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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 NTRS2026

Parametric-Based Heat Rejection Trade Study for Lunar and Martian Surface Operations

Establishing and maintaining a sustained presence on the lunar and/or Martian surfaces will require a diverse portfolio of surface elements (e.g., habitation, mobility, power generation, etc.). Many of these systems generate excess heat that must be rejected across a wide range of magnitudes, temperatures, and duty cycles and under variable environmental conditions. To identify the most promising heat rejection approaches for this diverse portfolio, a heat rejection trade study was conducted to evaluate the performance of different technology approaches across a spectrum of surface environments and heat-load requirements. The trade study consisted of three stages: (1) development of a parametric-based modeling framework, (2) creation of a database of heat rejection technologies, surface elements, and environmental conditions for the Moon and Mars, and (3) execution of a quantitative analysis of various heat rejection technologies across different operating conditions and surface elements. The modeling framework is developed in Python and Excel to prioritize small model size and hence low computational cost to enable large parametric sweeps while avoiding the reliance on proprietary software. Individual heat rejection processes are represented as simple Excel models, and a centralized Python script interfaces with the models to coordinate the parametric study. These simple sizing models were developed to take heat load requirements and environmental parameters as inputs and compute mass, power, and volume as outputs. Rather than assess each heat rejection technology separately for each surface element, a unified parametric space was developed to evaluate all technologies across all elements. This parametric space includes factors related to heat load (e.g., magnitude or temperature) and environment (e.g., surface temperature, sky temperature, solar flux). This effort generated a database containing information on over 60 heat rejection technologies and 30 surface elements. For each surface element, the expected heat rejection requirements were documented and analyzed to determine the most common needs shared across all elements. Environmental conditions at various lunar and Martian latitudes were also established for worst-case hot and worst-case cold scenarios. High-fidelity heat rejection models are currently under development. Preliminary trades between heat rejection technologies including radiators, venting technologies, convective coolers, and more have been conducted to identify promising options. This presentation will summarize the preliminary trade results and provide an overview and discussion of the expected heat loads and thermal environments for sustained surface operations on the Moon and Mars.

Heat Rejection↗
DOE OSTI2025

Exact block encoding of imaginary time evolution with universal quantum neural networks

We develop a constructive approach to generate quantum neural networks capable of representing the exact thermal states of all many-body qubit Hamiltonians. The Trotter expansion of the imaginary time propagator is implemented through an exact block encoding by means of a unitary, restricted Boltzmann machine architecture. Marginalization over the hidden-layer neurons (auxiliary qubits) creates the nonunitary action on the visible layer. Then, we introduce a unitary deep Boltzmann machine architecture in which the hidden-layer qubits are allowed to couple laterally to other hidden qubits. We prove that this wave-function is closed under the action of the imaginary time propagator and, more generally, can represent the action of a universal set of quantum gate operations. We provide analytic expressions for the coefficients for both architectures, thus enabling exact network representations of thermal states without stochastic optimization of the network parameters. In the limit of large imaginary time, the yields the ground state of the system. The number of qubits grows linearly with the number of interactions and total imaginary time for a fixed interaction order. Both networks can be readily implemented on quantum hardware via midcircuit measurements of auxiliary qubits. If only one auxiliary qubit is measured and reset, the circuit depth scales linearly with imaginary time and number of interactions, while the width is constant. Alternatively, one can employ a number of auxiliary qubits linearly proportional to the number of interactions, and circuit depth grows linearly with imaginary time only. Every midcircuit measurement has a postselection success probability, and the overall success probability is equal to the product of the probabilities of the midcircuit measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Criteria for Retention of 3013 S1 Containers Based on Relative Risk and Expert Judgment

An evaluation was performed to assess the suitability of thirty-three 3013 containers proposed for retention. These containers have moisture levels greater than 0.08 wt.% – the S1 population. The remainder of the S1 population stored at SRS will be down blended and disposed of by the end of 2028. Based on field surveillance and shelf-life data available to date as well as informed technical judgment, no container is currently expected to fail in its 50-year storage period. However, corrosion risk varies across the S1 population. Relative risks were evaluated using predicted Consensus Scores and their 95% Upper Prediction Limits (UPLs). The predicted values are based on a statistical model of Consensus Score as a function of moisture, chloride, and whether the packaged material was electrorefining scrap packaged at Hanford. Consensus Score has been shown to be a useful indicator of corrosion potential, and the UPL captures uncertainty in the model predictions, providing a conservative indicator of corrosion potential. Using UPLs to determine relative risks, together with expert review, three containers were identified as not suitable for retention, and the remainder were determined to be suitable.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

System for controller area network payload decoding

A system for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This system can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.

