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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Galactic Cosmic Ray Event-Based Risk Model (GERM) Code

This software describes the transport and energy deposition of the passage of galactic cosmic rays in astronaut tissues during space travel, or heavy ion beams in patients in cancer therapy. Space radiation risk is a probability distribution, and time-dependent biological events must be accounted for physical description of space radiation transport in tissues and cells. A stochastic model can calculate the probability density directly without unverified assumptions about shape of probability density function. The prior art of transport codes calculates the average flux and dose of particles behind spacecraft and tissue shielding. Because of the signaling times for activation and relaxation in the cell and tissue, transport code must describe temporal and microspatial density of functions to correlate DNA and oxidative damage with non-targeted effects of signals, bystander, etc. These are absolutely ignored or impossible in the prior art. The GERM code provides scientists data interpretation of experiments; modeling of beam line, shielding of target samples, and sample holders; and estimation of basic physical and biological outputs of their experiments. For mono-energetic ion beams, basic physical and biological properties are calculated for a selected ion type, such as kinetic energy, mass, charge number, absorbed dose, or fluence. Evaluated quantities are linear energy transfer (LET), range (R), absorption and fragmentation cross-sections, and the probability of nuclear interactions after 1 or 5 cm of water equivalent material. In addition, a set of biophysical properties is evaluated, such as the Poisson distribution for a specified cellular area, cell survival curves, and DNA damage yields per cell. Also, the GERM code calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle in a selected material. The GERM code makes the numerical estimates of basic physical and biophysical quantities of high-energy protons and heavy ions that have been studied at the NASA Space Radiation Laboratory (NSRL) for the purpose of simulating space radiation biological effects. In the first option, properties of monoenergetic beams are treated. In the second option, the transport of beams in different materials is treated. Similar biophysical properties as in the first option are evaluated for the primary ion and its secondary particles. Additional properties related to the nuclear fragmentation of the beam are evaluated. The GERM code is a computationally efficient Monte-Carlo heavy-ion-beam model. It includes accurate models of LET, range, residual energy, and straggling, and the quantum multiple scattering fragmentation (QMSGRG) nuclear database.

Cucinotta, Francis A.↗
NASA NTRS1981

The negative effect of hypokinesia involving injury and preventive measures

The optimum length of bed rest for athletes suffering from broken bones is considered. Negative effects of hypokinesia induced by bed rest include general weakness and deconditioning of the muscles as well as sleeplessness, headaches, muscle pain, constipation, unstable pulse and arterial pressure, and changes in reflexes. This is considered to be the result of a vegetative dysfunction induced by the decreased flow of nerve impulses and a decrease in interoceptive and exteroceptive signals. The briefest possible period of bed rest, followed by an increase in motor activity, the prescription of a large quantity of LFK, and an active program of physical therapy are recommended. The symptomology associated with hypokinesia disappears after one month of free motor activity.

Izakson, K. A.↗
NASA NTRS1990

An approach for relating the results of quantitative nondestructive evaluation to intrinsic properties of high-performance materials

One of the most difficult problems the manufacturing community has faced during recent years has been to accurately assess the physical state of anisotropic high-performance materials by nondestructive means. In order to advance the design of ultrasonic nondestructive testing systems, a more fundamental understanding of how ultrasonic waves travel and interact within the anisotropic material is needed. The relationship between the ultrasonic and engineering parameters needs to be explored to understand their mutual dependence. One common denominator is provided by the elastic constants. The preparation of specific graphite/epoxy samples to be used in the experimental investigation of the anisotropic properties (through the measurement of the elastic stiffness constants) is discussed. Accurate measurements of these constants will depend upon knowledge of refraction effects as well as the direction of group velocity propagation. The continuing effort for the development of improved visualization techniques for physical parameters is discussed. Group velocity images are presented and discussed. In order to fully understand the relationship between the ultrasonic and the common engineering parameters, the physical interpretation of the linear elastic coefficients (the quantities that relate applied stresses to resulting strains) are discussed. This discussion builds a more intuitional understanding of how the ultrasonic parameters are related to the traditional engineering parameters.

Miller, James G.↗
NASA NTRS1989

Spectral diagnostics from X-ray to radio wavelengths

A spectral diagnostic is defined as an observable spectral quantity that can be used to measure a physical parameter characterizing the observed plasma. The first step in establishing a spectral line is to determine whether the upper state of the transition is excited primarily by collisions (typically by electrons), by a radiative process, or by recombination (either direct or cascade). The ultraviolet portion of the spectrum is emphasized as it is well studied, the background photospheric continuum is weak for cool stars, and the spectrum consists of an interesting mix of lines formed by all three mechanisms.

