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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Wavelet Techniques Applied to Modeling Transitional/Turbulent Flows in Turbomachinery

Computer simulation is an essential part of the design and development of jet engines for the aeropropulsion industry. Engineers concerned with calculating the flow in jet engine components, such as compressors and turbines, need simple engineering models that accurately describe the complex flow of air and gases and that allow them to quickly estimate loads, losses, temperatures, and other design parameters. In this ongoing collaborative project, advanced wavelet analysis techniques are being used to gain insight into the complex flow phenomena. These insights, which cannot be achieved by commonly used methods, are being used to develop innovative new flow models and to improve existing ones. Wavelet techniques are very suitable for analyzing the complex turbulent and transitional flows pervasive in jet engines. These flows are characterized by intermittency and a multitude of scales. Wavelet analysis results in information about these scales and their locations. The distribution of scales is equivalent to the frequency spectrum provided by commonly used Fourier analysis techniques; however, no localization information is provided by Fourier analysis. In addition, wavelet techniques allow conditional sampling analyses of the individual scales, which is not possible by Fourier methods.

Source record↗
NASA NTRS2017

Computational Modeling of Space Physiology for Informing Spaceflight Countermeasure Design and Predictions of Efficacy

MOTIVATION: Spaceflight countermeasures mitigate the harmful effects of the space environment on astronaut health and performance. Exercise has historically been used as a countermeasure to physical deconditioning, and additional countermeasures including lower body negative pressure, blood flow occlusion and artificial gravity are being researched as countermeasures to spaceflight-induced fluid shifts. The NASA Digital Astronaut Project uses computational models of physiological systems to inform countermeasure design and to predict countermeasure efficacy.OVERVIEW: Computational modeling supports the development of the exercise devices that will be flown on NASAs new exploration crew vehicles. Biomechanical modeling is used to inform design requirements to ensure that exercises can be properly performed within the volume allocated for exercise and to determine whether the limited mass, volume and power requirements of the devices will affect biomechanical outcomes. Models of muscle atrophy and bone remodeling can predict device efficacy for protecting musculoskeletal health during long-duration missions. A lumped-parameter whole-body model of the fluids within the body, which includes the blood within the cardiovascular system, the cerebral spinal fluid, interstitial fluid and lymphatic system fluid, estimates compartmental changes in pressure and volume due to gravitational changes. These models simulate fluid shift countermeasure effects and predict the associated changes in tissue strain in areas of physiological interest to aid in predicting countermeasure effectiveness. SIGNIFICANCE: Development and testing of spaceflight countermeasure prototypes are resource-intensive efforts. Computational modeling can supplement this process by performing simulations that reduce the amount of necessary experimental testing. Outcomes of the simulations are often important for the definition of design requirements and the identification of factors essential in ensuring countermeasure efficacy.

biomedical data↗
NASA NTRS2004

Advanced Computational Modeling of Vapor Deposition in a High-pressure Reactor

In search of novel approaches to produce new materials for electro-optic technologies, advances have been achieved in the development of computer models for vapor deposition reactors in space. Numerical simulations are invaluable tools for costly and difficult processes, such as those experiments designed for high pressures and microgravity conditions. Indium nitride is a candidate compound for high-speed laser and photo diodes for optical communication system, as well as for semiconductor lasers operating into the blue and ultraviolet regions. But InN and other nitride compounds exhibit large thermal decomposition at its optimum growth temperature. In addition, epitaxy at lower temperatures and subatmospheric pressures incorporates indium droplets into the InN films. However, surface stabilization data indicate that InN could be grown at 900 K in high nitrogen pressures, and microgravity could provide laminar flow conditions. Numerical models for chemical vapor deposition have been developed, coupling complex chemical kinetics with fluid dynamic properties.

