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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Code Verification of Multiple Physics-Fidelity Models in Hypersonic Aerodynamics

Hypersonic aerodynamics models exist across a range of physics fidelities with associated computational expenses. These models may be run independently or in a multifidelity framework that leverages their complementary strengths of speed for lower-fidelity and accuracy for higher-fidelity models. This work presents applied code verification of two lower-fidelity models contained within the Sandia hypersonic aerodynamics code. Each model has a different form that requires individualized verification approaches, including comparison to analytical solutions as well as manufactured solutions with order-of-accuracy testing. In conclusion, results of this effort include the identification and resolution of code errors and shortcomings, as well as the demonstration of code correctness and consistency for both models.

Aerodynamics↗
DOE OSTI2024

A Four-Layer Cyber-Physical Security Model for Electric Machine Drives Considering Control Information Flow

Despite the IEEE Power Electronics Society (PELS) establishing Technical Committee 10 on Design Methodologies with a focus on the cyber-physical security of power electronics systems, a holistic design methodology for addressing security vulnerabilities remains underdeveloped. This gap largely stems from the limited integration of computer science and power/control engineering studies in this interdisciplinary field. Addressing the inadequacy of unilateral cyber or control perspectives, this article presents a novel four-layer cyber-physical security model specifically designed for electric machine drives. Central to this model is the innovative control information flow (CIF) model, residing within the control layer, which serves as a pivotal link between the cyber layer's vulnerable resources and the physical layer's state-space models. By mapping vulnerable resources to control variable space and tracing attack propagation, the CIF model facilitates accurate impact predictions based on tainted control laws. The effectiveness and validity of this proposed model are demonstrated through hardware experiments involving two typical cyber-attack scenarios, underscoring its potential as a comprehensive framework for multidisciplinary security strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Electromagnetic physics vectorization in the GeantV transport framework

The development of the GeantV Electromagnetic (EM) physics package has evolved following two necessary paths towards code modernization. A first phase required the revision of the main electromagnetic physics models and their implementation. The main objectives were to improve their accuracy, extend them to the new high-energy frontier posed by the Future Circular Collider (FCC) programme and allow a better adaptation to a multi-particle flow. Most of the EM physics models in GeantV have been reviewed from theoretical perspective and rewritten with vector-friendly implementations, being now available in scalar mode in the alpha release. The second phase consists of a thorough investigation on the possibility to vectorise the most CPU-intensive physics code parts, such as final state sampling. We have shown the feasibility of implementing electromagnetic physics models that take advantage of SIMD/SIMT architectures, thus obtaining gains in performance. After this phase, the time has come for the GeantV project to take a step forward towards the final proof of concept. This takes shape through the testing of the full simulation chain (transport + physics + geometry) running in vectorized mode. In this paper we will present the first benchmark results obtained after vectorizing a full set of electromagnetic physics models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics

Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks. However, they require extensive computational resources both for training and deployment. The problem is exacerbated as the amount and complexity of the data increases. Quantum-based vision transformer models could potentially alleviate this issue by reducing the training and operating time while maintaining the same predictive power. Although current quantum computers are not yet able to perform high-dimensional tasks, they do offer one of the most efficient solutions for the future. In this work, we construct several variations of a quantum hybrid vision transformer for a classification problem in high-energy physics (distinguishing photons and electrons in the electromagnetic calorimeter). We test them against classical vision transformer architectures. Our findings indicate that the hybrid models can achieve comparable performance to their classical analogs with a similar number of parameters.

Unlu, Eyup B. (ORCID:0000000266836463)↗
NASA NTRS2014

Physics Based Model for Cryogenic Chilldown and Loading. Part I: Algorithm

We report the progress in the development of the physics based model for cryogenic chilldown and loading. The chilldown and loading is model as fully separated non-equilibrium two-phase flow of cryogenic fluid thermally coupled to the pipe walls. The solution follow closely nearly-implicit and semi-implicit algorithms developed for autonomous control of thermal-hydraulic systems developed by Idaho National Laboratory. A special attention is paid to the treatment of instabilities. The model is applied to the analysis of chilldown in rapid loading system developed at NASA-Kennedy Space Center. The nontrivial characteristic feature of the analyzed chilldown regime is its active control by dump valves. The numerical predictions are in reasonable agreement with the experimental time traces. The obtained results pave the way to the development of autonomous loading operation on the ground and space.

