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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The “good,” the “bad,” and the “hidden” in neutron scattering and molecular dynamics of ionic aqueous solutions

We characterize a concentrated 7.3 m CaCl 2 solution, combining neutron diffraction with chloride isotopic substitution (Cl-NDIS) in null water and molecular dynamics (MD) simulations. We elucidate the solution structure, thermodynamic properties, and extent of ion pairing previously suggested as concentration-dependent and often not observed at lower concentrations. Our Cl-NDIS measurements designate the solvent-shared ion pairing as dominant and the contact ion pairing (CIP) as insignificant even under conditions close to the solubility limit. The MD models parameterized against neutron diffraction with calcium isotopic substitution (Ca-NDIS) overestimate CIP despite successfully reproducing most of the Cl-NDIS signal. This drawback originates from the fact that Ca 2+ –Cl - interactions were primarily “hidden” in the Ca-NDIS signal due to overlapping with Ca 2+ –O w and Ca 2+ –H w contributions to the total scattering. Contrary, MD models with moderate CIP and possessing generally good performance at high concentrations fail to reproduce the NDIS measurements accurately. Therefore, the electronic polarization, introduced in most of the recent MD models via scaling ionic charges, resolves some but not all parameterization drawbacks. We conclude that despite improving the quality of MD models “on average,” the question “which model is the best” has not been answered but replaced by the question “which model is better for a given research.” An overall “good” model can still be inappropriate or, in some instances, “bad” and, unfortunately, produce erroneous results. Additionally, the accurate interpretation of several NDIS datasets, complemented by MD simulations, can prevent such mistakes and help identify the strengths, weaknesses, and convenient applications for corresponding computational models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1973

Modification and updating of the Manned Activity Scheduling System (MASS) for shuttle and shuttle payloads analysis. Volume 1: Model specifications

The LRC MASS was modified and updated to include logic for efficient analysis of space shuttle payload operations. All MASS computer models were reviewed for compatibility with, and applicability to, the shuttle sortie mission. The MASS modification efforts were concentrated on two computer programs, the general scheduling model (GSM) and one day model (ODM). A new computer program, the DAYLIB Tape Processor, was developed to update the link between the GSM and ODM. The resulting MASS is an operationally efficient analytical tool that will allow a rapid assessment of shuttle and shuttle payload operations.

Ring, R. C.↗
NASA NTRSDate not supplied

GT2024-139262: Development of a Computational Fluid Dynamics Model for a High-Speed Centrifugal Compressor

Computational fluid dynamics (CFD) has become widely used in the design and analysis of turbomachinery components such as centrifugal compressors. However, CFD is only a limited representation of experimental cases and struggles to model complex flows or lack model details to increase computation speed. The High Efficiency Centrifugal Compressor (HECC) was designed by United Technologies Research Center for NASA to investigate the difficulties behind improving centrifugal compressor technology and to provide an open case to the turbomachinery research community. The CFD technology seen in previous numerical models of the compressor have improved since then. In this study, a high-fidelity computational model for the vaneless configuration of HECC operating at its design point was developed and compared to an equivalent experimental case. This model incorporated GPU versions of mesh generation code WAND and solver code LEO from AeroDynamic Solutions, Inc. Blade sections extracted from the solid model formed the basis of the impeller mesh, while the remainder of the stage was modeled as ducts. Standard day aerodynamic conditions, design rotational speed, design mass flow rate, and inlet flow angles served as additional inputs. A mesh sensitivity study was performed prior to using the model for analysis. The accuracy of the model’s predictions of impeller and stage performance were assessed by comparing the total pressure ratio, total temperature rise ratio, and adiabatic efficiency to the corresponding parameters observed in the experimental case. Results showed a general overprediction of compressor performance by the model. Predictions were more accurate for the impeller performance compared to those for the full compressor stage. Spanwise analyses showed that the model tended to follow flow behavior patterns exhibited in the experiment. Beyond this study, this model was used to examine HECC aerodynamics and performance in research following thereafter.

