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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Improved Systematic Pointing Error Model for the DSN Antennas

New pointing models have been developed for large reflector antennas whose construction is founded on elevation over azimuth mount. At JPL, the new models were applied to the Deep Space Network (DSN) 34-meter antenna s subnet for corrections of their systematic pointing errors; it achieved significant improvement in performance at Ka-band (32-GHz) and X-band (8.4-GHz). The new models provide pointing improvements relative to the traditional models by a factor of two to three, which translate to approximately 3-dB performance improvement at Ka-band. For radio science experiments where blind pointing performance is critical, the new innovation provides a new enabling technology. The model extends the traditional physical models with higher-order mathematical terms, thereby increasing the resolution of the model for a better fit to the underlying systematic imperfections that are the cause of antenna pointing errors. The philosophy of the traditional model was that all mathematical terms in the model must be traced to a physical phenomenon causing antenna pointing errors. The traditional physical terms are: antenna axis tilts, gravitational flexure, azimuth collimation, azimuth encoder fixed offset, azimuth and elevation skew, elevation encoder fixed offset, residual refraction, azimuth encoder scale error, and antenna pointing de-rotation terms for beam waveguide (BWG) antennas. Besides the addition of spherical harmonics terms, the new models differ from the traditional ones in that the coefficients for the cross-elevation and elevation corrections are completely independent and may be different, while in the traditional model, some of the terms are identical. In addition, the new software allows for all-sky or mission-specific model development, and can utilize the previously used model as an a priori estimate for the development of the updated models.

Rochblatt, David J.↗
DOE OSTI2026

A roadmap toward scaling, reasoning and self-evolving foundation models for nuclear and particle physics

Foundation models have revolutionized artificial intelligence, with Large Language Models demonstrating unprecedented capabilities in multimodal understanding, reasoning and tool use. Nuclear and particle physics stands at a critical juncture where similar transformative potential awaits realization. The field generates exabytes of experimental data, exascale simulations, and decades of theoretical insights — yet these remain largely disconnected from modern Artifical Intelligence (AI) capabilities, with most physics AI applications confined to narrow, task-specific models that suffer from domain shifting when applied to real experimental data. We present a roadmap for FM4NPP (Foundation Model for Nuclear and Particle Physics), systematically scaling from current proof-of-concept models to trillion-parameter architectures capable of autonomous discovery. Our approach advances three critical frontiers: unified data infrastructure integrating detector data, scientific knowledge and computational tools across global facilities; multi-facility foundation models enabling cross-experiment knowledge transfer and accelerated discovery; and agentic AI capabilities for reasoning and autonomous tool use. The resulting self-evolving FM4NPP will transform physics research by converting time-intensive data analysis, theory derivation and computational bottlenecks into rapid AI–human collaborative discovery. This paradigm shift promises to fundamentally accelerate scientific progress in nuclear and particle physics, enabling researchers to focus on high-level insights while AI handles routine analysis and explores vast parameter spaces beyond human capacity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2001

Study of Magnetic Damping Effect on Convection and Solidification Under G-Jitter Conditions

As shown in space flight experiments, g-jitter is a critical issue affecting solidification processing of materials in microgravity. This study aims to provide, through extensive numerical simulations and ground based experiments, an assessment of the use of magnetic fields in combination with microgravity to reduce the g-jitter induced convective flows in space processing systems. Analytical solutions and 2-D and 3-D numerical models for g-jitter driven flows in simple solidification systems with and without the presence of an applied magnetic field have been developed and extensive analyses were carried out. A physical model was also constructed and PIV measurements compared reasonably well with predictions from numerical models. Some key points may be summarized as follows: (1) the amplitude of the oscillating velocity decreases at a rate inversely proportional to the g-jitter frequency and with an increase in the applied magnetic field; (2) the induced flow oscillates at approximately the same frequency as the affecting g-jitter, but out of a phase angle; (3) the phase angle is a complicated function of geometry, applied magnetic field, temperature gradient and frequency; (4) g-jitter driven flows exhibit a complex fluid flow pattern evolving in time; (5) the damping effect is more effective for low frequency flows; and (6) the applied magnetic field helps to reduce the variation of solutal distribution along the solid-liquid interface. Work in progress includes developing numerical models for solidification phenomena with the presence of both g-jitter and magnetic fields and developing a ground-based physical model to verify numerical predictions.