heat transfer↗
DOE OSTI2014

Topos models for physics and topos theory

What is the role of topos theory in the topos models for quantum theory as used by Isham, Butterfield, Döring, Heunen, Landsman, Spitters, and others? In other words, what is the interplay between physical motivation for the models and the mathematical framework used in these models? Concretely, we show that the presheaf topos model of Butterfield, Isham, and Döring resembles classical physics when viewed from the internal language of the presheaf topos, similar to the copresheaf topos model of Heunen, Landsman, and Spitters. Both the presheaf and copresheaf models provide a “quantum logic” in the form of a complete Heyting algebra. Although these algebras are natural from a topos theoretic stance, we seek a physical interpretation for the logical operations. Finally, we investigate dynamics. In particular, we describe how an automorphism on the operator algebra induces a homeomorphism (or isomorphism of locales) on the associated state spaces of the topos models, and how elementary propositions and truth values transform under the action of this homeomorphism. Also with dynamics the focus is on the internal perspective of the topos.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Fission Product Quantification and Burnup Analysis via Gamma Spectrometry of the AGR-5/6/7 TRISO Fuel Experiment

Five capsules containing varying configurations of TRISO fuel compacts were irradiated in the most recent Advanced Gas Reactor (AGR) TRISO test campaign, AGR-5/6/7. As part of the post-irradiation examination (PIE) of this work all the fuel compacts and capsule graphite holders were measured with the Precision Gamma Scanner (PGS) to determine isotopic activities and locations using gamma spectrometry. Measuring the compacts allowed for the comparison of physics model depletion calculations with the measured compact activity to validate models. These measurements also are used to find compacts of interest with low fission product retention that may be tested in subsequent PIE. Measurements of the holders can confirm fission product distribution and migration. Of the isotopes measured, Cs-137 and Cs-134 were used to estimate location of failed particles as well as determine a measured burnup estimation. Results from Capsule 1 compacts and the holder indicate many fuel particle failures occurred. There are high amounts of cesium in the holder near Stack 9 of Capsule 1 that corresponds to low fractions?measured-to-calculated (M/C) ratio?of cesium remaining in those compacts. All other capsules had cesium retention ratios near 1. The isotope 110mAg had less consistent results. Capsule 2 compacts exhibited a M/C of 0.20, much lower than anticipated for the temperatures these compacts underwent. Capsule 3 experienced much higher temperatures, >1300C and had an average M/C of 0.75. These are the opposite results than what was predicted by past AGR experiments for the relationship between temperature and isotope retention. Capsule 4 and 5 showed good fission product retention and agreement with the physics model predictions. The burnup results from the PGS measurements showed strong agreement between the physics models and the measured values. Using a ratio of Cs-134/Cs-137 to calculate the burnup match closely with the physics models. Additionally, the Cs-137 burnup results were less accurate but extremely close to the model results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Train small, model big: Scalable physics simulators via reduced order modeling and domain decomposition

Numerous cutting-edge scientific technologies originate at the laboratory scale, but transitioning them to practical industry applications is a formidable challenge. Traditional pilot projects at intermediate scales are costly and time-consuming. An alternative, the pilot-scale model, relies on high-fidelity numerical simulations, but even these simulations can be computationally prohibitive at larger scales. To overcome these limitations, we propose a scalable, physics-constrained reduced order model (ROM) method. The ROM identifies critical physics modes from small-scale unit components, projecting governing equations onto these modes to create a reduced model that retains essential physics details. We also employ Discontinuous Galerkin Domain Decomposition (DG-DD) to apply ROM to unit components and interfaces, enabling the construction of large-scale global systems without data at such large scales. Here this method is demonstrated on the Poisson and Stokes flow equations, showing that it can solve equations about 15–40 times faster with only ~1% relative error. Furthermore, ROM takes one order of magnitude less memory than the full order model, enabling larger scale predictions at a given memory limitation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

A Two-Stage Physical-Based Model for Predicting Flow Stress of As-cast TiAl Alloy Under Hot Deformation Conditions

