Meshed Lunar Terrain Thermal Model Building Short Course
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Design, Build and Qualification of 28 Volt Lithium-Ion battery
Contents include the following: 1. GSFC performance specifications requirements. 2. AEA philosophy: battery configuration. 3. AEA battery design: SONY cell, cell screening, 20Ah and 60Ah. 4. Testing: 20Ah battery qualification (environmental); 60Ah battery LEO life cycle. 5. Conclusion.: summary and lessons learned.
Curzon, D.↗
Design, Build, and Testing of the Roman Space Telescope’s Wide Field Instrument Optical Stimulus System (SORC)
The Stimulus Of Ray Cones (SORC) is an optical stimulus system developed to verify, characterize, and calibrate the Roman Space Telescope’s (RST) Wide Field Instrument (WFI) under simulated operational conditions. SORC provides several critical test modes, the primary being point-source mode, which projects an image anywhere on the Focal Plane Assembly (FPA) detectors. This mode provides precise position knowledge of the FPA within WFI. Additional modes enable capturing WFI pupil alignment, evaluation of WFI’s selectable optical elements, and verification of focal plane fiber operation for higher-level system testing. The light source system incorporates a suite of narrowband and broadband fiber fed sources spanning the visible to near-infrared range, with options for pulsed or continuous wave illumination. SORC was designed and built at NASA’s Goddard Spaceflight Center (GSFC). Initial system-level testing occurred under ambient conditions in GSFC’s Spacecraft Systems Development and Integration Facility (SSDIF), followed by vacuum testing at operational temperatures (SORC at 214-222 K) in the Space Environment Simulator (SES). The system was then shipped to BAE Systems in Boulder, CO for post-shipment ambient and cryogenic testing before integration with WFI for two thermal vacuum test campaigns at cryogenic temperatures. This presentation will focus on design, development, and performance of SORC. Details of WFI verification and calibration using SORC, along with test results, have been published previously and will be referenced only as needed to describe SORC. The SORC ground test capability is critical to ensuring WFI meets stringent optical performance requirements, directly supporting the mission’s science objectives.
Stimulus of Ray Cones↗
Influences of Introduced Yttrium Oxide Particles on Superalloy 718
Additive manufacturing has proven useful for introducing oxide particles into superalloys, to provide oxide dispersion strengthening (ODS) at high temperatures. This study screened how such an approach can influence microstructure and failure modes for superalloy 718. The objective of this study was to compare tensile and creep failure modes for additively manufactured superalloy 718 having introduced oxide particles. Fine yttrium-oxide (yttria) particles were introduced into 718 powder using two different powder mixing methods. Additive manufacturing by laser powder bed fusion was then used to prepare specimen blanks for each case, oriented parallel and transverse to the building direction. These were subsequently given consistent stress relief, hot isostatic pressurization, and final heat treatments, then subjected to tensile and creep tests at varied temperatures. Additively manufactured 718 without ODS (“no-ODS”) had nearly equiaxed grains at 50 μm in width. Roll-mixed ODS material had a bimodal grain size distribution, with fine grains at 9 μm and coarse grains at 66 μm in average width. The fine grains were elongated about 5x in the building direction. Acoustic-mixed ODS materials had grains at 10 μm in width that were elongated about 10x in the build direction. Compared to no-ODS, roll-mixed and acoustic mixed ODS materials did not show improved tensile strengths at room temperature, 760 °C, and 1093 °C. The tensile failure strains parallel to the building direction of roll-mixed ODS material were lowest in these tests. Flattened clumps of yttria had formed transverse to the building direction in roll-mixed material, which were easily cracked. Creep rupture response at 760 °C in the building direction was highest for acoustic-mixed material test. Creep tests at 1093 °C showed varied results, with low rupture lives associated with enhanced cavitation at grain boundaries and surface oxidation. Transverse to the building direction, all three materials had low failure strains in tests at 760 °C. The elongated grain boundaries failed readily in all transverse specimens tested at 760 °C and 1093 °C.