Linsky, Jeffrey L.↗
NASA NTRS2003

Theory of Fiber Optical Bragg Grating: Revisited

The reflected signature of an optical fiber Bragg grating is analyzed using the transfer function method. This approach is capable to cast all relevant quantities into proper places and provides a better physical understanding. The relationship between reflected signal, number of periods, index of refraction, and reflected wave phase is elucidated. The condition for which the maximum reflectivity is achieved is fully examined. We also have derived an expression to predict the reflectivity minima accurately when the reflected wave is detuned. Furthermore, using the segmented potential approach, this model can handle arbitrary index of refraction profiles and compare the strength of optical reflectivity of different profiles. The condition of a non-uniform grating is also addressed.

Tai, H.↗
NASA NTRS2021

The M-dwarf Ultraviolet Spectroscopic Sample. I. Determining Stellar Parameters for Field Stars

Accurate stellar properties are essential for precise stellar astrophysics and exoplanetary science. In the M-dwarf regime, much effort has gone into defining empirical relations that can use readily accessible observables to assess physical stellar properties. Often, these relations for the quantity of interest are cast as a nonlinear function of available data; in Bayesian modeling, however, the reverse is needed. In this article, we introduce a new Bayesian framework to self-consistently and simultaneously apply multiple empirical calibrations to fully characterize the mass, luminosity, radius, and effective temperature of a field age M-dwarf. This framework includes a new M-dwarf mass–radius relation with a scatter of 3.1% at fixed mass. We further introduce the M-dwarf Ultraviolet Spectroscopic Sample (MUSS), and apply our methodology to provide consistent stellar parameters for these nearby low-mass stars, selected as having available spectroscopic data in the ultraviolet. These targets are of interest largely as either exoplanet hosts or benchmarks in multiwavelength stellar activity. We use the field MUSS stars to define a low-mass main sequence in the solar neighborhood through Gaussian Process (GP) regression. These results enable us to empirically measure a feature in the GP derivative at M ⊙ that indicates where the MUSS transitions from fully to partly convective interiors.

J. Sebastian Pineda↗
NASA NTRSDate not supplied

On the Integration of Loudness Over Time for the Prediction of Single-Event Annoyance

Loudness is the quality of human perception that is most related to acoustic intensity and energy, however there is no agreed-upon way of integrating loudness over time for the prediction of noise-induced annoyance. Most noise metrics used today for aircraft certification and regulation employ the “Equal-Energy Hypothesis” (EEH) and sum the acoustical energy of one or more noise events over time to create a single metric value that represents the entire exposure. Using the EEH creates a natural tradeoff between the peak energy and the duration of a single noise event, the “hypothesis” being that this tradeoff optimally predicts annoyance in a wide variety of situations. This work proposes a strategy for integrating a loudness-like time series which is flexible in two ways: First, it includes a parameter b ∈ [0,1]. When b = 0, the metric will return the peak of the time series. At b = .5, the metric will behave in accordance with the EEH. At b = 1, the metric will penalize the duration of the sound more than the EEH would. Second, transformations are given so that the strategy can be computed from, or generate quantities in units analogous to, decibels (or phon), physical units (acoustic pressure/power), or perceptually-scaled units (sone). This approach is demonstrated on a dataset of annoyance responses to single events of UAV and road vehicle noise that is fit with an augmented linear regression. Analyses based on this integration of A-weighted level and the output of the “Zwicker” loudness model yield similar results: that subjects may have been slightly more sensitive to the durations of the events than the EEH would suppose, but that the EEH cannot be disproven using these data. The time-integrated metrics outperform both time-averaged and centile-based metrics.

Psychoacoustics↗
NASA NTRSDate not supplied

On the Integration of Loudness Over Time for the Prediction of Single-Event Annoyance

