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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Preface to the Special Collection: Recollections in Space Physics

Space Physics is a comparatively young scientific discipline, tightly linked to the era of satellite based investigations and the discoveries that came with it. As such, we as a community are fortunate to have met, been taught and mentored by, and even become friends with, many of those who were around to witness the birth of, and in many cases establish, our field. Each of these "Pioneers" have remarkable stories to tell. These accounts from the dawn of the space age provide a glimpse into that era of scientific discovery and are an important part of our collective history that deserve to be shared and commemorated. These are stories of perseverance, ingenuity, luck, and sometimes failure. We are fortunate to live in an era in which these distinguished scientists are still with us and able to share their experiences.To commemorate the 100th Anniversary of the American Geophysical Union (AGU), we have solicited a special collection of recollection papers to memorialize these experiences. This is not the first such effort to capture these stories. In 1994 (Vol. 99, No. A10, pp. 19,099-19,212) and again in 1996 (Vol. 101, No. A5, pp. 10,477-10,585) JGRSpace Physics published special sections entitled, "Pioneers of Space Physics," in celebration of AGU's 75th anniversary. Later, in 1997, Gilmore and Spreiter (1997) added a set of recollection papers on the discovery of the magnetosphere. Together, these papers containing personal accounts from the pioneers of the space age covered the period of roughly 1958-1967.It is our great honor to present retrospective papers from several of our distinguished colleagues who contributed significantly to the explosion in understanding of space physics and aeronomy that occurred from roughly 1967-1980. We chose to start at the end of the previous set of recollections and cover the decade of the 1970s, during which our field greatly expanded. It is also a time in which the second generation of space scientists entered the field and made foundational discoveries that continue to define our discipline.As with the previous papers, we asked the authors to "recount some of the events leading to the emergence of space physics and to put the events into a professional as well as personal perspective." (Gombosi et al.,1994). The authors of this special collection of recollections were selected following the same guidelines as the previous efforts. We started from a long list of senior colleagues and narrowed the list to roughly two dozen distinguished scientists based on discipline and geographic balance. Some of our colleagues, when asked, felt they would be unable to devote the time or energy to such a recollection and declined. Sadly,in the intervening 25 years since the original effort, some of our colleagues who were most active during the early years of the space age have passed. Therefore, this new special collection, 25 years after the first one, is timely with the AGU centennial celebrations, but late in fully capturing the stories of the pioneers of space physics. This is unfortunate and we encourage future editors of this journal to commission specialsections of legacy perspectives with a faster cadence than a quarter of a century.

Kepko, Emil L.↗
NASA NTRS2021

An Information Theory Approach to Physical Domain Discovery

The project of physics discovery is often equivalent to finding the most concise description of a physical system. The description with optimum predictive capability for a dataset generated by a physical system is one that minimizes both predictive error on the dataset and the complexity of the description. The discovery of the governing physics of a system can therefore be viewed as a mathematical optimization problem. We outline here a method to optimize the description of arbitrarily complex physical systems by minimizing the entropy of the description of the system. The Recursive Domain Partitioning (RDP) procedure finds the optimum partitioning of each physical domain into subdomains, and the optimum predictive function within each subdomain. Penalty functions are introduced to limit the complexity of the predictive function within each domain. Examples are shown in 1D and 2D. In 1D, the technique effectively discovers the elastic and plastic regions within a stress-strain curve generated by simulations of amorphous carbon material, while in 2D the technique discovers the free-flow region and the inertially-obstructed flow region in the simulation of fluid flow across a plate.

