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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Nuclear Thermal Propulsion Dynamic Modeling with Modelica

Oak Ridge National Laboratory (ORNL) is participating in the nuclear thermal propulsion (NTP) research and development effort supported by the National Aeronautics and Space Administration (NASA). This effort involves collaboration between multiple research groups, representing various government agencies and industry partners. ORNL has developed a Modelica-based modeling package for dynamic system modeling of nuclear reactors called the Transient Simulation Framework of Reconfigurable Models (TRANSFORM). While this software has been successfully demonstrated in simulations of traditional pressurized-water reactors, boiling-water reactors, liquid-metal reactors and molten-salt reactors, it has also been adapted for non-traditional use in modeling hybrid energy systems, and tritium transport. This versatility is being applied to the current NTP project, where specific modules within TRANSFORM are being developed for and applied to transient modeling of the NASA NTP design. This paper presents the current state of the ORNL-NTP model, the utility of TRANSFORM methodologies in NTP transient simulations, the ability to develop NTP-specific modules, and future work for the model.

Rader, Jordan↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

A Novel Zr-1Nb Alloy and a New Look at Hydriding

A novel Zr-1Nb has begun development based on a working model that takes into account the hydrogen permeabilities for zirconium and niobium metals. The beta-Nb secondary phase particles (SPPs) in Zr-1Nb are believed to promote more rapid hydrogen dynamics in the alloy in comparison to other zirconium alloys. Furthermore, some hydrogen release is expected at the lower temperatures corresponding to outages when the partial pressure of H2 in the coolant is less. These characteristics lessen the negative synergism between corrosion and hydriding that is otherwise observed in cladding alloys without niobium. In accord with the working model, development of nanoscale precursors was initiated to enhance the performance of existing Zr-1Nb alloys. Their characteristics and properties can be compared to oxide-dispersion strengthened alloys, and material additions have been proposed to zirconium-based LWR cladding to guard further against hydriding and to fix the size of the SPPs for microstructure stability enhancements. A preparative route is being investigated that does not require mechanical alloying, and 10 nanometer molybdenum particles have been prepared which are part of the nanoscale precursors. If successful, the approach has implications for long term dry storage of used fuel and for new routes to nanoferritic and ODS alloys.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Comparison of model predictions with measured proton-induced production of nickel and iridium isotopes

Calculations were performed to support method development for simultaneous production of Ni and Ir isotopes. Here, the work scope included development of the physical target, irradiation configuration, and post-irradiation radiochemical separations. The assumptions of predictive models previously developed were refined based upon the precise experimental configuration selected, including Os target material and a set of stacked targets for concurrent production of Ni and Ir isotopes. Model predictions of the reaction cross sections with EMPIRE 3.21 and implied isotopic yields are compared with proton beam irradiation measurements to refine model parameters and guide future experiments.

Isotope Production↗
DOE OSTI2017

A finite element/level set model of polyurethane foam expansion and polymerization