Loudness is the quality of human perception that is most related to acoustic intensity and energy, however there is no agreed-upon way of integrating loudness over time for the prediction of noise-induced annoyance. Most noise metrics used today for aircraft certification and regulation employ the “Equal-Energy Hypothesis” (EEH) and sum the acoustical energy of one or more noise events over time to create a single metric value that represents the entire exposure. Using the EEH creates a natural tradeoff between the peak energy and the duration of a single noise event, the “hypothesis” being that this tradeoff optimally predicts annoyance in a wide variety of situations. This work proposes a strategy for integrating a loudness-like time series which is flexible in two ways: First, it includes a parameter b ∈ [0,1]. When b = 0, the metric will return the peak of the time series. At b = .5, the metric will behave in accordance with the EEH. At b = 1, the metric will penalize the duration of the sound more than the EEH would. Second, transformations are given so that the strategy can be computed from, or generate quantities in units analogous to, decibels (or phon), physical units (acoustic pressure/power), or perceptually-scaled units (sone). This approach is demonstrated on a dataset of annoyance responses to single events of UAV and road vehicle noise that is fit with an augmented linear regression. Analyses based on this integration of A-weighted level and the output of the “Zwicker” loudness model yield similar results: that subjects may have been slightly more sensitive to the durations of the events than the EEH would suppose, but that the EEH cannot be disproven using these data. The time-integrated metrics outperform both time-averaged and centile-based metrics.

Psychoacoustics↗
DOE OSTI2022

Status of Lattice QCD Calculations of One and Two-Nucleon Matrix Elements

Lattice QCD calculations of basic hadronic quantities are now often performed with systematic control over the physical pion mass, continuum and infinite volume limits. This progress signals a new era in which lattice QCD will be used to compute basic properties of hadrons, nucleons and light nuclei directly from the Standard Model (SM). In particular, we will be able to use lattice QCD, combined with Effective Field Theory, to make quantitative statements about the interaction of SM matter and potential beyond the SM physics ranging from direct dark matter detection to permanent electric dipole moments. I will briefly review the status and challenges of lattice QCD calculations of select one- and two-nucleon matrix elements. These calculations are significantly more challenging than the basic quantities computed with all systematics controlled, but substantial progress is being made.

Walker-Loud, Andre↗
NASA NTRS1986

Experimental and Theoretical Study of Propeller Spinner/Shank Interference

A fundamental experimental and theoretical investigation into the aerodynamic interference associated with propeller spinner and shank regions was conducted. The research program involved a theoretical assessment of solutions previously proposed, followed by a systematic experimental study to supplement the existing data base. As a result, a refined computational procedure was established for prediction of interference effects in terms of interference drag and resolved into propeller thrust and torque components. These quantities were examined with attention to engineering parameters such as two spinner finess ratios, three blade shank forms, and two/three/four/six/eight blades. Consideration of the physics of the phenomena aided in the logical deduction of two individual interference quantities (cascade effects and spinner/shank juncture interference). These interference effects were semi-empirically modeled using existing theories and placed into a compatible form with an existing propeller performance scheme which provided the basis for examples of application.

Cornell, C. C.↗
DOE OSTI2020

nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius δ goes to zero, and to the fractional Laplacian as δ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We provide theoretical convergence rates with respect to δ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, δ and α, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order α(γ), which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as δ. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

nPINNs: Nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius $\delta$ goes to zero, and to the fractional Laplacian as $\delta$ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We also provide theoretical convergence rates with respect to $\delta$ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, $\delta$ and $\alpha$, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order $\alpha(y)$, which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as $\delta$. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Hydrophobicity of Cryogenic Fluids for Fuel Transfer in Space Applicaitons

