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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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NASA NTRS1981

Systems design and analysis of the microwave radiometer spacecraft

Systems design and analysis data were generated for microwave radiometer spacecraft concept using the Large Advanced Space Systems (LASS) computer aided design and analysis program. Parametric analyses were conducted for perturbations off the nominal-orbital-altitude/antenna-reflector-size and for control/propulsion system options. Optimized spacecraft mass, structural element design, and on-orbit loading data are presented. Propulsion and rigid-body control systems sensitivities to current and advanced technology are established. Spacecraft-induced and environmental effects on antenna performance (surface accuracy, defocus, and boresight off-set) are quantified and structured material frequencies and modal shapes are defined.

Garrett, L. B.↗

NASA NTRS2010

Reliability Quantification of Advanced Stirling Convertor (ASC) Components

The Advanced Stirling Convertor, is intended to provide power for an unmanned planetary spacecraft and has an operational life requirement of 17 years. Over this 17 year mission, the ASC must provide power with desired performance and efficiency and require no corrective maintenance. Reliability demonstration testing for the ASC was found to be very limited due to schedule and resource constraints. Reliability demonstration must involve the application of analysis, system and component level testing, and simulation models, taken collectively. Therefore, computer simulation with limited test data verification is a viable approach to assess the reliability of ASC components. This approach is based on physics-of-failure mechanisms and involves the relationship among the design variables based on physics, mechanics, material behavior models, interaction of different components and their respective disciplines such as structures, materials, fluid, thermal, mechanical, electrical, etc. In addition, these models are based on the available test data, which can be updated, and analysis refined as more data and information becomes available. The failure mechanisms and causes of failure are included in the analysis, especially in light of the new information, in order to develop guidelines to improve design reliability and better operating controls to reduce the probability of failure. Quantified reliability assessment based on fundamental physical behavior of components and their relationship with other components has demonstrated itself to be a superior technique to conventional reliability approaches based on utilizing failure rates derived from similar equipment or simply expert judgment.

Shah, Ashwin R.↗

NASA NTRS1985

Coherent structures in a turbulent mixing layer - A comparison between direct numerical simulations and experiments

An eduction scheme has been developed in an attempt to determine the characteristics of large-scale vortical structures in a turbulent mixing layer. This analysis scheme has been applied to a set of experimental data taken in a new, larger mixing layer facility designed to minimize boundary and resonance effects. A similar scheme has been developed to apply to the results of a direct numerical simulation of a temporally growing mixing layer. A comparison of the two approaches shows important similarities in the coherent structures. The numerical simulations indicate that low levels of coherent forcing can dramatically change the evolution of the mixing layer. In the absence of such forcing, the numerical simulations and experiments show a lack of regularity in the transverse position, spacing, amplitude, shape and spanwise coherence of the large-scale vortical structures.

Metcalfe, R. W.↗

NASA NTRS2012

An Integrated Tool for System Analysis of Sample Return Vehicles

The next important step in space exploration is the return of sample materials from extraterrestrial locations to Earth for analysis. Most mission concepts that return sample material to Earth share one common element: an Earth entry vehicle. The analysis and design of entry vehicles is multidisciplinary in nature, requiring the application of mass sizing, flight mechanics, aerodynamics, aerothermodynamics, thermal analysis, structural analysis, and impact analysis tools. Integration of a multidisciplinary problem is a challenging task; the execution process and data transfer among disciplines should be automated and consistent. This paper describes an integrated analysis tool for the design and sizing of an Earth entry vehicle. The current tool includes the following disciplines: mass sizing, flight mechanics, aerodynamics, aerothermodynamics, and impact analysis tools. Python and Java languages are used for integration. Results are presented and compared with the results from previous studies.

Samareh, Jamshid A.↗

NASA NTRSDate not supplied

Design for Manufacturing of Structures with Automated Fiber Placement via Integration of Analysis and Process Planning

Under the Design for Manufacturing (DFM) task in the NASA HiCAM program, significant process has been made towards establishing a fully automated optimization process that spans the structural analysis, design, and manufacturing process planning for Automated Fiber Placement (AFP) structures. Previous efforts in this area established the data formats and mapping processes needed to exchange data between disciplines. The software tools used in the framework are HyperX (structural optimization), CAPP (process planning), and VCP (AFP path generation). The work has culminated with automation of data exchanges and an optimization process that drives the three software toward convergence. Within this framework, HyperX is used to generate mass-optimum composite designs which are iterated with VCP and CAPP to improve manufacturability of the designs. As the ply manufacturing process is adjusted by CAPP and VCP, HyperX continually re-assesses the structural integrity of the part and makes adjustments to the laminate as needed. For example, the position of fiber steering and tow overlaps/gaps can necessitate changes to the laminate design. Once the design converges, the end result is a stack of plies that meets both structural and manufacturing requirements. This paper presents the details of the framework and demonstrations the automated process on several parts with significant double-curvature.