The hot deformation behavior of Ti-30Al-4.2Mn-4.5Nb-0.2B alloy was investigated using the isothermal compression experiment at temperatures of 1020-1200 °C and strain rates of 0.001-1 s{sup −1}. The flow stress was sensitive to the deformation parameters like temperature and strain rate, which decreases with the increase in temperature and decrease in strain rates. Based on the true stress-true strain data, a two-stage physical-based model was proposed to describe the flow stress curve of as-cast TiAl alloy during hot deformation process. For establishing the model, at first, the flow curves of dynamic recovery (DRV) were modeled by employing stress-dislocation relation and adjusting dislocation annihilation coefficient Ω. Then, the flow curves of dynamic recrystallization (DRX) were modeled by considering the dynamic softening behavior into Avrami equation. Finally, the flow curves in the entire deformation stages could be described by embedding the predicted data of DRV model (i.e., flow stress before the critical strain) into the predicted data by DRX model (i.e., flow stress after the critical strain). The critical strain for initiation of DRX was determined by the double-differentiation method. To evaluate the applicability and effectiveness of DRX kinetics equation, the DRX curves were calculated and were consistent with the microstructure observation. Comparison between the experimental and predicted data shows that the proposed physical-based model can well forecast the flow stress under a wide working domain.

36 MATERIALS SCIENCE↗
NASA NTRS2020

Intercomparison of Precipitation Estimates Over the Southern Ocean from Atmospheric Reanalyses

Precipitation is a major component of the hydrologic cycle and plays a significant role in the sea-ice mass balance in the polar regions. Over the Southern Ocean, precipitation is particularly uncertain due to the lack of direct observations in this remote and harsh environment. Here we demonstrate that precipitation estimates from 8 global reanalyses produce similar spatial patterns between 2000-2010, although their annual means vary by about 250 mm yr -1 (or 26 percent of the median values) and there is little similarity in their representation of interannual variability. ERA-Interim produces the smallest and CFSR produces the largest amount of precipitation overall. Rainfall and snowfall are partitioned in five reanalyses; snowfall suffers from the same issues as the total precipitation comparison, with ERA-Interim producing about 128 mm less snowfall and JRA-55 about 103 mm more rainfall compared to the other reanalyses. When compared to CloudSat-derived snowfall, these five reanalyses indicate similar spatial patterns, but differ in their magnitude. All reanalyses indicate precipitation on nearly every day of the year, with spurious values occurring on an average of about 60 days yr -1 resulting in an accumulation of about 4.5 mm yr -1 . While similarities in spatial patterns among the reanalyses suggest a convergence, the large spread in magnitudes points to issues with the background models in adequately reproducing precipitation rates, and the differences in the model physics employed. Further improvements to model physics are required to achieve confidence in precipitation rate, as well as the phase and frequency of precipitation in these products.

Linette N. Boisvert↗
DOE OSTI2024

AGN-201 Digital Twin Concept of Operations

The AGN-201 is a nuclear reactor at Idaho State University (ISU) and is currently being used in the development of a digital twin (DT) with Idaho National Laboratory (INL). The goal is to create a DT (called the AGN-201 DT) which can monitor an operating nuclear reactor to determine when the reactor is being operated normally; normal operations are any operations that is declared by the ISU staff. To accomplish this, researchers at INL developed a DT ecosystem which can ingest data from the AGN-201 reactor. This data is fed into a series of machine learning (ML) and reactor physics models. The ML and reactor physics models assess the data and determine if the reactor is operating normally. Event(s) that are flagged as anomalies are investigated by the INL staff to determine if any undeclared experiments were conducted. The AGN-201 DT was verified in July/August 2023, when the ISU staff performed undeclared experiments and the INL staff were able to assess the anomalous events and determine what likely caused each event. This project was a steppingstone to promote the use of DTs for international safeguards and marks the first time a DT was able to monitor a nuclear reactor for this purpose. This document provides the concept of operations (CONOPS) for the AGN-201 digital twin (DT). The CONOPS describes how the AGN-201 and AGN-201 DT are expected to operate and details how each are established.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1990

Coupling of Coastal Zone Color Scanner data to a physical-biological model of the southeastern U.S. continental shelf ecosystem. I - CZCS data description and Lagrangian particle tracing experiments. II - An Eulerian model. III - Nutrient and phytoplankton fluxes and CZCS data assimilation

Surface phytoplankton biomass of the southeastern U.S. continental shelf area is discussed based on coastal zone color scanner (CZCS) images obtained in April 1980. Data of chlorophyll distributions are analyzed in conjunction with concurrent flow and temperature fields. Lagrangian particle tracing experiments show that the particles move consistently with the evolution of the chlorophyll patterns. A four-component physical-biological model for a horizontal plane at a nominal depth of 17 m is presented. Model simulations using various physical-biological dynamics and boundary conditions show that the variability of chlorophyll distributions is controlled by horizontal advection. Phytoplankton and nutrient fluxes, calculated using the model, show considerable variability with time. The chlorophyll distributions obtained from the CZCS images are assimilated into the model to improve the phytoplankton flux estimates.

