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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

ZFSP Proposal: Understanding the Physics and Chemistry of Iron Alloys relevant to the Conditions of Planetary Cores

Recent research results by the deep-Earth geoscience and astronomy communities have revealed a wealth of interesting and outstanding fundamental science questions about the interiors of terrestrial planets and exoplanets including the origin of the magnetic field in the iron core and its impact on the evolution of life, the alloying of light elements on physics and chemistry of the planet’s dynamic processes, and the discovery of super-rotation and heterogeneous behavior of the inner core. Resolving these questions requires a first-hand experimental database at relevant pressure-temperature (P-T) conditions, but is technically challenging using static diamond anvil cell (DAC) and dynamic shock wave efforts alone, due to their limited P-T conditions and analytical capabilities. However, the recent advent of new capabilities of Sandia’s Z-Machine, coupled with our recent developments in in-situ ellipsometry at the UT Austin and Sandia National Laboratory and first-principles calculations and geodynamic modelling capabilities at UC Berkeley, motivates the joint collaborative effort presented here. In this collaborative research, we will use the capabilities of Sandia’s Z-Machine to investigate the elastic, thermal transport and melting properties of material relevant to both Earth and exoplanets at their relative P-T conditions. Our long-term research goal is to better understand the physics and dynamics of planetary interiors through interdisciplinary collaborations between physicists, mineral physicists, theorists, geodynamicists, and astrophysicists. The conditions in the Earth and super-Earth can range up to ~300-500 GPa and ~5000-8000 K. We will couple these experiments with a unique set of diagnostics, to include a new ellipsometry capability, in order to sensitively measure conductivity of these materials, and phase changes, including melting. Specifically, the newly demonstrated capability of Z to shock materials to a given P-T along the Hugoniot, and then to apply a pressure ramp taking the material along an isentrope, provides a unique opportunity to obtain P-T conditions not accessible by traditional DAC technology, or through shocks simply along the Hugoniot. Our physics team will perform experiments and analyze results to determine the physics underlying iron alloys as high-energy planetary impactors, classic transition metal alloys, and iron-bearing periclase relevant to the interiors of rocky planets, including exoplanets. The experimental results so analyzed will provide data that our collaborative team of mineral physicists, geodynamics, seismologists, and astrophysicists will apply toward obtaining a fundamental understanding of the physical and chemical states of the terrestrial planets and exoplanets, including their chemical compositions, thermal transport behavior, origin of magnetic fields, and dynamic evolution of the planet.

58 GEOSCIENCES↗
DOE OSTI2015

Multi-Physics Demonstration Problem with the SHARP Reactor Simulation Toolkit

This report describes to employ SHARP to perform a first-of-a-kind analysis of the core radial expansion phenomenon in an SFR. This effort required significant advances in the framework Multi-Physics Demonstration Problem with the SHARP Reactor Simulation Toolkit used to drive the coupled simulations, manipulate the mesh in response to the deformation of the geometry, and generate the necessary modified mesh files. Furthermore, the model geometry is fairly complex, and consistent mesh generation for the three physics modules required significant effort. Fully-integrated simulations of a 7-assembly mini-core test problem have been performed, and the results are presented here. Physics models of a full-core model of the Advanced Burner Test Reactor have also been developed for each of the three physics modules. Standalone results of each of the three physics modules for the ABTR are presented here, which provides a demonstration of the feasibility of the fully-integrated simulation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Verification of Data-Driven Models of Physical Phenomena using Interpretable Approximation

Machine-learned models, specifically neural networks, are increasingly used as “closures” or “constitutive models” in engineering simulators to represent fine-scale physical phenomena that are too computationally expensive to resolve explicitly. However, these neural net models of unresolved physical phenomena tend to fail unpredictably and are therefore not used in mission-critical simulations. In this report, we describe new methods to authenticate them, i.e., to determine the (physical) information content of their training datasets, qualify the scenarios where they may be used and to verify that the neural net, as trained, adhere to physics theory. We demonstrate these methods with neural net closure of turbulent phenomena used in Reynolds Averaged Navier-Stokes equations. We show the types of turbulent physics extant in our training datasets, and, using a test flow of an impinging jet, identify the exact locations where the neural network would be extrapolating i.e., where it would be used outside the feature-space where it was trained. Using Generalized Linear Mixed Models, we also generate explanations of the neural net (à la Local Interpretable Model agnostic Explanations) at prototypes placed in the training data and compare them with approximate analytical models from turbulence theory. Finally, we verify our findings by reproducing them using two different methods.

