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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Battery Evaluation Profiles for X-57 and Future Urban Electric Aircraft

Battery energy density is one of the most critical design parameters for sizing all-electric aircraft, however it’s easily overestimated. Establishing the effective, usable energy density is confused by varying degrees of margin needed to account for structural and thermal management between different cell chemistry and pack designs. Therefore, a better methodology is needed to fairly compare emerging battery technologies for electric aircraft. Currently, there is a loss of critical information when vehicle trade studies are performed using “nominal” published cell-level performance metrics. Aircraft power demands rarely match these nominal power profiles, and aircraft designers lack the ability to accurately simulate the battery performance and temperature off-nominally unless the battery chemistry is well established. Conversely, battery suppliers have no generalized reference cases to publish more realistic performance metrics. This can lead to poor assumptions, such as aircraft studies assuming a fixed discharge efficiency of a battery, when in reality the usable energy in a pack is dependent on the power and thermal profile. Information needed to properly assess weight penalties for thermal management is also typically poorly characterized when assessing candidate batteries. This paper serves to better inform battery development, and similarly, provide aircraft designers with more realistic assumptions for applying knockdown margins in their designs. Detailed power and thermal performance estimates are provided, which provide a starting point for sizing power and thermal budgets using experimentally derived battery models. Results show that the X-57 battery-to-shaft efficiency is 77.3% for a particular optimized mission. Considering a 25% reserve on the battery capacity, this means that only roughly half of the original 55.3kWh ‘nominal’ pack energy can be converted to useful work during a mission. Further estimates on a clean-sheet VTOL optimization show an average 82.7% battery-to-shaft efficiency, using 98% peak efficiency inverters and 97.4% peak efficiency motors. Although higher battery efficiencies are possible, the resulting weight penalty negates improvement in vehicle performance. These trade-offs and resulting power profiles are provided as a starting point to better assess future battery designs.

Battery Electric Aircraft↗
NASA NTRS2026

Smart Process Planning for Automated Fiber Placement

Many industries, including aerospace, automotive, wind energy, maritime, and sporting goods, rely on strong, lightweight materials called composites. These materials are made by layering fibers, which can come in the form of narrow strips or wider sheets, and setting them in a polymer matrix. One of the most advanced ways to make these parts is through automated fiber placement, where a machine lays down the fibers in precise patterns. This method can create very efficient and strong designs, but it is complex, expensive, and often depends heavily on the experience of skilled engineers. Today, the design, manufacturing, and inspection stages of composite production are usually handled separately. This separation means that important information, such as how a part will be built or what defects might occur, is not always shared between stages. As a result, parts may not be as lightweight, strong, or defect-free as possible, and the process can take longer and cost more. This research develops a smart process planning system that connects design, manufacturing, and inspection into one continuous process. Built as software that works with existing tools, the system can automatically plan how the fibers are placed, predicting and reducing defects while improving both manufacturability and strength. The system optimizes not only individual layers but also how defects are distributed across all layers, preventing them from stacking up in ways that weaken the final part. It also uses inspection results from completed parts to improve future designs, creating a feedback loop where each stage informs the others. The system was tested by designing a composite panel using this new approach and comparing it to a panel made with state-of-the-art manual planning methods. The results showed that the system could intentionally control where defects appeared and increase the efficiency of the planning process. By unifying design, manufacturing, and inspection, this research shows a way to make advanced composite manufacturing more efficient, consistent, and cost-effective. This approach lowers the barrier to using automated fiber placement and opens the door for its wider adoption not only in aerospace but also in industries such as automotive, wind energy, maritime, and sporting goods, where strong and lightweight structures are essential.

