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839 records · Page 15

Spacecraft Conceptual Design for Returning Entire Near-Earth Asteroids

In situ resource utilization (ISRU) in general, and asteroid mining in particular are ideas that have been around for a long time, and for good reason. It is clear that ultimately human exploration beyond low-Earth orbit will have to utilize the material resources available in space. Historically, the lack of sufficiently capable in-space transportation has been one of the key impediments to the harvesting of near-Earth asteroid resources. With the advent of high-power (or order 40 kW) solar electric propulsion systems, that impediment is being removed. High-power solar electric propulsion (SEP) would be enabling for the exploitation of asteroid resources. The design of a 40-kW end-of-life SEP system is presented that could rendezvous with, capture, and subsequently transport a 1,000-metric-ton near-Earth asteroid back to cislunar space. The conceptual spacecraft design was developed by the Collaborative Modeling for Parametric Assessment of Space Systems (COMPASS) team at the Glenn Research Center in collaboration with the Keck Institute for Space Studies (KISS) team assembled to investigate the feasibility of an asteroid retrieval mission. Returning such an object to cislunar space would enable astronaut crews to inspect, sample, dissect, and ultimately determine how to extract the desired materials from the asteroid. This process could jump-start the entire ISRU industry.

solar electric propulsion

From Exploration Flight Test-1 to Artemis II--A NASA Langley's Orion Aerosciences Overview

The Orion Aerosciences program at NASA Langley has played a central role in advancing the aerodynamic and aeroheating prediction capabilities required for the Orion crew vehicle’s return from deep space. This presentation provides a technical overview of aerosciences contributions spanning Exploration Flight Test-1 (EFT-1), Artemis I, and the ongoing post-flight analysis of Artemis II. EFT-1 provided the first high-energy entry dataset for Orion, enabling critical validation of aerodynamic force and moment predictions, static and dynamic stability characteristics, and aeroheating environments at relevant flight Mach and Reynolds numbers. Flight-derived pressure data were used to refine the Flush Air Data System (FADS) methodology for atmospheric density reconstruction and to improve Best Estimated Trajectory (BET) solutions. The EFT-1 data also offered key insights into heat shield performance, including char layer recession, in-depth thermal response, and material retention behavior under flight conditions, informing updates to both thermal response models and uncertainty quantification practices. Building on EFT-1, Artemis I extended the database to true lunar-return conditions. Observations of heat shield performance, including localized char loss, bondline response, and recession variability, provided an unprecedented opportunity to reassess Thermal Protection System (TPS) and aeroheating modeling assumptions. Aerodynamic reconstruction efforts incorporated improved FADS calibration, enhanced atmospheric modeling, and refined force and moment databases to reduce trajectory and load uncertainties. Aeroheating comparisons between pre-flight predictions and flight data enabled targeted model updates, particularly in transitional flow environments and wake heating regions. For Artemis II, these lessons were systematically incorporated into the pre-flight prediction process. Updates included refined aerodynamic databases anchored to flight-validated corrections, improved density estimation and BET methodologies using enhanced database interpolation algorithm and FADS modeling, and revised aeroheating design environments informed by Artemis I material response observations. By the time of the workshop, Artemis II post-flight analysis will be underway, and preliminary findings will be presented where available, including early comparisons of aerodynamic reconstruction, atmospheric density estimation, and thermal protection system performance relative to updated predictions. Collectively, this body of work is a testament to the dedicated and multidisciplinary team whose sustained efforts have contributed to the program’s success and to the progressive maturation of Orion aerosciences modeling through numerical modeling, ground and flight data assimilation. The integrated advancement of aerodynamics, trajectory reconstruction, FADS-based density estimation, and aeroheating analysis has reduced predictive uncertainty and strengthened confidence for future crewed lunar and deep-space missions.

Orion

Artemis I Space Launch System Base Heat Shield Thermal Protection System Performance

The Space Launch System (SLS) Core Stage base heat shield experienced the highest external heating environments on the entire launch vehicle during Artemis I ascent flight. This result was consistent with design predictions. The base heat shield experiences P50 cork combustion dynamics at low altitudes, plume-induced recirculation at moderate altitudes and then in-space base flow physics out to Main Engine Cut-Off (MECO). The base heat shield thermal protection system (TPS) is composed of a P50 cork ablator which is bonded to a substrate. The heat shield protects the gimbal actuation system, RS-25 turbomachinery systems and other aft section sensitive components during ascent. This paper estimates the base heat shield TPS performance from Artemis I using flight data from the NASA Langley Research Center’s Scientifically Calibrated In-Flight Imagery (SCIFLI) Airborne Multispectral Imager (SAMI), development flight instrumentation (DFI) and other TPS recession flight measurements. Predictions from computational and ground test-derived engineering ablation models and observations are also applied. Since no base heat shield substrate thermocouple data were obtained for Artemis I, an estimate of the TPS performance data is derived here. This data assesses thermal margin of the SLS Core Stage base heat shield and best informs the Artemis II Crewed mission to the moon.