HiCAM↗

NASA NTRS2015

Design of a Thermal Precipitator for the Characterization of Smoke Particles from Common Spacecraft Materials

A thermal precipitator (TP) was designed to collect smoke aerosol particles for microscopic analysis in fire characterization research. Information on particle morphology, size and agglomerate structure obtained from these tests supplements additional aerosol data collected. Modeling of the thermal precipitator throughout the design process was performed with the COMSOL Multiphysics finite element software package, including the Eulerian flow field and thermal gradients in the fluid. The COMSOL Particle Tracing Module was subsequently used to determine particle deposition. Modeling provided optimized design parameters such as geometry, flow rate and temperatures. The thermal precipitator was built and testing verified the performance of the first iteration of the device. The thermal precipitator was successfully operated and provided quality particle samples for microscopic analysis, which furthered the body of knowledge on smoke particulates. This information is a key element of smoke characterization and will be useful for future spacecraft fire detection research.

Smoke↗

DOE OSTI2020

Probing the Local Site Disorder and Distortion in Pyrochlore High-Entropy Oxides

High-entropy oxides (HEOs) have attracted great interest in diverse fields because of their inherent opportunities to tailor and combine materials functionalities. The control of local order/disorder in the class is by extension a grand challenge toward realizing their vast potential. In this paper, we report the first examples of pyrochlore HEOs with five M-site cations, for Nd 2 M 2 O 7 , in which the local structure has been investigated by neutron diffraction and pair distribution function (PDF) analysis. The average structure of the pyrochlores is found to be orthorhombic Imma, in agreement with radius-ratio rules governing the structural archetype. The computed PDFs from density functional theory relaxed special quasirandom structure models are compared with real space PDFs in this work to evaluate M-site order/disorder. Reverse Monte Carlo combined with ab initio molecular dynamics and Metropolis Monte Carlo simulations demonstrates that Nd 2 (Ta 0.2 Sc 0.2 Sn 0.2 Hf 0.2 Zr 0.2 ) 2 O 7 is synthesized with its M-site local to nanoscale order highly randomized/disordered, while Nd 2 (Ti 0.2 Nb 0.2 Sn 0.2 Hf 0.2 Zr 0.2 ) 2 O 7+x exhibits a strong distortion of the TiO 6 octahedron and small degree of Ti chemical short-range order (SRO) on the subnanometer scale. Calculations suggest that this may be intrinsic, energetically favored SRO rather than due to sample processing. These results offer an important demonstration that the engineered variation of participating ions in HEOs, even among those with very similar radii, provides richly diverse opportunities to control local order/disorder motifs—and therefore materials properties for future designs. This work also hints at the exquisite level of detail that may be needed in computational and experimental data analysis to guide structure–property tuning in the emerging HEO materials class.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a Transcription Factor (TF)-gene regulatory network (TRN), which is modeled via a Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. Here we have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗

NASA NTRS1991

Basic research on design analysis methods for rotorcraft vibrations

The objective of the present work was to develop a method for identifying physically plausible finite element system models of airframe structures from test data. The assumed models were based on linear elastic behavior with general (nonproportional) damping. Physical plausibility of the identified system matrices was insured by restricting the identification process to designated physical parameters only and not simply to the elements of the system matrices themselves. For example, in a large finite element model the identified parameters might be restricted to the moduli for each of the different materials used in the structure. In the case of damping, a restricted set of damping values might be assigned to finite elements based on the material type and on the fabrication processes used. In this case, different damping values might be associated with riveted, bolted and bonded elements. The method itself is developed first, and several approaches are outlined for computing the identified parameter values. The method is applied first to a simple structure for which the 'measured' response is actually synthesized from an assumed model. Both stiffness and damping parameter values are accurately identified. The true test, however, is the application to a full-scale airframe structure. In this case, a NASTRAN model and actual measured modal parameters formed the basis for the identification of a restricted set of physically plausible stiffness and damping parameters.

Hanagud, S.↗

DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2008

A Thermal Precipitator for Fire Characterization Research

Characterization of the smoke from pyrolysis of common spacecraft materials provides insight for the design of future smoke detectors and post-fire clean-up equipment on the International Space Station. A thermal precipitator was designed to collect smoke aerosol particles for microscopic analysis in fire characterization research. Information on particle morphology, size and agglomerate structure obtained from these tests supplements additional aerosol data collected. Initial modeling for the thermal precipitator design was performed with the finite element software COMSOL Multiphysics, and includes the flow field and heat transfer in the device. The COMSOL Particle Tracing Module was used to determine particle deposition on SEM stubs which include TEM grids. Modeling provided optimized design parameters such as geometry, flow rate and temperatures. Microscopy results from fire characterization research using the thermal precipitator are presented.

