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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A velocity/stress mixed stabilized nodal finite element for elastodynamics: Analysis and computations with strongly and weakly enforced boundary conditions

A new nodal mixed finite element is proposed for the simulation of linear elastodynamics and wave propagation problems in time domain. Our method is based on equal-order interpolation discrete spaces for both the velocity (or displacement) and stress (or strain) tensor variables. The mixed form is derived using either the velocity/stress or velocity/strain pair of unknowns, the latter being instrumental in extensions of the method to nonlinear mechanics. The proposed approach works equally well on hexahedral or tetrahedral grids and, for this reason, it is suitable for time-domain engineering applications in complex geometry. The peculiarity of the proposed approach is the use of the rate form of the stress update equation, which yields a set of governing equations with the structure of a non-dissipative space/time Friedrichs’ system. We complement standard traction boundary conditions for the stress with strongly and weakly enforced boundary conditions for the velocity (or displacement). Weakly enforced boundary conditions are particularly suitable when considering complex geometrical shapes, because they do not require dedicated data structures for the imposition of the boundary degrees of freedom, but, rather, they utilize the structure of the variational formulation. Here we also show how the framework of weakly enforced boundary conditions can be used to develop variational forms for multi-domain simulations of heterogeneous media. A complete analysis including stability and convergence proofs is included, in the case of a space–time variational approach. A series of computational tests are used to demonstrate and verify the performance of the proposed approach.

42 ENGINEERING↗
DOE OSTI2026

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Performance evaluation of an air conditioning system based on quasi isothermal dehumidifcation

This work presents a first study of novel air conditioning system based on quasi isothermal dehumidification combined with dew-point evaporative cooling through the Maisotsenko cycle (M-Cycle). The dehumidification process is realized in desiccant coated heat exchanger with heat rejection system. The key idea behind the solution is to maximize the cooling effectiveness by creating favourable conditions for dehumidification and evaporative cooling process. This is achieved by rejecting the heat of sorption in the desiccant system and providing cold and dry air for M-Cycle air cooler, which allows the system to be powered by using low-grade heat energy at 50 °C. The analysis is performed on the basis of experimentally validated numerical models of each component combined in a unified model of the system. The goal of the study is to determine the performance of the solution under different operational conditions. In addition two types of M-Cycle air coolers (i.e. cross-flow and regenerative unit) are compared to establish which unit is more suitable as the source of cooling energy for the system. It was established that proposed system is able to provide comfortable room parameters (supply air temperature equal 17 °C and humidity ratio equal 0.010 kg/kg-da) for outdoor air temperatures ranging from 28 to 36 °C and outdoor air humidity ranging from 0.010 to 0.018 kg/kg-da. This means that the proposed solution is able to provide comfortable conditions in temperate and humid climates. Results also indicate that the system consisting of the regenerative M-Cycle HMX allows better cooling load management, offers higher thermal Coefficient of Performance (COP th ) and uses less water compared to the system with the cross-flow HMX. However, the system with cross-flow heat and mass exchanger (HMX) has a relatively greater electrical Coefficient of Performance (COP el ) in most cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Low- and high-order accurate boundary conditions: From Stokes to Darcy porous flow modeled with standard and improved Brinkman lattice Boltzmann schemes

