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Ban, Heng

Publications and source records attributed to Ban, Heng.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Transient behavior of oxide fuels with controlled microstructure and Cr2O3 additive

Abstract Microstructure and Cr 2 O 3 doping profoundly impact the thermal-mechanical properties and fracture of oxides fuels. It is a challenge to study the transient behavior of nuclear fuels under loss-of-coolant-event (LOCA). In this study, the crack behavior of UO 2 pellets with controlled grain structure and Cr 2 O 3 doping was tested with rapid power ramping (300−900 °C per min) mimicking a prototypical LOCA heating profile. Dense micron-sized UO 2 pellets display well-maintained integrity without cracking with the ramping up to 1500 °C at a heating rate of 8 °C per second. Fracture occurs in both pure and Cr 2 O 3 -doped dense nano-sized UO 2 pellets. The Cr 2 O 3 doped oxide fuel pellet with a larger grain size (~ 22.2 μm) displays the best performance under LOCA testing due to its highest thermal conductivity under high temperature. FEA calculations suggest a temperature gradient across the fuel pellet during transient testing, resulting in residual stress and cracking, which can be correlated with their thermal-mechanical properties.

Zhao, Dong (ORCID:0000000191377257)↗
DOE OSTI2024

Design Optimization and Measurement Uncertainty of an Electromagnetic Level Sensor for Liquid Metal Reactors

Here, this article describes the design and operation of a prototype mutual inductance level sensor (MILS) and the development and validation of a finite-element analysis (FEA) model describing its behavior. The MILS was designed for use in liquid sodium up to temperatures of 650 °C in the Mechanisms Engineering Test Loop (METL) at Argonne National Laboratory (ANL). Preliminary testing was performed in a room temperature test stand with aluminum acting as an analog for the sodium to better understand sensor performance and provide accurate code validation data. This experimental data, along with material properties found in literature, were used to validate an FEA model in ANSYS Maxwell. The validated ANSYS Maxwell model was used to examine the performance of the MILS in various environments and under various operating conditions. Simulations suggest the following: The MILS will perform adequately in liquid sodium and liquid lead at temperatures up to 650 °C . The temperature dependence of electrical conductivity imposes a temperature dependence on the sensor that requires proper compensation. The MILS signal sensitivity is maximized when mutual inductance between sensor coils is maximized, and sensor geometry should be selected to account for this factor. The operating frequency of the MILS can be optimized and is dependent on process fluid material, operating temperature, and materials/geometry of sensor system. Finally, the use of a stainless-steel isolating thimble does not adversely affect the sensor signal. The primary sources of error for this MILS system are the accuracy of the calibration standard against which the sensor is calibrated, and the temperature dependence of the sensor. This work contains all the necessary details to recreate the FEA model and results. This model can be used to optimize the performance of a MILS in any operating environment to read any electrically conductive working fluid.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Refinement and Exploration

This report documents activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2023 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective, and secure DI&C technologies for digital upgrades/designs. A risk assessment-informed framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk informed capability to quantitatively estimate the safety margin obtained from plant modernization, especially for safety-related DI&C systems, (2) support and supplement existing risk informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of safety-related DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the LWRS-developed framework provides a means to address relevant technical issues by: (1) defining a risk informed analysis process for DI&C upgrade that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying risk informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development and deployment of advanced DI&C technologies in nuclear power plants (NPPs). Adding diversity within a system or components is the primary means to eliminate and mitigate CCFs, but diversity also increases system complexity and may not address all sources of systematic failures. Optimization of diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in safety-related DI&C systems of NPPs and supporting relevant design optimization, the proposed framework provides: (a) A best-estimate, risk informed capability to address new technical digital issues quantitatively, focusing on software CCFs in safety-related DI&C systems of NPPs; (b) A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to predict and prevent risk in the early design stage of DI&C systems; (c) Technical bases and risk informed insights to assist users address the risk informed alternatives for evaluation of CCFs in safety-related DI&C systems of NPPs; and (d) A risk informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The research and development efforts of this project in FY 2023 are focused on refining current methods on software CCF modeling and estimation and exploring additional innovative approaches to risk assessment of DI&C systems to enable a more comprehensive and complete assessment of various safety-related DI&C design architectures. The primary audience of this report are DI&C designers, engineers, and probabilistic risk assessment (PRA) practitioners. This includes stakeholders, such as the nuclear utilities and regulators who consider the deployment and upgrade of DI&C systems, DI&C software developers and reviewers, and cybersecurity specialists. It should be noted that all the analyses are performed for the demonstration of the methodology, not for the evaluation of an actual digital control system. Results are obtained based on limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Experimental investigation of power transient flow boiling