Cardelino, Beatriz H.↗
NASA NTRS2004

Advanced Computational Modeling of Vapor Deposition in a High-Pressure Reactor

In search of novel approaches to produce new materials for electro-optic technologies, advances have been achieved in the development of computer models for vapor deposition reactors in space. Numerical simulations are invaluable tools for costly and difficult processes, such as those experiments designed for high pressures and microgravity conditions. Indium nitride is a candidate compound for high-speed laser and photo diodes for optical communication system, as well as for semiconductor lasers operating into the blue and ultraviolet regions. But InN and other nitride compounds exhibit large thermal decomposition at its optimum growth temperature. In addition, epitaxy at lower temperatures and subatmospheric pressures incorporates indium droplets into the InN films. However, surface stabilization data indicate that InN could be grown at 900 K in high nitrogen pressures, and microgravity could provide laminar flow conditions. Numerical models for chemical vapor deposition have been developed, coupling complex chemical kinetics with fluid dynamic properties.

Cardelino, Beatriz H.↗
DOE OSTI2025

Model Form Error Correction for a Black-Box Thermal Battery Heat Transfer Simulation

Thermal batteries are crucial for supplying power to high-consequence engineering applications such as rockets. Computational simulations have been developed to predict thermal battery behavior, but these simulations often suffer from modeling errors, including model form uncertainty. Addressing this uncertainty can be achieved by quantifying either the model discrepancy in the output or the model form error (MFE) in the governing equation. MFE is particularly valuable as it can be better extrapolated beyond observed outputs, which is essential for predictions involving changes in external system loading, system configuration and geometry, or output quantities. This paper employs a state estimation approach to estimate MFE using experimental data and then utilizes machine learning (ML) to model its relationship with state variables. A nonintrusive technique is used to estimate MFE in a black-box thermal battery heat transfer simulation. The trained machine learning model for MFE is then applied to correct simulation predictions under extrapolated initial conditions and battery configurations. In conclusion, the methodology's performance is evaluated using additional experimental data, demonstrating its effectiveness in improving prediction accuracy.

Batteries↗
NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗
NASA NTRS2005

Forecasting of Storm Surge Floods Using ADCIRC and Optimized DEMs

Increasing the accuracy of storm surge flood forecasts is essential for improving preparedness for hurricanes and other severe storms and, in particular, for optimizing evacuation scenarios. An interactive database, developed by WorldWinds, Inc., contains atlases of storm surge flood levels for the Louisiana/Mississippi gulf coast region. These atlases were developed to improve forecasting of flooding along the coastline and estuaries and in adjacent inland areas. Storm surge heights depend on a complex interaction of several factors, including: storm size, central minimum pressure, forward speed of motion, bottom topography near the point of landfall, astronomical tides, and most importantly, maximum wind speed. The information in the atlases was generated in over 100 computational simulations, partly by use of a parallel-processing version of the ADvanced CIRCulation (ADCIRC) model. ADCIRC is a nonlinear computational model of hydrodynamics, developed by the U.S. Army Corps of Engineers and the US Navy, as a family of two- and three-dimensional finite element based codes. It affords a capability for simulating tidal circulation and storm surge propagation over very large computational domains, while simultaneously providing high-resolution output in areas of complex shoreline and bathymetry. The ADCIRC finite-element grid for this project covered the Gulf of Mexico and contiguous basins, extending into the deep Atlantic Ocean with progressively higher resolution approaching the study area. The advantage of using ADCIRC over other storm surge models, such as SLOSH, is that input conditions can include all or part of wind stress, tides, wave stress, and river discharge, which serve to make the model output more accurate.

Valenti, Elizabeth↗
NASA NTRS1985

A computer simulation of the turbocharged turbo compounded diesel engine system: A description of the thermodynamic and heat transfer models

A computer simulation of the turbocharged turbocompounded direct-injection diesel engine system was developed in order to study the performance characteristics of the total system as major design parameters and materials are varied. Quasi-steady flow models of the compressor, turbines, manifolds, intercooler, and ducting are coupled with a multicylinder reciprocator diesel model, where each cylinder undergoes the same thermodynamic cycle. The master cylinder model describes the reciprocator intake, compression, combustion and exhaust processes in sufficient detail to define the mass and energy transfers in each subsystem of the total engine system. Appropriate thermal loading models relate the heat flow through critical system components to material properties and design details. From this information, the simulation predicts the performance gains, and assesses the system design trade-offs which would result from the introduction of selected heat transfer reduction materials in key system components, over a range of operating conditions.