Introduction Hydrophobicity is the tendency of a fluid to repel another material or fluid. Hydrophobiciy can be measured through the wetting angle of the fluid on the surface of the other material. The greater the wetting angle, the more hydrophobic the surface is against the fluid. Hydrophobicity properties can be caused by one of two means, either through physical properties or chemical properties [\citenum{NARBUTT2020121}]. Physical hydrophobicity due to the surface of a solid creating surface roughness/patterning, minimizing the contact area between the fluid and the surface. This can be observed throughout nature, such as the surface of lotus leaves or butterfly wings. This physical property can be induced on a variety of surfaces, namely through laser etching, allowing a surface to be finely lineated to imitate these natural surfaces while controlling quantity, depth, and patterns of the etching on the surface [\citenum{10.1063/1.4905616}]. Physical hydrophobicity is therefore dependent on several variables, including the surface that the fluid is on and properties of the fluid itself, such as density and surface tension, both of which are dependent on temperature and/or pressure. Chemical hydrophobicity [\citenum{MadeiraHydrophobocicicici}] is due to the inherent chemical properties of the materials being used. This is most commonly due to the molecular structures changing the polarity of the materials. Depending on a nonpolar material will repel a polar material, proving to be hydrophobic, conversely if the polarity of the materials is the same (polar – polar, nonpolar – nonpolar), they will attract each other. Cryogenics refers to the behavior of materials at very low temperatures (<\ang{-100} C) [\citenum{ZOHURI20181}]. In space applications many things are inherently cryogenic, therefore this is an important field. There has been little research in how hydrophobicity changes at cryogenic temperatures. Cryogenic fluids are commonly used as fuels for spacecraft, therefore, integrating a hydrophobic surface can increase the transfer rate of the fuel. To test this, several experiments were set up to determine the hydrophobic properties of cryogenic fluids, including nitrogen (LN2), argon (LAr), oxygen (LOx), hydrogen (LH2), and methane (CH4), at cryogenic temperatures. Etched Wafer Testing A silicon wafer cut from a crystal of silicon [100] was used to model the potential hydrophobicity of various cryogenic fluids. Silicon [100] references to the crystallographic orientation of the silicon crystals in the wafer. These wafers were then laser etched to create a surface that is more likely to be hydrophobic. To test the hydrophobicity of the wafers at cryogenic temperatures, the temperatures of the wafers must be reduced to the same temperature as the cryogenic fluid being used to prevent rapid boil-off. To achieve this a double-walled vacuum insulated glass chamber was utilized. The chamber is open, with a double-walled glass that can be placed in a vacuum to remove condensation from the outside to allow easier viewing of the experiment. Furthermore, the silicon wafer's temperature must be lowered to the temperature of LN2, as well as maintain the temperature throughout the experiment. To achieve this a piece of 6061 aluminum was used, creating a stand-off for the wafer and it would allow for the insulation of the temperature of the wafers. The container was then filled with LN2 and once the LN2 stabilized the vaporization and the levels of LN2 dropped under the height of the wafer, the wafer was then allowed to air dry. Once the wafer was dry from the LN2, drops of LN2 were placed on the surface of the wafer for observation. During the first trial, the LN2 that was dropped on the surface displayed nonhydrophobic behaviors, spreading out along the surface, with a minimal wetting angle (too small to be measured). This process was repeated for liquid argon. Further testing with other cryogenic liquids will require different testing apparatuses due to being more volatile. Furthermore, several papers [\citenum{voltvolt7}] have suggested that running a voltage can induce a hydrophobic effect throughout a surface, further testing will include a voltage (constant and oscillating) to determine if voltage influences inducing hydrophobicity at cryogenic temperatures. Chemical Testing Coating the interior walls of the fuel tanks and fuel lines can successfully create a hydrophobic surface. The inherent problem is finding a material that can be used to coat the surface, furthermore, at these temperatures, the coating will remain solid, which could pose issues in maintaining the hydrophobic properties. To test this water (polar) is hydrophobic against oils and fats (nonpolar), therefore the same experiment as the etched wafer testing was conducted to determine if the hydrophobic properties will persist as the nonpolar material remains solid and the polar material remains a fluid. The water remained hydrophobic, allowing testing can expand to the cryogenic fluids. However, another hurdle is faced in finding a chemically opposite material to the cryogenic fluids being tested. As many of the cryogenic fluids being tested are diatomic, they are inherently nonpolar, therefore, the material used for the hydrophobic coating must be polar. Polar greases and lubricants are difficult to come by, however, lithium stearate, appears to be a potential candidate for creating a coated hydrophobic surface for cryogenic fluids at cryogenic temperatures.

Cryogenics↗
DOE OSTI2020

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

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

36 MATERIALS SCIENCE↗
DOE OSTI2020

Volumetric analysis and mesh generation of real and artificial microstructural geometries

Producing a viable finite element mesh of realistic microstructural structural geometry is a critical step in analyzing the thermo-mechanical behavior of complex multi-material composites. Advancements in imaging technology such as micro computed tomography have allowed modelers to access high resolution mesoscale geometries for direct numerical simulation. However, converting from voxel based 3D images to usable finite element meshes has been challenging. A robust method including algorithms and software scripts for generating finite element meshes from 3D imaged microstructures is presented in this paper. It includes a routine for inserting cohesive elements around material interfaces to enable modeling of interface properties including delamination and damage. The algorithms and procedures presented in this method leverage currently available software packages for processing surface based geometry into volume based meshes. In addition to converting real geometry from physical imaging systems, algorithms for producing numerically generated and statistically equivalent microstructural geometry are also included. These artificial microstructures can be a valuable resource for modelers when physical specimens do not exist or are limited in quantity: Method establishes a workflow from voxel data to viable finite element mesh including interface information, and Includes a method for synthetic geometry generation based on metrics from real microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Validation of the single-event method for low-energy electron transport via stopping power calculations with $\mathrm{MCNP}$