To advance the mechanistic understanding of power transients in flow boiling, experimental investigations are performed to capture the thermal response of four different kinds of cladding materials, including FeCrAls, to Fuchs reactivity initiated accident transients and linear ramp power transients. The transition boiling regime is phenomenologically reflected in power-transient flow boiling, which is different from steady-state boiling. In light of this, the critical heat flux that is usually featured with temperature overshooting does not indicate thermal safety margin of cladding materials as conservatively as the maximum heat flux, which is greater than critical heat flux in the power transient boiling curve. This is physically attributed to the thermal energy deposition on the cladding wall. In addition, the thermal energy released from the cladding wall results in different boiling heat transfer coefficient for the decreasing and increasing power stages respectively during the Fuchs power transient. The maximum heat flux difference gap, which appears due to changes in cladding materials and transient time scales, is appreciable under an intermediate heat convection regime. However, this difference gap is gradually weakened by the progressive increasing of mass flux and/or inlet subcooling due to the enhanced dominance of heat convection over heat conduction. Finally, the small difference gap of maximum heat flux is found to be insignificant under the weak heat convection because of power transient induced annular flow instability.

42 ENGINEERING↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Development of a plug-and-play anti-noise module for fault diagnosis of rotating machines in nuclear power plants

The health of rotating machines is crucial to the stable operation and safety of nuclear power plants. However, research on machine learning-based fault diagnosis of rotating machines in the nuclear industry is still in its infancy. The signal noise generated in the plant may negatively affect the effectiveness of the analysis. In this paper, a plug-and-play anti-noise machine learning module is proposed to fill the knowledge and capability gap. The modules are loaded into a convolutional neural network called deep residual network (ResNet) to obtain a new model with noise reduction capability. The basic idea is that the module is able to identify noise features and include them in the subsequent analysis, effectively filtering noise at the feature level of the network. Nine variants of the new model are compared with the original ResNet as well as four classical machine learning models to test the effectiveness of the module and to examine the impact of the module's loading modes on the performance of the new model. Finally, this research helps facilitate the application of machine learning in the plant noise environment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

An Integrated Framework for Risk Assessment of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration

This report documents the activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective and secure DI&C technologies for digital upgrades/designs. A framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the risk impact of plant modernization, considering the introduction of high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the framework provides a means to address relevant technical issues by: (1) defining a risk-informed analysis process for DI&C upgrade, that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development and deployment of advanced DI&C technologies on nuclear power plant (NPPs).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

An Application of a Modified Beta Factor Method for the Analysis of Software Common Cause Failures

This paper presents an approach for modeling software common cause failures (CCFs) within digital instrumentation and control (I&C) systems. CCFs consist of a concurrent failure between two or more components due to a shared failure cause and coupling mechanism. This work emphasizes the importance of identifying software-centric attributes related to the coupling mechanisms necessary for simultaneous failures of redundant software components. The groups of components that share coupling mechanisms are called common cause component groups (CCCGs). Most CCF models rely on operational data as the basis for establishing CCCG parameters and predicting CCFs. This work is motivated by two primary concerns: (1) a lack of operational and CCF data for estimating software CCF model parameters; and (2) the need to model single components as part of multiple CCCGs simultaneously. A hybrid approach was developed to account for these concerns by leveraging existing techniques: a modified beta factor model allows single components to be placed within multiple CCCGs, while a second technique provides software-specific model parameters for each CCCG. This hybrid approach provides a means to overcome the limitations of conventional methods while offering support for design decisions under the limited data scenario.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem

Some research topics belonging to classification problems in the nuclear industry, such as fault diagnosis and accident identification, can be solved by feature extraction and subsequent application of statistical machine learning classifiers. Recently, deep neural network-based methods with automatic feature extraction and high accuracy have gained wide attention. They usually require large-scale training data, however, plant fault or accident data are scarce or difficult to obtain. Here this paper proposes a convolutional network (CNN)-based transfer learning method to solve this problem. The network's shallow layer is derived from a pre-trained CNN based on the ImageNet database to automatically extract features, and the deep layer is customized to match the classification problem. Data in non-image formats are converted to image formats and subsequently used to train the network. Case studies of rotating machines fault diagnosis show that the proposed method requires only limited training data to achieve high accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2006

Viscosity Measurement for Tellurium Melt

The viscosity of high temperature Te melt was measured using a new technique in which a rotating magnetic field was applied to the melt sealed in a suspended ampoule, and the torque exerted by rotating melt flow on the ampoule wall was measured. Governing equations for the coupled melt flow and ampoule torsional oscillation were solved, and the viscosity was extracted from the experimental data by numerical fitting. The computational result showed good agreement with experimental data. The melt velocity transient initiated by the rotating magnetic field reached a stable condition quickly, allowing the viscosity and electrical conductivity of the melt to be determined in a short period.

Lin, Bochuan↗
NASA NTRS2005

Thermophysical and Optical Properties of Semiconducting Ga2Te3 Melt

The majority of bulk semiconductor single crystals are presently grown from their melts. The thermophysical and optical properties of the melts provide a fundamental understanding of the melt structure and can be used to optimize the growth conditions to obtain higher quality crystals. In this paper, we report several thermophysical and optical properties for Ga2Te3 melts, such as electrical conductivity, viscosity, and optical transmission for temperatures ranging from the melting point up to approximately 990 C. The conductivity and viscosity of the melts are determined using the transient torque technique. The optical transmission of the melts is measured between the wavelengths of 300 and 2000 nm by an dual beam reversed-optics spectrophotometer. The measured properties are in good agreement with the published data. The conductivities indicate that the Ga2Te3 melt is semiconductor-like. The anomalous behavior in the measured properties are used as an indication of a structural transformation in the Ga2Te3 melt and discussed in terms of Eyring's and Bachinskii's predicted behaviors for homogeneous melts.

Li, Chao↗
NASA NTRS2005

Thermal Conductivity Based on Modified Laser Flash Measurement

The laser flash method is a standard method for thermal diffusivity measurement. It employs single-pulse heating of one side of a thin specimen and measures the temperature response of the other side. The thermal diffusivity of the specimen can be obtained based on a one-dimensional transient heat transfer analysis. This paper reports the development of a theory that includes a transparent reference layer with known thermal property attached to the back of sample. With the inclusion of heat conduction from the sample to the reference layer in the theoretical analysis, the thermal conductivity and thermal diffusivity of sample can be extracted from the temperature response data. Furthermore, a procedure is established to select two points from the data to calculate these properties. The uncertainty analysis indicates that this method can be used with acceptable levels of uncertainty.