Daniel Shea↗
NASA NTRSDate not supplied

Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE) – Physical Workload Approximation

The Human Physiology, Performance, Protection & Operations Laboratory (H-3PO) at NASA Johnson Space Center (JSC) is developing a hybrid reality exploration surface analog, “Assessments of Physiology and Cognition in Hybrid-reality Environments” (APACHE). The goal of APACHE is to create a planetary extravehicular activity (EVA) simulation environment that provides a representative physical and cognitive workload approximation using a combination of virtual reality (VR), physical reality, and hybrid reality (HR) techniques. To develop and characterize the physical workload approximation within the APACHE environment, a two-part approach was implemented. In part 1, baseline physical work load during ambulation within APACHE was evaluated and compared with that in other planetary EVA analog environments and with existing data sets from Apollo planetary EVAs and reduced gravity testing of prototype planetary spacesuits. For this evaluation, 10 subjects were asked to ambulate in three surface analog environments: a passive treadmill in APACHE, natural terrain in an outdoor field environment, and a standard motorized treadmill. Subjects’ heart rate and metabolic rate (VO2/VCO2) were measured and compared among the different test conditions and existing data sets. Gait parameters were also collected to compare with suited mechanics and to understand the role of gait kinematics in physical workload. In part 2, the aim is to evaluate the addition of a custom weighted body suit to the aforementioned surface analog environments and the ability to titrate the suit configuration to provide the best possible physical workload approximation for simulation of lunar and Martian EVAs.

Alex Baughman↗
NASA NTRSDate not supplied

Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE) – Physical Workload Approximation

The Human Physiology, Performance, Protection & Operations Laboratory (H-3PO) at NASA Johnson Space Center (JSC) is developing a hybrid reality exploration surface analog, “Assessments of Physiology And Cognition in Hybrid-reality Environments” (APACHE). The goal of APACHE is to create a planetary extravehicular activity (EVA) simulation environment that provides a representative physical and cognitive workload approximation using a combination of virtual reality (VR), physical reality, and hybrid reality (HR) techniques. To develop and characterize the physical workload approximation within the APACHE environment, a two-part approach was implemented. In part 1, baseline physical workload during ambulation within APACHE was evaluated and compared with that in other planetary EVA analog environments and with existing data sets from Apollo planetary EVAs and reduced gravity testing of prototype planetary spacesuits. For this evaluation, 10 subjects were asked to ambulate in three surface analog environments: a passive treadmill in APACHE, natural terrain in an outdoor field environment, and a standard motorized treadmill. Subjects’ heart rate and metabolic rate (VO2/VCO2)were measured and compared among the different test conditions and existing data sets. Gait parameters were also collected to compare with suited mechanics and to understand the role of gait kinematics in physical workload. In part 2, the aim is to evaluate the addition of a custom weighted body suit to the aforementioned surface analog environments and the ability to titrate the suit configuration to provide the best possible physical workload approximation for simulation of lunar and Martian EVAs.

Alexander J Baughman↗
NASA NTRSDate not supplied

NASA Physics of Failure (PoF) for Reliability

An item’s reliability or longevity is dependent not only on its design but also on how it is used, manufactured, tested, and the stresses it has or will experience. Stresses include operational and environmental exposures to thermal, voltage, current, age/exposure, mechanical, and radiation mechanisms. Therefore, in reliability analysis, it is important to consider the contributions of all these factors when predicting the failure rates of components. Historically, there has been a reliance on handbook data (e.g., MIL-HDBK-217), but experience has shown that these values and distributions are not representative of actual performance. Therefore, to make more credible reliability and risk assessments for its missions, NASA must transition to estimating likelihoods of failure based on an item’s reliability or longevity factors (or the physical susceptibilities and strengths impacting the design’s performance) has or will experience, whenever possible. To facilitate this transition, a Handbook on Methodology for Physics of Failure Based Reliability Assessments has been developed by NASA to assist in applying physics experiences or experimental physics for empirical analysis and conceptualized physics exposures or theoretical physics for deterministic analysis, to develop and aggregate realistic likelihoods of failure leading to more credible forecasts of item performance and longevity. In addition, since it is NASA’s intention that this document continues to evolve based on community lessons learned and the introduction of new assessment methodologies, NASA is encouraging and appreciates the contributions of current and future authors to maintain and enhance this handbook and its supporting case studies.

