Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “PHYSICS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2025

Physics-constrained superresolution diffusion for six-dimensional phase space diagnostics

Adaptive physics-constrained superresolution diffusion is developed for noninvasive virtual diagnostics of the six-dimensional (6D) phase space density of charged particle beams. An adaptive variational autoencoder embeds initial beam condition images and scalar measurements to a low-dimensional latent space from which a 32 6 pixel 6D tensor representation of the beam's 6D phase space density is generated. Projecting from a 6D tensor generates physically consistent two-dimensional projections. Physics-guided superresolution diffusion transforms low-resolution images of the 6D density to high resolution 256 × 256 pixel images. Unsupervised adaptive latent space tuning enables tracking of time-varying beams without knowledge of time-varying initial conditions. The method is demonstrated with experimental data and multiparticle simulations at the HiRES UED. The general approach is applicable to a wide range of complex dynamic systems evolving in high-dimensional phase space. The method is shown to be robust to distribution shift without retraining. Published by the American Physical Society 2025

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Cyber-Physical Power Systems Protection: The Byzantine Cybersecurity Framework

Cybersecurity of smart grids have been topic of much interest in recent years. As this critical infrastructure operation increases dependency on automated processes and controls, exposure to cyber-physical threats become inevitable. Considering cyber-physical security of the grid, much focus of attention has been made towards smart grids real-time monitoring solutions, including the state estimation process. Analyzing the relevant literature, one can note though that seldom research has been done on cyber-physical security of smart grids protection systems. Protection systems have intangible value towards grid reliability. This paper presents a cybersecurity framework for smart grids protection systems. A physics-based inspired machine learning solution is at the core process of the framework. Processed relay inputs and outputs are used by a deep predictive coding network. Formal models, a quasi-static state estimator, provides an oracle when low confidence decision is reached. Evolving knowledge is derived through reinforcement learning. Implementation aspects considering the Pacific Northwest National Laboratory Electricity Infrastructure Operations Center are presented. Built as an extra control layer to protection systems, without hard-to-derive parameters, highlights potential aspects towards real-life applications.

Bretas, Arturo Suman↗
DOE OSTI2025

Meta-Learning Enhanced Physics-Informed Graph Attention Convolutional Network for Distribution Power System State Estimation

Promptly perceiving distribution system states is challenged by frequent topology changes and uncertain power injections. To address these issues, a Meta-learning enhanced physics-informed graph attention convolutional network (Meta-PIGACN) model is proposed to handle topological variability in distribution system state estimation (DSSE). Specifically, physics information is integrated into the graph convolutional network, enabling a physics-informed edge-weighting process that incorporates physical information to control the aggregation of neighboring nodes. Besides, the graph attention mechanism automatically adjusts the importance of different neighboring nodes, allowing the capture and preservation of inherent system features across varying topologies, thereby improving state estimation accuracy. Furthermore, meta-learning is proposed to acquire empirical knowledge across multiple topologies so that the model can rapidly adapt to new configurations through iterative gradient descent updates even in large-scale systems. In conclusion, the simulation results based on the 33/118/1746-node distribution systems show the high accuracy and efficiency of the proposed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Cy-Phy ADS: Cyber Physical Anomaly Detection Framework for EV Charging Systems

Today’s large-scale Electric Vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and physical threats. Timely identification and mitigation of these threats are critical for improving human safety, avoiding economic losses, and preventing catastrophic system failures. By addressing this, our work presents a ResNet Autoencoder (AE) based Cyber-Physical Anomaly Detection System (Cy-Phy ADS) for detecting anomalies in EV Controller Area Network (CAN) protocol communication. It consists of four main components: Cyber-Physical Feature Extractor, ResNet AE-based Anomaly Detection Framework, Cyber-Physical Health Metric (CPHM), and Visualization Dashboard. The presented framework was trained and tested using CAN data collected from the EV charging system testbed at the Idaho National Laboratory. The presented Cy-Phy ADS compared against six widely used unsupervised anomaly detection algorithms: One Class Support Vector Machine (OCSVM), Variational Autoencoder (VAE), LSTM Autoencoder (LSTM AE), Isolation Forest (IForest), Principle Component Analysis (PCA) and Local Outlier Factor (LOF). Here the presented approach showed the highest accuracy among the compared methods. Further, the proposed approach showed comparable performance in terms of precision, F1, and False positive rate. It also showed the lowest training and inference time compared to the neural network-based baseline algorithms compared against with. Additionally, the Cy-Phy ADS has advantages such as unsupervised training, the ability to provide a holistic metric for system health characterization, and non-linear feature extraction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones

Tropical cyclones (TCs) are among the most destructive weather systems. Realistically and efficiently detecting and tracking TCs are critical for assessing their impacts and risks. In particular, the eye is a signature feature of a mature TC. Therefore, knowing the eyes’ locations and movements is crucial for both operational weather forecasts and climate risk assessments. Recently, a multilevel robustness framework has been introduced to study the critical points of time-varying vector fields. The framework quantifies the robustness (i.e., structural stability) of critical points across varying neighborhoods. By relating the multilevel robustness with critical point tracking, the framework has demonstrated its potential in cyclone tracking. An advantage is that it identifies cyclonic features using only 2D wind vector fields, which is encouraging as most tracking algorithms require multiple dynamic and thermodynamic variables at different altitudes. A disadvantage is that the framework does not scale well computationally for datasets containing a large number of cyclones. Herein this paper introduces a topologically robust physics-informed tracking framework (TROPHY) for TC tracking. The main idea is to integrate physical knowledge of TC to drastically improve the computational efficiency of multilevel robustness framework for large-scale climate datasets. First, during preprocessing, we propose a physics-informed feature selection strategy to filter 90% of critical points that are short-lived and have low stability, thus preserving good candidates for TC tracking. Second, during in-processing, we impose constraints during the multilevel robustness computation to focus only on physics-informed neighborhoods of TCs. We apply TROPHY to 30 years of 2D wind fields from reanalysis data in ERA5 and generate a number of TC tracks. In comparison with the observed tracks, we demonstrate that TROPHY can capture TC characteristics (e.g., frequency, intensity, duration, latitudes with maximum intensity, and genesis) that are comparable to and sometimes even better than a well-validated TC tracking algorithm that requires multiple dynamic and thermodynamic scalar fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Understanding relative metal prices and availability: Combining physical and economic perspectives

Abstract Scientists typically use physical indicators, such as average crustal abundance, and energy requirements in production, as measures of the availability of mineral resources. Economists, on the other hand, typically rely on measures such as prices, and extraction costs. This study investigates the role of crustal abundance, energy requirements, and other physical indicators in determining relative metal prices. The relationship between prices, and physical indicators is estimated statistically for a sample of 22 chemical elements over the period 1970–2013 using an economic market model. The results suggest that energy requirements in production explain 43% of observed variation in metal prices, crustal abundance 21%, and other physical indicators (toxicity, native metal status, and melting point) combine to explain 12% of the observed differences in metal prices.

Watson, Brett J.↗
DOE OSTI2014

Free software for performing physical analysis of systems for digital radiography and mammography

Purpose: In this paper, the authors present a free software for assisting users in achieving the physical characterization of x-ray digital systems and image quality checks. Methods: The program was developed as a plugin of a well-known public-domain suite ImageJ. The software can assist users in calculating various physical parameters such as the response curve (also termed signal transfer property), modulation transfer function (MTF), noise power spectra (NPS), and detective quantum efficiency (DQE). It also includes the computation of some image quality checks: defective pixel analysis, uniformity, dark analysis, and lag. Results: The software was made available in 2009 and has been used during the last couple of years by many users who gave us valuable feedback for improving its usability. It was tested for achieving the physical characterization of several clinical systems for digital radiography and mammography. Various published papers made use of the outcomes of the plugin. Conclusions: This software is potentially beneficial to a variety of users: physicists working in hospitals, staff working in radiological departments, such as medical physicists, physicians, engineers. The plugin, together with a brief user manual, are freely available and can be found online ( http://www.medphys.it/downloads.htm ). With our plugin users can estimate all three most important parameters used for physical characterization (MTF, NPS, and also DQE). The plugin can run on any operating system equipped with ImageJ suite. The authors validated the software by comparing MTF and NPS curves on a common set of images with those obtained with other dedicated programs, achieving a very good agreement.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

