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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Abstract Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management

Accurate subsurface reservoir pressure control is extremely challenging due to geological heterogeneity and multiphase fluid-flow dynamics. Predicting behavior in this setting relies on high-fidelity physics-based simulations that are computationally expensive. Yet, the uncertain, heterogeneous properties that control these flows make it necessary to perform many of these expensive simulations, which is often prohibitive. To address these challenges, we introduce a physics-informed machine learning workflow that couples a fully differentiable multiphase flow simulator, which is implemented in the DPFEHM framework with a convolutional neural network (CNN). The CNN learns to predict fluid extraction rates from heterogeneous permeability fields to enforce pressure limits at critical reservoir locations. By incorporating transient multiphase flow physics into the training process, our method enables more practical and accurate predictions for realistic injection-extraction scenarios compared to previous works. To speed up training, we pretrain the model on single-phase, steady-state simulations and then finetune it on full multiphase scenarios, which dramatically reduces the computational cost. We demonstrate that high-accuracy training can be achieved with fewer than three thousand full-physics multiphase flow simulations – compared to previous estimates requiring up to ten million. This drastic reduction in the number of simulations is achieved by leveraging transfer learning from much less expensive single phase simulations.

25 ENERGY STORAGE↗
DOE OSTI2025

Electrodermal activity as a proxy for sweat rate monitoring during physical and mental activities

Electrodermal activity has long been used for mental activity monitoring by measuring skin conductance at specific locations, such as fingertips, with high sweat gland density. However, electrodermal activity has not been considered useful for physical activity monitoring, where large sweat volumes are generated, resulting in the accumulation of sweat at the skin–electrode interface and, thus, preventing further dynamic response to sweating events. Here we show that electrodermal activity can be used as a proxy for sweat loss measurement under both low and high physical activity levels. We use wearable sweat sensors that consist of water-permeable electrodes and microfluidic-based sweat analysers, and show that skin conductance is proportional to the instantaneous sweat loss. We demonstrate that sweat loss during exercise can be estimated by integrating skin conductance over time, which can be applied to assess the body hydration status of exercisers. From multisite measurements of skin conductance, we show that the wrist, forearm and upper arm are reflective of physical activity levels, whereas the finger is indicative of mental activity. Simultaneous measurement of two different sites selectively decouples mental and physical activities.

Biomedical engineering↗
DOE OSTI2023

Inferring adversarial behaviour in cyber‐physical power systems using a Bayesian attack graph approach

Abstract Highly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high‐risk access paths of cyber intruders that target grid resilience. However, cyber‐physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk‐based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber‐Adversarial Power System (Bayes‐CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes‐CAPS builds initial Bayesian attack graphs for realistic power system cyber‐physical models. These models are adaptable using collected data from the system under study. Then, Bayes‐CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber‐physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.

Sahu, Abhijeet↗
DOE OSTI2012

Solid state dosimeters used in medical physics 'A review'

Many solid-state detectors have been successfully used to perform the quality control and in vivo dosimetry in medical physics, both in diagnostic radiology and radiotherapy, as they have high sensitivity in a small volume; most of them do not require electrical connection and have dosimetric characteristics of interest such as: good accuracy and reproducibility, as well as a response independent of the energy of radiation, some of them. For this reason, the selection of an appropriate detector for use in medical physics must take into account the energy mass absorption coefficient relative to water for photon sources and the mass stopping power relative to water for beta emitters and electron beams in the energy range of interest in medical physics, as well as the effective atomic number of materials that constitute them. This paper presents a review of the dosimetric characteristics of the solid state dosimeters most suitable for use in medical physics.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Rapid 3D nanoscale coherent imaging via physics-aware deep learning

Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-CDI-NN, a deep convolutional neural network and differential programing framework trained to predict 3D structure and strain, solely from input 3D X-ray coherent scattering data. Our networks are designed to be “physics-aware” in multiple aspects; in that the physics of the X-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods. Our integrated machine learning and differential programing solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2012