Ishizaka, Joji↗
DOE OSTI2019

Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network based Equivalent Model

The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.

Parameter optimization, V2X, Powertrain modeling↗
DOE OSTI2024

Hamiltonian learning using machine-learning models trained with continuous measurements

Here, we build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Duality defect in a deformed transverse-field Ising model

Physical quantities with long lifetimes have both theoretical significance in the study of quantum many-body systems and practical implications for quantum technologies. In this manuscript, we investigate the roles played by topological defects in the construction of quasiconserved quantities, using as a prototypical example the Kramers-Wannier duality defect in a deformed one-dimensional quantum transverse-field Ising model. We construct the duality defect Hamiltonian in three different ways: half-chain Kramers-Wannier transformation, utilization of techniques in the Ising fusion category, and defect-modified weak integrability breaking deformation. The third method is also applicable for the study of generic integrable defects under weak integrability breaking deformations. We also work out the deformation of defect-modified higher charges in the model and study their slower decay behavior. Furthermore, we consider the corresponding duality defect twisted deformed Floquet transverse-field Ising model and investigate the stability of the isolated zero mode associated with the duality defect in the integrable Floquet Ising model, under such weak integrability breaking deformation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Uncertainty quantification of a physics-informed model based on sparse identification of a Thermal Energy Distribution System

Integrated energy systems (IES)s are crucial for enhancing the economy and efficiency of power generation sources (e.g., nuclear energy) necessary to unleash American energy dominance. These systems can be integrated with thermal energy storage (TES) and intermittent renewable energies to optimize overall energy use, peak-load regulation, and demand-side responses. However, the stabilization of energy generation, transport, and utilization introduces operational complexities that exceed the challenges of managing each sub-component individually. Currently, though IESs rely on human operators for efficiency and stability, reducing human error risk and enhancing performance through automation is highly desirable. Recent advances at Idaho National Laboratory have demonstrated successful control of the Thermal Energy Distributed System (TEDS). However, the automatic control system depends on a deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC) model, which are trained based on simulation data from physics-based simulations. Because of uncertainties in physics-based simulation, SINDyC model results in large discrepancies against experimental data and cannot be reliably used in automatic control. In this paper, we present an innovative approach to address these discrepancies by quantifying uncertainties and developing a more robust model. We first generated trajectories by using first-principles physics codes to encapsulate the experiment. Next, we trained thousands of models by randomly sampling these trajectories. We then collapsed all those models into one probabilistic SINDyC by fitting a multivariate Gaussian distribution onto the resulting coefficient’s distribution. Despite its simplicity, our approach successfully produced 95% confidence intervals that captured the experimental trajectories. It even did so with a higher probability and better U-pooling score across six of the seven relevant quantities of interest (QoIs), as compared to other classical approaches. In conclusion, ongoing research is focusing on generating new experimental trajectories to validate this approach, and on employing Bayesian calibration to refine parametric uncertainties and guide future model development efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Subtropical marine low stratiform cloud deck spatial errors in the E3SMv1 Atmosphere Model

Marine low-level clouds continue to be poorly simulated in models despite many studies and field experiments devoted to their improvement. Here we focus on the spatial errors in the cloud decks in the Department of Energy Earth system model (E3SM) relative to the satellite climatology by calculating centroid distances, area ratios, and overlap ratios. Since model dynamics is better simulated than clouds, these errors are attributed primarily to the model physics. To gain additional insight, we performed a sensitivity run in which model winds were nudged to those of reanalysis. This results in a large change (but not necessarily an improvement) in the simulated cloud decks. These differences between simulations are mainly due to the interactions between model dynamics and physics. These results suggest that both model physics (widely recognized) and its interaction with dynamics (less recognized) are important to model improvement in simulating these low-level clouds.

Brunke, Michael A.↗
DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