Polyurethane foams are used widely for encapsulation and structural purposes because they are inexpensive, straightforward to process, amenable to a wide range of density variations (1 lb/ft3 - 50 lb/ft3), and able to fill complex molds quickly and effectively. Computational model of the filling and curing process are needed to reduce defects such as voids, out-of-specification density, density gradients, foam decomposition from high temperatures due to exotherms, and incomplete filling. This paper details the development of a computational fluid dynamics model of a moderate density PMDI structural foam, PMDI-10. PMDI is an isocyanate-based polyurethane foam, which is chemically blown with water. The polyol reacts with isocyanate to produces the polymer. PMDI- 10 is catalyzed giving it a short pot life: it foams and polymerizes to a solid within 5 minutes during normal processing. To achieve a higher density, the foam is over-packed to twice or more of its free rise density of 10 lb/ft3. The goal for modeling is to represent the expansion, filling of molds, and the polymerization of the foam. This will be used to reduce defects, optimize the mold design, troubleshoot the processed, and predict the final foam properties. A homogenized continuum model foaming and curing was developed based on reaction kinetics, documented in a recent paper; it uses a simplified mathematical formalism that decouples these two reactions. The chemo-rheology of PMDI is measured experimentally and fit to a generalized- Newtonian viscosity model that is dependent on the extent of cure, gas fraction, and temperature. The conservation equations, including the equations of motion, an energy balance, and three rate equations are solved via a stabilized finite element method. The equations are combined with a level set method to determine the location of the foam-gas interface as it evolves to fill the mold. Understanding the thermal history and loads on the foam due to exothermicity and oven curing is very important to the results, since the kinetics, viscosity, and other material properties are all sensitive to temperature. Results from the model are compared to experimental flow visualization data and post-test X-ray computed tomography (CT) data for the density. Several geometries are investigated including two configurations of a mock structural part and a bar geometry to specifically test the density model. We have found that the model predicts both average density and filling profiles well. However, it under predicts density gradients, especially in the gravity direction. Further model improvements are also discussed for future work.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Frequency Domain Reflectometry NDE for Aging Cables in Nuclear Power Plants

Cable insulation polymers are among the more susceptible materials to age-related degradation within a nuclear power plant. This is recognized by both regulators and utilities, so all plants have developed cable aging management programs to detect damage before critical component failure in compliance with regulatory guidelines. Although a wide range of tools are available to evaluate cables and cable systems, cable aging management programs vary in how condition monitoring and nondestructive examinations are conducted as utilities search for the most reliable and cost-effective ways to assess cable system condition. Frequency domain reflectometry (FDR) is emerging as one valuable tool to locate and assess damaged portions of a cable system with minimal cost and only requires access in most cases to one of the cable terminal ends. Since laboratory studies to evaluate the use of FDR for inspection of aged cables can be expensive and data interpretation may be confounded by multiple factors which influence results, a model-based approach is desired to parametrically investigate the effect of insulation material damage in a controlled manner. This work describes development of a physics-based FDR model which uses finite element simulations of cable segments in conjunction with cascaded circuit element simulations to efficiently study a cable system. One or more segments of the cable system model have altered physical or electrical properties which represent the degree of damage and the location of the damage in the system. This circuit model is then subjected to a simulated FDR examination. The modeling approach is verified using several experimental cases and by comparing it to a commercial simulator suitable for simulation of some cable configurations. The model is used to examine a broad range of parameters including defect length, defect profile, degree of degradation, number and location of defects, FDR bandwidth, and addition of impedance-matched extensions to minimize the end-shadow effect.

Cable Inspection, Frequency Domain Reflectometry↗
DOE OSTI2018

Modeling Anaerobic Soil Organic Carbon Decomposition in Arctic Polygon Tundra: Insights into Soil Geochemical Influences on Carbon Mineralization: Modeling Archive

This anaerobic carbon decomposition model framework was developed with explicit representation of fermentation, methanogenesis and iron reduction by combining three well-known modeling approaches developed in different disciplines. A pool-based model to represent upstream carbon transformations and replenishment of dissolved organic carbon (DOC) pool, a thermodynamically-based model to calculate rate kinetics and biomass growth for methanogenesis and Fe(III) reduction, and a humic ion-binding model for aqueous phase speciation and pH calculations. The combined models are implemented in the open source geochemical model PHREEQC (V3.0). Installation of PHREEQC is required to run this model. This dataset contains one zipped file and one pdf.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints

Jointing techniques like the Self-Piercing Riveting (SPR), Resistance Spot Welding (RSW) and Rivet-Weld (RW) joints are used for mass production of dissimilar material joints due to their high performance, short cycle time, and adaptability. However, the service life and safety usage of these joints can be largely impacted by the galvanic corrosion due to the difference in equilibrium potentials between the metals with the presence of electrolyte. Here, in this paper, we focus on Al-Fe galvanic corrosion and develop physics-informed machine learning based surrogate model for statistical corrosion analysis, which enables the reliability analysis of dissimilar material joints under corrosion environment. In this study, a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode. Geometric and environmental factors including crevice gap, roughness of anode, conductivity, and the temperature of the electrolyte are investigated. Further, a thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al joints. It is found that the electrolyte conductivity has the largest effects on the material loss and needs to be managed closely for better corrosion control. This will help in designing and manufacturing joints with improved corrosion performance.