aerothermodynamics

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

Space Shuttle Main Engine Propellant Path Leak Detection Using Sequential Image Processing

Initial research in this study using theoretical radiation transport models established that the occurrence of a leak is accompanies by a sudden but sustained change in intensity in a given region of an image. In this phase, temporal processing of video images on a frame-by-frame basis was used to detect leaks within a given field of view. The leak detection algorithm developed in this study consists of a digital highpass filter cascaded with a moving average filter. The absolute value of the resulting discrete sequence is then taken and compared to a threshold value to produce the binary leak/no leak decision at each point in the image. Alternatively, averaging over the full frame of the output image produces a single time-varying mean value estimate that is indicative of the intensity and extent of a leak. Laboratory experiments were conducted in which artificially created leaks on a simulated SSME background were produced and recorded from a visible wavelength video camera. This data was processed frame-by-frame over the time interval of interest using an image processor implementation of the leak detection algorithm. In addition, a 20 second video sequence of an actual SSME failure was analyzed using this technique. The resulting output image sequences and plots of the full frame mean value versus time verify the effectiveness of the system.

L Montgomery Smith

Prediction of prompt NO(x) in hydrocarbon air flames

The gas turbine industry is directing particular attention to very low NOx combustors, whether for aircraft or land based CCGT systems. These low NOx combustors frequently use liquid fuels or natural gas burning under very lean premixed conditions with air or under rich-lean conditions, although only the first case is studied here. In land based systems, diluted steam or nitrogen are sometimes injected into the combustion chambers to reduce flame temperature. The NOx emissions from such systems are the product of three chemical mechanisms which are interrelated: the hydrocarbon prompt NO, the thermal NO (extended Zeldovich mechanism), and the nitrous oxide route to NO. Formation of NO2 from NO also occurs, as well as emission of carbon monoxide and unburnt hydrocarbons. When the fuel-oxidant proportion decreases towards leaner conditions, flame temperatures are lowered, resulting in the total NO being reduced and the thermal-NO contribution greatly diminished to the benefit of the remaining two mechanisms of NO formation. While knowledge of the elementary reactions and their chemical kinetics concerning methane and simple hydrocarbons combustion has existed for a number of years, its use for computer modeling is limited to simple flow dynamics configurations. Nevertheless, understanding of such combusting flows under a wide range of experimental conditions allows for analogies or speculations with more complex actual systems. Such understanding can be achieved by means of one dimensional laminar premixed flame modeling, with a full chemical mechanism which incorporates the three routes of NO formation. Complementary to this understanding is the modeling of the actual combustion system using a full description of the fluid dynamics coupled with a reduced chemical scheme, which is then compared against the first model. The objective of this investigation is to evaluate the relative importance of the three mechanisms of NO formation in lean premixed methane-air combustion with increasing pressure using the one dimensional plug flow package PREMIX, and to test the validity of a three dimensional model with a global chemical mechanism against the one dimensional model in the atmospheric pressure case. Methane is chosen because it is the only mechanism which is reasonably well known and is a good guide to the behavior of other hydrocarbons. The mixture ratio chosen is richer than that in lean gas turbines, but the combustion of this mixture with the low preheat gives realistic gas turbine final flame temperatures. Conditions of NO2 formation are also analyzed in the one dimensional model and results extrapolated to the case of gas turbines.

Valerie Dupont

Improving Adhesive Bondline Time of Flight Predictions During Autoclave Cure Utilizing Machine Learning

Composite materials are increasingly being used in aerospace applications due to their superior strength-to-weight ratio compared to commonly used metals. A current limitation to widespread adoption is the certification of adhesively bonded joints. One approach to improving adhesive bonding in composites is accurately measuring the thickness of adhesive bondlines in composite laminates. Precise bondline thickness control is essential for aerospace applications where adhesive layer thickness directly affects joint fracture properties and structural performance. This study focused on implementing machine learning techniques to determine the ultrasonic time of flight (directly correlated to thickness) in adhesive bondlines throughout autoclave cure cycles. A high-temperature (use up to 180°C) ultrasonic scanning system was deployed in an autoclave to provide time of flight data through composite panels. Three experiments were conducted on the curing of 305 mm × 305 mm unidirectional composite panels. In the first experiment, a piecewise function was fit for the temperature correction factor to account for changing autoclave temperatures. Due to deficiencies in the first calibration experiment, a second experiment was run, and the results were used to train a machine learning model. The revised experiment, in combination with the machine learning model, significantly increased the accuracy of the bondline time of flight predictions (~14% error reduced to <1%). Data was processed using the Regression Learner Application in MATLAB®, with a Support Vector Machine selected for the model. The result was a machine learning algorithm capable of reliably quantifying ultrasonic time of flight through adhesive bondlines. The third experiment provided independent test data for the machine learning model, demonstrating that the model produces accurate predictions from data beyond its training set.