Computer-Aided Process Planning↗
NASA NTRSDate not supplied

The Origin of Palimpsests

Palimpsests are the most enigmatic impact features observed on icy worlds of the Solar System. Palimpsests are large impact features which are bright, old, mostly flat, and roughly circular. They range from >150 to ~350 km in diameter and sometimes exhibit recognizable associated radial secondary ejecta crater strings beyond their rims. Curiously, among all the Solar System's icy worlds, palimpsests are exclusively seen on the icy Galilean moons. Palimpsests are obviously different than similarly sized impact features seen on other icy satellites. For example, Evander, a 350 km diameter peak-ring impact basin on Saturn’s moon Dione, has a very low depth/diameter ratio of 0.008, indicating significant viscous relaxation. Nevertheless, it still retains short wavelength topography including a prominent central peak, peak ring, and a continuous rim, none of which are seen at palimpsests. Palimpsests display an approximately circular, and dominating, expanse of undulating plains, which is level with surrounding terrain and shows no recognizable rim. At the center of palimpsests are smooth central plains that are enclosed by a low scarp. Central plains material superposes the surrounding undulating plains and is littered with small blocks reminiscent of “small chaos” on Europa. Palimpsests have yet to be explored using modern impact simulation tools. We have begun to investigate two competing hypotheses: (A) Palimpsests form on targets which have a preexisting near-subsurface layer that is liquid or solid but very weak, and is excavated as proximal ejecta; or (B) They form in a solid ice target underlain by a steep thermal gradient that strongly influences the resulting impact feature, including the production of impact-induced melting at depth that is then a major component of proximal ejecta. Our results will have profound implications for the early thermal history of the icy Galilean satellites. We will test our two hypotheses for the formation of palimpsests on Ganymede (where they are both best observed and preserved) by means of 2D iSALE hydrodynamic simulations of impacts into ice targets that will be structured according to the scenarios for each hypothesis, with select runs in 3D. We have already conducted a pilot study using 2D iSALE that has yielded promising results, which we will report out at the meeting.

palimpsests↗
NASA NTRS2025

Enhancing Nasa Sounding Rocket Capabilities Through Composite Material Adoption

Sounding rockets are a suborbital research platform employed by NASA for heliophysics, astrophysics, and geospace science investigations, and for technology development. They follow semi-parabolic trajectories and are launched using surplus military, and commercial, solid-propellant rocket motors. NASA sounding rocket payloads are comprised of standard, modular subassemblies, designed to perform specific functions critical to achieving mission success criteria. Since the program’s inception, payload length and mass have trended upwards, limiting the capability of the platform to reach exospheric apogees and decreasing the time above critical altitudes to observe solar and celestial targets. To offset this trend, composite materials may be of use in payload and subsystem design to decrease payload mass. This investigation seeks to understand if there is a practical benefit to introducing composite materials on the NSRP by assessing mission specific impacts to those missions conducted in fiscal year 2024. To accurately estimate the mass of a composites-based payload, a standard subsystem (NIACS) underwent structural reconfiguration. Materials were selected that align with NSRP design, manufacturing, and testing standards. NIACS reconfiguration resulted in a mass reduction of 19.6% which was then applied to the payload system in entirety. Using vehicle performance carpet plots, delta apogee and time above the Kármán line were determined. Performance improvements were then evaluated against that mission’s success criteria to determine if that mission would have benefitted from using composite materials. On average, each mission would have gained 59.8 kilometers apogee and 62.9 seconds above the Kármán line. However, only nine of the seventeen missions would have benefitted from these performance improvements. Two of the missions could have used a lower-impulse launch vehicle to achieve mission success. Upleg stability met programmatic criteria; downleg instability and dynamic pressure improved, showing that ballast would not offset the mass reduction of using composite materials. Cost analysis showed that production costs will increase 39%. For the two missions that could have used a lower-impulse launch vehicle, the cost of the launch vehicle would have offset payload production costs. In summary, composite materials are not recommended for universal use on NASA sounding rocket payloads due to unclear programmatic benefits and added cost. However, they are justified for optical astrophysics and heliophysics missions, high-apogee (exosphere) requirements, or when they reduce payload costs versus higher impulse launch vehicles. Reflown, recoverable subsystems should use composites to enhance apogee and observation time.