SU-E-I-65: The Joint Commission's Requirements for Annual Diagnostic Physics Testing of Nuclear Medicine Equipment, and a Clinically Relevant Methodology for Testing Low-Contrast Resolution

Purpose: To introduce the Joint Commission's requirements for annual diagnostic physics testing of all nuclear medicine equipment, effective 7/1/2014, and to highlight an acceptable methodology for testing lowcontrast resolution of the nuclear medicine imaging system. Methods: The Joint Commission's required diagnostic physics evaluations are to be conducted for all of the image types produced clinically by each scanner. Other accrediting bodies, such as the ACR and the IAC, have similar imaging metrics, but do not emphasize testing low-contrast resolution as it relates clinically. The proposed method for testing low contrast resolution introduces quantitative metrics that are clinically relevant. The acquisition protocol and calculation of contrast levels will utilize a modified version of the protocol defined in AAPM Report #52. Results: Using the Rose criterion for lesion detection with a SNRpixel = 4.335 and a CNRlesion = 4, the minimum contrast levels for 25.4 mm and 31.8 mm cold spheres were calculated to be 0.317 and 0.283, respectively. These contrast levels are the minimum threshold that must be attained to guard against false positive lesion detection. Conclusion: Low contrast resolution, or detectability, can be properly tested in a manner that is clinically relevant by measuring the contrast level of cold spheres within a Jaszczak phantom using pixel values within ROI's placed in the background and cold sphere regions. The measured contrast levels are then compared to a minimum threshold calculated using the Rose criterion and a CNRlesion = 4. The measured contrast levels must either meet or exceed this minimum threshold to prove acceptable lesion detectability. This research and development activity was performed by the authors while employed at West Physics Consulting, LLC. It is presented with the consent of West Physics, which has authorized the dissemination of the information and/or techniques described in the work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

TU-E-BRD-01: President’s Symposium: The Necessity of Innovation in Medical Physics

This abstract will not blow you away, but speed-painting presenter Erik Wahl will certainly make a truly unique AAPM symposium that you will not want to miss. Along with clinical director John Bayouth and scientific leader Jeff Siewerdsen, this session will highlight innovation. To avoid being button pushers and irrelevant investigators of yesterday’s science, we must innovate. This is particularly challenging in the changing landscape of declining research funding and healthcare reimbursement. But all hope is not lost, Medical Physics is a field born out of innovation. As scientists we quickly translated the man-made and natural phenomena of radiation into a tool that could diagnose broken bones, locate foreign objects imbedded within the body, and treat a spectrum of diseases. As hyperbolae surrounding the curative powers of radiation overcame society, physicists continued their systematic pursuit of a fundamental understanding of radiation and applied their knowledge to enable the diagnostic and therapeutic power of this new tool. Health economics and the decline in research funding have put the Medical Physicist in a precarious position: how do we optimally participate in medical research and advanced patient care in the face of many competing needs? Today's diagnostic imaging and therapeutic approaches are tremendously sophisticated. Researchers and commercial vendors are producing technologies at a remarkable rate; to enable their safe and effective implementation Medical Physicists must work from a fundamental understanding of these technologies. This requires all of us, clinically practicing Medical Physicists, Researchers and Educators alike, to combine our training in scientific methods with innovation. Innovation is the key to our past, a necessity for our contemporary challenges, and critical for the future of Medical Physics. The keynote speakers for the 2014 AAPM Presidential Symposium session will address the way we approach these vitally important technologies for diagnosis and therapy into opportunities to innovate. The speed-painting artist and lecturer Erik Wahl will finish the symposium with a fast-paced and entertaining presentation on embracing the future by creating disruptive innovation strategies. Learning Objectives: Identify connection between Medical Physics and Innovation. Understand how Innovation enables Clinical Medical Physicists to implement novel technologies. Learn how innovative Medical Physics solutions can address significant and relevant challenges in science. Become inspired to pursue a new scientific understanding, positive change in clinical practice, and benefit to patients.

60 APPLIED LIFE SCIENCES↗