THE PHYSICS OF THE NEUTRINO MECHANISM OF CORE-COLLAPSE SUPERNOVAE

Although it is known that the stalled accretion shock in models of core-collapse supernovae turns into explosion when the neutrino luminosity from the proto-neutron star (PNS) exceeds a critical value (L{sup crit}{sub {nu},core}) (the 'neutrino mechanism'), the physics of L{sup crit}{sub {nu},core} has never been systematically explored. We solve the accretion problem between the PNS surface and the accretion shock. We quantify the deep connection between the general problem of accretion flows with bounding shocks and the neutrino mechanism. In particular, we show that there is a maximum, critical sound speed above which the shock jump conditions cannot be satisfied and steady-state accretion is impossible. This physics is general and does not depend on a specific heating mechanism. For the simple model of pressure-less free fall onto a shock bounding an isothermal accretion flow, we show that shock solutions are possible only for sound speed c{sub T} < c{sup crit}{sub T} and that c{sup 2}{sub T}/v{sub esc}{sup 2} = 3/16 = 0.1875 at c{sup crit}{sub T}. We generalize this result to the supernova problem, showing that the same physics determines L{sup crit}{sub {nu},core}. The critical condition for explosion can be written as c{sup 2}{sub S}/v{sup 2}{sub esc} {approx_equal} 0.19, where c{sub S} is the adiabatic sound speed. This 'antesonic' condition describes L{sup crit}{sub {nu},core} over a broad range of parameters, and other criteria proposed in the literature fail to capture this physics. We show that the accretion luminosity reduces L{sup crit}{sub {nu},core} non-trivially. A larger PNS radius decreases L{sup crit}{sub {nu},core}, implying that a stiff high-density equation of state may be preferred. Finally, using an analytic model, we provide evidence that the reduction of L{sup crit}{sub {nu},core} seen in recent multi-dimensional simulations results from reduced cooling efficiency, rather than an increased heating rate.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

CONSTRAINING CLUSTER PHYSICS WITH THE SHAPE OF X-RAY CLUSTERS: COMPARISON OF LOCAL X-RAY CLUSTERS VERSUS {Lambda}CDM CLUSTERS

Recent simulations of cluster formation have demonstrated that condensation of baryons into central galaxies during cluster formation can drive the shape of the gas distribution in galaxy clusters significantly rounder out to their virial radius. These simulations generally predict stellar fractions within cluster virial radii that are {approx}2-3 times larger than the stellar masses deduced from observations. In this paper, we compare ellipticity profiles of simulated clusters performed with varying input physics (radiative cooling, star formation, and supernova feedback) to the cluster ellipticity profiles derived from Chandra and ROSAT observations, in an effort to constrain the fraction of gas that cools and condenses into the central galaxies within clusters. We find that local relaxed clusters have an average ellipticity of {epsilon} = 0.18 {+-} 0.05 in the radial range of 0.04 {<=} r/r{sub 500} {<=} 1. At larger radii r > 0.1r{sub 500}, the observed ellipticity profiles agree well with the predictions of non-radiative simulations. In contrast, the ellipticity profiles of simulated clusters that include dissipative gas physics deviate significantly from the observed ellipticity profiles at all radii. The dissipative simulations overpredict (underpredict) ellipticity in the inner (outer) regions of galaxy clusters. By comparing simulations with and without dissipative gas physics, we show that gas cooling causes the gas distribution to be more oblate in the central regions, but makes the outer gas distribution more spherical. We find that late-time gas cooling and star formation are responsible for the significantly oblate gas distributions in cluster cores, but the gas shapes outside of cluster cores are set primarily by baryon dissipation at high redshift (z {>=} 2). Our results indicate that the shapes of X-ray emitting gas in galaxy clusters, especially at large radii, can be used to place constraints on cluster gas physics, making it potential probes of the history of baryonic cooling in galaxy clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

PHYSICS OF A PARTIALLY IONIZED GAS RELEVANT TO GALAXY FORMATION SIMULATIONS-THE IONIZATION POTENTIAL ENERGY RESERVOIR

Simulation codes for galaxy formation and evolution take on board as many physical processes as possible beyond the standard gravitational and hydrodynamical physics. Most of this extra physics takes place below the resolution level of the simulations and is added in a ''sub-grid'' fashion. However, these sub-grid processes affect the macroscopic hydrodynamical properties of the gas and thus couple to the ''on-grid'' physics that is explicitly integrated during the simulation. In this paper, we focus on the link between partial ionization and the hydrodynamical equations. We show that the energy stored in ions and free electrons constitutes a potential energy term which breaks the linear dependence of the internal energy on temperature. Correctly taking into account ionization hence requires modifying both the equation of state and the energy-temperature relation. We implemented these changes in the cosmological simulation code GADGET2. As an example of the effects of these changes, we study the propagation of Sedov-Taylor shock waves through an ionizing medium. This serves as a proxy for the absorption of supernova feedback energy by the interstellar medium. Depending on the density and temperature of the surrounding gas, we find that up to 50% of the feedback energy is spent ionizing the gas rather than heating it. Thus, it can be expected that properly taking into account ionization effects in galaxy evolution simulations will drastically reduce the effects of thermal feedback. To the best of our knowledge, this potential energy term is not used in current simulations of galaxy formation and evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Extragalactic interstellar extinction curves: Indicators of local physical conditions