oxide dispersion strengthening↗
Practical Battery Thermal Modeling Techniques
Lithium-ion batteries are thermo-electrochemical devices, whereby nearly every facet of their functionality and performance are thermally driven. As a result, it is important to have thermal modeling techniques that effectively capture the intricacies of both the electrochemical nature of the battery and also the complex thermal network that typically results from the design of the battery thermal management system. Here we present a thermal modeling workflow and a set of general assumptions for how to construct a thermal model of a Li-ion battery pack. We use a 14-cell bank of 18650-format Li-ion cells, loosely based on a proposed alternative battery design for Orion, as the example. Although the workflow is performed with Thermal Desktop and related utilities, the focus of this presentation is less about software specific techniques, but rather is focused on the assumptions and conditions that should be used in a model (regardless of the tool used to build the model). Example cases and results will be presented for charge, discharge, and thermal runaway.
lithium-ion battery↗
Usage-based Lifing of Lithium-Ion Battery with HybridPhysics-Informed Neural Networks
Lithium-ion batteries are commonly used to power unmanned aircraft vehicles (UAVs).The ability to model and forecast the remaining useful life of these batteries enables UAV reliability assurance. Building accurate models for battery state of charge and state of health based on first principles is challenging due to the complex electrochemistry that governs battery operations and computational complexity required to solve them. Therefore, reduced order models are often used due to their ability to capture the overall battery discharge. Un-fortunately, these simplifications lead to residual discrepancy between model predictions and observed data. In this paper, we present a hybrid modeling approach merging reduced-order models and neural networks. In this approach, while most of the input-output relationship is captured by Nernst and Butler-Volmer equations, data-driven kernels reduce the gap between predictions and observations. We validate our approach using data publicly available through the NASA Prognostics Center of Excellence repository. Results showed that our hybrid battery prognosis model can be successfully calibrated, even with a limited number of observations.
Lithium-ion Battery↗
Enabling Interoperability in Earth System Digital Twins (ESDT): Integrating Observations, Models, and AI for Actionable Insights Through NASA'S Intelligent Systems Technology Program
NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.
Earth System↗
An Investigation of State-Space Model Fidelity for SSME Data
In previous studies, a variety of unsupervised anomaly detection techniques for anomaly detection were applied to SSME (Space Shuttle Main Engine) data. The observed results indicated that the identification of certain anomalies were specific to the algorithmic method under consideration. This is the reason why one of the follow-on goals of these previous investigations was to build an architecture to support the best capabilities of all algorithms. We appeal to that goal here by investigating a cascade, serial architecture for the best performing and most suitable candidates from previous studies. As a precursor to a formal ROC (Receiver Operating Characteristic) curve analysis for validation of resulting anomaly detection algorithms, our primary focus here is to investigate the model fidelity as measured by variants of the AIC (Akaike Information Criterion) for state-space based models. We show that placing constraints on a state-space model during or after the training of the model introduces a modest level of suboptimality. Furthermore, we compare the fidelity of all candidate models including those embodying the cascade, serial architecture. We make recommendations on the most suitable candidates for application to subsequent anomaly detection studies as measured by AIC-based criteria.
Space Shuttles↗
PROTECT: Production and Reuse of Thermally Efficient Ceramic Thermal Protection Systems
PROTECT (Production and Reuse Of Thermally Efficient Ceramic TPS) is a NASA Early Career Initiative focused on developing the next generation of reusable ceramic thermal protection systems (TPS). This project addresses key challenges in TPS design, including temperature capability, thermal stability, and refurbishment time, by integrating novel material development with predictive modeling. Leveraging enhanced capabilities in NASA’s Porous Microstructure Analysis (PuMA) software, PROTECT introduces new modeling tools to predict the thermal and mechanical behavior of fibrous ceramic materials. These tools enable accurate prediction of performance metrics such as thermal conductivity and structural integrity, reducing reliance on costly physical testing. Preliminary advances in these areas will be presented. To support model validation, PROTECT is building a comprehensive database of raw material properties using advanced characterization techniques, including micro computed tomography (CT) scanning in collaboration with the University of Illinois Urbana-Champaign (UIUC). The presentation will detail the sampling workflows and analysis methods used to generate this detailed microstructural data and how it is used to develop improved models in PuMA. This multi-center collaboration, spanning NASA (JSC, ARC, KSC, GRC), Oak Ridge National Laboratory, UIUC, and SpaceX, is developing tailored TPS solutions for LEO, lunar, and Martian missions. By bridging heritage systems with the demands of modern spaceflight, PROTECT contributes to the advancement of reusable TPS technologies for future exploration missions.
