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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Updates to the Predictive Materials Modeling Software Tools

Updates on NASA's efforts to build predictive material models from the micro-scale to the macro-scale are presented. To complement the mission design cycle process and reduce the need for extensive testing, NASA is developing modeling and simulation tools that enable characterizing material properties and response to hot plasma experienced during atmospheric entry. The PuMA (microstructure analysis), PATO (macroscale material response), SPARTA (direct simulation Monte Carlo) and ARCHeS (arc heater modeling) codes are described. A range of applications, encompassing calculating effective material properties, study of high shear boundary layer flow over woven materials, new model of silicone-based coatings, new model of mechanical erosion, as well as approach to loose multi-physics coupling, are presented.

material modeling↗
NASA NTRSDate not supplied

Updates on the Predictive Materials Modeling Software Tools

Updates on NASA‘s efforts to build a Predictive Material Modeling (PMM) framework from the micro-scale to the macro-scale are presented in this abstract. The PMM effort is part of the Entry Systems Modeling (ESM) project under NASA’s Game Changing Development (GCD) program. To reduce the need for extensive testing and accelerate the design cycle process, ESM is developing simulation and modeling tools that enable the characterization of the properties of thermal protection materials and their response to extremely hot plasma. The Porous Microstructure Analysis (PuMA) software has been developed to compute effective material properties and perform material response simulations on digitized microstructures of porous media. PuMA is able to import three-dimensional digital images obtained from X-ray microtomography or to generate artificial microstructures that mimic real materials. PuMA also provides a module for interactive 3D visualizations. Version 3, which was recently released as open-source, includes modules to compute simple morphological properties such as porosity, volume fractions, pore diameter, and specific surface area. Additional capabilities include the determination of effective thermal and electrical conductivity (both radiative and solid conduction - including the ability to simulate local anisotropy for the latter); effective diffusivity and tortuosity from the continuum to the rarefied regime; techniques to determine the local material orientation, as well as mechanical properties (elasticity coefficients), and permeability. Computed properties are then used to inform a macro-scale material response model, such as those implemented in the Porous material Analysis Toolbox based on OpenFOAM (PATO) software developed within ESM. The computational model in PATO is a generic heat and mass transfer model for porous reactive materials containing several solid phases and a single gas phase. The detailed chemical interactions occurring between the solid phases and the gas phase are modeled at the pore scale, assuming Local Thermal Equilibrium. Recent efforts include the development of a mechanical erosion model as well as a unified model allowing an intrinsic coupling between fluid and material. Comparison to flight data (Mars Science Laboratory [MSL] Entry Descent and Landing Instrument [MEDLI] and Mars 2020 MEDLI2) is critical in order to validate these computational tools. Examples of ablative material response using the code will be presented, including 3D simulations of the full-scale heatshield of the MSL capsule. The simulations demonstrated the ability of the modern material response code, PATO, to handle the material response of geometrically complex and large domains through the use of massively parallel computations.

material modeling↗
NASA NTRS2008

Predicting Material Performance in the Space Environment from Laboratory Test Data, Static Design Environments, and Space Weather Models

Qualifying materials for use in the space environment is typically accomplished with laboratory exposures to simulated UV/EUV, atomic oxygen, and charged particle radiation environments with in-situ or subsequent measurements of material properties of interest to the particular application. Choice of environment exposure levels are derived from static design environments intended to represent either mean or extreme conditions that are anticipated to be encountered during a mission. The real space environment however is quite variable. Predictions of the on orbit performance of a material qualified to laboratory environments can be done using information on 'space weather' variations in the real environment. This presentation will first review the variability of space environments of concern for material degradation and then demonstrate techniques for using test data to predict material performance in a variety of space environments from low Earth orbit to interplanetary space using historical measurements and space weather models.

Minow, Josep I.↗
NASA NTRS1985

Evaluation and prediction of long-term environmental effects on non metallic materials

Predictive modeling of environmental conditions on nonmetallic materials was studied. The in-flight data of the atomic oxygen reaction with carbon and osmium, the laboratory and in-flight data of the atomic oxygen reaction with polymeric films and the effect of electron irradiation on the rates of oxidation are discussed. No information is found that can be used to model such effects on composites. The effects of the space environment on thermal control coatings and its effect on the space station are examined.