The present contribution focuses on the accuracy of reflection-type boundary conditions in the Stokes–Brinkman–Darcy modeling of porous flows solved with the lattice Boltzmann method (LBM), which we operate with the two-relaxation-time (TRT) collision and the Brinkman-force based scheme (BF), called BF-TRT scheme. In parallel, we compare it with the Stokes–Brinkman–Darcy linear finite element method (FEM) where the Dirichlet boundary conditions are enforced on grid vertices. In bulk, both BF-TRT and FEM share the same defect: in their discretization a correction to the modeled Brinkman equation appears, given by the discrete Laplacian of the velocity-proportional resistance force. This correction modifies the effective Brinkman viscosity, playing a crucial role in the triggering of spurious oscillations in the bulk solution. While the exact form of this defect is available in lattice-aligned, straight or diagonal, flows; in arbitrary flow/lattice orientations its approximation is constructed. At boundaries, we verify that such a Brinkman viscosity correction has an even more harmful impact. Already at the first order, it shifts the location of the no-slip wall condition supported by traditional LBM boundary schemes, such as the bounce-back rule. For that reason, this work develops a new class of boundary schemes to prescribe the Dirichlet velocity condition at an arbitrary wall/boundary-node distance and that supports a higher order accuracy in the accommodation of the TRT-Brinkman solutions. For their modeling, we consider the standard BF scheme and its improved version, called IBF; this latter is generalized in this work to suppress or to reduce the viscosity correction in arbitrarily oriented flows. Our framework extends the one- and two-point families of linear and parabolic link-wise boundary schemes, respectively called B-LI and B-MLI, which avoid the interference of the Brinkman viscosity correction in their closure relations. The performance of LBM and FEM is thoroughly evaluated in three benchmark tests, which are run throughout three distinctive permeability regimes. The first configuration is a horizontal porous channel, studied with a symbolic approach, where we construct the exact solutions of FEM and BF/IBF with different boundary schemes. The second problem refers to an inclined porous channel flow, which brings in as new challenge the formation of spurious boundary layers in LBM; that is, numerical artefacts that arise due to a deficient accommodation of the bulk solution by the low-accurate boundary scheme. The third problem considers a porous flow past a periodic square array of solid cylinders, which intensifies the previous two tests with the simulation of a more complex flow pattern. The ensemble of numerical tests provides guidelines on the effect of grid resolution and the TRT free collision parameter over the accuracy and the quality of the velocity field, spanning from Stokes to Darcy permeability regimes. It is shown that, with the use of the high-order accurate boundary schemes, the simple, uniform-mesh-based TRT-LBM formulation can even surpass the accuracy of FEM employing hardworking body-fitted meshes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗
DOE OSTI2022

Generalizability analysis of tool condition monitoring ensemble machine learning models

Tool condition monitoring (TCM) is an essential research area for the optimization and automation of metal machining processes, and could help manufacturers reduce costs, production time, machine downtime, energy use, and part scrappage. However, TCM systems developed in prior studies have struggled to reach the high level of generalizability which is necessary for industrial applications. This study addresses TCM system generalizability to new machining conditions, how variations in machining and environmental conditions may be used to improve model generalizability, and ensemble machine learning techniques for TCM. Further, milling tool life experiments were conducted using various machining conditions, and the processes' sound, spindle power, and axial load signals were collected. Different machine learning models were evaluated for the prediction of tool wear levels, including four individual models and five ensemble models. Changes in cutting speed were found to display a large effect on model performance, while the chip load showed some effect, and the feed rate had little effect. A simulated noise data augmentation technique for model improvement is applied within TCM for the first time, and resulted in increased model generalizability and reduced overfitting. Across several performance metrics the extremely randomized trees ensemble machine learning model generally performed the best for this application, achieving a leave-one-group-out cross validation accuracy score of 92.4 %, a 10-fold cross validation score of 98.9 %, and an averaged accuracy across 11 generalizability tests of 87.3 %.

42 ENGINEERING↗
DOE OSTI2017

How do operating conditions affect As(III) removal by iron electrocoagulation?

Iron electrocoagulation (Fe-EC) has been shown to effectively remove arsenic from contaminated groundwater at low cost and has the potential to improve access to safe drinking water for millions of people. Understanding how operating conditions, such as the Fe dosage rate and the O 2 recharge rate, affect arsenic removal at different pH values is crucial to maximize the performance of Fe-EC under economic constraints. In this work, we improved upon an existing computational model to investigate the combined effects of pH, Fe dosage rate, and O 2 recharge rate on arsenic removal in Fe-EC. We showed that the impact of the Fe dosage rate strongly depends on pH and on the O 2 recharge rate, which has important practical implications. We identified the process limiting arsenic removal (As(III) oxidation versus As(V) adsorption) at different pH values, which allowed us to interpret the effect of operating conditions on Fe-EC performance. Finally, we assessed the robustness of the trends predicted by the model, which assumes a constant pH, against lab experiments reproducing more realistic conditions where pH is allowed to drift during treatment as a result of equilibration with atmospheric CO 2 . Furthermore, our results provide a nuanced understanding of how operating conditions impact arsenic removal by Fe-EC and can inform decisions regarding the operation of this technology in a range of groundwaters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Scattering through a straight quantum waveguide with combined boundary conditions