42 ENGINEERING↗
DOE OSTI2017

Sulfur Atoms Adsorbed on Cu(100) at Low Coverage: Characterization and Stability against Complexation

By using scanning tunneling microscopy, we characterize the size and bias-dependent shape of sulfur atoms on Cu(100) at low coverage (below 0.1 monolayers) and low temperature (quenched from 300 to 5 K). Sulfur atoms populate the Cu(100) terraces more heavily than steps at low coverage, but as coverage approaches 0.1 monolayers, close-packed step edges become fully populated, with sulfur atoms occupying sites on top of the step. Density functional theory (DFT) corroborates the preferential population of terraces at low coverage as well as the step adsorption site. In experiment, small regions with p(2 × 2)-like atomic arrangements emerge on the terraces as sulfur coverage approaches 0.1 monolayer. Using DFT, a lattice gas model has been developed, and Monte Carlo simulations based on this model have been compared with the observed terrace configurations. A model containing eight pairwise interaction energies, all repulsive, gives qualitative agreement. Experiment shows that atomic adsorbed sulfur is the only species on Cu(100) up to a coverage of 0.09 monolayers. There are no Cu–S complexes. Conversely, prior work has shown that a Cu 2 S 3 complex forms on Cu(111) under comparable conditions. On the basis of DFT, this difference can be attributed mainly to stronger adsorption of sulfur on Cu(100) as compared with Cu(111).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Simulating Atmospheric Boundary Layer Turbulence with Nek5000/RS

We present large-eddy-simulation (LES) modeling approaches for the simulation of atmospheric boundary layer turbulence that are of direct relevance to wind energy production. In this report, we study a GABLS benchmark problem using high-order spectral element code Nek5000/RS, which is supported under the DOE’s Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) project, targeting application simulations on various acceleration-device based exascale computing platforms [1, 2]. We demonstrate our newly developed subgrid-scale (SGS) models based on high-pass filter (HPF), mean-field eddy viscosity (MFEV), and Smagorinsky (SMG) with no-slip and traction boundary conditions, provided with low-order statistics, convergence and turbulent structure analysis. The model fidelity and scaling performance of Nek5000/RS on DOE’s leadership computing platforms in comparison to those of AMR-Wind, a block-structured second-order finite-volume code with adaptive-mesh-refinement capabilities, are discussed in [3].

17 WIND ENERGY↗
DOE OSTI2021

BISON Simulation Development for ALD Coated Particles (Progress Report, FY21)

Argonne has on ongoing effort to perform atomic layer deposition coatings on micron-scale fuel particles. Initial results showed cracking of the coating layer above a specific coating thickness, which motived the development of a BISON model for the coated particle system to help explain the behavior. This report describes the initial development of the BISON model, the materials models used, and the conditions used in the simulation. A 2D model has been developed, with sensitivity studies performed on several key parameters. Based on the 2D model results, and 3D model was also developed, with results from all calculations described. First principles calculations were also performed on the fuel/coating interface to help describe the observed behavior. Potential future activities are also described.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Coupled Lake‐Atmosphere‐Land Physics Uncertainties in a Great Lakes Regional Climate Model