Papazian, H.↗
NASA NTRS2000

Modeling Natural Space Ionizing Radiation Effects on External Materials

Predicting the effective life of materials for space applications has become increasingly critical with the drive to reduce mission cost. Programs have considered many solutions to reduce launch costs including novel, low mass materials and thin thermal blankets to reduce spacecraft mass. Determining the long-term survivability of these materials before launch is critical for mission success. This presentation will describe an analysis performed on the outer layer of the passive thermal control blanket of the Hubble Space Telescope. This layer had degraded for unknown reasons during the mission, however ionizing radiation (IR) induced embrittlement was suspected. A methodology was developed which allowed direct comparison between the energy deposition of the natural environment and that of the laboratory generated environment. Commercial codes were used to predict the natural space IR environment model energy deposition in the material from both natural and laboratory IR sources, and design the most efficient test. Results were optimized for total and local energy deposition with an iterative spreadsheet. This method has been used successfully for several laboratory tests at the Marshall Space Flight Center. The study showed that the natural space IR environment, by itself, did not cause the premature degradation observed in the thermal blanket.

Alstatt, Richard L.↗
NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

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 ML 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 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 ML models and the defining model parameters and architectures within the Granta MI Platform. 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 the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, 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.

Brandon L. Hearley↗
NASA NTRS1974

Prediction and verification of creep behavior in metallic materials and components, for the space shuttle thermal protection system. Volume 1, phase 1: Cyclic materials creep predictions

Cyclic creep response was investigated and design methods applicable to thermal protection system structures were developed. The steady-state (constant temperature and load) and cyclic creep response characteristics of four alloys were studied. Steady-state creep data were gathered through a literature survey to establish reference data bases. These data bases were used to develop empirical equations describing creep as a function of time, temperature, and stress and as a basis of comparison for test data. Steady-state creep tests and tensile cyclic tests were conducted. The following factors were investigated: material thickness and rolling direction; material cyclic creep response under varying loads and temperatures; constant stress and temperature cycles representing flight conditions; changing stresses present in a creeping beam as a result of stress redistribution; and complex stress and temperature profiles representative of space shuttle orbiter trajectories. A computer program was written, applying creep hardening theories and empirical equations for creep, to aid in analysis of test data. Results are considered applicable to a variety of structures which are cyclicly exposed to creep producing thermal environments.

Davis, J. W.↗
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 NTRS1975

Life prediction of materials exposed to monotonic and cyclic loading: Bibliography

This bibliography is comprised of approximately 1200 reference citations related to the mechanics of failure in aerospace structures. Most of the references are for information on life prediction for materials exposed to monotonic and cyclic loading in elevated temperature environments such as that in the hot end of a gas turbine engine. Additional citations listed are for documents on the thermal and mechanical effects on solar cells in the cryogenic vacuum environment; radiation effects on high temperature mechanical properties; and high cycle fatigue technology as applicable to gas turbine engine bearings. The bibliography represents a search of the literature published in the period April 1962 through April 1974 and is largely limited to documents published in the United States. It is a companion volume to NASA CR-134750, Life Prediction of Materials Exposed to Monotonic and cyclic Loading - A Technology Survey.

Carpenter, J. L., Jr.↗
NASA NTRS1975

Life prediction of materials exposed to monotonic and cyclic loading: A new technology survey

Reviewed and evaluated technical abstracts for about 100 significant documents are reported relating primarily to life prediction for structural materials exposed to monotonic and cyclic loading, particularly in elevated temperature environments. The abstracts in the report are mostly for publications in the period April 1962 through April 1974. The purpose of this report is to provide, in quick reference form, a dependable source for current information

Stuhrke, W. F.↗
NASA NTRS1994

Damage and strength of composite materials: Trends, predictions, and challenges

Research on damage mechanisms and ultimate strength of composite materials relevant to scaling issues will be addressed in this viewgraph presentation. The use of fracture mechanics and Weibull statistics to predict scaling effects for the onset of isolated damage mechanisms will be highlighted. The ability of simple fracture mechanics models to predict trends that are useful in parametric or preliminary designs studies will be reviewed. The limitations of these simple models for complex loading conditions will also be noted. The difficulty in developing generic criteria for the growth of these mechanisms needed in progressive damage models to predict strength will be addressed. A specific example for a problem where failure is a direct consequence of progressive delamination will be explored. A damage threshold/fail-safety concept for addressing composite damage tolerance will be discussed.