Verma, Kiren E.↗
NASA NTRS2020

Evaluation of Fatigue Damage Accumulation Functions for Delamination Initiation and Propagation

The present report follows on the cohesive fatigue damage model methodology proposed in NASA-TP-2018-219838. In that report, an empirical function describing the incremental damage due to cyclic loading was used to calculate fatigue damage within a cohesive formulation. The form of the function was developed such that, when integrated at a constant stress amplitude from no damage to failure, it produces a life versus load response that is consistent with an S-N diagram. Therefore, the parameters of the damage model could be obtained by fitting the model predictions to an S-N diagram. The finite element analyses performed demonstrate that the cohesive fatigue accumulation function provides a link between the S-N diagram that describes crack initiation, and the Paris law that characterizes the rate of crack propagation. However, when the model was proposed, it was not known whether the form of the damage accumulation function associated with a desired S-N diagram is unique and, if not, if the link between S-N and the Paris law is unique and independent of the fatigue function selected. In the effort described herein, several alternative forms of the damage function that reproduce the desired features of S-N diagrams were found and evaluated. The effects of each of these functions on the predicted parameters of the Paris law and the propagation threshold are discussed. The results indicate that the predicted exponent m of the Paris law is indeed independent of the damage accumulation function. However, different functions predict different values for the pre-factor C of the Paris law. Therefore, the proper damage accumulation function must be selected by comparison with experiments. One of the new damage accumulation functions proposed herein was found to be particularly useful for analysis because of the ease with which the model parameters can be determined with a minimal amount of experimental information. The effectiveness of the proposed methodology and damage function was demonstrated by conducting analyses of a double cantilever beam test, a mixed-mode bending test, and a three-point bending test of a skin/doubler specimen. The results indicate that the same set of model parameters can provide accurate predictions of the rate of fatigue crack propagation for a variety of material interfaces, mode mixities, load levels, and stress ratios.

DKIN/Stiffener Debonding↗
NASA NTRS2026

Gateway Element and Payload Materials Outgassing Analyses: HALO, HERMES, and ERSA

Gateway was intended to be humanity’s first space station around the Moon, but its development has been paused as the National Aeronautics and Space Administration (NASA) shifts focus to achieving the United States’ National Space Policy goals. Instead of an orbiting lunar outpost, NASA will now pursue the development of a lunar surface base to support a sustained human presence on the Moon. Before the program’s pause, Gateway’s Induced Environments team worked to ensure payloads and elements (i.e., modules) complied with induced environment requirements. Methods developed and insights gained from this work will have applicability to NASA’s Moon Base and the potential repurposing of Gateway elements and payloads, as well as to induced environments modeling for future space stations. The Gateway program’s induced environment included molecular contamination, electric thruster plume sputter and redeposition, and lunar dust transfer from the Human Landing System (HLS). Primary sources of external molecular contamination included materials outgassing, chemical thruster plume contamination, and vacuum venting. The focus of this paper will be on element- and payload-level materials outgassing analyses performed for Gateway Configuration 1, extending the previously-developed framework for Gateway system-level external molecular contamination modeling. Gateway Configuration 1 consisted of the Power and Propulsion Element (PPE) and the Habitation and Logistics Outpost (HALO). It also included payloads like the European Radiation Sensor Array (ERSA) attached to PPE and the Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) attached to HALO. The element- and payload-level analyses to be introduced in this paper for HALO, HERMES, and ERSA enabled high-fidelity descriptions of Gateway’s external molecular contamination environment. Approaches to geometric modeling, meshing, outgassing rate assignment, molecular transport modeling, and analysis methodology will be presented. Element and payload contaminant deposition onto sensitive Gateway receiver surfaces will be summarized and results compared to induced environment requirements. While these results incorporate refinements made over the course of the program, they were not intended to be final. Therefore, modeling assumptions and inputs, potential improvements, and lessons-learned will be documented to inform future work on Moon Base, repurposed elements and payloads, and other space stations.

Gateway↗
NASA NTRS2026

Hybrid Thermally Efficient Core (HyTEC) Phase 1 3-Stage Compressor Rig Final Report - Unlimited Rights

The HyTEC Phase 1 3-Stage Compressor Rig project was conducted to mature and validate front-stage aerodynamic technologies in support of HyTEC Phase 2 and GEA’s Compact Core compressor. Building on the prior ERA Phase 2 Baseline Rig, GEA and NASA collaborated on the design and testing of the HyTEC rig, incorporating advanced technologies such as increased radius ratio, higher aerodynamic loading, and advanced casing treatment. The HyTEC test campaign leveraged similar design and instrumentation philosophy, test execution approach, and the same facility (NASA W-7) to enable clear comparison with the legacy ERA rig results. The HyTEC rig was designed to deliver benefits in efficiency, tip loading, operability, and aeromechanics, with success measured against defined TPMs that were anchored to the ERA baseline rig data.