Assanis, D. N.↗
NASA NTRS1974

Four-D global reference atmosphere technical description, part 1

An empirical atmospheric model was developed which generates values for pressure, density, temperature, and winds from surface levels to orbital altitudes. The output parameters consist of components for: (1) latitude, longitude, and altitude dependent monthly and annual means; (2) quasi-biennial oscillations; and (3) random perturbations to simulate partially the variability due to synoptic, diurnal, planetary wave, and gravity wave variations. Quasi-biennial and random variation perturbations are computed from parameters determined from various empirical studies and are added to the monthly mean values. This model has been developed as a computer program called PROFILE which can be used to generate altitude profiles of atmospheric parameters along any simulated trajectory through the atmosphere. The PROFILE program was developed for design applications in the space shuttle program. Other applications of the model are discussed, such as for global circulation and diffusion studies, and for generating profiles for comparison with other atmospheric measurement techniques, (e.g. satellite measured temperature profiles).

Justus, C. G.↗
NASA NTRS2006

Forecasting of Storm-Surge Floods Using ADCIRC and Optimized DEMs

Increasing the accuracy of storm-surge flood forecasts is essential for improving preparedness for hurricanes and other severe storms and, in particular, for optimizing evacuation scenarios. An interactive database, developed by WorldWinds, Inc., contains atlases of storm-surge flood levels for the Louisiana/Mississippi gulf coast region. These atlases were developed to improve forecasting of flooding along the coastline and estuaries and in adjacent inland areas. Storm-surge heights depend on a complex interaction of several factors, including: storm size, central minimum pressure, forward speed of motion, bottom topography near the point of landfall, astronomical tides, and, most importantly, maximum wind speed. The information in the atlases was generated in over 100 computational simulations, partly by use of a parallel-processing version of the ADvanced CIRCulation (ADCIRC) model. ADCIRC is a nonlinear computational model of hydrodynamics, developed by the U.S. Army Corps of Engineers and the US Navy, as a family of two- and three-dimensional finite-element-based codes. It affords a capability for simulating tidal circulation and storm-surge propagation over very large computational domains, while simultaneously providing high-resolution output in areas of complex shoreline and bathymetry. The ADCIRC finite-element grid for this project covered the Gulf of Mexico and contiguous basins, extending into the deep Atlantic Ocean with progressively higher resolution approaching the study area. The advantage of using ADCIRC over other storm-surge models, such as SLOSH, is that input conditions can include all or part of wind stress, tides, wave stress, and river discharge, which serve to make the model output more accurate. To keep the computational load manageable, this work was conducted using only the wind stress, calculated by using historical data from Hurricane Camille, as the input condition for the model. Hurricane storm-surge simulations were performed on an eight-node Linux computer cluster. Each node contained dual 2-GHz processors, 2GB of memory, and a 40GB hard drive. The digital elevation model (DEM) for this region was specified using a combination of Navy data (over water), NOAA data (for the coastline), and optimized Interferometric Synthetic Aperture Radar data (over land). This high-resolution topographical data of the Mississippi coastal region provided the ADCIRC model with improved input with which to calculate improved storm-surge forecasts.

Valenti, Elizabeth↗
DOE OSTI2022

Development and Validation of Resistance-Capacitance Model for Phase Change Material Embedded in Porous Media

A simple and effective way to enhance PCM thermal conductivity is by embedding porous materials with high thermal conductivities. In this work, CFD was used to evaluate the melting of PCM embedded in metal foam supplied with constant heat flux. Good agreement between experimental and numerical results has been found with a mean PCM local temperature deviation of 1.62K and 2.97K in two locations. The CFD simulation time was 56 hours for 6.5 hours of flow time. A computationally efficient resistance capacitance-based model (RCM) was developed to improve simulation speed. The proposed RCM exhibited mean local temperature deviations of 1.86K and 2.96K when compared against the experimental data for two thermocouple positions, however, it was approximately 2.5x10 5 times faster than CFD. The charging time deviation was less than 1% compared to CFD, and no significant change in thermal energy stored was observed since the temperature differences were small enough to result in insignificant sensible heat deviation. A simulation cost-effectiveness index (CEI) considering accuracy and computational affordability was introduced; the CEI of the RCM solver is 10 3 times better compared to CFD for the prediction of local PCM temperature.