Normalized interstellar extinction curves (ISECs) in the Milky Way and other galaxies show a variety of shapes. This variety is attributed to differences along different sight lines in the abundances of the several dust and gas components contributing to extinction. In this paper we propose that these abundance differences are not arbitrary but are a specific consequence of the physical conditions on those sight lines. If this proposal is correct, then it implies that ISECs contain information about physical conditions in the regions generating extinction. This may be particularly important for high redshift galaxies where information on the conditions may be difficult to obtain. We adopt a model of extinction carriers in which the solid and gaseous components are not immutable but respond time-dependently to the local physics. We validate this model by fitting extinction curves measured on sight lines in the Magellanic Clouds and obtained for the gamma-ray burst afterglow GRB 080605. We present results for this model as follows: (1) we show that computed ISECs are controlled by a small number of physical parameters, (2) we demonstrate the sensitivity of computed ISECs to these parameters, (3) we compute as examples ISECs for particular galaxy types, and (4) we note that different galaxy types have different shapes of ISEC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

The physical nature of the cosmic accretion of baryons and dark matter into halos and their galaxies

The cosmic accretion of both dark matter and baryons into halos is typically measured using some evolving virial relation, but recent work suggests that most halo growth at late cosmic time (z≲2) is not physical but is rather the by-product of an evolving virial radius (“pseudo-evolution”). Using Omega25, a suite of cosmological simulations that incorporate both dark matter and gas dynamics with differing treatments of gas cooling, star formation, and thermal feedback, we systematically explore the physics that governs cosmic accretion into halos and their galaxies. Physically meaningful cosmic accretion of both dark matter and baryons occurs at z≳1 across our halo mass range: M{sub 200m}=10{sup 11−14} M{sub ⊙}. However, dark matter, because it is dissipationless, is deposited (in a time-average sense) at ≳R{sub 200m}(z) in a shell-like manner, such that dark matter mass and density experience little-to-no physical growth at any radius within a halo at z<1. In contrast, gas, because it is able to cool radiatively, experiences significant accretion at all radii, at a rate that roughly tracks the accretion rate at R{sub 200m}, at all redshifts. Infalling gas starts to decouple from dark matter at ≈2 R{sub 200m} and continues to accrete to smaller radii until the onset of strong angular-momentum support at ≈0.1 R{sub 200m}. Thus, while the growth of dark matter is subject to pseudo-evolution, the growth of baryons is not. The fact that the accretion rate of gas on galactic scales tracks the accretion rate near R{sub 200m} provides insight into the tight relations between the masses/sizes of galaxies and those of their host halos across cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Physical interpretation of large Lorentz violation via Weyl semimetals

The physical intepretation of effective field theories of fundamental interactions incorporating large Lorentz violation is a long-standing challenge, known as the concordance problem. In condensed-matter physics, certain Weyl semimetals with emergent Lorentz invariance exhibit large Lorentz violation, thereby offering prospective laboratory analogues for exploration of this issue. We take advantage of the mathematical equivalence between the descriptions of large Lorentz violation in fundamental and condensed-matter physics to investigate the primary aspects of the concordance problem, which arise when the coefficients for Lorentz violation are large or the observer frame is highly boosted. Using thermodynamic arguments, we present a physical solution to the concordance problem and explore some implications.

Lorentz violation↗
DOE OSTI2021

Watch and learn—a generalized approach for transferrable learning in deep neural networks via physical principles

Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific breakthroughs in computer vision and natural language processing where the weights learned in state-of-the-art models can be used to initialize models for other tasks which dramatically improve their performance and save computational time. Here we demonstrate an unsupervised learning approach augmented with basic physical principles that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. Our approach, distribution-consistent learning, DCL, is a general strategy that works for a variety of canonical statistical mechanical models (Ising and Potts) as well as disordered interaction potentials. Using data collected from a single set of observation conditions, DCL successfully extrapolates across all temperatures, thermodynamic phases, and can be applied to different length-scales. This constitutes a fully transferrable physics-based learning in a generalizable approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Impact of new physics on the JUNO-long-baseline synergy in the neutrino mass ordering determination