PoF↗
DOE OSTI2015

Validation of the Physics Analysis used to Characterize the AGR-1 TRISO Fuel Irradiation Test

The results of a detailed physics depletion calculation used to characterize the AGR-1 TRISO-coated particle fuel test irradiated in the Advanced Test Reactor (ATR) at the Idaho National Laboratory are compared to measured data for the purpose of validation. The particle fuel was irradiated for 13 ATR power cycles over three calendar years. The physics analysis predicts compact burnups ranging from 11.30-19.56% FIMA and cumulative neutron fast fluence from 2.21?4.39E+25 n/m 2 under simulated high-temperature gas-cooled reactor conditions in the ATR. The physics depletion calculation can provide a full characterization of all 72 irradiated TRISO-coated particle compacts during and post-irradiation, so validation of this physics calculation was a top priority. The validation of the physics analysis was done through comparisons with available measured experimental data which included: 1) high-resolution gamma scans for compact activity and burnup, 2) mass spectrometry for compact burnup, 3) flux wires for cumulative fast fluence, and 4) mass spectrometry for individual actinide and fission product concentrations. The measured data are generally in very good agreement with the calculated results, and therefore provide an adequate validation of the physics analysis and the results used to characterize the irradiated AGR-1 TRISO fuel.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Unsupervised Deep Learning Can Identify Physical Parameters

Parametrizing a problem is one of the biggest obstacles toward the application of physical methods to novel systems. In this research, we show that deep learning methods can be used to accelerate this parametrization process. We use an unsupervised learning algorithm, a variational autoencoder, to extract meaningful physical parameters from unlabeled time-series generated from classical physical systems. For example, given time series data of projectile motion and the instruction to represent the data in a small number of unspecified parameters, the model can discover, without further direction, the concepts of velocity and gravity. Using this method, one can uncover information about the physics of a system, even when the relevant physical parameters are unknown to the experimenter. This has obvious applications to novel systems in which parameters are not known a priori. Moreover, errors in the reconstruction of the parameters can be used to reveal meaningful info rmation about state transitions, which can be conceptualized as a generalization of similar unsupervised learning methods used to detect phase transitions in the thermodynamics of particle systems to systems that evolve in the time domain. While our proof-of-concept applications are modest, this method shows potential for making progress in a wide array of problems both in physics and outside of the field.

Veillette, John↗
DOE OSTI2021

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution

Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system's physical characteristics, determining the optimal structure and optimization strategy for these models can be difficult. In this work, we explore methods for discovering neural state space dynamics models for system identification. Starting with a design space of block-oriented state space models and structured linear maps with strong physical priors, we encode these components into a model genome alongside network structure, penalty constraints, and optimization hyperparameters. Demonstrating the overall utility of the design space, we employ an asynchronous genetic search algorithm that alternates between model selection and optimization and obtains accurate physically consistent models of three physical systems: an aerodynamics body, a continuous stirred tank reactor, and a two tank interacting system.

genetic algorithms, neural architecture search, ne↗
DOE OSTI2023

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

artificial intelligence, machine learning↗
DOE OSTI2024

Multi-physics Modeling of Radiative Heat Transfer and Fluid Flow for the Reactor Cavity Cooling System