25 ENERGY STORAGE↗
DOE OSTI2022

Modeling Analog Tile-Based Accelerators Using SST

Analog computing has been widely proposed to improve the energy efficiency of multiple important workloads including neural network operations, and other linear algebra kernels. To properly evaluate analog computing and explore more complex workloads such as systems consisting of multiple analog data paths, system level simulations are required. Moreover, prior work on system architectures for analog computing often rely on custom simulators creating signficant additional design effort and complicating comparisons between different systems. To remedy these issues, this report describes the design and implementation of a flexible tile-based analog accelerator element for the Structural Simulation Toolkit (SST). The element focuses on heavily on the tile controller—an often neglected aspect of prior work—that is sufficiently versatile to simulate a wide range of different tile operations including neural network layers, signal processing kernels, and generic linear algebra operations without major constraints. The tile model also interoperates with existing SST memory and network models to reduce the overall development load and enable future simulation of heterogeneous systems with both conventional digital logic and analog compute tiles. Finally, both the tile and array models are designed to easily support future extensions as new analog operations and applications that can benefit from analog computing are developed.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Investigation of beamed-energy ERH thruster performance

The objective of this study was to determine the performance of an External Radiation Heated (ERH) thruster. In this thruster, high intensity laser energy is focused to ignite either a Laser Supported Combustion (LSC) wave or a Laser Supported Detonation (LSD) wave. Thrust is generated as the LSC or LSD wave propagates over the thruster's surface, or in the proposed thruster configuration, the vehicle afterbody. Thrust models for the LSC and LSD waves were developed and simulated on a computer. Performance parameters investigated include the effect of laser intensity, flight Mach number, and altitude on mean-thrust and coupling coefficient of the ERH thruster. Results from these models suggest that the ERH thruster using LSC/LSD wave ignition could provide propulsion performance considerably greater than any propulsion system currently available.

Myrabo, Leik N.↗
NASA NTRS1998

The detection of clouds, aerosols and marine atmospheric boundary layer characteristics from simulated GLAS data

Scheduled for launch in 2001 as part of NASA's Earth Observing System (EOS), the Geoscience Laser Altimeter System (GLAS) will provide continuous laser sounding of the earth's atmosphere from space for the first time. From its polar orbit about 600 km above the surface, GLAS will employ a 40 Hz solid state laser operating at 1064 nm to measure topography to an accuracy of 10 cm. Simultaneously, the atmospheric channels (1064 and 532 nm) of GLAS will provide profiles of atmospheric backscatter from 40 km to the ground with 75 meter vertical resolution (Spinhirne and Palm, 1996). These measurements will give scientists an unprecedented global data set on the vertical structure of clouds and aerosols which will greatly aid research efforts aimed at understanding their effects on climate and their role in climate change (Hartman, 1994). To better understand and predict the performance of the GLAS atmospheric channels, a computer model was developed to simulate the type of signal that the instrument would likely produce. The model uses aircraft lidar data and provides realistic simulated GLAS data sets over large areas spanning a wide range of atmospheric conditions. These simulated GLAS datasets are invaluable for designing and testing algorithms for the retrieval of parameters such as cloud and aerosol layer height, optical depth and extinction cross section. This work is currently proceeding and in this paper we will present results of the cloud and aerosol detection algorithm with emphasis on the detection of Marine Atmospheric Boundary Layer (MABL) aerosol. In addition, we use a recently developed technique to ascertain the feasability of estimating MABL moisture and temperature structure from spaceborne systems such as GLAS.