Li, Ben Q.↗
NASA NTRS1993

Numerical modeling of physical vapor transport under microgravity conditions: Effect of thermal creep and stress

One of the most promising applications of microgravity (micro-g) environments is the manufacture of exotic and high-quality crystals in closed cylindrical ampoules using physical vapor transport (PVT) processes. The quality enhancements are believed to be due to the absence of buoyant convection in the weightless environment - resulting in diffusion-limited transport of the vapor. In a typical experiment, solid-phase sample material is initially contained at one end of the ampoule. The sample is made to sublime into the vapor phase and deposit onto the opposite end by maintaining the source at an elevated temperature with respect to the deposit. Identification of the physical factors governing both the rates and uniformity of crystal growth, and the optimization of the micro-g technology, will require an accurate modeling of the vapor transport within the ampoule. Previous micro-g modeling efforts have approached the problem from a 'classical' convective/diffusion formulation, in which convection is driven by the action of buoyancy on thermal and solutal density differences. The general conclusion of these works have been that in low gravity environments the effect of buoyancy on vapor transport is negligible, and vapor transport occurs in a diffusion-limited mode. However, it has been recently recognized than in the non-isothermal (and often low total pressure) conditions encountered in ampoules, the commonly-assumed no-slip boundary condition to the differential equations governing fluid motion can be grossly unrepresentative of the actual situation. Specifically, the temperature gradients can give rise to thermal creep flows at the ampoule side walls. In addition, temperature gradients in the vapor itself can, through the action of thermal stress, lead to bulk fluid convection.

Mackowski, Daniel W.↗
NASA NTRS1988

A physically consistent model for artificial dissipation in transonic potential flow computations

The effect that artificial dissipation has on numerical solutions of the transonic Full Potential Equation (FPE) are investigated by comparing the artificially dissipative FPE to a Physically Dissipative Potential (PDP) equation. Analytic expressions were derived from the variables C and M sub c that are used in the artificial density formulation. It was shown that these new values generate artificial dissipation which is equivalent to the physical dissipation existing in the PDP equation. The new expression for the variables C and M sub c can easily be incorporated into the existing full potential codes which are based either on the artificial density or on the artificial viscosity formulation. A comparison of Physically Dissipative Potential (PDP), Artificial Density or Viscosity (ADV), Artificial Mass Flux (AMF), and ADV with variable C and M sub c formulation (MCC) is also presented.

Dulikravich, George S.↗
NASA NTRS1989

NDE in aerospace - Requirements for science, sensors and sense

The complexity of modern nondestructive evaluation (NDE) arises from four main factors: quantitative measurement science, physical models for computational analysis, realistic interfacing with engineering decisions, and direct access to management priorities. Recent advances in the four factors of NDE are addressed. Physical models of acoustic propagation are presented that have led to the development of measurement technologies advancing the ability to assure that materials and structures will perform as designed. In addition, a brief discussion is given of current research for future mission needs such as smart structures that sense their own health. Such advances permit projects to integrate design for inspection into their plans, bringing NDE into engineering and management priorities. The measurement focus is on ultrasonics with generous case examples. Problem solutions highlighted include critical stress in fasteners, residual stress in steel, NDE laminography, and solid rocket motor NDE.

Heyman, Joseph S.↗
DOE OSTI2025

VERITAS : A density-functional theory-based multiband kinetic model for understanding x-ray spectroscopy of dense plasmas

X-ray spectroscopy has long been a powerful diagnostic tool for hot, dilute plasmas, providing insights into plasma conditions by measuring line shifts and broadenings of atomic transitions. The technique critically depends on the accuracy of atomic physics models used to interpret spectroscopic measurements for inferring plasma properties such as free-electron density and temperature. Over the past decades, the atomic and plasma physics communities have developed robust atomic physics models to account for various processes in hot, dilute classical plasmas. While these models have been successful in that regime, their applicability becomes uncertain when interpreting x-ray spectroscopy experiments of above-solid-density plasmas. Given that finite-temperature density-functional theory (DFT) offers a more accurate description of dense plasma environments, we present the development of a DFT-based multi-band kinetic model, VERITAS, designed to improve the interpretation of x-ray spectroscopic measurements in high-density plasmas produced by laser-driven spherical implosions. This work details the VERITAS model and its application to both time-integrated and time-resolved x-ray spectra from implosion experiments on OMEGA. The advantages and limitations of the VERITAS model will also be discussed, along with potential directions for advancing x-ray spectroscopy of dense and superdense plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2007

