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NASA NTRSDate not supplied

Modeling and Analysis of Stirling Power Convertors

Modeling and Analysis of Stirling Power Convertors Luis A. Rodriguez1 Steven M. Geng, Terry V. Reid, Scott D. Wilson NASA Glenn Research Center, Cleveland, OH, 44135, USA NASA Glenn’s Thermal Energy Conversion Branch is supporting the development of the next generation free-piston Stirling power convertors. American Superconductor (AMSC) and Sunpower Inc. are the two firms under contract to develop the Flexure Isotope Stirling Convertor (FISC) and the Sunpower Robust Stirling Convertor (SRSC), respectively. To comprehend and forecast convertor performance, Sage, ANSYS® Maxwell, and ANSYS® Fluent were used to model the Stirling thermodynamic cycle, alternator electromagnetics, and piston and displacer dynamics. I. Introduction Stirling convertors are being developed by NASA as a potential steady source of electrical power for NASA’s future scientific space missions. Currently, NASA Glenn Research Center has two corporations under contract, American Superconductor (AMSC) and Sunpower Inc., for the development of the next generation of free-piston Stirling convertors for dynamic radioisotope power systems. AMSC is developing the Flexure Isotope Stirling Convertor (FISC), which uses flexures to prevent side motion and rubbing of the piston. Similarly, Sunpower Inc, is developing the Sunpower Robust Stirling Convertor (SRSC). The SRSC uses gas bearings to prevent radial contact of the moving piston. As convertor development continues, it is increasingly important to understand and predict the interactions of components in the system, how they respond to one another, and how they perform as a response to changes in operating conditions. A suitable and enlightening way to demonstrate and foresee these interactions is with the use of accurate modeling software. Sage, ANSYS® Maxwell, and ANSYS® Fluent are the current modeling tools used by NASA to analytically determine convertor performance. Sage is a one-dimensional object-oriented commercial software package used for modeling and optimizing Stirling convertors for Dynamic Radioisotope Power Systems (DRPS) and it is one of the most accurate Stirling convertor codes in use by NASA. This code is the successor to GLIMPS (Globally-Implicit Stirling Cycle Simulation) and GLOP (GLIMPS Optimization) software created by Gedeon Associates [1]. Model input parameters are typically material/gas type, component physical dimensions, temperatures, frequency, charge pressure, and number of time/space nodes. Sage is used to model both the FISC’s and SRSC’s Stirling cycle thermodynamics and piston/displacer dynamics. Performance maps were created and analyzed for both power systems to better understand the relationship between the following conditions: cold-end temperature, hot-end temperature, piston/displacer amplitudes, pressure drop, and thermal input power. The synergy between these conditions will help determine parameter sensitivity. ANSYS® Maxwell was used to create a three-dimensional (3-D) axisymmetric model for both FISC and SRSC alternators. The significant physical components included in each model are the magnets, magnet carrier, outer/inner laminations, and the coil. Inputs to the model are piston amplitude, piston frequency, alternator load, coil resistance, tuning capacitance, and specific material properties. The alternator models calculate terminal voltage, current, piston/current phase, voltage/current phase, coil inductance, terminal power and efficiency. The RI2 losses, core (hysteresis and eddy) losses, and magnet/can eddy losses are also a part of the final results. ANSYS® Fluent is used to build 3-D computational fluid dynamic (CFD) models to examine the Stirling cycle thermodynamics for both the FISC and SRSC systems. Three-dimensional Computer Aided Design (CAD) models were used to create the physical components of each convertor. Steady-state simulations were conducted for hardware testing, prediction of environmental losses during testing, and generation of radiation look-up tables. The model inputs to the aforementioned analysis are the material properties and boundary thermal conditions. The steady-state model calculates temperature and heat flow distributions. Transient 3-D calculations were also part of the CFD analysis. In this study a physically reduced version of the FISC is used to obtain a prediction of available engine power. For the gas bearing SRSC, the transient effort is used to obtain a prediction of bearing pad performance and its sensitivity to micro-channel geometric variation. The model inputs to the transient simulations are the piston amplitude, displacer amplitude, frequency, displacer/piston phase angle, dynamic deforming CFD grid, temperature boundary conditions, and user defined files describing motion profile of piston/displacer. The results of the model are temperature distributions, heat distributions, and PV power produced at pre-determined conditions.