42 ENGINEERING↗
DOE OSTI2022

GIF Supercritical Water Cooled Reactor: Proliferation Resistance and Physical Protection White Paper

This report is part of a series of six white papers, prepared jointly by the Proliferation Resistance and Physical Protection Working Group (PRPPWG) and the six System Steering Committees (SSCs) and provisional System Steering Committees (pSSCs). This publication is an update to a similar series published in 2011 presenting the status of Proliferation Resistance & Physical Protection (PR&PP) characteristics for each of the six systems selected by the Generation IV International Forum (GIF) for further research and development, namely: the Sodium-cooled fast Reactor (SFR), the Very high temperature reactor (VHTR), the gas-cooled fast reactor (GFR), the Molten salt reactor (MSR) and the Supercritical water–cooled reactor (SCWR). This white paper represents the status of Proliferation Resistance and Physical Protection (PR&PP) characteristics for the Supercritical Water-cooled Fast reactor (SCFR) reference designs selected by the Generation IV International Forum (GIF) SCWR System Steering Committee (SSC). The intent is to generate preliminary information about the PR&PP features of the SCWR reactor technology and to provide insights for optimizing their PR&PP performance for the benefit of SCWR system designers. It updates the SCWR analysis published in the 2011 report “Proliferation Resistance and Physical Protection of the Six Generation IV Nuclear Energy Systems”, prepared Jointly by the Proliferation Resistance and Physical Protection Working Group (PRPPWG) and the System Steering Committees and provisional System Steering Committees of the Generation IV International Forum, taking into account the evolution of both the systems, the GIF R&D activities, and an increased understanding of the PR&PP features. The white paper, prepared jointly by the GIF PRPPWG and the GIF SCWR SSC, follows the highlevel paradigm of the GIF PR&PP Evaluation Methodology to investigate the PR&PP features of the eight proposed GIF SCWR designs. Two small modular reactors, the Canadian SSR and the Canada/China/Europe ECC-SMART are also mentioned. An overview of the fuel cycles for the GIF designs are provided. For PR, the document analyses and discusses the proliferation resistance aspects in terms of robustness against State-based threats associated with diversion of materials, misuse of facilities, breakout scenarios, and production in clandestine facilities. Similarly, for PP, the document discusses the robustness against theft of material and sabotage by non-State actors. The document follows a common template adopted by all the white papers in the updated series.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

MOOSE Reactor Module Meshing Enhancements to Support Reactor Physics Analysis

The U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) program develops an integrated suite of advanced reactor physics tools built upon the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Each code generally requires an input finite element mesh on which the physics solution is calculated, reported, and transferred to other physics codes. The meshing process is often burdensome for the complex geometries present in reactors due to lack of easy-to-use, open-source meshing tools. To address the bottleneck associated with meshing complex geometries found in nuclear reactors, NEAMS initiated the development of the MOOSE Reactor Module starting in FY21. The Reactor Module builds off the existing MOOSE Mesh System to include targeted meshing capabilities such as the ability to generate hexagonal pin cells, assemblies with ducts, rotating control drums, cores, peripheral zones around a core, as well as the automatic labeling (“reporting IDs”) of pin, assembly, and planar zones to simplify post-processing of results. As a Physics Module in MOOSE, the Reactor Module is open-source, available with any MOOSE installation, directly compatible with MOOSE-based tools, and can be invoked from MOOSEbased applications to generate meshes. Functionality from the Reactor Module has been applied to several advanced reactor concepts to demonstrate user workflow improvements and accuracy. The primary objective of the Reactor Module is to improve useability of MOOSEbased tools by streamlining mesh generation and output inspection processes. During FY22, the functionality of the Reactor Module (and accompanying Mesh System) has been expanded based on user needs. First, the Reactor Geometry Mesh Builder capability developed primarily in FY21 has been refactored and merged to the public MOOSE repository. This capability wraps underlying Reactor Module mesh generators into a “Pin – Assembly – Core” workflow appropriate for conventional Cartesian and hexagonal geometries, and notably assigns material IDs during mesh generation stage and generates only the minimal number of blocks needed in order to reduce computational burden. Biasing and boundary layer options have been added to the base mesh generators as required by thermal hydraulics solvers. The reporting ID functionality has been expanded to differentiate ring-wise and azimuthal sectors within a pin for use with depletion algorithms, and VectorPostProcessor and Reporter objects are now available to integrate solution variables across zones based on ID combinations. Functionality to trim hexagonal meshes along the center or periphery has been developed so users may leverage symmetry and reflective boundary conditions to reduce the mesh size. A flexible and powerful tool to fill the space between two sidesets has been introduced to the framework and can be used for transition layers such as stitching two assemblies together with different numbers of pins, or for complex geometries which do not follow conventional Cartesian/hexagonal patterns. Finally, additional verification problems were performed with NEAMS physics tools in complement with existing NEAMS work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Hydropower Cyber-Physical Configurations