The determination of the neutrino mass ordering is one of the flagship goals in particle physics. A well-known and powerful synergy emerges when combining high-precision measurements of the effective atmospheric mass-squared splitting from electron antineutrino disappearance in reactor experiments with that from muon (anti)neutrino disappearance in accelerator-based long-baseline experiments. To fully exploit this synergy, percent-level precision in the atmospheric mass splitting is required—a target that JUNO is expected to achieve within a few months of data taking. This motivated the formulation of a mass ordering sum rule for neutrino disappearance channels, which shows that by combining data from T2K and NOvA with JUNO after one year of operation, the neutrino mass ordering can be determined at the 3⁢𝜎 confidence level. Since JUNO has recently started taking data, it is timely to ask whether this sum rule remains robust in the presence of new physics. We identify the necessary conditions for new physics to affect the sum rule and demonstrate that, in some cases, such effects could lead to an incorrect inference of the mass ordering. As concrete examples, we consider scalar nonstandard interactions (SNSI) and neutrinos coupled to an ultralight scalar field. We find that, for SNSI, current constraints render any modification of the sum rule negligible, whereas in the latter case, the inference of the ordering requires caution. Nevertheless, these effects can be disentangled, illustrating how the sum rule can also be used to search for new physics.

Alves, Gustavo F. S. [Fermi National Accelerator L↗
DOE OSTI2025

Domain-specific text embedding model for accelerator physics

Accelerator physics presents unique challenges for natural language processing (NLP) due to its specialized terminology and complex concepts. A key component in overcoming these challenges is the development of robust text embedding models that transform textual data into dense vector representations, facilitating efficient information retrieval and semantic understanding. In this work, we introduce AccPhysBERT, a sentence embedding model fine-tuned specifically for accelerator physics. Our model demonstrates superior performance across a range of downstream NLP tasks, surpassing existing models in capturing the domain-specific nuances of the field. We further showcase its practical applications, including semantic paper-reviewer matching and integration into retrieval-augmented generation systems, highlighting its potential to enhance information retrieval and knowledge discovery in accelerator physics. Published by the American Physical Society 2025

Hellert, Thorsten (ORCID:0000000227970926)↗
DOE OSTI2021

Quantum physical reality of polar-nonpolar oxide heterostructures

Conducting interfaces between polar and nonpolar insulating oxides, e.g., LaAlO 3 /SrTiO 3 , have generated interest for both fundamental physics and oxide-electronics applications. Current understanding is based on an amalgamation of a classical electrostatic model (polar catastrophe model) that was originally derived for semi-infinite solids and quantum density-functional-theory (DFT) results on ultrathin films. Here we report comprehensive DFT calculations that unveil a very different purely quantum physical reality. We show that, for ultrathin polar films, the interfacial dipole does not control the electrostatic potential in the polar film—the surface and interface play equal roles, and the absence or presence of centrosymmetry in the physical LAO film results in different, purely quantum mechanisms for the generation of a conducting interface, neither involving physical-charged transfer. Predictions are made that can be tested and can guide technology development.

36 MATERIALS SCIENCE↗
DOE OSTI2025

New strategies for new physics search with Λ b → Λ ν ν ¯ decays

We examine the new physics sensitivity of the rare decay Λ b → Λ ν ν ¯ , which can be accessible at future Z -pole machines like Future Circular Collider and Circular Electron Positron Collider. We find that the longitudinal polarization of Λ b baryons produced in Z decays introduces a novel observable, the forward-backward asymmetry A FB ↑ in the angle between the outgoing Λ momentum and the Λ b spin. We provide Standard Model predictions for the Λ b → Λ ν ν ¯ branching ratio and A FB ↑ , and show that future precision measurements of these observables are complementary and probe new physics scales comparable to other b → s ν ν ¯ and b → s ℓ + ℓ − processes. We also show that the zero crossing of the forward-backward asymmetry offers a robust test of form factor calculations independent of new physics. Published by the American Physical Society 2025

Altmannshofer, Wolfgang (ORCID:0000000316212561)↗
DOE OSTI2023

Embedding hard physical constraints in neural network coarse-graining of three-dimensional turbulence

In recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of partial differential equations is enforcing physical constraints and boundary conditions. In this work, we propose a general framework to directly embed the notion of an incompressible fluid into convolutional neural networks, and apply this to coarse-graining of turbulent flow. These physics-embedded neural networks leverage interpretable strategies from numerical methods and computational fluid dynamics to enforce physical laws and boundary conditions by taking advantage the mathematical properties of the underlying equations. Here, we demonstrate results on three-dimensional fully developed turbulence, showing that this technique drastically improves local conservation of mass, without sacrificing performance according to several other metrics characterizing the fluid flow.

97 MATHEMATICS AND COMPUTING↗