Scattering through a straight two-dimensional quantum waveguide R×(0,d) with Dirichlet boundary conditions on (R{sub −}{sup *}×(y=0))∪(R{sub +}{sup *}×(y=d)) and Neumann boundary condition on (R{sub −}{sup *}×(y=d))∪(R{sub +}{sup *}×(y=0)) is considered using stationary scattering theory. The existence of a matching conditions solution at x = 0 is proved. The use of stationary scattering theory is justified showing its relation to the wave packets motion. As an illustration, the matching conditions are also solved numerically and the transition probabilities are shown.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Growth condition dependence of photoluminescence polarization in (100) GaAs/AlGaAs quantum wells at room temperature

We conducted systematic measurements on the carrier lifetime (τ{sub c}), spin relaxation time (τ{sub s}), and circular polarization of photoluminescence (P{sub circ}) in (100) GaAs/AlGaAs multiple quantum wells grown by molecular beam epitaxy (MBE). The τ{sub c} values are strongly affected by MBE growth conditions (0.4–9 ns), whereas the τ{sub s} are almost constant at about 0.13 ns. The result suggests that spin detection efficiency [τ{sub s}/(τ{sub c} + τ{sub s})], which is expected to be proportional to a steady-state P{sub circ}, is largely dependent on growth condition. We confirmed that the P{sub circ} has similar dependence on growth condition to those of τ{sub s}/(τ{sub c} + τ{sub s}) values. The study thus indicates that choosing the appropriate growth condition of the QW is indispensable for obtaining a high P{sub circ} from a spin-polarized light-emitting diode (spin-LED)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Boundary conditions at the walls with thermionic electron emission in two temperature modeling of “thermal” plasmas

In this paper, we propose new boundary conditions for the electric potential, the electron energy equation, and the energy equation for heavy particles (ions and neutrals) at the hot walls with thermionic electron emission for two-temperature thermal arc models. The derived boundary conditions assume that the walls are made from refractory metals and, consequently, the erosion of the wall is small and can be neglected. In these boundary conditions, the plasma sheath formed at the electrode is viewed as the interface between the plasma and the wall. The derived boundary conditions allow the calculation of the heat flux to the walls from the plasma. This allows the calculation of the thermionic electron current that makes the model of electrode-plasma interaction self-consistent.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

The inverse problem of recovering the source in a parabolic equation under a condition of nonlocal observation

We study the inverse problem for a parabolic equation of recovering the source, that is, the right-hand side F(x,t)=h(x,t)f(x), where the function f(x) is unknown. To find f(x), along with the initial and boundary conditions, we also introduce an additional condition of nonlocal observation of the form ∫{sub 0}{sup T}u(x,t) dμ(t)=χ(x). We prove the Fredholm property for the problem stated in this way, and obtain sufficient conditions for the existence and uniqueness of a solution. These conditions are of the form of readily verifiable inequalities and put no restrictions on the value of T>0 or the diameter of the domain Ω under consideration. The proof uses a priori estimates and the qualitative properties of solutions of initial-boundary value problems for parabolic equations. Bibliography: 40 titles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Cable Condition Monitoring Strategy for Safe and Reliable Plant Operation