Meyer, Marit↗

NASA NTRS2018

Structural Certification of Human-Rated Inflatable Space Structures

This paper details the results of an initial study to develop a certification plan for human-rated inflatable space structures, including guidelines for qualification testing. Habitable softgoods inflatables are multi-layered shell structures that use high-strength webbing, cordage and broadcloth fabric to carry the skin loads of a variety of volumetric shapes and structural architectures. The primary objectives of this study are to define the key parameters that affect these structures and propose a statistically robust approach to defining safety and knockdown factors based on test and analysis. Current NASA standards for habitable inflatable space structures use a factor of safety of 4, which was inherited from airship design criteria. An updated approach to defining a design factor, taking into account material strength variability, load variability in the article, number of test samples, and damage and degradation effects is specified. Accurate analytical modeling of these structures is hindered by the difficulty of obtaining accurate and consistent material data due to load-history- dependent, nonlinear load versus strain behavior. A building block approach to certification is detailed that uses stochastic modeling and statistical test design and analysis to address the unique challenges these high-strength softgoods structures present. Human-rated inflatable modules are a transformative capability for launching much larger habitable volumes into space than is possible with rigid shell structures. This research aims to provide the framework for certifying these structures for future human space exploration missions.

Jones, Thomas C.↗

NASA NTRS2013

Characterizing the Response of Composite Panels to a Pyroshock Induced Environment using Design of Experiments Methodology

This experimental study seeks to quantify the impact various composite parameters have on the structural response of a composite structure in a pyroshock environment. The prediction of an aerospace structure's response to pyroshock induced loading is largely dependent on empirical databases created from collections of development and flight test data. While there is significant structural response data due to pyroshock induced loading for metallic structures, there is much less data available for composite structures. One challenge of developing a composite pyroshock response database as well as empirical prediction methods for composite structures is the large number of parameters associated with composite materials. This experimental study uses data from a test series planned using design of experiments (DOE) methods. Statistical analysis methods are then used to identify which composite material parameters most greatly influence a flat composite panel's structural response to pyroshock induced loading. The parameters considered are panel thickness, type of ply, ply orientation, and pyroshock level induced into the panel. The results of this test will aid in future large scale testing by eliminating insignificant parameters as well as aid in the development of empirical scaling methods for composite structures' response to pyroshock induced loading.

Parsons, David S.↗

NASA NTRS2013

Characterizing the Response of Composite Panels to a Pyroshock Induced Environment Using Design of Experiments Methodology

Parsons, David S.↗

NASA NTRS2013

Characterizing the Response of Composite Panels to a Pyroshock Induced Environment Using Design of Experiments Methodology

This experimental study seeks to quantify the impact various composite parameters have on the structural response of a composite structure in a pyroshock environment. The prediction of an aerospace structure's response to shock induced loading is largely dependent on empirical databases created from collections of development and flight test data. While there is significant structural response data due to shock induced loading for metallic structures, there is much less data available for composite structures. One challenge of developing a composite shock response database as well as empirical prediction methods for composite structures is the large number of parameters associated with composite materials. This experimental study uses data from a test series planned using design of experiments (DOE) methods. Statistical analysis methods are then used to identify which composite material parameters most greatly influence a flat composite panel's structural response to shock induced loading. The parameters considered are panel thickness, type of ply, ply orientation, and shock level induced into the panel. The results of this test will aid in future large scale testing by eliminating insignificant parameters as well as aid in the development of empirical scaling methods for composite structures' response to shock induced loading.

Parsons, David↗

NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗

NASA NTRS2022

Application Table: A Bridge Connecting the Designing “With-The-Material” and “The-Material” Paradigms

Over the last few decades, advances in high-performance computing, new material characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. In 2016 NASA sponsored a 2040 Vision study (which appeared in 2018) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. The study envisions a cyber-physical-social ecosystem comprised of experimentally verified and validated (V & V) computational models, tools, and techniques, along with the associated digital tapestry, that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of “fit-for-purpose” materials, components, and systems. Consequently, the development of a robust information management system that incorporates (across the full life cycle) both experimental (real data) and virtual data resulting from the application of various simulation tools (at single or multiple length scales), therefore enabling the virtual design and optimization of materials throughout their processing – internal structure – property – performance envelope, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality, and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. This is particularly true when attempting to merge ICME practices with recent additive manufacturing technology which will enable production of the resulting 2040 Vision material and structural designs. At NASA Glenn Research Center we are exploring the future of material science through the use of novel characterization methodologies, high performance computing, and recently an emphasis on integrated computational materials engineering (ICME). Herein, recent efforts to incorporate an Application Table within NASA Glenn Research Center’s ICME Granta MI database is presented. The goal is to provide a place where material and structural application information/requirements can be linked so as to marry the “design the-material” and the “design-with-material” paradigms and thereby enable application-driven design and optimization of materials and structures by providing a central location that links material processing at various length scales to the application’s performance requirements. This paper discusses the specifics of this Application Table as well as best practices and key principles for the development of a robust materials information management system to enable the 2040 Vision integrated materials and structures ecosystem. Furthermore, it presents the intended role of the Application Table in the future of ICME design of “fit-for-purpose” materials, showing the need for a well-established framework that can successfully bridge the gap between the design “the material” and design “with-the-material” paradigms.

Materials↗