Propulsion, Refractory, and Coating Materials↗
Simulations of Yarn Micro-Mechanics of Woven Heat Shield Materials
Carbon and phenolic fibers are commonly used in ablative thermal protection materials, such as 3-dimensional Mid-Density Carbon Phenolic (3MDCP), a 3D-woven composite comprised of mixed-fiber yarn bundles. Predicting the micro-mechanical response and fracture of twisted yarns composed of brittle and ductile fibers requires a modeling approach that captures per-fiber yielding, fiber fracture, and inter-fiber friction and contact. This work presents an extended bonded particle model (BPM) for discrete element method (DEM) simulation of fiber and yarn mechanics, implemented in LAMMPS. The model builds on the incremental bond formulation of Guo et al. and introduces a piecewise elasto-plastic constitutive law for axial extension, enabling representation of fibers that yield before failure. 3MDCP yarns were constructed using measured fiber radius distributions and helical twist geometry. Tensile simulations of single-ply 3MDCP yarns show good agreement with vender stress–strain results. Fiber breakage models also show details on yarn breakage propagration, centered radially in the yarn. Yarn breakage of multi-ply 3MDCP also matched experimental observations in per-ply breakage; however, predicted yarn breakage strength were found higher than experimental observations.
Discrete Element Method↗
Simulations of Yarn Micro-Mechanics of Woven Heat Shield Materials (3MDCP)
Carbon and phenolic fibers are commonly used in ablative thermal protection materials, such as 3-dimensional Mid-Density Carbon Phenolic (3MDCP), a 3D-woven composite comprised of mixed-fiber yarn bundles. Predicting the micro-mechanical response and fracture of twisted yarns composed of brittle and ductile fibers requires a modeling approach that captures per-fiber yielding, fiber fracture, and inter-fiber friction and contact. This work presents an extended bonded particle model (BPM) for discrete element method (DEM) simulation of fiber and yarn mechanics, implemented in LAMMPS. The model builds on the incremental bond formulation of Guo et al. and introduces a piecewise elasto-plastic constitutive law for axial extension, enabling representation of fibers that yield before failure. 3MDCP yarns were constructed using measured fiber radius distributions and helical twist geometry. Tensile simulations of single-ply 3MDCP yarns show good agreement with vender stress–strain results. Fiber breakage models also show details on yarn breakage propagration, centered radially in the yarn. Yarn breakage of multi-ply 3MDCP also matched experimental observations in per-ply breakage; however, predicted yarn breakage strength were found higher than experimental observations.
Woven↗
Pragmatic Stress Prediction on Additively Manufactured Coupons
Prediction of residual stresses from process parameters for additively manufactured large metal parts is computationally expensive. NASA is currently developing meter-scale parts with direct energy deposition. Practically, the predictive computational methods need to efficiently scale-up to meter-scale parts. Coupled thermal-mechanical multi-physics simulations have been developed with the pragmatic method using ABAQUS, COMSOL Multiphysics, ALE3D software. The residual stresses are a result of the manufacturing process which creates thermal cycling of the build layers. The pragmatic method uses lumped thermal layers for stress predictions to reduce computational costs. The stress predictions as well as deformations of the different codes are compared with each other and with ANSYS Additive using identical material models, boundary and initial conditions. The codes were used to simulate three different geometries: a thin wall, hollow cylinder and twin-cantilever part. The coupon parts were then manufactured with Inconel-625. The residual stresses in these parts were measured using X-ray diffraction as well as neutron beam diffraction at NIST. The stress measurements for the two technologies are compared. The pragmatic stress prediction method enabled predictions of the multi-centimeter scale parts using desktop computer workstations in only a few hours for each coupon. The results of the simulated stress predictions compared favorably with the measured stresses even though thermally lumped layers were employed. Finally, a two-meter scale nozzle was simulated using ANSYS Additive. The simulations were used to examine the build orientation trade-space with respect to resulting geometric deformation. The predicted deformations were compared to measurements of an actual subscale part manufactured with direct energy deposition.
pragmatic method↗
P-band Antenna Array for NASA's Space Exploration Synthetic Aperture Radar (SESAR) Instrument
A dual-polarized, wideband, lightweight P-band (435 MHz) antenna element and array for spaceborne radar applications has been developed for NASA’s SESAR (Space Exploration Synthetic Aperture Radar) instrument for planetary synthetic aperture radar (SAR) applications. The antenna array is based on lightweight, low-profile antenna elements that form the building blocks of configurable “array panels” that can in turn be used to build larger arrays consisting of multiple panels. The element design combines a novel, mostly planar conductor antenna element geometry with a robust lightweight composite material construction.