High-temperature gas-cooled reactors (HTGRs) are notable for their high thermal efficiency and potential for combined heat and power applications. These reactors are particularly appealing due to their advanced passive safety features. HTGRs utilize passive safety systems that function without requiring active components like pumps or compressors during emergencies. These reactor designs depend on a Reactor Cavity Cooling System (RCCS) to manage decay heat removal from the reactor pressure vessel (RPV) during accident conditions. The RCCS consists of vertical rectangular channels known as "risers" or riser ducts positioned around the RPV. These risers receive heat from the RPV through both convective and radiative heat transfer mechanisms. Understanding the interplay of multiple physical phenomena, such as fluid dynamics, heat transfer, and neutron interactions, is essential for the effective design and operation of nuclear reactors, particularly for systems like the RCCS. Multi-physics simulations provide a comprehensive approach to studying these interactions, offering detailed insights and enhancing accuracy. They are especially important in RCCS designs, where the interaction between radiative and convective heat transfer can significantly impact system performance. By leveraging multi-physics simulations, complex reactor behaviors can be modeled without compromising the fidelity of the underlying physical processes. This work aims to establish a robust methodology for coupling multiple physical processes in an air-cooled RCCS. By focusing on validating this multi-physics approach, the study involves designing test cases that simulate various conditions to verify the numerical models employed. The outcomes of this research will provide critical insights for accurately modeling and optimizing complex nuclear systems like the RCCS.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

New physics in the visible final states of B → D(*) τν

We derive compact expressions for the helicity amplitudes of the many-body B → D (*) (→ DY)τ(→ Xν)ν decays, specifically for X = ℓν or π and Y = π or γ. We include contributions from all ten possible new physics four-Fermi operators with arbitrary couplings. Our results capture interference effects in the full phase space of the visible τ and D * decay products which are missed in analyses that treat the τ or D * or both as stable. The τ interference effects are sizable, formally of order m τ /m B for the standard model, and may be of order unity in the presence of new physics. Treating interference correctly is essential when considering kinematic distributions of the τ or D * decay products, and when including experimentally unavoidable phase space cuts. Our amplitude-level results also allow for efficient exploration of new physics effects in the fully differential phase space, by enabling experiments to perform such studies on fully simulated Monte Carlo datasets via efficient event reweighing. As an example, we explore a class of new physics interactions that can fit the observed R(D (*) ) ratios, and show that analyses including more differential kinematic information can provide greater discriminating power for new physics, than single kinematic variables alone.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Efficient use of quantum computers for collider physics

Most observables at particle colliders involve physics at a wide variety of distance scales. Due to asymptotic freedom of the strong interaction, the physics at short distances can be calculated reliably using perturbative techniques, while long distance physics is non-perturbative in nature. Factorization theorems separate the contributions from different scales, allowing to identify the pieces that can be determined perturbatively from those that require non-perturbative information, and if the non-perturbative pieces can be reliably determined, one can use experimental measurements to extract the short distance effects, sensitive to possible new physics. Without the ability to compute the non-perturbative ingredients from first principles one typically identifies observables for which the non-perturbative information is universal in the sense that it can be extracted from some experimental observables and then used to predict other observables. In this paper we argue that the future ability to use quantum computers to calculate non-perturbative matrix elements from first principles will allow to make predictions for observables with non-universal non-perturbative long-distance physics.

Algorithms and Theoretical Developments↗
DOE OSTI2024

Cultural Shifts in High Energy Physics Collaboration from the Cold War to the Present: A Historical and Philosophical Perspective

Here, this article employs empirical history and the philosophy of science to study cultural convergences and divergences in international collaborations in high energy physics. We examine two cases: (1) E-36, an experiment on small angle proton-proton scattering conducted during the Cold War at the National Accelerator Laboratory (NAL) in the USA by Soviet and US scientists and (2) an ongoing collaborative experiment, NICA, at the Joint Institute for Nuclear Research (JINR, Dubna), which is a project devoted to heavy-ion physics. The JINR, particularly its Laboratory of High Energy Physics (formerly the “Laboratory of High Energies”) is the main mediating actor between these two cases (i.e., E-36 and NICA), as the majority of Soviet participants in E-36 were representatives of the Institute. Using empirical data collected through archival searches, field observations conducted at JINR in 2018–2019, and in-depth interviews, we tell a story of cultural differences in high energy physics by applying the concepts of ‘trading zones’ (P. Galison) and the translation of interests in actor-networks (B. Latour, M. Callon and others). We analyze three types of cultural diversity (specialization, nationality, and generational) in light of the implications of temporal context and the dichotomy between East and West, showing the roles cultural diversity plays in scientific collaboration (which is an integral part of as well as obstacle to scientific research that can nevertheless provide learning opportunities). Our study aims to demonstrate how disunity and diversity may function in scientific research and how high energy physics collaborations can remain productive despite sometimes deep divergences, including those between East and West.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Learning functional priors and posteriors from data and physics