Electrical cables provide essential functions, such as delivery of power or instrumentation signals for monitoring systems. Most cables installed in industrial applications are constructed with organic polymer insulations that can become brittle, crack, or degrade over time from exposure to harsh environmental conditions, such as elevated temperatures, moisture, vibration, mechanical shock, and radiation. This paper describes an overall strategy for assessing the health and managing the aging of cables during the operating life of an industrial facility. This strategy involves performing condition assessments and monitoring of electrical cables using both in situ and laboratory testing techniques. It includes in situ testing to identify anomalies in the circuits, such as degraded terminations, splices, connections, and degraded sections of cable insulation, as well as as-found evaluations to determine the current condition of installed cables. Furthermore, these cable condition evaluations provide important information about the current state of the cable circuits. Moreover, the test results can be used to trend/monitor age-related degradation and estimate the remaining useful life of installed cables.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Visualization of R410A Flow Boiling Inside Horizontal Smooth Tubes under Diabatic Conditions

This article presents a novel approach to visualize the flow boiling in round tubes under “diabatic conditions”. In the visualization test section, a 3D printed round tube is placed inside a glass tube with secondary fluid at a higher temperature flowing in the annulus between the two tubes thus providing diabatic conditions. Experimental work was conducted for evaporating R410A flow in a horizontal smooth round tube with the inner diameter of 6.3 mm. Compared to the traditional adiabatic visualization technique, the more realistic evaporative condition is simulated and the developing evaporation process is captured. New insights for the flow behavior at low vapor quality are brought by this diabatic method. The vapor plug occurs at very low vapor quality, and this is attributed to the coalescence of bubbles generated on the heated tube wall. For the intermittent flow, liquid slug triggered by Kelvin–Helmholtz instability affects the nucleation on the tube wall. It brings the fresh liquid to rewet the dry surface and activate the nucleation sites. Finally, heat flux condition affects the heat transfer mechanisms during the flow boiling, and the heat transfer through the nucleate boiling mechanism increases as the heat flux increases.

3D printed↗
DOE OSTI2024

Robust errant beam prognostics with conditional modeling for particle accelerators

Abstract Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Order conditions for nonlinearly partitioned Runge-Kutta methods

Recently, a new class of nonlinearly partitioned Runge–Kutta (NPRK) methods was proposed for nonlinearly partitioned systems of autonomous ordinary differential equations y' = F(y, y). The target class of problems are those in which different scales, stiffnesses, or physics are coupled in a nonlinear way, wherein the desired partition cannot be written in a classical additive or component-wise fashion. Here we use a rooted-tree analysis to derive full-order conditions for NPRKM methods, where M denotes the number of nonlinear partitions. Due to the nonlinear coupling and thereby the mixed product differentials, it turns out that the standard node-colored rooted tree analysis used in analyzing ODE integrators does not naturally apply. Instead we develop a new edge-colored rooted-tree framework to address the nonlinear coupling. The resulting order conditions are enumerated, are provided directly for up to fourth order with M = 2 and third order with M = 3, and are related to existing order conditions of additive and partitioned RK methods. We conclude with an example that shows how the nonlinear order conditions can be used to obtain an embedded estimate of the state-dependent nonlinear coupling strength in a dynamical system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Final Technical Report Recovery Act: Online Nonintrusive Condition Monitoring and Fault Detection for Wind Turbines

