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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

MPD Thruster Performance Analytic Models

Magnetoplasmadynamic (MPD) thrusters are capable of accelerating quasi-neutral plasmas to high exhaust velocities using Megawatts (MW) of electric power. These characteristics make such devices worthy of consideration for demanding, far-term missions such as the human exploration of Mars or beyond. Assessment of MPD thrusters at the system and mission level is often difficult due to their status as ongoing experimental research topics rather than developed thrusters. However, in order to assess MPD thrusters utility in later missions, some adequate characterization of performance, or more exactly, projected performance, and system level definition are required for use in analyses. The most recent physical models of self-field MPD thrusters have been examined, assessed, and reconfigured for use by systems and mission analysts. The physical models allow for rational projections of thruster performance based on physical parameters that can be measured in the laboratory. The models and their implications for the design of future MPD thrusters are presented.

Gilland, James↗
DOE OSTI2011

Physics and modeling of an end-Hall (gridless) ion source

In an end-Hall source, an ion beam is extracted from a magnetized plasma and accelerated by the plasma electric field without grids. The principle of end-Hall sources is similar to that of Hall effect thrusters (or closed-drift thrusters), but their design is optimized for processing applications (ion beam assisted deposition or substrate cleaning) rather than propulsion. The beam divergence is larger in end-Hall ion sources, and these sources can operate at low ion energies. Although end-Hall sources are commonly used in the surface processing industry, no detailed modeling of these sources is available, and their operation is quite empirical. In this paper, a self-consistent, two-dimensional, quasineutral model of an end-Hall ion source is developed and used in order to improve the understanding of the basic physics of these plasma sources and to quantify the parameters controlling the properties of the extracted ion beam.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2017

Computational and Experimental Fluid-Structure Interaction Analysis of a High-Lift Wing with a Slat-Cove Filler for Noise Reduction

Airframe noise is a significant component of overall noise produced by transport aircraft during landing and approach (low speed maneuvers). A significant source for this noise is the cove of the leading-edge slat. The slat-cove filler (SCF) has been shown to be effective at mitigating slat noise. The objective of this work is to understand the fluid-structure interaction (FSI) behavior of a superelastic shape memory alloy (SMA) SCF in flow using both computational and physical models of a high-lift wing. Initial understanding of flow around the SCF and wing is obtained using computational fluid dynamics (CFD) analysis at various angles of attack. A framework compatible with an SMA constitutive model (implemented as a user material subroutine) is used to perform FSI analysis for multiple flow and configuration cases. A scaled physical model of the high-lift wing is constructed and tested in the Texas A&M 3 ft-by-4-foot wind tunnel. Initial validation of both CFD and FSI analysis is conducted by comparing lift, drag and pressure distributions with experimental results.

Scholten, William D.↗
DOE OSTI2019

Development of a Liquid-Fueled Molten Salt Reactor Safeguards Model.

This work describes the ongoing work to develop a molten salt reactor (MSR) model and associated tools for safeguards analysis. A new flowsheet was developed in collaboration with Oak Ridge National Laboratory (ORNL) for the Molten Salt Demonstration Reactor (MSDR). This design was chosen by ORNL as a generic baseline design that could be used for safeguards research. The model has simple chemical processing that is less extensive than the two-fluid flowsheet developed in the last year. A detailed TRITON reactor physics model, provided by ORNL, was implemented into the process model. The process model now includes reactor parameters such as K-eff and decay heat, which could be used as part of an advanced safeguards approach. Finally, a set of generic safeguards tools based on current safeguards approaches were developed. These tools are flexible and can be used with most MSR flowsheets. ACKNOWLEDGEMENTS This work was funded by the Materials Protection Accounting and Control Technologies (MPACT) working group as part of the Fuel Cycle Technologies Program under the U.S. Department of Energy, Office of Nuclear Energy. The authors would also like to acknowledge Ben Betz ler for his work on the reactor physics models that were incorporated into the work and the continued collaboration with ORNL staff.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

Air Flow Modeling in the Wind Tunnel of the FHWA Aerodynamics Laboratory at Turner-Fairbank Highway Research Center