Synthetic Aperture Radar↗
PROTECT: Production and Reuse of Thermally Efficient Ceramic Thermal Protection Systems
PROTECT (Production and Reuse Of Thermally Efficient Ceramic TPS) is a NASA Early Career Initiative focused on developing the next generation of reusable ceramic thermal protection systems (TPS). This project addresses key challenges in TPS design, including temperature capability, thermal stability, and refurbishment time, by integrating novel material development with predictive modeling. Leveraging enhanced capabilities in NASA’s Porous Microstructure Analysis (PuMA) software, PROTECT introduces new modeling tools to predict the thermal and mechanical behavior of fibrous ceramic materials. These tools enable accurate prediction of performance metrics such as thermal conductivity and structural integrity, reducing reliance on costly physical testing. Preliminary advances in these areas will be presented. To support model validation, PROTECT is building a comprehensive database of raw material properties using advanced characterization techniques, including micro computed tomography (CT) scanning in collaboration with the University of Illinois Urbana-Champaign (UIUC). The presentation will detail the sampling workflows and analysis methods used to generate this detailed microstructural data and how it is used to develop improved models in PuMA. This multi-center collaboration, spanning NASA (JSC, ARC, KSC, GRC), Oak Ridge National Laboratory, UIUC, and SpaceX, is developing tailored TPS solutions for LEO, lunar, and Martian missions. By bridging heritage systems with the demands of modern spaceflight, PROTECT contributes to the advancement of reusable TPS technologies for future exploration missions.
Propulsion, Refractory, and Coating Materials↗
Advanced Materials for the Lunar Surface: Multiscale Computational Design of Refractory Alloys and Carbides
Emerging operational environments, such as the lunar surface, present novel challenges for NASA and drive the need for advanced materials in applications like fission surface power systems. To address these demands, computational materials science is rapidly evolving to augment or replace costly and hazardous empirical testing. Although materials selection at NASA remains predominantly experimentally driven, advanced simulation methodologies are being steadily integrated into the engineering lifecycle. This work details the application of multiscale simulation techniques—including first-principles calculations, CALPHAD, dislocation dynamics, and molecular dynamics—at NASA's Ames Research Center to evaluate advanced materials for extreme environments. First, we present contributions to the Space Nuclear Propulsion Project. Be-cause propellant channel coatings in nuclear thermal rockets must withstand high-pressure, high-temperature hydro-gen, optimizing these materials is critical. First-principles calculations were employed to establish a rigorous quantitative and qualitative understanding of the behavior of the refractory carbides ZrC, NbC, and their mixtures in high-enthalpy hydrogen environments. This necessitated the generation of high-fidelity thermodynamic models for both stoichiometric and carbon-depleted carbides, both with and without the presence of hydrogen. Furthermore, we highlight efforts under the Refractory Alloy Additive Manufacturing Build Optimization (RAAMBO) project, where existing and novel alloy compositions were assessed for additive manufacturing printability and subsequent performance in applications such as heat pipes and rocket nozzle extensions. This was accomplished through a comprehensive multiscale simulation framework that bridged the gap from the nanometer to the millimeter scale. Across both initiatives, rigorous validation against empirical data was prioritized. By systematically employing a verified and validated computational frame-work, we demonstrate how simulation effectively supports multidisciplinary engineering efforts, builds project-wide confidence, and drives critical materials development.