Robert Henry Burth↗
NASA NTRS1997

Future Aerospace Technology in the Service of the Alliance: Sustained Hypersonic Flight - Volume 3

Sustained Hypersonic Flight capability is envisioned for several future military and civilian applications, such as long range immediate reaction reconnaissance, high speed interception of air targets, long range precision strike against hardened or time critical targets, and access to space. The inherent reduction in time-to-target and low vulnerability will permit new operational tactics. The symposium outlined mission needs and operational scenarios for hypersonic vehicles. Addressed were: Technological issues and challenges in external hypersonic aerodynamics and design, propulsion and engine/airframe integration, military ramjet applications, overall system design including structures and materials development needs, and test facilities. Ground based test facilities cannot provide full simulation at Mach numbers above 5. Therefore, the use of Computational Fluid Dynamics design tools is essential. For routine use of the computational tools in analysis, design and optimization, it is necessary to reduce the time for the entire computational process by two orders of magnitude. Hypersonic boundary layer transition remains a critical design issue because of the important impact on engine drag and on heating, which can affect the choice of materials and thermal protection systems. In addition, reliable prediction of scramjet net thrust is an absolute must in resolving hypersonic air breathing vehicle design issues. Due to current facility and computational shortfalls, the development of future hypersonic flight systems requires research flight tests in the technology areas of boundary layer transition and air-breathing propulsion engine performance. For sustained hypersonic flight beyond Mach 6, the supersonic combustion ramjet (scramjet) engine is the only choice for the near future. Only this air-breathing concept offers a significant promise of large reductions in required propellant fractions, increased payload fractions, and reduced size vehicles, together with a foreseeable technological feasibility. Airframe/engine integration, combustor design and thermal management are the predominant engineering tasks. Fuels, hydrogen or hydrocarbon, must be matched to the operational needs of military or civil use. Experience in existing ramjet propelled missiles capable of speeds up to Mach 4 can support the development effort. The potential mission and cost benefits of sustained hypersonic flight to both military and civil applications are tremendous. From the budget point of view, the possibility of sharing development costs between military and civil programs offers a specific advantage.

Combustion chambers↗
NASA NTRS2026

X-Hab 2026: LiDAR-Powered Autonomous Charging Service Capability for Surface Rovers and Systems

This document details the timeline of the LiDAR-Powered Autonomous Charging Service Capability for Surface Rovers and Systems project, initiated by the Fall 2025 semester class and completed by the Spring 2026 semester class. This project focuses on developing a fully autonomous system composed of a mobile surface rover and an induction charging station with a robotic arm, both controlled by their own NVIDIA Jetson Orin Nano. Structural improvements to the rover suspension system and body eliminated excessive camber, reduced stress and strain on the plexiglass body, and improved maneuverability and durability of the rover. The charging station robotic arm was fully redesigned to increase reach while minimizing weight and increasing misalignment tolerance during docking on uneven terrain. Electrical system improvements addressed previous torque and power limitations of both the rover and charging station arm. High-torque servo motors were selected based on updated calculations which incorporated terrain slope and Factor of Safety, enabling zero-point turning for the rover and increased payload capacity of the charging station arm. Significant progress was made in autonomy and perception. The rover now employs 3D LiDAR and SLAM mapping for localization, mapping, and path planning. The Battery Monitoring System (BMS) was created to coordinate battery management between the rover and charging station. The BMS provides continuous monitoring of battery state of charge, temperature, current, and will enable the autonomous initiation, execution, and termination of the charging cycle via Bluetooth communication. Testing of the WIBOTIC induction charging system demonstrated reliable power transfer under both aligned and misaligned conditions. This project demonstrated the ability of an autonomously navigating surface rover to independently plan a path to the charging station, dock, and the charging station to autonomously deploy a robotic charging arm and initiate charging of the rover. This work details the progress made to demonstrate the feasibility of autonomous surface rover navigation and recharging systems.