centrifugal compressor↗
NASA NTRS2011

Analysis of ALTAIR 1998 Meteor Radar Data

We describe a new analysis of a set of 32 UHF meteor radar traces recorded with the 422 MHz ALTAIR radar facility in November 1998. Emphasis is on the velocity measurements, and on inferences that can be drawn from them regarding the meteor masses and mass densities. We find that the velocity vs altitude data can be fitted as quadratic functions of the path integrals of the atmospheric densities vs distance, and deceleration rates derived from those fits all show the expected behavior of increasing with decreasing altitude. We also describe a computer model of the coupled processes of collisional heating, radiative cooling, evaporative cooling and ablation, and deceleration - for meteors composed of defined mixtures of mineral constituents. For each of the cases in the data set we ran the model starting with the measured initial velocity and trajectory inclination, and with various trial values of the quantity mPs 2 (the initial mass times the mass density squared), and then compared the computed deceleration vs altitude curves vs the measured ones. In this way we arrived at the best-fit values of the mPs 2 for each of the measured meteor traces. Then further, assuming various trial values of the density Ps, we compared the computed mass vs altitude curves with similar curves for the same set of meteors determined previously from the measured radar cross sections and an electrostatic scattering model. In this way we arrived at estimates of the best-fit mass densities Ps for each of the cases. Keywords meteor ALTAIR radar analysis 1 Introduction This paper describes a new analysis of a set of 422 MHz meteor scatter radar data recorded with the ALTAIR High-Power-Large-Aperture radar facility at Kwajalein Atoll on 18 November 1998. The exceptional accuracy/precision of the ALTAIR tracking data allow us to determine quite accurate meteor trajectories, velocities and deceleration rates. The measurements and velocity/deceleration data analysis are described in Sections II and III. The main point of this paper is to use these deceleration rate data, together with results from a computer model, to determine values of the quantities mPs 2 (the meteor mass times its material density squared); and further, by combining these m s 2 values with meteor mass estimates for the same set of meteors determined separately from measured radar scattering

Zinn, J.↗
DOE OSTI2022

Computational study of inertial migration of prolate particles in a straight rectangular channel

Inertial migration of spherical particles has been investigated extensively using experiments, theory, and computational modeling. Yet, a systematic investigation of the effect of particle shape on inertial migration is still lacking. Herein, we numerically mapped the migration dynamics of a prolate particle in a straight rectangular microchannel using smoothed particle hydrodynamics at moderate Reynolds number flows. After validation, we applied our model to 2:1 and 3:1 shape aspect ratio particles at multiple confinement ratios. Their effects on the final focusing position, rotational behavior, and transitional dynamics were studied. In addition to the commonly reported tumbling motion, for the first time, we identified a new logrolling behavior of a prolate ellipsoidal particle in the confined channel. This new behavior occurs when the confinement ratio is above an approximate threshold value of K = 0.72. Our microfluidic experiments using cell aggregates with similar shape aspect ratio and confinement ratio confirmed this new predicted logrolling motion. In this study, we also found that the same particle can undergo different rotational modes, including kayaking behavior, depending on its initial cross-sectional position and orientation. Furthermore, we examined the migration speed, angular velocity, and rotation period as well as their dependence on both particle shape aspect ratio and confinement ratio. Our findings are especially relevant to the applications where particle shape and alignment are used for sorting and analysis, such as the use of barcoded particles for biochemical assays through optical reading, or the shape-based enrichment of microalgae, bacteria, and chromosomes.

42 ENGINEERING↗
DOE OSTI2022

Aerodynamic Analysis of Wind Loading on Parabolic Trough Collectors Using High-Fidelity CFD Modeling

Wind loading on parabolic solar collector support structures is the key driver for material costs. As identified by NREL's CSP Best Practices project [1], ability to predict wind-loading in deep-arrays and the impact of edge effects has potential to decrease levelized cost of electricity (LCoE) by 15%. In this study, we present a high-fidelity computational model that can model the flow around deep arrays of parabolic troughs under varying turbulent inflow conditions. Ability to accurately reproduce atmospheric boundary layer (ABL) profiles is first demonstrated through Large Eddy Simulation (LES) simulations and compared against wind tunnel measurements. Thereafter, unsteady turbulent flow past single row and six row configurations is computed using hybrid RANS/LES simulations. Investigations of wind loading, through parametric tilt angle variations of single row and on waked collectors in six-row configuration, are used to highlight design considerations for parabolic troughs only possible through high-fidelity unsteady calculations.

aerodynamics↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
DOE OSTI2021

Nonequilibrium Physics of Multiphase Flow in Porous Media: Wettability and Disorder

Carbon capture and geologic storage, dissociation of methane hydrates in permafrost, enhanced oil recovery, and water dropout in low-temperature fuel cells, all have something in common: two or more fluids flow simultaneously through a porous medium; and the displacement of one fluid by another is often unstable (either due to gravity or viscous forces). Yet, our ability to model multiphase flow in porous media has remained a challenge. The traditional equations are unable to predict, explain, or even reproduce, the formation of the complex patterns observed in experiments. The overarching goal of the project is to develop new physical understanding of the role of wettability and disorder in multiphase flow through permeable media, and to develop new mathematical and computational models at the pore scale and at the continuum scale.