The U.S. Department of Energy’s Water Power Technologies Office funded Pacific Northwest National Laboratory, Argonne National Laboratory, and the National Renewable Energy Laboratory to develop a typology to characterize the variety and pervasiveness of cyber-physical configurations across the nation’s hydropower fleet. Outreach to owners and operators returned configurations for 275 hydropower plants or approximately 12% of the fleet. Components (OT and IT), systems, and connections among systems differed among plants according to function, age, position in the river cascade, and many other factors. Seven cyber-physical configuration types labeled A through I, included from 2 to dozens of plants. They were differentiated by how pervasive data and control connections were among cyber-physical components and how frequently control signals paired with data signals in a feedback loop. The flow of data and control within each type implies what cybersecurity vulnerabilities may exist, and what mitigation actions may be most effective. A self-assessment approach allows plant operators to identify the configuration type similar to their plant and link to the lessons learned and best practices information. The cyber-physical typology reinforces the idea that hydropower facilities vary widely, but it also identifies groups that highlight similarities in how their cyber-physical components interact. This helps address fleetwide cybersecurity needs by identifying a reasonable number of configuration types that share risks, vulnerabilities, and potential mitigations.

13 HYDRO ENERGY↗
DOE OSTI2024

Open Call LDRD: Physically Informed Autoencoders for Galactic Redshift Regression

Physical constraints have been suggested to make neural network models more generalizable, act scientifically plausible, and be more data-efficient over unconstrained baselines. In this report, we present preliminary work on evaluating the effects of adding soft physical constraints to computer vision neural networks trained to estimate the conditional density of redshift on input galaxy images for the Sloan Digital Sky Survey. We introduce physically motivated soft constraint terms that are not implemented with differential or integral operators. We frame this work as a simple ablation study where the effect of including soft physical constraints is compared to an unconstrained baseline. We compare networks using standard point estimate metrics for photometric redshift estimation, as well as metrics to evaluate how faithful our conditional density estimate represents the probability over the ensemble of our test dataset. We find no evidence that the implemented soft physical constraints are more effective regularizers than augmentation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Scalable Control Co-design for Resilient-by-Design Cyber Physical Systems

Critical infrastructure networks, such as power and transportation networks, are often modelled as cyber-physical systems. With ever increasing complexity of these systems, there is a need for newer and more relevant metrics and design tools that will co-optimize the physical system components and control policies to guarantee resilience against cyber and natural threats. To this end, a simulation-based control co-design computational framework that will concurrently determine the system and control parameters of a cyber-physical system to meet pre-specified resilience, operational and economic objectives has been developed. The capabilities of the developed co-design engine are demonstrated by designing the physical components and control parameters of a microgrid system that will meet its resiliency objectives when subjected to various cyber and physical threats.

42 ENGINEERING↗
DOE OSTI2021

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.

Chan, Jay↗
DOE OSTI2019

Atomic Absorption Line Diagnostics for the Physical Properties of Red Supergiants

Red supergiants (RSGs) are evolved massive stars that represent extremes, in both their physical sizes and their cool temperatures, of the massive star population. The effective temperature (T {sub eff}) is the most critical physical property needed to place an RSG on the Hertzsprung–Russell Diagram, due to the stars’ cool temperatures and resulting large bolometric corrections. Several recent papers have examined the potential utility of atomic line equivalent widths (EWs) in cool supergiant (CSG) spectra for determining T {sub eff} and other physical properties and found strong correlations between Ti i and Fe i spectral features and T {sub eff} in earlier-type CSGs (G and early K) but poor correlations in M-type stars, a spectral subtype that makes up a significant fraction of RSGs. We have extended this work by measuring the EWs of Ti, Fe, and Ca lines in late K- and M-type RSGs in the Milky Way, Large Magellanic Cloud, and Small Magellanic Cloud, and compared these results to the predictions of the theoretical stellar LTE atmosphere models (MARCS) stellar atmosphere models. Our analyses show a poor correlation between T {sub eff} and the Fe i and Ti i lines in our observations (at odds with strong correlations predicted by stellar atmosphere models), but do find statistically significant correlations between T {sub eff} and the Ca ii triplet (CaT) features of Milky Way RSGs, suggesting that this could be a potential diagnostic tool for determining T {sub eff} in M-type supergiants. We also examine correlations between these spectral features and other physical properties of RSGs (including metallicity, surface gravity, and bolometric magnitude), and consider the underlying physics driving the evolution of atomic line spectra in RSGs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Neutrino Physics and Astrophysics at Colliders

