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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin

Accurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data. Many complex and dangerous impact scenarios are difficult to conduct real-world experiments on to collect sufficient samples. To capture all impact scenarios and fully leverage the advantages of AI-based detection technologies, advanced methods involve combining real-world structural monitoring data with corresponding numerical models to construct digital twins. These methods continuously refine the created numerical models with limited real-world data and provide diverse impact scenarios through numerical model simulations. However, there are inevitable differences between digital models and physical models that are challenging to correct through mechanical means. This discrepancy in data distribution between the two models significantly hinders the application of digital twin technology in impact/event identification tasks. To address this challenge, this study proposes a novel model based on autoencoders, named Transfer-AE. Transfer-AE encodes the common features of digital twins in the latent space to bridge the uncertainty gap at a macro scale between numerical models and physical models and synchronously fits the magnitude and location of the impact load in the decoder. This enables consistent detection results for the same impact event, whether the sample comes from the numerical model or the physical model. Transfer-AE includes two operating modes: Mode 1 has a fixed computational complexity with stable inference speed, but the training cost and difficulty increase with data distribution. Mode 2's computational complexity increases with data distribution, but it has a fixed training cost and speed. In both cases involving the geodesic dome structure simulating a deep space habitat and the IASC-ASCE benchmark structure, Transfer-AE demonstrated the best performance in impact localization and quantification tasks compared to mainstream domain-adaptive transfer models.

Chengjia Han↗
NASA NTRS1992

Comparison of the USU ionospheric model with the UCL-Sheffield coupled thermospheric-ionospheric model

Several physical models of the high-latitude ionosphere have been developed that describe the time-dependent evolution of the E- and F-region plasma density. The models require a variety of inputs, including solar EUV fluxes, magnetospheric convection, auroral precipitation, and neutral atmosphere. Of specific relevance to this study is how the neutral atmosphere is incorporated into the ionospheric models. For the USU ionospheric model, the neutral atmosphere is the MSIS 1986 empirical model, while for the UCL-Sheffield coupled thermospheric-ionospheric model the neutral atmosphere is computed simultaneously with the ionosphere. Both models were run for similar solar and magnetospheric conditions (solar maximum, moderate geomagnetic activity, and winter solstice). Solar maximum conditions ensured a strong coupling between the ionosphere and thermosphere, which provided the possibility of a large ionospheric difference between the two physical models. This was further enhanced by choosing winter conditions so that the densities were not dominated by sunlight. The comparison of the two models indicated that both models predict the same morphological features with similar ionospheric densities, generally within about 30 percent.

Sojka, J. J.↗
NASA NTRS2013

Natural Air-Sea Flux of CO2 in Simulations of the NASA-GISS Climate Model: Sensitivity to the Physical Ocean Model Formulation

Results from twin control simulations of the preindustrial CO2 gas exchange (natural flux of CO2) between the ocean and the atmosphere are presented here using the NASA-GISS climate model, in which the same atmospheric component (modelE2) is coupled to two different ocean models, the Russell ocean model and HYCOM. Both incarnations of the GISS climate model are also coupled to the same ocean biogeochemistry module (NOBM) which estimates prognostic distributions for biotic and abiotic fields that influence the air-sea flux of CO2. Model intercomparison is carried out at equilibrium conditions and model differences are contrasted with biases from present day climatologies. Although the models agree on the spatial patterns of the air-sea flux of CO2, they disagree on the strength of the North Atlantic and Southern Ocean sinks mainly because of kinematic (winds) and chemistry (pCO2) differences rather than thermodynamic (SST) ones. Biology/chemistry dissimilarities in the models stem from the different parameterizations of advective and diffusive processes, such as overturning, mixing and horizontal tracer advection and to a lesser degree from parameterizations of biogeochemical processes such as gravitational settling and sinking. The global meridional overturning circulation illustrates much of the different behavior of the biological pump in the two models, together with differences in mixed layer depth which are responsible for different SST, DIC and nutrient distributions in the two models and consequently different atmospheric feedbacks (in the wind, net heat and freshwater fluxes into the ocean).