The penetration of wind power has increased greatly over the last decade in the United States and across the world. The U.S. wind power industry installed 1,118 MW of new capacity in the first quarter of 2011 alone and entered the second quarter with another 5,600 MW under construction. By 2030, wind energy is expected to provide 20% of the U.S. electricity needs. As the number of wind turbines continues to grow, the need for effective condition monitoring and fault detection (CMFD) systems becomes increasingly important [3]. Online CMFD is an effective means of not only improving the reliability, capacity factor, and lifetime, but it also reduces the downtime, energy loss, and operation and maintenance (O&M) of wind turbines. The goal of this project is to develop novel online nonintrusive CMFD technologies for wind turbines. The proposed technologies use only the current measurements that have been used by the control and protection system of a wind turbine generator (WTG); no additional sensors or data acquisition devices are needed. Current signals are reliable and easily accessible from the ground without intruding on the wind turbine generators (WTGs) that are situated on high towers and installed in remote areas. Therefore, current-based CMFD techniques have great economic benefits and the potential to be adopted by the wind energy industry. Specifically, the following objectives and results have been achieved in this project: (1) Analyzed the effects of faults in a WTG on the generator currents of the WTG operating at variable rotating speed conditions from the perspective of amplitude and frequency modulations of the current measurements; (2) Developed effective amplitude and frequency demodulation methods for appropriate signal conditioning of the current measurements to improve the accuracy and reliability of wind turbine CMFD; (3) Developed a 1P-invariant power spectrum density (PSD) method for effective signature extraction of wind turbine faults with characteristic frequencies in the current or current demodulated signals, where 1P stands for the shaft rotating frequency of a WTG; (4) Developed a wavelet filter for effective signature extraction of wind turbine faults without characteristic frequencies in the current or current demodulated signals; (5) Developed an effective adaptive noise cancellation method as an alternative to the wavelet filter method for signature extraction of wind turbine faults without characteristic frequencies in the current or current demodulated signals; (6) Developed a statistical analysis-based impulse detection method for effective fault signature extraction and evaluation of WTGs based on the 1P-invariant PSD of the current or current demodulated signals; (7) Validated the proposed current-based wind turbine CMFD technologies through extensive computer simulations and experiments for small direct-drive WTGs without gearboxes; and (8) Showed, through extensive experiments for small direct-drive WTGs, that the performance of the proposed current-based wind turbine CMFD technologies is comparable to traditional vibration-based methods. The proposed technologies have been successfully applied for detection of major failures in blades, shafts, bearings, and generators of small direct-drive WTGs. The proposed technologies can be easily integrated into existing wind turbine control, protection, and monitoring systems and can be implemented remotely from the wind turbines being monitored. The proposed technologies provide an alternative to vibration-sensor-based CMFD. This will reduce the cost and hardware complexity of wind turbine CMFD systems. The proposed technologies can also be combined with vibration-sensor-based methods to improve the accuracy and reliability of wind turbine CMFD systems. When there are problems with sensors, the proposed technologies will ensure proper CMFD for the wind turbines, including their sensing systems. In conclusion, the proposed technologies offer an effective means to achieve condition-based smart maintenance for wind turbines and have a great potential to be adopted by the wind energy industry due to their almost no-cost, nonintrusive features. Although only validated for small direct-drive wind turbines without gearboxes, the proposed technologies are also applicable for CMFD of large-size wind turbines with and without gearboxes. However, additional investigations are recommended in order to apply the proposed technologies to those large-size wind turbines.

17 WIND ENERGY↗
DOE OSTI2016

Rate Theory Modeling and Simulation of Silicide Fuel at LWR Conditions

As a promising candidate for the accident tolerant fuel (ATF) used in light water reactors (LWRs), the fuel performance of uranium silicide (U 3 Si 2 ) at LWR conditions needs to be well understood. In this report, rate theory model was developed based on existing experimental data and density functional theory (DFT) calculations so as to predict the fission gas behavior in U 3 Si 2 at LWR conditions. The fission gas behavior of U 3 Si 2 can be divided into three temperature regimes. During steady-state operation, the majority of the fission gas stays in intragranular bubbles, whereas the dominance of intergranular bubbles and fission gas release only occurs beyond 1000 K. The steady-state rate theory model was also used as reference to establish a gaseous swelling correlation of U 3 Si 2 for the BISON code. Meanwhile, the overpressurized bubble model was also developed so that the fission gas behavior at LOCA can be simulated. LOCA simulation showed that intragranular bubbles are still dominant after a 70 second LOCA, resulting in a controllable gaseous swelling. The fission gas behavior of U 3 Si 2 at LWR conditions is benign according to the rate theory prediction at both steady-state and LOCA conditions, which provides important references to the qualification of U 3 Si 2 as a LWR fuel material with excellent fuel performance and enhanced accident tolerance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