Megan Steele↗
NASA NTRS1993

A SIMULINK Environment for Flight Dynamics and Control Analysis - Application to the DHC-2 'Beaver', Part 1. Implementation of a Model Library in SIMULINK

The design of advanced Automatic Aircraft Control Systems (AACS's) can be improved upon considerably if the designer can access all models and tools required for control system design and analysis through a graphical user-interface, from within one software environment. This MSc-thesis presents the first step in the development of such an environment, which is currently being done at the Section for Stability and Control of Delft University of Technology, Faculty of Aerospace Engineering. The environment is implemented within the commercially available software package MATLAB/SIMULINK. The report consists of two parts. Part I gives a detailed description of the AACS design environment. The heart of this environment is formed by the SIMULINK implementation of a nonlinear aircraft model in block-diagram format. The model has been worked out for the old laboratory aircraft of the Faculty, the De Havilland DHC-2 'Beaver', but due to its modular structure, it can easily be adapted for other aircraft. Part I also describes MATLAB programs which can be applied for finding steady-state trimmed-flight conditions and for linearization of the aircraft model, and it shows how the built-in simulation routines of SIMULINK have been used for open-loop analysis of the aircraft dynamics. Apart from the implementation of the models and tools, a thorough treatment of the theoretical backgrounds is presented. Part II of this report presents a part of an autopilot design process for the 'Beaver' aircraft, which clearly demonstrates the power and flexibility of the AACS design environment from part I. Evaluations of all longitudinal and lateral control laws by means of nonlinear simulations are treated in detail. The AACS design environment from part I proved to be a very useful tool for designing the control laws of the 'Beaver' autopilot within a very tight time-schedule. The autopilot design process itself will be used as a guideline for future AACS research at the Faculty of Aerospace Engineering. Flight tests of the 'Beaver' autopilot, done after evaluating the control laws in the SIMULINK package, proved to be quite successful. In the future, the AACS design package will evolve into a standardized, integrated design environment which can be applied to virtually any type of aircraft. The AACS design cycle will be shortened further by developing tools for automatically porting control laws from the MATLAB/SIMULINK environment to a piloted real-time flight simulator and the Flight Control Computers of the aircraft.

Aircraft Modelling↗
NASA NTRS2026

Mathematical Model of a Regenerative Fuel Cell for System Optimization

This thesis developed a system-level optimization model of a regenerative fuel cell (RFC) system for long-duration, off-world energy storage applications. Prior RFC design studies have typically been limited to reduced parameter sets and simplified constraints due to computational limitations relative to the number of relevant degrees of freedom. As a result, important nonlinear interactions between subsystems have not been fully captured. This work began to address that gap by developing a higher-fidelity, nonlinear optimization framework that incorporates a broader set of design variables and coupled constraints, enabling a multidimensional model that captures the coupled behavior of RFC subsystems and demonstrates the feasibility of applying optimization to such systems. An expanded system-level optimization approach was established that captures interactions between electrochemical performance, structural requirements, and storage design. This enabled a more comprehensive evaluation of trade-offs than conventional formulations. The model integrates four coupled subsystems: a fuel cell, an electrolyzer, reactant gas, and high-pressure storage tanks, and was formulated to accommodate a wide range of mission parameters, including operational time and required output power. It incorporates constraints on available solar array power, reactant mass balance between production and consumption, and pressure-dependent storage requirements. To enable reliable convergence, the optimization problem was reformulated to reduce dimensionality and improve numerical stability, with subsystem models organized for efficient evaluation. Problem dimensionality was reduced by consolidating lower-level design variables into higher-level representative quantities, and subsystem behavior was evaluated within the optimization loop. A multi-start initialization strategy was employed to mitigate sensitivity to local minima and improve solution quality, while nonlinear relationships were solved using robust numerical methods. The results showed that convergence was achieved across a range of required output power values. Specific energy reached a maximum at a critical mission power level, where the electrolyzer power matched the available solar input and operated near its voltage and current density limits. Beyond this point, further increases in required power resulted in less mass-efficient operation, increasing total system mass and reducing overall performance. The developed model represents an advancement in RFC system-level optimization by enabling analysis of a broader and more tightly coupled design space than previous considerations. While convergence behavior and computational cost remain challenges, the methods introduced improve solvability and allow inclusion of additional design variables with minimal loss of physical fidelity. However, the numerical results should not be interpreted as definitive design recommendations, as the model includes simplifying assumptions and omits several higher-order effects. Future work should extend this framework by incorporating additional subsystems and loss mechanisms, such as thermal management, parasitic power consumption, and reactant losses, to improve fidelity and ensure more representative design conclusions.

Electrochemistry↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