Lin, Bochuan↗
NASA NTRS2004

Torque Transient of Magnetically Drive Flow for Viscosity Measurement

Viscosity is a good indicator of structural changes for complex liquids, such as semiconductor melts with chain or ring structures. This paper discusses the theoretical and experimental results of the transient torque technique for non-intrusive viscosity measurement. Such a technique is essential for the high temperature viscosity measurement of high pressure and toxic semiconductor melts. In this paper, our previous work on oscillating cup technique was expanded to the transient process of a magnetically driven melt flow in a damped oscillation system. Based on the analytical solution for the fluid flow and cup oscillation, a semi-empirical model was established to extract the fluid viscosity. The analytical and experimental results indicated that such a technique has the advantage of short measurement time and straight forward data analysis procedures

Ban, Heng↗
NASA NTRS2003

Structural Fluctuations and Thermophysical Properties of Molten II-VI Compounds

The objectives of the project are to conduct ground-based experimental and theoretical research on the structural fluctuations and thermophysical properties of molten II-VI compounds to enhance the basic understanding of the existing flight experiments in microgravity materials science programs as well as to study the fundamental heterophase fluctuation phenomena in these melts by: 1) conducting neutron scattering analysis and measuring quantitatively the relevant thermophysical properties of the II-VI melts (such as viscosity, electrical conductivity, thermal diffusivity and density) as well as the relaxation characteristics of these properties to advance the understanding of the structural properties and the relaxation phenomena in these melts and 2) performing theoretical analyses on the melt systems to interpret the experimental results. All the facilities required for the experimental measurements have been procured, installed and tested. It has long been recognized that liquid Te presents a unique case having properties between those of metals and semiconductors. The electrical conductivity for Te melt increases rapidly at melting point, indicating a semiconductor-metal transition. Te melts comprise two features, which are usually considered to be incompatible with each other: covalently bound atoms and metallic-like behavior. Why do Te liquids show metallic behavior? is one of the long-standing issues in liquid metal physics. Since thermophysical properties are very sensitive to the structural variations of a melt, we have conducted extensive thermophysical measurements on Te melt.

Su, Ching-Hua↗
NASA NTRS2003

Thermal Property Measurement of Semiconductor Melt using Modified Laser Flash Method

This study further developed standard laser flash method to measure multiple thermal properties of semiconductor melts. The modified method can determine thermal diffusivity, thermal conductivity, and specific heat capacity of the melt simultaneously. The transient heat transfer process in the melt and its quartz container was numerically studied in detail. A fitting procedure based on numerical simulation results and the least root-mean-square error fitting to the experimental data was used to extract the values of specific heat capacity, thermal conductivity and thermal diffusivity. This modified method is a step forward from the standard laser flash method, which is usually used to measure thermal diffusivity of solids. The result for tellurium (Te) at 873 K: specific heat capacity 300.2 Joules per kilogram K, thermal conductivity 3.50 Watts per meter K, thermal diffusivity 2.04 x 10(exp -6) square meters per second, are within the range reported in literature. The uncertainty analysis showed the quantitative effect of sample geometry, transient temperature measured, and the energy of the laser pulse.

Lin, Bochuan↗
NASA NTRS2003

Modified Laser Flash Method for Thermal Properties Measurements and the Influence of Heat Convection

The study examined the effect of natural convection in applying the modified laser flash method to measure thermal properties of semiconductor melts. Common laser flash method uses a laser pulse to heat one side of a thin circular sample and measures the temperature response of the other side. Thermal diffusivity can be calculations based on a heat conduction analysis. For semiconductor melt, the sample is contained in a specially designed quartz cell with optical windows on both sides. When laser heats the vertical melt surface, the resulting natural convection can introduce errors in calculation based on heat conduction model alone. The effect of natural convection was studied by CFD simulations with experimental verification by temperature measurement. The CFD results indicated that natural convection would decrease the time needed for the rear side to reach its peak temperature, and also decrease the peak temperature slightly in our experimental configuration. Using the experimental data, the calculation using only heat conduction model resulted in a thermal diffusivity value is about 7.7% lower than that from the model with natural convection. Specific heat capacity was about the same, and the difference is within 1.6%, regardless of heat transfer models.

Lin, Bochuan↗