Nonzero neutrino masses guarantee new physics and neutrinos are excellent probes of extreme environments in the Universe. The recent collider neutrino experimental program, including FASER$ν$ and SND@LHC, along with the planned Forward Physics Facility at the High-Luminosity Large Hadron Collider, is opening a new window into neutrino physics and astrophysics. In this article, we review recent achievements and prospects of collider neutrino experiments, including key achievements such as the first measurements of collider neutrino interactions at unprecedented energies and the exploration of new physics scenarios, like dark matter candidates, sterile neutrinos, and non-standard neutrino interactions. For concreteness, we will focus on the significant scientific opportunities presented by the Forward Physics Facility, which will enable precision measurements of neutrino cross sections and proton structure at low parton momentum fraction. Furthermore, collider neutrino studies will substantially reduce systematic uncertainties in calculating atmospheric neutrino fluxes, thereby improving astrophysical neutrino observations as well as advancing our understanding of cosmic-ray interactions.

Machado, Pedro [Fermilab] (ORCID:0000000291187354)↗
DOE OSTI2023

An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015

Terrestrial water budget (TWB) data over large domains are of high interest for various hydrological applications. Spatiotemporally continuous and physically consistent estimations of TWB rely on land surface models (LSMs). As an augmentation of the operational North American Land Data Assimilation System Phase 2 (NLDAS-2) four-LSM ensemble, this paper describes a dataset simulated from an ensemble of 48 physics configurations of the Noah LSM with multi-physics options (Noah-MP). The 48 Noah-MP physics configurations are selected to give a representative cross-section of commonly used LSMs for parameterizing runoff, atmospheric surface layer turbulence, soil moisture limitation on photosynthesis, and stomatal conductance. The dataset spans from 1980 to 2015 over the conterminous United States (CONUS) at a monthly temporal resolution and a 1/8° spatial resolution. The dataset variables include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The dataset is available at https://doi.org/10.5281/zenodo.7109816. Evaluations carried out in this study and previous investigations show that the ensemble per forms well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff. Noah-MP complements the NLDAS models well, and adding Noah-MP consistently improves the NLDAS es timations of the above variables in most areas of CONUS. Besides, the perturbed-physics ensemble facilitates the identification of model deficiencies. The parameterizations of shallow snow, spatially varying groundwater dynamics, and near-surface atmospheric turbulence should be improved in future model versions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Physically regularized machine learning emulators of aerosol activation

Abstract. The activation of aerosol into cloud droplets is an important step in the formation of clouds and strongly influences the radiative budget of the Earth. Explicitly simulating aerosol activation in Earth system models is challenging due to the computational complexity required to resolve the necessary chemical and physical processes and their interactions. As such, various parameterizations have been developed to approximate these details at reduced computational cost and accuracy. Here, we explore how machine learning emulators can be used to bridge this gap in computational cost and parameterization accuracy. We evaluate a set of emulators of a detailed cloud parcel model using physically regularized machine learning regression techniques. We find that the emulators can reproduce the parcel model at higher accuracy than many existing parameterizations. Furthermore, physical regularization tends to improve emulator accuracy, most significantly when emulating very low activation fractions. This work demonstrates the value of physical constraints in machine learning model development and enables the implementation of improved hybrid physical and machine learning models of aerosol activation into next-generation Earth system models.

58 GEOSCIENCES↗
DOE OSTI2024

Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models

Abstract. Motivated by reducing errors in the energy budget related to enthalpy fluxes within the Energy Exascale Earth System Model (E3SM), we study several physics–dynamics coupling approaches. Using idealized physics, a moist rising bubble test case, and the E3SM's nonhydrostatic dynamical core, we consider unapproximated and approximated thermodynamics applied at constant pressure or constant volume. With the standard dynamics and physics time-split implementation, we describe how the constant-pressure and constant-volume approaches use different mechanisms to transform physics tendencies into dynamical motion and show that only the constant-volume approach is consistent with the underlying equations. Using time step convergence studies, we show that the two approaches both converge but to slightly different solutions. We reproduce the large inconsistencies between the energy flux internal to the model and the energy flux of precipitation when using approximate thermodynamics, which can only be removed by considering variable latent heats, both when computing the latent heating from phase change and when applying this heating to update the temperature. Finally, we show that in the nonhydrostatic case, for physics applied at constant pressure, the general relation that enthalpy is locally conserved no longer holds. In this case, the conserved quantity is enthalpy plus an additional term proportional to the difference between hydrostatic pressure and full pressure.