In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Saddle point approaches in strong field physics and generation of attosecond pulses

Attoscience is the emerging field that accesses the fastest electronic processes occurring at the atomic and molecular length scales with attosecond (1 as = 10 –18 s) time resolution having wide ranging physical, chemical, material science and biological applications. The quintessential and one of the most fundamental processes in this domain is the generation of phase locked XUV attosecond pulses. The theoretical approach to understand the process incorporates a fully quantum or semi classical or relativistic description of coherent charge dynamics in intense ultrashort electromagnetic fields driving a quantum system (an atom, a molecule, solid band gap materials or surface plasmas). Modelling of such physical and dynamical systems in science and also in many other branches often leads to equations represented in terms of complex multi-dimensional integrals. These integrals can often be solved using the stationary phase approximation, which leads to a series of equations identifying the points in the multi-dimensional space, having most significant contributions in their evaluation. These points are usually indicated as saddle points. The description of the dynamics of quantum mechanical or relativistic systems that results from such an approach enables near to classical physics intuitive perceptions of the processes under investigation. Thus, the saddle point methods are very powerful and valuable general theoretical tools to obtain asymptotic expressions of such solutions and help also to gain physical insights on the underlying phenomena. Such techniques developed in the past have been adapted to study the emission of as pulses by different physical systems and have been widely employed in calculating and estimating the response of matter to intense electromagnetic pulses on ultrafast time scales. Here we provide an extensive disposition of the saddle point approaches unifying their ubiquitous applications within the domain of attoscience valid for simple atomic to more complex condensed matter systems undergoing ultrafast dynamics and present current trends and advancements in the field. In this review we would delineate the methodology, present a synthesis of seminal works and describe the state of the art applications. Finally we also address ultrashort time dynamics of novel materials that have gained much attention recently, namely lower dimensional material systems and micro-plasma systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Lagrangian large eddy simulations via physics-informed machine learning

High-Reynolds number homogeneous isotropic turbulence (HIT) is fully described within the Navier–Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as large eddy simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about effects of subgrid scales on the resolved scales. Here, we take an alternative approach and design LES heuristics stated in terms of Lagrangian particles moving with the flow. Our Lagrangian LES, thus L-LES, is described by equations generalizing the weakly compressible smoothed particle hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from direct numerical simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The subgrid-scale contributions are modeled separately with physical constraints to account for the effects from unresolved scales. We build the resulting model under the differentiable programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our L-LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Physics beyond colliders at CERN: beyond the Standard Model working group report

The Physics Beyond Colliders initiative is an exploratory study aimed at exploiting the full scientific potential of the CERN’s accelerator complex and scientific infrastructures through projects complementary to the LHC and other possible future colliders. These projects will target fundamental physics questions in modern particle physics. This document presents the status of the proposals presented in the framework of the Beyond the Standard Model physics working group, and explore their physics reach and the impact that CERN could have in the next 10-20 years on the international landscape.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Applications of Nuclear and Particle Physics Technology: Particles & Detection — A Brief Overview

Here, a brief overview of the technology applications with significant societal benefit that have their origins in nuclear and particle physics research is presented. It is shown through representative examples that applications of nuclear physics can be classified into two basic areas: 1) applying the results of experimental nuclear physics and 2) applying the tools of experimental nuclear physics. Examples of the application of the tools of experimental nuclear and particle physics research are provided in the fields of accelerator and detector based technologies namely synchrotron light sources, nuclear medicine, ion implantation and radiation therapy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