Monte Carlo simulations of low-energy ( <50 keV) electron transport in matter are essential for a broad range of application fields. Several Monte Carlo codes have developed specialized treatments for this case, but a comprehensive validation of low-energy electron transport for general-purpose simulations remains lacking in the literature. One approach to accomplish this validation is calculation of stopping power using low-energy electron transport physics, as stopping power is a fundamental radiation transport quantity which must be simulated accurately for nearly any application. Here in this work, we use the Monte Carlo N-Particle (MCNP) radiation transport code with the single-event method for electron transport to calculate stopping powers of low-energy electrons (50 eV to 30 keV) in 41 elemental solids, 14 compound solids, and five rare gas solids, comparing simulation results to published semi-empirical stopping power calculations from optical measurements. In general, the simulations give good agreement (typically within ±10%) with semi-empirical stopping power values at higher energies: 300 eV and above for most elemental solids, 1 keV and above for compound solids, and 400 eV and above for rare gas solids. Agreement between MCNP and semi-empirical values is worse below these energies. The most significant source of error is the EPRDATA14 cross section data, which does not account for changes in electronic structure due to solid-state bonding, particularly in compound materials. The simplistic model of atomic excitation used to generate the EPRDATA14 cross sections is another key source of error. Additionally, the breakdown of the continuous slowing-down approximation introduces significant uncertainty at low energies, although this is a limitation of the calculation method and not of the simulation procedure. Accounting for these and other uncertainty sources, the single event method in MCNP is robust and able to give good accuracy for a variety of low-energy electron transport problems through diverse kinds of materials.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Modeling and simulation of transitional Rayleigh–Taylor flow with partially averaged Navier–Stokes equations

In this work, the partially averaged Navier–Stokes (PANS) equations are used to predict the variable-density Rayleigh–Taylor (RT) flow at Atwood number 0.5 and maximum Reynolds number 500. This is a prototypical problem of material mixing, featuring laminar, transitional, and turbulent flow, instabilities and coherent structures, density fluctuations, and production of turbulence kinetic energy by both shear and buoyancy mechanisms. These features pose numerous challenges to modeling and simulation, making the RT flow ideal to develop the validation space of the recently proposed PANS Besnard–Harlow–Rauenzahn-linear eddy viscosity model closure. The numerical simulations are conducted at different levels of physical resolution and test three approaches to set the parameters $f_\phi$ defining the range of physically resolved scales. The computations demonstrate the efficiency (accuracy vs cost) of the PANS model predicting the spatiotemporal development of the RT flow. Results comparable to large-eddy simulations and direct numerical simulations are obtained at significantly lower physical resolution without the limitations of the Reynolds-averaged Navier–Stokes equations in these transitional flows. The data also illustrate the importance of appropriate selection of the physical resolution and the resolved fraction of each dependent quantity $\phi$ of the turbulent closure, $f_\phi$. These two aspects determine the ability of the model to resolve the flow phenomena not amenable to modeling by the closure and, as such, the computations’ fidelity.

Navier Stokes equations↗
DOE OSTI2025

Estimating ionization states and continuum lowering from ab initio path integral Monte Carlo simulations for warm dense hydrogen

Warm dense matter (WDM) is an active field of research, with applications ranging from astrophysics to inertial confinement fusion. Ionization degree and continuum lowering are important quantities to understand how materials behave under these conditions, but can be difficult to diagnose since experimental campaigns are limited and often require model-dependent analysis. This is especially true for hydrogen, which has a comparably low scattering cross section, making high-quality data particularly difficult to obtain. Consequently, building equation of state tables often relies on simulations in combination with untested approximations to extract properties from experiments. Here, we investigate an approach for extracting the ionization potential depression and ionization degree—quantities which are otherwise not directly accessible from the physical model—from first-principles path integral Monte Carlo (PIMC) simulations utilizing a chemical model. In contrast to experimental measurements, where noise and nonequilibrium effects add to the uncertainty of the inferred parameters, PIMC simulations provide a clean signal with well-defined thermodynamic conditions. Comparisons against commonly used models show a qualitative agreement, but we find deviations primarily for the high-density and high-temperature cases. We also demonstrate the decreasing sensitivity of the dynamic structure factor with respect to both ionization and continuum lowering for increasing scattering angles in x-ray Thomson scattering experiments. Our work has important implications for the design of future experiments, but also offers qualitative understanding of structure factors and the imaginary-time correlation function obtained from first-principles quantum Monte Carlo simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