Hybrid Modeling Improves Health and Performance Monitoring

Scientific Monitoring Inc. was awarded a Phase I Small Business Innovation Research (SBIR) project by NASA's Dryden Flight Research Center to create a new, simplified health-monitoring approach for flight vehicles and flight equipment. The project developed a hybrid physical model concept that provided a structured approach to simplifying complex design models for use in health monitoring, allowing the output or performance of the equipment to be compared to what the design models predicted, so that deterioration or impending failure could be detected before there would be an impact on the equipment's operational capability. Based on the original modeling technology, Scientific Monitoring released I-Trend, a commercial health- and performance-monitoring software product named for its intelligent trending, diagnostics, and prognostics capabilities, as part of the company's complete ICEMS (Intelligent Condition-based Equipment Management System) suite of monitoring and advanced alerting software. I-Trend uses the hybrid physical model to better characterize the nature of health or performance alarms that result in "no fault found" false alarms. Additionally, the use of physical principles helps I-Trend identify problems sooner. I-Trend technology is currently in use in several commercial aviation programs, and the U.S. Air Force recently tapped Scientific Monitoring to develop next-generation engine health-management software for monitoring its fleet of jet engines. Scientific Monitoring has continued the original NASA work, this time under a Phase III SBIR contract with a joint NASA-Pratt & Whitney aviation security program on propulsion-controlled aircraft under missile-damaged aircraft conditions.

Source record↗
DOE OSTI2026

A physics informed model for the prediction of surface energies and the shape of metal particles

Surface energies of metal-based systems are important for determining the Wulff-constructed shapes of metal nanoparticles and understanding the stability. We have developed a physics informed method to predict the total energy of metal-based systems across a wide range of configurations. Our method has been tested against density functional theory (DFT) calculations for late transition metals. This method enables on-the-fly surface energy predictions based on the fundamental of materials property and allows for the Wulff construction of metal particles for a random number of elemental atoms and without the need for DFT calculations. By making a division between atoms in the different layers of the model system, we can improve the accuracy of the model, suggesting a dissimilarity between the electronic structure due to an alternating compression and expansion of atomic layers. We find that our model accurately and effectively provides valuable insights into the distribution and stability of nanoparticles.

Mandal, Shyama Charan (ORCID:0000000245882874)↗
NASA NTRS2001

Use of Remotely-Sensed Rainfall Data in Global Modeling and Data Assimilation

Precipitation observations derived from microwave sensors available from the Tropical Rainfall Measuring Mission (TRMM) and the proposed Global Precipitation Mission (GPM) can provide crucial information needed for improving global modeling, data assimilation, and numerical weather prediction. New methodologies are being developed at NASA to make effective use of this new data type in these applications. Currently, global analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. We show that assimilating 6-h averaged TRMM rainfall retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper tropospheric moisture in the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System. The improved analysis also leads to improved short-range forecasts in the tropics. The above results were obtained using a variational assimilation procedure that uses rainfall observations to derive moisture and temperature tendency corrections every 6 hours to compensate for errors arising from imperfect initial conditions and deficiencies in the model physics. We will describe a developmental path towards using space-borne rainfall data to empirically estimate and correct for state-dependent systematic errors in parameterized model physics. The study provides a demonstration of the potential of using remote-sensed rainfall data from microwave instruments to improve the 4-dimensional global datasets for climate analysis and numerical weather prediction.

Hou, Arthur↗
NASA NTRS1993

Physical model-set identification for robust control of flexible structures

An approach to dynamic system identification is presented taking into account the goal of enhancing robust control performance of flexible structures. Identification techniques are derived which take advantage of the physics of structural dynamics and can provide realistic bounds for all potential parameter uncertainties. The developed approach includes input optimization which distributes excitation energy in such a way that the influence of residual uncertainties on robust control performance is reduced.

Karlov, Valeri I.↗
DOE OSTI2026

Machine learning at the Spallation Neutron Source accelerator and target

We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.

Accelerator Physics↗
NASA NTRS1981

Applicability of Satellite Freeze Forecasting and Cold Climate Mapping to the Other Parts of the United States

Tasks performed to determine the value of using GOES satellite thermal imagery to enhance fruit crop production in Michigan are described. An overview is presented of the system developed for image processing and thermal image and surface environmental data bases prepared to assess the physical models developed in Florida. These data bases were used to identify correlations between satellite apparent temperatures patterns and Earth surface factors. Significant freeze events in 1981 and the physical models used to provide a perspective on how Florida models can be applied in the context of the Michigan environment are discussed.