computational materials↗
Battery Pack Shape Optimization using Transient Heat Conduction Coupled with Cell-Discharge Analysis
Battery electric systems exhibit significant time-dependence, especially when evaluated in the context of an aircraft mission profile with continually changing power demands. Additionally, when evaluating battery-powered aircraft concepts, it is important to accurately compute the temperature of the batteries and properly characterize the thermal response of the system. The temperature of the batteries has a significant impact on cell performance, in addition to safety considerations of maintaining battery temperatures below their operating limit. Because of these considerations, battery models for preliminary design and optimization of aircraft should include the capability to accurately compute the temperature distribution within the battery pack. Furthermore, battery pack designs should be as light-weight as possible to maximize the pack energy density, while also considering battery temperature limits. Here, we demonstrate a simultaneous trajectory and shape optimization of a battery pack concept, using a transient heat transfer finite element model coupled with a time-varying cell-discharge battery model to provide this capability. Including the transient finite element problem in the loop enables accurate temperatures that can be passed back to the cell discharge model, while the cell discharge model can supply the finite element model with time-varying heat boundary conditions to the finite element problem, further benefiting the fidelity of the thermal response of the batteries. We first demonstrate the coupling capability between the battery cell-discharge model and the transient finite-element heat transfer through an optimization which computes the optimal current profile for the battery pack while ensuring the battery temperatures remain below their operational limit. We then build on this optimization by adding shape optimization to the problem, which allows us to consider a composite objective function which also minimizes the mass of the battery pack, while also producing an optimal current discharge profile.
Optimization↗
Battery Pack Shape Optimization using Transient Heat Conduction Coupled with Cell-Discharge Analysis
Battery electric systems exhibit significant time-dependence, especially when evaluated in the context of an aircraft mission profile with continually changing power demands. Additionally, when evaluating battery-powered aircraft concepts, it is important to accurately compute the temperature of the batteries and properly characterize the thermal response of the system. The temperature of the batteries has a significant impact on cell performance, in addition to safety considerations of maintaining battery temperatures below their operating limit. Because of these considerations, battery models for preliminary design and optimization of aircraft should include the capability to accurately compute the temperature distribution within the battery pack. Furthermore, battery pack designs should be as light-weight as possible to maximize the pack energy density, while also considering battery temperature limits. Here, we demonstrate a simultaneous trajectory and shape optimization of a battery pack concept, using a transient heat transfer finite element model coupled with a time-varying cell-discharge battery model to provide this capability. The transient finite-element analysis is done using TACS, and the cell-discharge battery model uses OpenMDAO and dymos. Including the transient finite element problem in the loop enables accurate temperatures that can be passed back to the cell discharge model, while the cell discharge model can supply the finite element model with time-varying heat boundary conditions, further benefiting the fidelity of the thermal response of the batteries. We first demonstrate the coupling capability between the battery cell-discharge model and the transient finite-element heat transfer through an optimization which computes the optimal current profile for the battery pack while ensuring the battery temperatures remain below their operational limit. We then build on this optimization by adding shape optimization to the problem, which allows us to consider a composite objective function which also minimizes the mass of the battery pack, while also producing an optimal current discharge profile.
Optimization↗
Performance of Two Battery Prognostic Applications used by Two Octocopters for Safe Low Altitude Autonomous Flight Operations
This paper addresses the problem of building trust in online predictions of the remaining available flying time for two different electric Unmanned Aerial Vehicles (eUAVs) powered by lithium-ion-polymer batteries. Flight tests for various automation research missions for the two vehicles were monitored using two on-board battery health management applications to make predictions of the remaining flying time (RFT) for each eUAV and to predict the state of the battery. Playback of the voltage, current and temperature profiles of the battery discharge were used to assess the accuracy of the estimation of the voltage and the charge states of the models as well as the estimate of the RFT. The reference ground truth values were the observed landing time and the measured battery pack resting pack voltage 20 minutes after the flight. The predicted RFT, state of charge (SoC), and state of energy (SoE) were compared with the observed results. Noise values of one standard deviation from the mean values of the internal charge states of the battery model during a reference run were used to vary the states during simulation. One application used an equivalent circuit model of the electrical dynamics of the battery pack, and the other application used a reduced-order electrochemistry model. The variation of the model state components was compared to the variation in the estimate of the RFT and the variation in the SoE to estimate a confidence factor. Variation in the estimates caused by factors affecting the off-line laboratory parameter identification experiments is considered. Variation in the estimates due to environmental factors are discussed.
Assurance↗