Palm, Stephen P.↗
DOE OSTI2026

Computationally inexpensive part-scale thermal history of additive friction-stir deposition

This study presents an analytical model for steady-state power generation and tool heat loss in additive friction-stir deposition (AFSD), developed to enable part-scale thermal simulation while remaining computationally inexpensive. The model predicts total generated power, yielding 3.7–4.7 kW across deposition temperature setpoints of 400–460 °C for the deposition of AA6061 with a Be-Cu tool. This corresponds to 90–95% of the reported spindle power. Tool heat loss is experimentally determined by calibrating a steady-state energy balance between the generated power, the substrate-deposition thermal gradient, and a temperature dependent tool heat loss term: q tool (T) = a + b (T - 400°C) with a = 2.7 x 10 6 Wm -2 and b = 9.5 x 10 3 Wm -2 K -1 . The calibration indicates that about 69% of the generated heat is conducted into the tool for this configuration, which is much higher than previously reported. The calibrated heat-source is implemented in finite element software (Adamantine) to simulate the transient thermal history of a 100 cm 3 representative build in 8 min on a standard desktop (at 0.635 mm build-height resolution). For the first three layers, the substrate temperatures between simulation and experiment are within 10% mean absolute percentage error. Sensitivity analysis indicates that uncertainties in average deposition temperature and deformation localization (stir-zone geometry, depth, and spatial dependance of strain-rate and flow stress) dominate model variance, motivating additional experimental verification.

Additive Friction-Stir Deposition↗
DOE OSTI2021

Deterioration of concrete due to ASR: Experiments and multiscale modeling

The process of ASR (Alkali-Silica Reaction) induced expansion and damage in pavement concrete specimens is investigated using laboratory experiments and computational modeling. In the experimental program, the concrete specimens are subject to CS-CPT (climate simulation concrete prism test) to obtain ASR induced expansion with and without external supply of alkali. The dissolution rates of the granodiorite used in the concrete mix and the gel formation rates are determined under concrete-like conditions (pH 13.8, with/without Ca(OH){sub 2} and NaCl) at different temperatures. A micromechanics based computational model with aggregate-scale diffusion and reaction kinetics coupled to an Eigenstrain based micromechanics damage model is developed for the simulation of ASR induced expansion and damage. Data from the experimental program are used to calibrate and validate the computational model. Model predictions show that for the given concrete mixture, ASR induced expansion in the specimen exposed to water is predominantly governed by microcracking in the aggregate, while the expansion in the specimen subjected to external alkali supply is governed by microcracking in both the aggregates and the cement paste.

36 MATERIALS SCIENCE↗
DOE OSTI2018

System-level risk assessment of carbon sequestration into a naturally fractured aquifer at Kevin Dome, Montana

As actual CO2 injection is unlikely to take place at Kevin Dome, Montana, the Big Sky Carbon Sequestration Partnership has turned to maximizing the value of existing data acquired at the site. We present the risk assessment work done using the National Risk Assessment Partnership (NRAP) to Kevin Dome, Montana. Geologic CO2 sequestration in saline aquifers poses certain risks including CO2/brine leakage through wells or non-sealing faults into ground water or land surface. These risks are difficult to quantify due to data availability and uncertainty. One solution is running large numbers of numerical simulations on the primary CO2 injection reservoir, shallow reservoirs/aquifers, faults, and wells to assess leakage risks and uncertainties. However, a full-physics simulation is usually too computationally expensive. NRAP integrated assessment model (NRAP-IAM) uses reduced order models (ROMs) developed from numerical reservoir simulations of a primary CO2 injection reservoir to address this issue. A powerful stochastic framework allows NRAP-IAM to explore complex interactions among many uncertain variables and evaluate the likely performance of potential sequestration sites. In this study, we investigate the sensitivity of a variety of uncertain parameters to CO2/brine leakage through (1) legacy wellbore and (2) fault pathways. We found major uncertain parameters to which the potential CO2 leakage through legacy wellbore is sensitive including values of fracture permeability, end-point CO2 relative permeability, capillary pressure, and permeability of confining rocks. CO2 and brine leakage through fault pathways is sensitive to fracture permeability, length of the faults, and fault displacement.

Nguyen, Minh↗
NASA NTRS1973

User's instructions for the cardiovascular Walters model

The model is a combined, steady-state cardiovascular and thermal model. It was originally developed for interactive use, but was converted to batch mode simulation for the Sigma 3 computer. The model has the purpose to compute steady-state circulatory and thermal variables in response to exercise work loads and environmental factors. During a computer simulation run, several selected variables are printed at each time step. End conditions are also printed at the completion of the run.

Croston, R. C.↗