Source record↗
DOE OSTI2025

Resimulation-based self-supervised learning for pretraining physics foundation models

Self-supervised learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose resimulation-based self-supervised representation learning (RS3L), a novel simulation-based SSL strategy that employs a method of resimulation to drive data augmentation for contrastive learning in the physical sciences, particularly, in fields that rely on stochastic simulators. By intervening in the middle of the simulation process and rerunning simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how RS3L pretraining enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Mid-Fidelity Computational Fluid Dynamics Analysis of the Elytron 4S UAV Concept

The Elytron 4S Unmanned Aerial Vehicle (UAV) Concept was developed to combine the advantages of fixed- and rotary-wing technology. The 4S Concept is a box-wing configuration with rotors mounted on a centrally located tiltwing. The UAV is intended to be capable of both conventional takeoff and landing (CTOL) and vertical takeoff and landing (VTOL), and is envisioned to excel in UAV performance because of the combined efficiency of fixed-wing aircraft and the hover and VTOL capabilities of regular drones or quadcopters. A mid-fidelity Unsteady Reynolds-Averaged Navier-Stokes (URANS) approach using Rotorcraft CFD (RotCFD) is performed to analyze and characterize the performance of the aircraft. The flow field is coupled with a rotor model based on blade-element momentum theory to model the 4S UAV rotors. Turbulence is modeled using a realizable k- turbulence model with special wall function. The code is used to generate aerodynamic forces and moments on the body at cruise conditions, and during VTOL. The results and their uncertainties are characterized, and an angle- of-attack and sideslip sweep are computed, both with and without rotors on. Simulations are compared with the wind tunnel tests in the 7- by 10-Foot U.S. Army Wind Tunnel at NASA Ames Research Center, performed in 2017. Results show promising comparison with experimental data, despite a late change in rotor size and rudder size of the physical model that cause the expected deviations from the simulation. A slight change in the net thrust value, when rotors are modeled, is observed because of the rotor diameter increase on the physical model. A noticeable difference in the directional stability was observed because of the increased rudder surface and added strakes. These changes were implemented to improve on the design as simulated, which is observed in the results. The simulation results paved the way to the first successful flight of the UAV Concept.

Koning, Witold J. K.↗
NASA NTRS2016

Physics Based Model for Cryogenic Chilldown and Loading. Part III: Correlations

In this report we discuss the details of the correlations used to recognize flow patterns and predict frictional losses, heat and mass transfer in the cryogenic two phase flow. The emphasis are put on the formulation of the correlation problem in terms of concise parametric and functional spaces allowing for efficient online search of the model parameters and accurate prediction of the phenomena observed during cryogenic loading. A special attention is paid to the discussion of the correlation dependence on the gravity. In this context the physics of stability, friction, and boiling in the two-phase flow that underlies the required correlations is discussed.

two-phase flow↗
DOE OSTI2024

Exascale-Enabled Models and Algorithms for Microelectronics Applications (MicroEleX) v1

The MicroEleX code package contains a variety of models and algorithms for physical modeling of microelectronic circuitry, including electrostatics, electrodynamics, superconducting physics, micromagnetics, multi-ferroic systems, and quantum transport. MicroEleX leverages the AMReX software framework to provide scalability on GPU-based supercomputing architectures. The code is open source and designed to be algorithmically flexible so developers can incorporate enhanced or customized physics.

Nonaka, Andy↗
NASA NTRS2002

Rocket Engine Oscillation Diagnostics

Rocket engine oscillating data can reveal many physical phenomena ranging from unsteady flow and acoustics to rotordynamics and structural dynamics. Because of this, engine diagnostics based on oscillation data should employ both signal analysis and physical modeling. This paper describes an approach to rocket engine oscillation diagnostics, types of problems encountered, and example problems solved. Determination of design guidelines and environments (or loads) from oscillating phenomena is required during initial stages of rocket engine design, while the additional tasks of health monitoring, incipient failure detection, and anomaly diagnostics occur during engine development and operation. Oscillations in rocket engines are typically related to flow driven acoustics, flow excited structures, or rotational forces. Additional sources of oscillatory energy are combustion and cavitation. Included in the example problems is a sampling of signal analysis tools employed in diagnostics. The rocket engine hardware includes combustion devices, valves, turbopumps, and ducts. Simple models of an oscillating fluid system or structure can be constructed to estimate pertinent dynamic parameters governing the unsteady behavior of engine systems or components. In the example problems it is shown that simple physical modeling when combined with signal analysis can be successfully employed to diagnose complex rocket engine oscillatory phenomena.

Nesman, Tom↗