Abstract This study develops a surrogate‐based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and land surface. Perturbed physics ensembles of the model during the 2018 summer are used to train a neural network surrogate model to predict lake surface temperature (LST) and near‐surface air temperature (T2m). Average physics uncertainties are determined to be 1.5C for LST and T2m over land, and 1.9C for T2m over lake, but these have significant spatiotemporal variations. We find that atmospheric physics parameterizations alone are the dominant sources of uncertainty (45%–53%), while lake and land parameterizations account for 33% and 38% of the uncertainty of LST and T2m over land respectively. Interactions of atmosphere physics parameterizations with those of the land and lake contribute to an additional 13%–17% of the total variance. LST and T2m over the lake are more uncertain in the deeper northern lakes, particularly during the rapid warming phase that occurs in late spring/early summer. The LST uncertainty increases with sensitivity to the lake model's surface wind stress scheme. T2m over land is more uncertain over forested areas in the north, where it is most sensitive to the land surface model, than the more agricultural land in the south, where it is most sensitive to the atmospheric planetary boundary and surface layer scheme. Uncertainty also increases in the southwest during multiday temperature declines with higher sensitivity to the land surface model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-F-BRF-01: A GPU Framework for Developing Interactive High-Resolution Patient-Specific Biomechanical Models

Purpose: To develop a GPU-based framework that can generate highresolution and patient-specific biomechanical models from a given simulation CT and contoured structures, optimized to run at interactive speeds, for addressing adaptive radiotherapy objectives. Method: A Massspring-damping (MSD) model was generated from a given simulation CT. The model's mass elements were generated for every voxel of anatomy, and positioned in a deformation space in the GPU memory. MSD connections were established between neighboring mass elements in a dense distribution. Contoured internal structures allowed control over elastic material properties of different tissues. Once the model was initialized in GPU memory, skeletal anatomy was actuated using rigid-body transformations, while soft tissues were governed by elastic corrective forces and constraints, which included tensile forces, shear forces, and spring damping forces. The model was validated by applying a known load to a soft tissue block and comparing the observed deformation to ground truth calculations from established elastic mechanics. Results: Our analyses showed that both local and global load experiments yielded results with a correlation coefficient R{sup 2} > 0.98 compared to ground truth. Models were generated for several anatomical regions. Head and neck models accurately simulated posture changes by rotating the skeletal anatomy in three dimensions. Pelvic models were developed for realistic deformations for changes in bladder volume. Thoracic models demonstrated breast deformation due to gravity when changing treatment position from supine to prone. The GPU framework performed at greater than 30 iterations per second for over 1 million mass elements with up to 26 MSD connections each. Conclusions: Realistic simulations of site-specific, complex posture and physiological changes were simulated at interactive speeds using patient data. Incorporating such a model with live patient tracking would facilitate real time assessment of variations of the actual anatomy and delivered dose for adaptive intervention and re-planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Fast pyrolysis of glucose‐based carbohydrates with added NaCl part 1: Experiments and development of a mechanistic model

Sodium ions, one of the natural inorganic constituents in lignocellulosic biomass, significantly alter pyrolysis behavior and resulting chemical speciation. Here, experiments were conducted using a micropyrolyzer to investigate the catalytic effects of NaCl on fast pyrolysis of glucose‐based carbohydrates (glucose, cellobiose, maltohexaose, and cellulose), and on a major product of cellulose pyrolysis, levoglucosan (LVG). A mechanistic model that addressed the significant catalytic effects of NaCl on the product distribution was developed. The model incorporated interactions of Na + with cellulosic chains and low molecular weight species, reactions mediated by Na + including dehydration, cyclic/Grob fragmentation, ring‐opening/closing, isomerization, and char formation, and a degradation network of LVG in the presence of Na + . Rate coefficients of elementary steps were specified based on Arrhenius parameters. The mechanistic model for cellulose included 768 reactions of 222 species, which included 252 reactions of 150 species comprising the mechanistic model of glucose decomposition in the presence of NaCl. © 2015 American Institute of Chemical Engineers AIChE J , 62: 766–777, 2016

Zhou, Xiaowei↗
DOE OSTI2022

Multisource Mobile Transfer Learning Algorithm Based on Dynamic Model Compression