Computational fluid dynamics (CFD) modeling is widely used in industry for design and in the research community to support, compliment, and extend the scope of experimental studies. Analysis of transportation infrastructure using high performance cluster computing with CFD and structural mechanics software is done at the Transportation Research and Analysis Computing Center (TRACC) at Argonne National Laboratory. These resources, available at TRACC, were used to perform advanced three-dimensional computational simulations of the wind tunnel laboratory at the Turner-Fairbank Highway Research Center (TFHRC). The goals were to verify the CFD model of the laboratory wind tunnel and then to use versions of the model to provide the capability to (1) perform larger parametric series of tests than can be easily done in the laboratory with available budget and time, (2) to extend testing to wind speeds that cannot be achieved in the laboratory, and (3) to run types of tests that are very difficult or impossible to run in the laboratory. Modern CFD software has many physics models and domain meshing options. Models, including the choice of turbulence and other physics models and settings, the computational mesh, and the solver settings, need to be validated against measurements to verify that the results are sufficiently accurate for use in engineering applications. The wind tunnel model was built and tested, by comparing to experimental measurements, to provide a valuable tool to perform these types of studies in the future as a complement and extension to TFHRC’s experimental capabilities. Wind tunnel testing at TFHRC is conducted in a subsonic open-jet wind tunnel with a 1.83 m (6 foot) by 1.83 m (6 foot) cross section. A three component dual force-balance system is used to measure forces acting on tested models, and a three degree of freedom suspension system is used for dynamic response tests. Pictures of the room are shown in Figure 1-1 to Figure 1-4. A detailed CAD geometry and CFD model of the wind tunnel laboratory at TFHRC was built and tested. Results were compared against experimental wind velocity measurements at a large number of locations around the room. This testing included an assessment of the air flow uniformity provided by the tunnel to the test zone and assessment of room geometry effects, such as influence of the proximity the room walls, the non-symmetrical position of the tunnel in the room, and the influence of the room setup on the air flow in the room. This information is useful both for simplifying the computational model and in deciding whether or not moving, or removing, some of the furniture or other movable objects in the room will change the flow in the test zone.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Machine learning for geophysical characterization of brittleness: Tuscaloosa Marine Shale case study

Brittleness is one of the most important reservoir properties for unconventional reservoir exploration and production. Better knowledge about the brittleness distribution can help to optimize the hydraulic fracturing operation and lower costs. However, there are very few reliable and effective physical models to predict the spatial distribution of brittleness. We have developed a machine learning-based method to predict subsurface brittleness by using multidiscipline data sets, such as seismic attributes, rock physics, and petrophysics information, which allows us to implement the prediction without using a physical model. The method is applied on a data set from Tuscaloosa Marine Shale, and the predicted rock physics template is close to the calculated value from conventional inverted elastic parameters. Therefore, the proposed method helps determine areas of the reservoir that have optimal geomechanical properties for successful hydraulic fracturing.

Geochemistry & Geophysics↗
DOE OSTI2022

Hypothesis testing via AI: Generating physically interpretable models of scientific data with machine learning (Full Technical Report)

Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.

97 MATHEMATICS AND COMPUTING↗
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↗
DOE OSTI2017

A Physical Model-based Correction for Charge Traps in the Hubble Space Telescope ’s Wide Field Camera 3 Near-IR Detector and Its Applications to Transiting Exoplanets and Brown Dwarfs

The Hubble Space Telescope Wide Field Camera 3 (WFC3) near-IR channel is extensively used in time-resolved observations, especially for transiting exoplanet spectroscopy as well as brown dwarf and directly imaged exoplanet rotational phase mapping. The ramp effect is the dominant source of systematics in the WFC3 for time-resolved observations, which limits its photometric precision. Current mitigation strategies are based on empirical fits and require additional orbits to help the telescope reach a thermal equilibrium . We show that the ramp-effect profiles can be explained and corrected with high fidelity using charge trapping theories. We also present a model for this process that can be used to predict and to correct charge trap systematics. Our model is based on a very small number of parameters that are intrinsic to the detector. We find that these parameters are very stable between the different data sets, and we provide best-fit values. Our model is tested with more than 120 orbits (∼40 visits) of WFC3 observations and is proved to be able to provide near photon noise limited corrections for observations made with both staring and scanning modes of transiting exoplanets as well as for starting-mode observations of brown dwarfs. After our model correction, the light curve of the first orbit in each visit has the same photometric precision as subsequent orbits, so data from the first orbit no longer need to be discarded. Near-IR arrays with the same physical characteristics (e.g., JWST/NIRCam ) may also benefit from the extension of this model if similar systematic profiles are observed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗
DOE OSTI2015

DREAM3D simulations of inner-belt dynamics

A 1973 paper by Lyons and Thorne explains the two-belt structure for electrons in the inner magnetosphere as a balance between inward radial diffusion and loss to the atmosphere, where the loss to the atmosphere is enabled by pitch-angle scattering from Coulomb and wave-particle interactions. In the 1973 paper, equilibrium solutions to a decoupled set of 1D radial diffusion equations, one for each value of the first invariant of motion, μ, were computed to produce the equilibrium two-belt structure. Each 1D radial diffusion equation incorporated an L-and μ-dependent `lifetime' due to the Coulomb and wave-particle interactions. This decoupling of the problem is appropriate under the assumption that radial diffusion is slow in comparison to pitch-angle scattering. However, for some values of μ and L the lifetime associated with pitch-angle scattering is comparable to the timescale associated with radial diffusion, suggesting that the true equilibrium solutions might reflect `coupled modes' involving pitch-angle scattering and radial diffusion and thus requiring a 3D diffusion model. In the work we show here, we have computed the equilibrium solutions using our 3D diffusion model, DREAM3D, that allows for such coupling. We find that the 3D equilibrium solutions are quite similar to the solutions shown in the 1973 paper when we use the same physical models for radial diffusion and pitch-angle scattering from hiss. However, we show that the equilibrium solutions are quite sensitive to various aspects of the physics model employed in the 1973 paper that can be improved, suggesting that additional work needs to be done to understand the two-belt structure.