Luis A Rodriguez↗

NASA NTRS1973

Characterization of heat transfer in nutrient materials, part 2

A thermal model is analyzed that takes into account phase changes in the nutrient material. The behavior of fluids in low gravity environments is discussed along with low gravity heat transfer. Thermal contact resistance in the Skylab food heater is analyzed. The original model is modified to include: equivalent conductance due to radiation, radial equivalent conductance, wall equivalent conductance, and equivalent heat capacity. A constant wall-temperature model is presented.

Cox, J. E.↗

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 NTRS2017

3D Material Response Analysis of PICA Pyrolysis Experiments

Primarily interested in improving ablation modeling for use in inverse reconstruction of flight environments on ablative heat shields. Ablation model is essentially a component of the heat flux sensor, so model uncertainties lead to measurement uncertainties. Non-equilibrium processes have been known to be significant in low density ablators for a long time, but increased accuracy requirements of the reconstruction process necessitates incorporating this physical effect. Attempting to develop a pyrolysis model for implementation in material response based on the PICA data produced by Bessire and Minton. Pyrolysis gas species molar yields as a function of temperature and heating rate. Several problems encountered while trying to fit Arrhenius models to the data led to further investigation of the experimental setup.

Oliver, Brandon A.↗

NASA NTRSDate not supplied

Benchmarking and Performance of the NASA Multiscale Analysis Tool

The NASA Multiscale Analysis Tool (NASMAT) is as a “plug and play,” software package which utilizes multiscale recursive micromechanics as a platform for massively multiscale modeling of hierarchical materials and structures subjected to thermomechanical. This paper is intended to give an overview of the design of NASMAT and how the design supports modularity, upgradability and maintainability, interoperability, and utility. First, the software architecture and hierarchy will be explored. Details on each of the 11 NASMAT procedures and the arrangement of NASMAT data will be presented. Application program interfaces (APIs) that were developed to facilitate the communication of NASMAT with other programs will be described. The intended application for NASMAT is massively multiscale modeling on high performance computing systems. As such, results benchmarking the performance of the integration of NASMAT with the Abaqus commercial finite element method software are also presented.

Multiscale Modeling↗

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 NTRS2002

Probabilistic Fatigue: Computational Simulation

Fatigue is a primary consideration in the design of aerospace structures for long term durability and reliability. There are several types of fatigue that must be considered in the design. These include low cycle, high cycle, combined for different cyclic loading conditions - for example, mechanical, thermal, erosion, etc. The traditional approach to evaluate fatigue has been to conduct many tests in the various service-environment conditions that the component will be subjected to in a specific design. This approach is reasonable and robust for that specific design. However, it is time consuming, costly and needs to be repeated for designs in different operating conditions in general. Recent research has demonstrated that fatigue of structural components/structures can be evaluated by computational simulation based on a novel paradigm. Main features in this novel paradigm are progressive telescoping scale mechanics, progressive scale substructuring and progressive structural fracture, encompassed with probabilistic simulation. These generic features of this approach are to probabilistically telescope scale local material point damage all the way up to the structural component and to probabilistically scale decompose structural loads and boundary conditions all the way down to material point. Additional features include a multifactor interaction model that probabilistically describes material properties evolution, any changes due to various cyclic load and other mutually interacting effects. The objective of the proposed paper is to describe this novel paradigm of computational simulation and present typical fatigue results for structural components. Additionally, advantages, versatility and inclusiveness of computational simulation versus testing are discussed. Guidelines for complementing simulated results with strategic testing are outlined. Typical results are shown for computational simulation of fatigue in metallic composite structures to demonstrate the versatility of this novel paradigm in predicting a priori fatigue life.