58 GEOSCIENCES↗
NASA NTRS1993

Multi-dimensional modeling of pressurization and expulsion of multi-phase hydrogen propellant

A multidimensional computational model of the pressurization process in a slush hydrogen propellant storage tank was developed and its accuracy evaluated by comparison to experimental data measured for a 5 ft diameter spherical tank. The fluid mechanic, thermodynamic, and heat transfer processes within the ullage are represented by a finite-volume model. the heat and mass fluxes at the ullage boundary were computed in auxiliary analyses and specified as input to the finite-volume model. the model was shown to be in reasonable agreement with the experimental data. A parameter study was undertaken to examine the dependence of the pressurization process on initial ullage temperature distribution and pressurant mass flow rate. It is shown that for a given heat flux rate at the ullage boundary, the pressurization process is nearly independent of initial temperature distribution. The mass flow rate study revealed decreasing pressurant mass requirements with increasing pressurant mass flow rate. Further, significant differences were identified between the ullage temperature and velocity fields predicted for pressurization of slush and those predicted for pressurization of liquid hydrogen. A simplified model of the pressurization process was constructed in search of a dimensionless characterization of the pressurization process. It is shown that the relationship derived from this simplified model collapses all of the pressure history data generated during this study into a single curve.

Source record↗
NASA NTRS2006

Boundary Condition for Modeling Semiconductor Nanostructures

A recently proposed boundary condition for atomistic computational modeling of semiconductor nanostructures (particularly, quantum dots) is an improved alternative to two prior such boundary conditions. As explained, this boundary condition helps to reduce the amount of computation while maintaining accuracy.

Lee, Seungwon↗
NASA NTRS2003

Atomistic Modeling of Nanostructures via the BFS Quantum Approximate Method

Ideally, computational modeling techniques for nanoscopic physics would be able to perform free of limitations on the type and number of elements, while providing comparable accuracy when dealing with bulk or surface problems. Computational efficiency is also desirable, if not mandatory, for properly dealing with the complexity of typical nano-strucured systems. A quantum approximate technique, the BFS method for alloys, which attempts to meet these demands, is introduced for the calculation of the energetics of nanostructures. The versatility of the technique is demonstrated through analysis of diverse systems, including multi-phase precipitation in a five element Ni-Al-Ti-Cr-Cu alloy and the formation of mixed composition Co-Cu islands on a metallic Cu(III) substrate.

Bozzolo, Guillermo↗
DOE OSTI2020

Predictive Modeling and Diagnostic Monitoring of Extreme Science Workflows (Final Report)

This proposal addresses a critical issue of performance prediction identified in the report from the ASCR \Computational Modeling of Big Networks (COMBINE)" workshop: "end-to-end performance is not predictable due to a variety of factors. Even when some performance forecasts or predictions can be made, they often cannot explain the reasons why some predictions fail." We will develop new analytical models to predict the end-to-end performance of scientific workflows on DOE computing infrastructures, and use simulations and experimentation to validate and refine these models, as well as to pinpoint the sources of model inaccuracy. We will also use these models to help diagnose application and infrastructure problems, and to adapt the system based on this diagnosis. This section provides background in the areas relevant to the proposed work. RPI’s specific tasks within the Panorama project are as follows: (1) Develop Aspen-Simulation interface for Workflow Model Driven Simulation. (2) Validate manual performance models of two target workflow scenarios with empirical measurement and simulation. (3) Extend ROSS-Aspen API to simulate workflow descriptions when required. (4) Validate Aspen performance models of two target workflow scenarios with automatic performance model empirical measurement and simulation. (5) Design and implement final system to automatically generate Aspen performance models from workflow descriptions (including methods to compensate for limitations of Aspen analytical models). (6) Validate improved Aspen performance models with target workflow on production infrastructure. To date, all the project milestones assigned to us where reached within the best of our abilities over the course of the project performance period. Below describes the key outcome from our collaborative research in a system named, Durango .

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Correlation tracking study for meter-class solar telescope on space shuttle

The theory and expected performance level of correlation trackers used to control the pointing of a solar telescope in space using white light granulation as a target were studied. Three specific trackers were modeled and their performance levels predicted for telescopes of various apertures. The performance of the computer model trackers on computer enhanced granulation photographs was evaluated. Parametric equations for predicting tracker performance are presented.