Gas exchange↗
NASA NTRS2003

The CEOP Inter-Monsoon Studies (CIMS)

Prediction of climate relies on models, and better model prediction depends on good model physics. Improving model physics requires the maximal utilization of climate data of the past, present and future. CEOP provides the first example of a comprehensive, integrated global and regional data set, consisting of globally gridded data, reference site in-situ observations, model location time series (MOLTS), and integrated satellite data for a two-year period covering two complete annual cycles of 2003-2004. The monsoon regions are the most important socio-economically in terms of devastation by floods and droughts, and potential impacts from climate change md fluctuatinns nf the hydrologic cyc!e. Scientifically, it is most challenging, because of complex interactions of atmosphere, land and oceans, local vs. remote forcings in contributing to climate variability and change in the region. Given that many common features, and physical teleconnection exist among different monsoon regions, an international research focus on monsoon must be coordinated and sustained. Current models of the monsoon are grossly inadequate for regional predictions. For improvement, models must be confronted with relevant observations, and model physic developers must be made to be aware of the wealth of information from existing climate data, field measurements, and satellite data that can be used to improve models. Model transferability studles must be conducted. CIMS is a major initiative under CEOP to engage the modeling and the observational communities to join in a coordinated effort to study the monsoons. The objectives of CIMS are (a) To provide a better understanding of fundamental physical processes (diurnal cycle, annual cycle, and intraseasonal oscillations) in monsoon regions around the world and (b) To demonstrate the synergy and utility of CEOP data in providing a pathway for model physics evaluation and improvement. In this talk, I will present the basic concepts of CIMS and the key scientific problems facing monsoon climates and provide examples of common monsoon features, and possible monsoon induced teleconnections linking different parts of the world.

Lau, William K. M.↗
DOE OSTI2026

MeVPrtl: An Event Generator for Dark Sector Particles in the Short-Baseline Neutrino Program

MeVPrtl is a modular event generator of beyond the Standard Model (BSM) physics particles developed for use in the Short-Baseline Neutrino (SBN) Program. A large class of BSM physics models predict that new particles could be produced in the intense Booster Neutrino Beam (BNB) and Neutrinos at the Main Injector (NuMI) beams at Fermilab, travel to the SBN Program detectors, and decay into Standard Model (SM) particles. These new physics models are motivated by dark matter, the neutrino mass scale, and a solution to the strong CP problem. MeVPrtl provides an interface to implement the overlapping phenomenology of these models, and to connect them with meson flux inputs and object outputs used by the SBN Program's LArSoft-based detector simulation. Implementations for the Higgs portal, heavy neutral lepton, and heavy QCD axion models exist within MeVPrtl. In this paper these implementations and their validation, as well as details of the MeVPrtl interface, are specified.

Abratenko Ao, P.↗
NASA NTRS1991

Computer graphics testbed to simulate and test vision systems for space applications

A system was developed for displaying computer graphics images of space objects and the use of the system was demonstrated as a testbed for evaluating vision systems for space applications. In order to evaluate vision systems, it is desirable to be able to control all factors involved in creating the images used for processing by the vision system. Considerable time and expense is involved in building accurate physical models of space objects. Also, precise location of the model relative to the viewer and accurate location of the light source require additional effort. As part of this project, graphics models of space objects such as the Solarmax satellite are created that the user can control the light direction and the relative position of the object and the viewer. The work is also aimed at providing control of hue, shading, noise and shadows for use in demonstrating and testing imaging processing techniques. The simulated camera data can provide XYZ coordinates, pitch, yaw, and roll for the models. A physical model is also being used to provide comparison of camera images with the graphics images.

Cheatham, John B.↗
NASA NTRS1990

Supersonic combustor modeling

The physical phenomena involved when a supersonic flow undergoes chemical reaction are discussed. Detailed physical models of convective and diffusive mixing, and finite rate chemical reaction in supersonic flow are presented. Numerical algorithms used to solve the equations governing these processes are introduced. Computer programs using these algorithms are used to analyze the structure of the reacting mixing layer. It is concluded that, as in subsonic flow, exothermic heat release in unconfined supersonic flows retards fuel/air mixing. Non mixing is shown to be a potential problem in reducing the efficiency of supersonic as well as subsonic combustion. Techniques for enhancing fuel/air mixing and combustion are described.