58 GEOSCIENCES↗
DOE OSTI2023

Sensitivities of subgrid-scale physics schemes, meteorological forcing, and topographic radiation in atmosphere-through-bedrock integrated process models: a case study in the Upper Colorado River basin

Abstract. Mountain hydrology is controlled by interacting processes extending from the atmosphere through the bedrock. Integrated process models (IPMs), one of the main tools needed to interpret observations and refine conceptual models of the mountainous water cycle, require meteorological forcing that simulates the atmospheric process to predict hydroclimate then subsequently impacts surface–subsurface hydrology. Complex terrain and extreme spatial heterogeneity in mountainous environments drive uncertainty in several key considerations in IPM configurations and require further quantification and sensitivity analyses. Here, we present an IPM using the Weather Research and Forecasting (WRF) model which forces an integrated hydrologic model, ParFlow-CLM, implemented over a domain centered over the East River watershed (ERW), located in the Upper Colorado River basin (UCRB). The ERW is a heavily instrumented 300 km2 region in the headwaters of the UCRB near Crested Butte, CO, with a growing atmosphere-through-bedrock observation network. Through a series of experiments in the water year 2019 (WY19), we use four meteorological forcings derived from commonly used reanalysis datasets, three subgrid-scale physics scheme configurations in WRF, and two terrain shading options within WRF to test the relative importance of these experimental design choices for key hydrometeorological metrics including precipitation and snowpack, as well as evapotranspiration, groundwater storage, and discharge simulated by the ParFlow-CLM. Our hypothesis is that uncertainty from synoptic-scale forcings produces a much larger spread in surface–subsurface hydrologic fields than subgrid-scale physics scheme choice. Results reveal that the WRF subgrid-scale physics configuration leads to larger spatiotemporal variance in simulated hydrometeorological conditions, whereas variance across meteorological forcing with common subgrid-scale physics configurations is more spatiotemporally constrained. Despite reasonably simulating precipitation, a delay in simulated discharge peak is due to a systematic cold bias across WRF simulations, suggesting the need for bias correction. Discharge shows greater variance in response to the WRF simulations across subgrid-scale physics schemes (26 %) rather than meteorological forcing (6 %). The topographic radiation option has minor effects on the watershed-average hydrometeorological processes but adds profound spatial heterogeneity to local energy budgets (±30 W m−2 in shortwave radiation and 1 K air temperature differences in late summer). This is the first presentation of sensitivity analyses that provide support to help guide the scientific community to develop observational constraints on atmosphere-through-bedrock processes and their interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Influence of wind turbine design parameters on linearized physics-based models in OpenFAST

Abstract. While most physics involved in wind energy are nonlinear, linearization of the underlying nonlinear wind system equations is often important for understanding the system response and exploiting well-established methods and tools for analyzing linear systems. Linearized models are important for eigenanalysis (to derive structural natural frequencies, damping ratios, and mode shapes), controls design (based on linear state-space models), etc. In controls co-design, wherein methods often rely on linearized time-domain models of the physics, the physical structure (often called the plant) and controller are designed and optimized concurrently, so it is important to understand how changes to the physical design affect the linearized system. This work summarizes efforts done to understand the impact of design parameter variations in the physical system (e.g., mass, stiffness, geometry, and aerodynamic and hydrodynamic coefficients) on the linearized system using OpenFAST.

17 WIND ENERGY↗
DOE OSTI2021

Physical Activity Types and Programs Recommended by Primary Care Providers Treating Adults With Arthritis, DocStyles 2018

Primary care providers (PCPs) can offer counseling to adults with arthritis on physical activity, which can reduce pain and improve physical function, mental health, and numerous other health outcomes. We analyzed cross-sectional 2018 DocStyles data for 1,366 PCPs who reported they always or sometimes recommend physical activity to adults with arthritis. Most PCPs sampled (88.2%) recommended walking, swimming, or cycling; 65.5% did not recommend any evidence-based, arthritis-appropriate physical activity programs recognized by the Centers for Disease Control and Prevention. Opportunities exist for public health awareness campaigns to educate PCPs about evidence-based physical activity programs proven to optimize health for adults with arthritis when more than counseling is needed.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1971

Reference earth orbital research and applications investigations (blue book). Volume 3: Physics

The definition of physics experiments to be conducted aboard the space station is presented. The four functional program elements are: (1) space physics research laboratory, (2) plasma physics and environmental perturbation laboratory, (3) cosmic ray physics laboratory, and (4) physics and chemistry laboratory. The experiments to be conducted by each facility are defined and the crew member requirements to accomplish the experiments are presented.

Source record↗