Chamis, Christos C.↗

NASA NTRS2015

Application of the Refined Zigzag Theory to the Modeling of Delaminations in Laminated Composites

The Refined Zigzag Theory is applied to the modeling of delaminations in laminated composites. The commonly used cohesive zone approach is adapted for use within a continuum mechanics model, and then used to predict the onset and propagation of delamination in five cross-ply composite beams. The resin-rich area between individual composite plies is modeled explicitly using thin, discrete layers with isotropic material properties. A damage model is applied to these resin-rich layers to enable tracking of delamination propagation. The displacement jump across the damaged interfacial resin layer is captured using the zigzag function of the Refined Zigzag Theory. The overall model predicts the initiation of delamination to within 8% compared to experimental results and the load drop after propagation is represented accurately.

Groh, Rainer M. J.↗

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 NTRS2023

PMT F7 Silver Standard Material Properties Characterization

PMT F7 epoxy resin was characterized to capture the material's thermochemical and thermo-mechanical properties. DSC testing was performed and the data was used to develop a cure kinetics model which predicts the material cure response and specific heat capacity. TMA and DMA testing were performed and the data was used to develop thermo-mechanical models which predict the material CTE, cure shrinkage, and modulus evolution with cure. Convergent Manufacturing Technologies US determined test parameters, analyzed data, and performed model fitting. NASA completed tests and provided data to Convergent Manufacturing Technologies US.

Epoxy↗

NASA NTRS1987

A mathematical model for predicting cyclic voltammograms of electronically conductive polypyrrole

Polypyrrole is an attractive polymer for use as a high-energy-density secondary battery because of its potential as an inexpensive, lightweight, and noncorrosive electrode material. A mathematical model to simulate cyclic voltammograms for polypyrrole is presented. The model is for a conductive porous electrode film on a rotating disk electrode (RDE) and is used to predict the spatial and time dependence of concentration, overpotential, and stored charge profiles within a polypyrrole film. The model includes both faradic and capacitance charge components in the total current density expression.

Yeu, Taewhan↗

NASA NTRS1988

A mathematical model for predicting cyclic voltammograms of electronically conductive polypyrrole

Yeu, Taewhan↗

NASA NTRS1992

Three-dimensional numerical modeling of indium phosphide Point-Contact Solar Cells

The Point-Contact Solar Cell (PCSC) geometry has proven very effective for silicon cells. To date, it has not been implemented in III-V materials. In addition, modeling such a geometry is very difficult because of its three-dimensional nature. We have developed a three-dimensional finite element modeling code (FIESTA ROC). In this paper, we present results from a three-dimensional modeling study of InP point-contact solar cells.

Clark, Ralph O.↗

NASA NTRS2004

Statistical Analysis of CMC Constituent and Processing Data

Ceramic Matrix Composites (CMCs) are the next "big thing" in high-temperature structural materials. In the case of jet engines, it is widely believed that the metallic superalloys currently being utilized for hot structures (combustors, shrouds, turbine vanes and blades) are nearing their potential limits of improvement. In order to allow for increased turbine temperatures to increase engine efficiency, material scientists have begun looking toward advanced CMCs and SiC/SiC composites in particular. Ceramic composites provide greater strength-to-weight ratios at higher temperatures than metallic alloys, but at the same time require greater challenges in micro-structural optimization that in turn increases the cost of the material as well as increases the risk of variability in the material s thermo-structural behavior. to model various potential CMC engine materials and examines the current variability in these properties due to variability in component processing conditions and constituent materials; then, to see how processing and constituent variations effect key strength, stiffness, and thermal properties of the finished components. Basically, this means trying to model variations in the component s behavior by knowing what went into creating it. inter-phase and manufactured by chemical vapor infiltration (CVI) and melt infiltration (MI) were considered. Examinations of: (1) the percent constituents by volume, (2) the inter-phase thickness, (3) variations in the total porosity, and (4) variations in the chemical composition of the Sic fiber are carried out and modeled using various codes used here at NASA-Glenn (PCGina, NASALife, CEMCAN, etc...). The effects of these variations and the ranking of their respective influences on the various thermo-mechanical material properties are studied and compared to available test data. The properties of the materials as well as minor changes to geometry are then made to the computer model and the detrimental effects observed using statistical analysis software. The ultimate purpose of this study is to determine what variations in material processing can lead to the most critical changes in the materials property. The work I have taken part in this summer explores, in general, the key properties needed In this study SiC/SiC composites of varying architectures, utilizing a boron-nitride (BN)