Riggins, David W.↗
DOE OSTI2026

Convergence Criteria for Multiphysics Simulations

The behavior of engineered systems is often influenced by multiple physical phenomena, such as mechanical deformation, heat transfer, and chemical species transport and reactions. There are often strong interactions between these phenomena, and there is increasing interest in applying coupled-physics models to improve understanding of physical behavior under complex environmental conditions. Multiple simulation frameworks that facilitate coupled-physics simulations are in widespread use, and these employ a variety of techniques to account for interactions between those physics. Many frameworks solve the physics models independently and transfer results between them. Alternatively, a single monolithic system of equations for every physics model can be formed and solved. Each of these approaches has its benefits and drawbacks, and the optimal approach varies depending on the nature of the problem. The open-source MOOSE framework was developed targeting solution of large-scale multiphysics problems. Although it provides options for all these coupling approaches, its standard approach for multiphysics solutions is to form and solve a single monolithic system of equations containing the unknowns for all physics models. MOOSE provides a streamlined approach for users to define the solution variables, the terms in the partial differential equations pertaining to each variable, and interactions between solution variables. One aspect of the monolithic solution approach that can be problematic, however, is defining appropriate convergence criteria for the nonlinear system. A standard approach is to determine convergence is to simply take a norm of the residual vector corresponding to the full vector of unknowns. However, if the residual vector contains variables for multiple physics models, the magnitudes of those variables can differ significantly, and the variables can converge at significantly different rates from each other. It is important to ensure that the variables for each of the physics are converged, and also ensure that the convergence criteria are not excessively stringent in cases when there is little change in the solution. This talk presents representative multiphysics problems to highlight these issues, and shows strategies for convergence criteria in MOOSE that are robust for multiphysics models under a variety of conditions.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Extracting and Interpreting Electrochemical Impedance Spectra (EIS) from Physics-Based Models of Lithium-Ion Batteries

This paper implements a highly efficient algorithm to extract electrochemical impedance spectra (EIS) from physics-based battery models (e.g., a P2D model). The mathematical approach is different from how EIS is practiced experimentally. Experimentally, the voltage (current) is harmonically perturbed over a wide range of frequencies and the amplitude and phase shift of the corresponding current (voltage) is measured. The experimental approach can be implemented in simulation software, but is computationally expensive. The approach here is to determine locally linear state-space models from the full physical model. The four Jacobian matrices that are the basis of the state-space models can be derived by numerical differentiation of the physical model. The EIS is then extracted from the state-space model using computationally efficient matrix-manipulation techniques. The algorithm can evaluate the full EIS at an instant in time during a transient, independent of whether the battery is in a stationary state. The approach is also able to separate the full-cell impedance to evaluate partial EIS, such as for a battery anode alone. Although such partial EIS is difficult to measure experimentally, the partial EIS provides valuable insights in interpreting the full-cell EIS.

25 ENERGY STORAGE↗
DOE OSTI2024

High-Fidelity Neutronics Model of a Realistic Heat Pipe Microreactor Report

The creation of a digital twin requires two parallel products: a virtual model and a physical asset. This report details the development the high-fidelity reactor physics model of a realistic heat pipe micro reactor for the virtual model. The realistic heat pipe micro reactor provides a generic model that research can utilize to make safeguards considerations. The model will be leveraged for capturing diversion and misuse scenarios expected to take place in a microreactor. A python wrapper has been developed to rapidly explore various acquisition pathways by manipulating the reactor physics model on-the-fly.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Aspen Open Jets: unlocking LHC data for foundation models in particle physics

Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collected by the CMS experiment at the Large Hadron Collider can be useful in pre-training foundation models for HEP. Specifically, we introduce the AspenOpenJets (AOJs) dataset, consisting of approximately 178 M high p T jets derived from CMS 2016 Open Data. We show how pre-training the OmniJet-α foundation model on AOJs improves performance on generative tasks with significant domain shift: generating boosted top and QCD jets from the simulated JetClass dataset. In addition to demonstrating the power of pre-training of a jet-based foundation model on actual proton–proton collision data, we provide the ML-ready derived AOJs dataset for further public use.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2014

Cabin Environment Physics Risk Model

This paper presents a Cabin Environment Physics Risk (CEPR) model that predicts the time for an initial failure of Environmental Control and Life Support System (ECLSS) functionality to propagate into a hazardous environment and trigger a loss-of-crew (LOC) event. This physics-of failure model allows a probabilistic risk assessment of a crewed spacecraft to account for the cabin environment, which can serve as a buffer to protect the crew during an abort from orbit and ultimately enable a safe return. The results of the CEPR model replace the assumption that failure of the crew critical ECLSS functionality causes LOC instantly, and provide a more accurate representation of the spacecraft's risk posture. The instant-LOC assumption is shown to be excessively conservative and, moreover, can impact the relative risk drivers identified for the spacecraft. This, in turn, could lead the design team to allocate mass for equipment to reduce overly conservative risk estimates in a suboptimal configuration, which inherently increases the overall risk to the crew. For example, available mass could be poorly used to add redundant ECLSS components that have a negligible benefit but appear to make the vehicle safer due to poor assumptions about the propagation time of ECLSS failures.

Risk↗
DOE OSTI2024

Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models

Abstract We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data. This work provides a novel scheme to perform masked modeling based pre-training to learn permutation invariant functions on sets. More generally, this work provides a step towards building large foundation models for HEP that can be generically pre-trained with self-supervised learning and later fine-tuned for a variety of down-stream tasks. In MPM, particles in a set are masked and the training objective is to recover their identity, as defined by a discretized token representation of a pre-trained vector quantized variational autoencoder. We study the efficacy of the method in samples of high energy jets at collider physics experiments, including studies on the impact of discretization, permutation invariance, and ordering. We also study the fine-tuning capability of the model, showing that it can be adapted to tasks such as supervised and weakly supervised jet classification, and that the model can transfer efficiently with small fine-tuning data sets to new classes and new data domains.

Heinrich, Lukas (ORCID:0000000240487584)↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
NASA NTRS1988

Diagnosis by integrating model-based reasoning with knowledge-based reasoning

Our research investigates how observations can be categorized by integrating a qualitative physical model with experiential knowledge. Our domain is diagnosis of pathologic gait in humans, in which the observations are the gait motions, muscle activity during gait, and physical exam data, and the diagnostic hypotheses are the potential muscle weaknesses, muscle mistimings, and joint restrictions. Patients with underlying neurological disorders typically have several malfunctions. Among the problems that need to be faced are: the ambiguity of the observations, the ambiguity of the qualitative physical model, correspondence of the observations and hypotheses to the qualitative physical model, the inherent uncertainty of experiential knowledge, and the combinatorics involved in forming composite hypotheses. Our system divides the work so that the knowledge-based reasoning suggests which hypotheses appear more likely than others, the qualitative physical model is used to determine which hypotheses explain which observations, and another process combines these functionalities to construct a composite hypothesis based on explanatory power and plausibility. We speculate that the reasoning architecture of our system is generally applicable to complex domains in which a less-than-perfect physical model and less-than-perfect experiential knowledge need to be combined to perform diagnosis.

Bylander, Tom↗
NASA NTRS1983

Aerothermal modeling program, phase 1

The physical modeling embodied in the computational fluid dynamics codes is discussed. The objectives were to identify shortcomings in the models and to provide a program plan to improve the quantitative accuracy. The physical models studied were for: turbulent mass and momentum transport, heat release, liquid fuel spray, and gaseous radiation. The approach adopted was to test the models against appropriate benchmark-quality test cases from experiments in the literature for the constituent flows that together make up the combustor real flow.

Sturgess, G. J.↗