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

Gao, Peng↗
DOE OSTI2016

Building Protection Against External Ionizing Fallout Radiation

A nuclear explosion has the potential to injure or kill tens to hundreds of thousands of people through exposure to fallout (external gamma) radiation. Existing buildings can protect their occupants (reducing external radiation exposures) by placing material and distance between fallout particles and indoor individuals. This protection is not well captured in current fallout risk assessment models and so the US Department of Defense is implementing the Regional Shelter Analysis methodology to improve the ability of the Hazard Prediction and Assessment Capability (HPAC) model to account for building protection. This report supports the HPAC improvement effort by identifying a set of building attributes (next page) that, when collectively specified, are sufficient to calculate reasonably accurate, i.e., within a factor of 2, fallout shelter quality estimates for many individual buildings. The set of building attributes were determined by first identifying the key physics controlling building protection from fallout radiation and then assessing which building attributes are relevant to the identified physics. This approach was evaluated by developing a screening model (PFscreen) based on the identified physics and comparing the screening model results against the set of existing independent experimental, theoretical, and modeled building protection estimates. In the interests of transparency, we have developed a benchmark dataset containing (a) most of the relevant primary experimental data published by prior generations of fallout protection scientists as well as (b) the screening model results.

42 ENGINEERING↗
DOE OSTI2021

Thermal Energy Storage Model Development within the Integrated Energy Systems Hybrid Repository

This publication details newly created energy storage models developed within the HYBRID Modelica repository as part of the Department of Energy Office of Nuclear Energy (DOE-NE) Integrated Energy Systems (IES) program, led by Idaho National Laboratory (INL). Model development to-date includes creation of dynamic systems-level models of concrete, latent heat, and packed-bed thermocline energy storage technologies for deployment in the IES-based HYBRID repository. Models are developed using the latest publicly available data and incorporate the possibility of control strategy inclusion for use with the existing IES modeling, analysis, and optimization toolset. Simulations showcase the abilities of each technology to cyclically charge and discharge when exposed to time-varying boundary conditions. In addition, low-level surrogate models for some of the thermal energy storage technologies were created using Python. These lower order Python files are much cheaper to run (i.e., computationally faster) and thus operate well when incorporated within the stochastic optimization problems run for the IES program. Moreover, economic data was collected for use within the INL-developed Framework for Optimization of ResourCes and Economics (FORCE). This information is incorporated within the FORCE platform. This work has resulted in creation of systems-level models for concrete, latent heat, and thermocline thermal energy storage systems with associated control systems. Now that these models are available, they can be utilized within different integrated energy park concepts to understand optimal system operation, control, and dispatching. Moreover, given the generic nature of the models, industrial partner technologies (e.g., Storworks Power, EnergyNest) can be quickly added to the repository using the existing models as a basis. Additional dynamic models for thermal energy storage concepts can be developed and added to the HYBRID repository as needed.

25 ENERGY STORAGE↗
DOE OSTI2022

Follow-On Status Report on FY2022 Model Development within the Integrated Energy Systems Hybrid Repository

This publication details newly created energy storage and reactor models developed within the HYBRID modeling repository as part of the Department of Energy Office of Nuclear Energy (DOE-NE) Integrated Energy Systems (IES) program, led by Idaho National Laboratory (INL). Model development to-date includes creation of dynamic systems-level models of a pebble bed high temperature gas reactor (HTGR), compressed air energy storage (CAES), and Modelica standard library based two-tank sensible heat storage (SHS) in the IES-based HYBRID repository. Models are developed using the latest publicly available data and incorporate the possibility of control strategy inclusion for use with the existing IES modeling, analysis, and optimization toolset. Simulations showcase the abilities of each technology to flexibly operate in ways consistent with IES operation expectations. When these models are available, they can be utilized within different integrated energy park concepts to understand optimal system operation, control, and dispatching. Moreover, given the generic nature of the models, industrial partner technologies can be quickly added to the repository using the existing models as a basis. Additional dynamic models for thermal energy storage concepts can be developed and added to the HYBRID repository as needed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