Machine Learning

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

Changes in Arctic and Antarctic Sea-Ice Properties and Processes

Although sea ice covers ~9% of the world’s oceans, its presence, and absence, vastly alter the energetic balance of Earth’s climate. Because of geographic differences, Arctic and Antarctic sea ice have different dominant properties and processes, which produce different responses to natural variability and anthropogenic change. In this Review, we survey changes in sea-ice properties and processes of the Arctic and Antarctic environments. In the Arctic, the melt season has lengthened by ~7.4 days decade −1 (1979-2024). There has been a corresponding decrease in winter sea-ice thickness (1.6 m mean total over 1980-2023), summer surface albedo (0.03 decade −1 over 1979-2020), and spring snow depth (2.5 cm decade −1 over 1954-2024). In comparison, corresponding changes in the Antarctic are small overall due to contrasting regional and temporal trends. From a dynamic perspective, sea-ice motion in both hemispheres has increased, with 0.63 cm s −1 decade −1 in the Arctic (1978-2024) and 0.69 cm s −1 decade −1 in the Antarctic (1982-2024), whereas trends in polynya occurrence differ by region. Future research should prioritize sea-ice observations that can inform how the climate system responds to natural variability and anthropogenic change, be standardized to increase uptake by the broader scientific community, and target process-oriented interactions to support model development.

Melinda A Webster

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN

Summary of Research Report

Ten papers, published in various publications, on buckling, and the effects of imperfections on various structures are presented. These papers are: (1) Buckling mode localization in elastic plates due to misplacement in the stiffner location; (2) On vibrational imperfection sensitivity on Augusti's model structure in the vicinity of a non-linear static state; (3) Imperfection sensitivity due to elastic moduli in the Roorda Koiter frame; (4) Buckling mode localization in a multi-span periodic structure with a disorder in a single span; (5) Prediction of natural frequency and buckling load variability due to uncertainty in material properties by convex modeling; (6) Derivation of multi-dimensional ellipsoidal convex model for experimental data; (7) Passive control of buckling deformation via Anderson localization phenomenon; (8)Effect of the thickness and initial im perfection on buckling on composite cylindrical shells: asymptotic analysis and numerical results by BOSOR4 and PANDA2; (9) Worst case estimation of homology design by convex analysis; (10) Buckling of structures with uncertain imperfections - Personal perspective.

Isaac Elishakoff

Battery Cell Thermal Runaway Calorimeter

We currently have several methods for determining total energy output of an 18650 lithium ion cell. We do not, however, have a good method for determining the fraction of energy that dissipates via conduction through the cell can vs. the energy that is released in the form of ejecta. Knowledge of this fraction informs the design of our models, battery packs, and storage devices; (a) No longer need to assume cell stays together in modeling (b) Increase efficiency of TR mitigation (c) Shave off excess protection.

Darcy, Eric

High-Order Methods in NASA’s Next Generation of Computational Fluid Dynamics Tools