Obrien, T. Kevin↗
NASA NTRS1972

Development of uniform and predictable battery materials for nickel-cadmium aerospace cells

The objective of this study was to analyze battery materials and manufacturing methods with the aim of developing uniform and predictable battery plates for nickel cadmium aerospace cells. The results are presented of an extensive factorially-designed experiment aimed at illuminating, in a comparative study, the effect of plaque preparation, plaque thickness, impregnation process, and loading level with active material. The following impregnation processes were investigated: (1) chemical conversion, (2) electrochemical conversion, and (3) high temperature electrochemical impregnation. The test parameter on which the conclusion regarding the manufacturing variables are based was the capacity change during a simulated near-earth orbit cycle regime. As operational variables, the effect of various charge and discharge rates was investigated. During the 100 test cycles, overall capacity changes of positive plates and the differences between the various plates were found to be small. Plates prepared by the high temperature electrochemical impregnation of slurry-coated plaque (at the conditions used) showed surface buildup of active material. They also retained slightly less of their capacity during cycling compared to other plate preparations.

Source record↗
NASA NTRS1971

Development of uniform and predictable battery materials for nickel-cadmium aerospace cells

Battery materials and manufacturing methods were analyzed with the aim of developing uniform and predictable battery plates for nickel cadmium aerospace cells. A study is presented for the high temperature electrochemical impregnation process for the preparation of nickel cadmium battery plates. This comparative study is set up as a factorially designed experiment to examine both manufacturing and operational variables and any interaction that might exist between them. The manufacturing variables in the factorial design include plaque preparative method, plaque porosity and thickness, impregnation method, and loading, The operational variables are type of duty cycle, charge and discharge rate, extent of overcharge, and depth of discharge.

Source record↗
NASA NTRS1985

Two simplified procedures for predicting cyclic material response from a strain history

Simplified inelastic analysis procedures were developed at NASA Lewis and Pratt & Whitney Aircraft for predicting the stress-strain response at the critical location of a thermomechanically cycled structure. These procedures are intended primarily for use as economical structural analysis tools in the early design stages of aircraft engine hot section components where nonlinear finite-element analyses would be prohibitively expensive. Both simplified methods use as input the total strain history calculated from a linear elastic analysis. The elastic results are modified to approximate the characteristics of the inelastic cycle by incremental solution techniques. A von Mises yield criterion is used to determine the onset of active plasticity. The fundamental assumption of these methods is that the inelastic strain is local and constrained from redistribution by the surrounding elastic material.

Kaufman, A.↗
NASA NTRS2009

Prediction of Material Properties of Nanostructured Polymer Composites Using Atomistic Simulations

Atomistic models of epoxy polymers were built in order to assess the effect of structure at the nanometer scale on the resulting bulk properties such as elastic modulus and thermal conductivity. Atomistic models of both bulk polymer and carbon nanotube polymer composites were built. For the bulk models, the effect of moisture content and temperature on the resulting elastic constants was calculated. A relatively consistent decrease in modulus was seen with increasing temperature. The dependence of modulus on moisture content was less consistent. This behavior was seen for two different epoxy systems, one containing a difunctional epoxy molecule and the other a tetrafunctional epoxy molecule. Both epoxy structures were crosslinked with diamine curing agents. Multifunctional properties were calculated with the nanocomposite models. Molecular dynamics simulation was used to estimate the interfacial thermal (Kapitza) resistance between the carbon nanotube and the surrounding epoxy matrix. These estimated values were used in a multiscale model in order to predict the thermal conductivity of a nanocomposite as a function of the nanometer scaled molecular structure.

Hinkley, J.A.↗