Smithson, R. C.↗
DOE OSTI2022

Adaptive Generation of Training Data for ML Reduced Model Creation

Machine learning proxy models are often used to speed up or completely replace complex computational models. The greatly reduced and deterministic computational costs enable new use cases such as digital twin control systems and global optimization. The challenge of building these proxy models is generating the training data. A naive uniform sampling of the input space can result in a non-uniform sampling of the output space of a model. This can cause gaps in the training data coverage that can miss finer scale details resulting in poor accuracy. While larger and larger data sets could eventually fill in these gaps, the computational burden of full-scale simulation codes can make this prohibitive. In this paper, we present an adaptive data generation method that utilizes uncertainty estimation to identify regions where training data should be augmented. By targeting data generation to areas of need, representative data sets can be generated efficiently. The effectiveness of this method will be demonstrated on a simple one-dimensional function and a complex multidimensional physics model.

Cianciosa, Mark↗
DOE OSTI2023

Predicting rare events using neural networks and short-trajectory data

Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic dynamical systems. When the event is rare in comparison with the timescales of simulation and/or measurement needed to resolve the elemental dynamics, accurate prediction from direct observations becomes challenging. In such cases a more effective approach is to cast statistics of interest as solutions to Feynman-Kac equations (partial differential equations). Here, we develop an approach to solve Feynman-Kac equations by training neural networks on short-trajectory data. Our approach is based on a Markov approximation but otherwise avoids assumptions about the underlying model and dynamics. This makes it applicable to treating complex computational models and observational data. Additionally, we illustrate the advantages of our method using a low-dimensional model that facilitates visualization, and this analysis motivates an adaptive sampling strategy that allows on-the-fly identification of and addition of data to regions important for predicting the statistics of interest. Finally, we demonstrate that we can compute accurate statistics for a 75-dimensional model of sudden stratospheric warming. This system provides a stringent test bed for our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Verification of MCNP Critical Benchmark Model of U233-COMP-THERM-004

Los Alamos National Laboratory (LANL) has been working on creating a new centralized repository for MCNP models of critical benchmark experiments. The initial model of U233-COMP-THERM-004 was derived from the Whisper Suite provided with MCNP6.2, and was compared against the ICSBEP handbook chapter for the benchmark. Many notable errors were found in the initial model and were revised accordingly. Comparing the computational model of the old and new models confirmed that any Whisper results that relied upon the old model are still valid.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Neutronics Calculation Advances at Los Alamos: Manhattan Project to Monte Carlo

The history and advances of neutronics calculations at Los Alamos during the Manhattan Project through the present are reviewed. Substantial improvements to neutron diffusion methods and the invention of both the Monte Carlo neutron transport methods in 1947 and deterministic discrete ordinates Sn in 1953 were all made at Los Alamos just after the Manhattan Project. We briefly summarize early simpler and more approximate neutronics methods and then describe the need to better predict neutronics behavior through consideration of theoretical equations, models and algorithms, experimental measurements, and available computing capabilities and their limitations. This paper briefly covers key advances in deterministic methods during the Manhattan Project. These capabilities, coupled with increasing postwar defense needs and the invention of electronic computing with the Electronic Numeric Integrator and Computer, known as ENIAC, and the Mathematical Analyzer Numerical Integrator and Automatic Computer Model, known as MANIAC, led to the creation of Monte Carlo and deterministic discrete ordinates neutronics transport methods. We note the important role that the scientific comradery between the Los Alamos scientists played in the process. This paper briefly covers the early methods, algorithms, computers, and electronic and women pioneers that enabled Monte Carlo to spread to all areas of science. We focus heavily on these early developments and the subsequent creation of the MCNP® code, advances in its associated nuclear data, and its applications to problems of national defense at Los Alamos.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRSDate not supplied

Liquid Hydrogen Tank Chill and No-Vent Fill Prediction using Computational Fluid Dynamics

Cryogenic tank chill and fill is an important cryogenic fluid management technology that supports and enables many of NASA’s long-duration space missions. For no-vent fill, the receiver tank pressure remains below the supply tank pressure during the entire duration of the fill so that the tank does not require venting. This is especially advantageous for tank fill operations in low gravity where the position of the liquid is not always known and venting the tank may cause loss of propellant by venting liquid. In lieu of expensive tests conducted on-orbit, accurate computational models capable of predicting receiver tank pressure during cryogenic propellant tank fill may be used to reduce system and propellant mass as well as mission risk. However, these numerical models must be validated or anchored to test data. This study presents computational fluid dynamics (CFD) models with conjugate heat transfer that are used to predict a liquid hydrogen tank chill and no-vent fill ground test conducted at Lewis Research Center (now Glenn Research Center) in 1991. The specific test case chosen for model validation implemented an upward-facing jet near the bottom of a cylindrical 34-liter tank. CFD predictions are compared to experimental measurements of tank pressure, fill level, and wall temperature. The CFD results show reasonable agreement to the test data but overpredict the pressure collapse near the end of the fill despite the liquid jet penetrating the liquid-vapor interface.

Computational Fluid Dynamics↗