, which are times of interest to spent fuel transportation and storage applications. The multi-group neutron spectrum provided by Monte Carlo code OpenMC was utilized to collapse into one-group cross sections. The decay heats for different fuel assemblies were obtained and compared with measurement data. The validation results show satisfactory agreement between NUIT results and reported measurement data. This work helps to obtain uncertainty margins in decay heat predictions with NUIT code.
Interpolation Influence on the Fast Fourier Transform Based Calculation of Three-Dimensional Dose Rate Field
Authors: Dong Xinwen , Fang Sheng , Zhuang Shuhan
Published: November 2022

The calculation of three-dimensional dose rate fields plays a key role in radiation dose rate estimation and the service for the nuclear emergency. The recent fast calculation method based on the Fast Fourier Transform (FFT) method can greatly speed up the calculation without losing accuracy, which is promising for operational usage in nuclear emergency response systems. But it can only be used for a uniform grid. Unfortunately, most atmospheric dispersion models use a non-uniform grid, which prevents the direct application of FFT-based calculation. Therefore, interpolation is required beforehand to use the Fourier transform, which may introduce errors and affect computing efficiency. In this paper, an atmospheric dispersion modeling case of a typical nuclear power plant (NPP) is used to investigate the efficiency of different interpolation methods, which are based on a non-uniform grid. These methods are linear interpolation and nearest-neighbor interpolation. The sensitive analysis of grid resolution is investigated in the slices of x, y, and z at typical positions, which confirms the smooth-out and speed-up effects in rough grids. A grid size over 10 m at any slice commonly causes losses of change details of dose rate fields. Given the same resolution of 50 m × 50 m × 50 m, the nearest neighbor performs a 717 times calculation faster than the linear method, which preserves more change details of dose rate fields as well. For complex calculation tasks, e.,g., non-uniform NPP buildings, the nearest neighbor interpolation method is recommended with a resolution of 10 m × 10 m × 10 m to make a good balance between accuracy and speed.
The Impact of Corporate Social Responsibility on Public Acceptance of Nuclear Power: A Mediation Model
Authors: Xu Zhong , Ma Jianhong , Li Xuning , Yang Ting , Gui Xinyu
Published: November 2022

Nuclear power, as a clean energy source, has been facing a blow after the Fukushima nuclear accident. Low public acceptance has severely restricted the nuclear power industry’s development. China government attaches great importance to the public acceptance of nuclear power and takes it as an important indicator for the approval of nuclear power projects. In the research of consumer domain, corporate social responsibility (CSR) is positively correlated with consumer support for enterprises, and the public is the ultimate consumer of nuclear power products. On this basis, hypotheses are proposed in this study: the public’s perception of the social responsibility of nuclear power enterprises can improve the public acceptance of nuclear power. Taking 211 residents around a nuclear power plant as the research object, this paper explores the impact of CSR perception on public acceptance of nuclear power and its path through questionnaire survey. The data analysis results of valid questionnaires showed that: (1) The percived CSR of nuclear power companies in China is divided into two dimensions, legal responsibility, economic&other responsibility, (2) The perceived legal responsibility has a positive effect on the public acceptance of nuclear power, and this effect is realized through the partial mediating effect of knowledge, dread risk perception and benefit perception.
Research on the Influence of Degassing on the Release of Fission Gases in PWRs
Authors: Ye Yaoxin , Fu Pengtao , Li Zhijun
Published: November 2022

Once fuel failures occur in a reactor core, the fission gas will release from the UO2 pellet to the fuel-to-sheath gap and eventually enter the primary coolant through the cladding wall breach. The crew of nuclear power plant regularly sample the primary coolant and characterize the fuel integrity based on the fission gases release model. If the activity of the primary coolant is too high and exceeds a certain limit, the degassing operation will be performed by the crew, causing a significant variation of primary coolant activity. As a result, the influence of degassing on the fission gases release should be taken into consideration in such situation. In this work, a modified model of fission gases release considering the degassing effect was established based on the fission product release mechanism and the authentic degassing processes of a nuclear power plant. The modified fission gases release model with degassing consideration was then validated by the measured primary coolant activity in a nuclear power plant. Finally, the model was applied on characterizing the fuel integrity of a fuel cycle with defective rods and frequent strong degassing operation. The dependence of release-to-birth ratio on decay constant of fission gases was determined using the modified model and the defect size was predicted. The predicted defect size was in good agreement with the sipping test result.