58 GEOSCIENCES↗
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.↗
DOE OSTI2016

WEC-SIM Phase 1 Validation Testing -- Numerical Modeling of Experiments: Preprint

The Wave Energy Converter Simulator (WEC-Sim) is an open-source code jointly developed by Sandia National Laboratories and the National Renewable Energy Laboratory. It is used to model wave energy converters subjected to operational and extreme waves. In order for the WEC-Sim code to be beneficial to the wave energy community, code verification and physical model validation is necessary. This paper describes numerical modeling of the wave tank testing for the 1:33-scale experimental testing of the floating oscillating surge wave energy converter. The comparison between WEC-Sim and the Phase 1 experimental data set serves as code validation. This paper is a follow-up to the WEC-Sim paper on experimental testing, and describes the WEC-Sim numerical simulations for the floating oscillating surge wave energy converter.

16 TIDAL AND WAVE POWER↗
DOE OSTI2013

Source Physics Experiment modeling: YEARLY REPORT FOR THE PERIOD Jan-Dec 2012

The Office of Nuclear Detonation Detection identifies characterization of the explosion source as a fundamental research area. Our ability to characterize explosion sources from earthquakes relies on empirical laws that are not covering all the possible cases of location, site structure and yields of possible explosions. Our empirical knowledge needs to be completed by a physical understanding of seismic wave generation. NA22 has funded the LANL SPE seismic project to develop a new source model under the Ground-Based Nuclear Explosion Monitoring Research and Development (GNEM-R&D) Program. NA22 also funds computational support of the seismic source model development. Hydrodynamic computations using realistic material response models combined with a comprehensive series of field experiments provided by the SPE program are needed for test and validation of the new seismic source models. The following three essential elements, (1) model development, (2) numerical simulations of non-linear material response, and (2) field experimentation, will be brought to bear on a predictive capability for broadband P and S wavefields with a sound physical basis for seismic monitoring applications. This research will advance our ability to address yield estimation for UNE conducted in proliferant nations and provide a better understanding of the coupling of explosion sources to the seismic wavefield.

58 GEOSCIENCES↗
DOE OSTI2016

Response of precipitation extremes to idealized global warming in an aqua-planet climate model: towards a robust projection across different horizontal resolutions

Current climate models produce quite heterogeneous projections for the responses of precipitation extremes to future climate change. To help understand the range of projections from multimodel ensembles, a series of idealized 'aquaplanet' Atmospheric General Circulation Model (AGCM) runs have been performed with the Community Atmosphere Model CAM3. These runs have been analysed to identify the effects of horizontal resolution on precipitation extreme projections under two simple global warming scenarios. We adopt the aquaplanet framework for our simulations to remove any sensitivity to the spatial resolution of external inputs and to focus on the roles of model physics and dynamics. Results show that a uniform increase of sea surface temperature (SST) and an increase of low-to-high latitude SST gradient both lead to increase of precipitation and precipitation extremes for most latitudes. The perturbed SSTs generally have stronger impacts on precipitation extremes than on mean precipitation. Horizontal model resolution strongly affects the global warming signals in the extreme precipitation in tropical and subtropical regions but not in high latitude regions. This study illustrates that the effects of horizontal resolution have to be taken into account to develop more robust projections of precipitation extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Properties of jets measured from tracks in proton-proton collisions at center-of-mass energy √s=7 TeV with the ATLAS detector

Jets are identified and their properties studied in center-of-mass energy √s=7 TeV proton-proton collisions at the Large Hadron Collider using charged particles measured by the ATLAS inner detector. Events are selected using a minimum bias trigger, allowing jets at very low transverse momentum to be observed and their characteristics in the transition to high-momentum fully perturbative jets to be studied. Jets are reconstructed using the anti-k t algorithm applied to charged particles with two radius parameter choices, 0.4 and 0.6. An inclusive charged jet transverse momentum cross section measurement from 4 GeV to 100 GeV is shown for four ranges in rapidity extending to 1.9 and corrected to charged particle-level truth jets. The transverse momenta and longitudinal momentum fractions of charged particles within jets are measured, along with the charged particle multiplicity and the particle density as a function of radial distance from the jet axis. Comparison of the data with the theoretical models implemented in existing tunings of Monte Carlo event generators indicates reasonable overall agreement between data and Monte Carlo. These comparisons are sensitive to Monte Carlo parton showering, hadronization, and soft physics models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