The missions of the National Aeronautics and Space Administration (NASA) routinely produce unique requirements and challenges for development and application of Computational Fluid Dynamics (CFD) methods. NASA presently embodies four distinct Mission Directorates: Aeronautics Research, Exploration Systems, Science, and Space Operations. These missions generate requirements for systems that operate in a wide variety of environments. They range from the high-speed flight of aerodynamically optimized vehicles operating in the earth’s atmosphere to spacecraft designed for missions that don’t favor aerodynamic optimization, some operating in the atmosphere of planets and planetary moons such as Mars and Venus or Saturn’s moon Titan. Systems supporting these vehicles, such as rocket and jet propulsion, reaction control systems, fluid and thermal transfer systems, etc. can also generate their own unique set of flow phenomena that challenge today’s CFD methodology. Through the NASA Engineering and Safety Center (NESC), NASA annually conducts state-of-the-discipline assessments in fifteen distinct engineering disciplines. These assessments are performed by the NASA Technical Fellows that lead Technical Discipline Teams (TDT) of recognized experts in these fifteen areas. In the Aerosciences discipline, three topics have been identified as the top challenges for the discipline: aero-plume interaction prediction, unsteady separated flows, and aerothermodynamic prediction. These challenge areas are defined by the Agency’s high-risk projects and problems on which the NESC is requested to perform independent testing, analysis, and assessments. When viewed as a whole, these tests, analyses, and assessments provide a clear view of the recurring technical challenges facing Agency engineers and researchers and can be used to guide future research and technology development. The present state-of-the-art in the application of CFD at NASA is the use of Reynolds-Averaged Navier- Stokes (RANS) solvers, primarily executed in a steady-state mode of operation. In isolated cases, Unsteady RANS (URANS) solvers have been employed when steady RANS solutions produce poorly converging or oscillating results or in cases, such as aeroelastic analysis, which require unsteady aerodynamic simulation. For most traditional external and internal aerodynamic flows, structured overset grids or unstructured grids are employed to minimize geometric modeling and grid generation times. Grid adaptation, primarily as a series of coarse-grain intermediate processing steps is also seeing use on particularly complex flow problems and configurations. In the case of aerothermodynamic flows, engineers have been forced to continue to employ structured grid techniques as the present unstructured grid methodology has proven inadequate in the prediction of surface heating. In the area of aero-plume interaction modeling, two-gas, frozen chemistry simulation is generally the state-of-the- art, with some production solvers capable of predicting flows with only a single gas component. Prediction of flows falling into the afore-mentioned top Aerosciences technical challenges have severely stressed the present state-of-the-art in CFD prediction and for some problems, such as unsteady separated flows and aero-plume interaction cases, engineers have begun employing Large Eddy Simulation (LES) and Hybrid RANS/LES techniques. In some isolated aero-propulsion interaction cases, chemically reacting flow simulations have been applied. These methods are highly evolutionary and engineers have little experience in their application, so they cannot be heavily relied upon in today’s application environment. Therefore, this leads one to muse over which numerical technologies will be included in the CFD tools that will be employed 30 years in the future. This presentation will describe specific technical problems that have stressed NASA’s traditional CFD methods to their breaking point and will link these issues to the Agency’s top Aerosciences technical challenges. The discussion will then shift to the characteristics of future CFD solvers that will be required to attack these challenges and how these characteristics differ from the present state-of-the- art. High-order methods certainly appear to have a place in the development of future CFD tools and some of the physical characteristics of our most challenging problems suggest that high-order methods are the only way to effectively solve them. But there are some relatively severe implementation issues that face these methods, particularly in the area of general applicability and robust operation as an engineering tool. Desired characteristics of next-generation CFD solvers will be discussed and the author’s view of which emerging numerical technologies might be employed to address these attributes will also be presented

David M Schuster

RTE: A Computer Code for Rocket Thermal Evaluation

The numerical model for a rocket thermal analysis code (RTE) is discussed. RTE is a comprehensive thermal analysis code for thermal analysis of regeneratively cooled rocket engines. The input to the code consists of the composition of fuel/oxidant mixture and flow rates, chamber pressure, coolant temperature and pressure. dimensions of the engine, materials and the number of nodes in different parts of the engine. The code allows for temperature variation in axial, radial and circumferential directions. By implementing an iterative scheme, it provides nodal temperature distribution, rates of heat transfer, hot gas and coolant thermal and transport properties. The fuel/oxidant mixture ratio can be varied along the thrust chamber. This feature allows the user to incorporate a non-equilibrium model or an energy release model for the hot-gas-side. The user has the option of bypassing the hot-gas-side calculations and directly inputting the gas-side fluxes. This feature is used to link RTE to a boundary layer module for the hot-gas-side heat flux calculations.

Mohammad H N Naraghi

Softening the Gap between Wöhler and Paris – New Approaches for Fatigue Analysis –

Fatigue analysis tools can vary across industries. For example, automotive engineers often use the Wöhler (S-N) approach to design for safe-life, while aerospace engineers prioritize damage tolerance and inspection intervals, relying instead on crack growth models such as Paris’ law. Although both approaches may deal with the control of cracks in similar materials, their analysis tools and material characterizations are fundamentally distinct. This divide mirrors the classic split between stress-based strength analysis and linear elastic fracture mechanics. However, modern nonlinear models that incorporate material softening, such as cohesive laws, blur this boundary and capture fracture behaviors across scales. This presentation describes the CF23 fatigue model, which uses cohesive softening to link S-N crack initiation with crack propagation rates. CF23 spans the full fatigue spectrum, from initial propagation transients to steady-state growth and threshold conditions, offering a unified framework that bridges Wöhler and Paris-based methodologies. Example applications include fatigue crack propagation transients in adhesive interfaces and skin/stiffener separation.

cohesive elements