Fornuff, Jonathan↗

NASA NTRS1990

Probabilistic high cycle fatigue failure analysis with application to liquid propellant rocket engines

A probabilistic high cycle fatigue (HCF) failure analysis of a welded duct in a rocket engine of the Space Shuttle main engine class is described. A state-of-the-art HCF failure prediction method was used in a Monte Carlo simulation to generate a distribution of failure lives. A stochastic stress/life model is used for material characterization, and a composite stress history is generated for accurately deriving the stress cycles for the fatigue-damage calculations. The HCF failure model expresses fatigue life as a function of stochastic parameters including environment, loads, material properties, geometry, and model specification errors. A series of HCF failure life analyses were performed to study the impact of a fixed parameter and to assess the importance of each stochastic input parameter through marginal analyses.

Sutharshana, S.↗

NASA NTRS1992

Two-dimensional finite element model of the pultrusion process

Composite materials used in the fabrication of industrial products/components are under constant development. Applications vary widely from consumer products to high-performance aerospace components. The pultrusion process is one of the important methods of production of composite materials. In order to develop a fundamental understanding of this process, a computational model employing the finite element method is developed which enables a prediction of the material temperature and degree-of-cure at any time during the process. The model is comprehensive; it can readily be employed to perform parametric studies of the process and to aid in the development of efficient design procedures for this type of material system. Comparisons are made between model predictions and experimental results and good agreement is observed.

Hackett, Robert M.↗

NASA NTRSDate not supplied

Modeling of Response of Thermoplastic Composites Under High Energy Impact

This work investigates the response of thermoplastic composites under impact loading. Panels fabricated with the material system under consideration – carbon fiber reinforced low melt semi-crystalline resin TC1225 LMPAEK T700G (T700/LMPAEK) – were subject to impact with an Aluminum projectile, at low and high velocities. At lower velocities, the projectile rebounded from the panel after impact and caused damage e.g. delaminations. Finite element models were developed to model this test using LS-DYNA®, with material model MAT299, a continuum damage model based on the LaRC04 criteria, to capture the dynamic response and damage modes in the T700/LMPAEK panels. Interlaminar delaminations were modeled using the cohesive contact formulation available within LS-DYNA. Previous work by the authors showed that delaminations were consistently underpredicted in size, relative to the test. It was surmised that this was due to effects of strain rate on intralaminar and interlaminar response of the T700/LMPAEK material system. Therefore, the current work is focused on incorporating strain rate effects in modeling the interlaminar response. Rate dependence was incorporated using ‘one-way tie-break contact’ in LS-DYNA, wherein, the Mode-II fracture toughness of the LMPAEK resin, G IIc , was assumed to be an exponential function of the strain rate. In particular, G IIc , was modeled to exponentially decrease at higher strain rates. Models with this form of rate dependence favorably correlated with experimental data regarding delamination extent and rebound velocity, relative to prior models without strain rate effects.

Composite Materials↗

NASA NTRS1986

Integrated research in constitutive modelling at elevated temperatures, part 2

Four current viscoplastic models are compared experimentally with Inconel 718 at 1100 F. A series of tests were performed to create a sufficient data base from which to evaluate material constants. The models used include Bodner's anisotropic model; Krieg, Swearengen, and Rhode's model; Schmidt and Miller's model; and Walker's exponential model.

Haisler, W. E.↗