Axial Compressors↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
DOE OSTI2026

NuMI Graphite Target Fin Removal Assembly

Materials scientists want to study the irradiated graphite target from the Neutrino at the Main Injector (NuMI) beam line. In order to gather these irradiated graphite fins from the target canister, it will have to be done remotely. Therefore, a removal assembly will need to be created in order to do this efficiently and well. This project aims to make progressive steps towards deploying the final target removal assembly. Furthermore, calculations related to the removal assembly design were done, and tests were planned to discover further information about the feasibility of this design. The main result from this project is the deflection of the cantilever arm, which is 0.354 inches due to the weight of the beam and cutting tool. Additionally, more results of this project are the testing assembly CAD model and the feasibility of design. This project concludes that there is still more work that needs to be done for the removal assembly to be deployed, but there were foundational steps that were made in the implementation of this design. Finally, there should be further steps and tests to determine the feasibility of this removal assembly.

Malovski, Azra [NIU, DeKalb]↗
DOE OSTI2026

Ramp-release experiments for strength measurements: Strain-rate dependence

This paper presents an enhanced analysis method for investigating material properties at high strain rates, extending the capability of established experimental techniques to gain more information. The ramp-release method has been applied to many experiments reported at high (≈10 5 − 10 6 s −1 ) strain-rates. More recent data gathered at the National Ignition Facility (NIF) has enabled higher (≈10 8 s −1 ) strain-rates to be studied. Here, we present an initial application of ramp-release analysis to NIF ramp-compression data, illustrating both the opportunities and the practical challenges of extending these methods to laser-driven platforms. The higher strain-rates accessed at the NIF mean that there is more strain-rate enhancement to strength, and the experimental configuration means that this enhancement is more readily seen in the data. This is enabled by the capability of avoiding peak-compression attenuation through the sample thickness with a designed hold period made possible by the pulse-shaping capability of NIF. We propose that this combination of experimental conditions and an enhanced analysis method enables the strain-rate enhancement to strength to be studied, and potentially for this to inform physics models at smaller scales than the continuum.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Crossover from quantum to classical transport

Understanding the crossover from quantum to classical transport phenomena has become of fundamental importance not only for technological applications due to the creation of sub-10nm transistors – an important building block of our modern life – but also for elucidating the role played by quantum mechanics in the evolutionary fitness of biological complexes. This article provides a basic introduction into the nature of charge and energy transport in the quantum and classical regimes. Here, it will identify the characteristic transport properties in both limits, and demonstrate how they can be connected through the loss of quantum mechanical coherence. I will identify the salient features of the crossover physics, and demonstrate their importance in opening new transport regimes and for understanding efficient and robust energy transport in biological complexes.

charge↗
NASA NTRS1991

Impact of Inertia, Friction, and Backlash upon Force Control in Telemanipulation

The mechanical behavior of master controllers of telemanipulators has been a concern of both designers and implementors of telerobotic systems. In general, the literature recommends that telemanipulator systems be constructed that minimize inertia, friction, and backlash in an effort to improve telemanipulative performance. For the most part, these recommendations are founded upon theoretical analysis or simply intuition. Although these recommendations are not challenged on their merit, the material results are measured of building and fielding telemanipulators that possess less than ideal mechanical behaviors. Experiments are described in which forces in a mechanical system with human input are evaluated as a function of mechanical characteristics such as inertia, friction, and backlash. Results indicate that the ability of the human to maintain gripping forces was relatively unaffected by dynamic characteristics in the range studied, suggesting that telemanipulator design in this range should be based on task level force control requirements rather than human factors.

Neil A Duffie↗
DOE OSTI2026

Phase 1 Engagement Summary: Rural Alabama

An Alabama community coalition received technical assistance through the U.S. Department of Energy's Communities Local Energy Action Program (LEAP) to expand awareness of alternative transportation fuels through stakeholder engagement efforts. Phase 1 of the project centered on outreach, relationship-building, and the identification of community priorities. Through two workshops held in October and December 2024, the coalition engaged over 120 stakeholders - including county commissioners, school district representatives, and public and private fleet operators. These sessions included presentations from technical experts and industry partners, as well as a vehicle showcase and structured discussions to assess stakeholder needs and concerns. This report summarizes these efforts and identifies three key priority areas for Phase 2 of the project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Computer Science↗
DOE OSTI2025

Maximizing Free Energy Gain

Maximizing the amount of work harvested from an environment is important for a wide variety of biological and technological processes, from energy-harvesting processes such as photosynthesis to energy storage systems such as fuels and batteries. Here, we consider the maximization of free energy—and by extension, the maximum extractable work—that can be gained by a classical or quantum system that undergoes driving by its environment. We consider how the free energy gain depends on the initial state of the system while also accounting for the cost of preparing the system. We provide simple necessary and sufficient conditions for increasing the gain of free energy by varying the initial state. We also derive simple formulae that relate the free energy gained using the optimal initial state rather than another suboptimal initial state. Finally, we demonstrate that the problem of finding the optimal initial state may have two distinct regimes, one easy and one difficult, depending on the temperatures used for preparation and work extraction. We illustrate our results on a simple model of an information engine.

Physics↗
NASA NTRS1990

Autonomous Power Expert Fault Diagnostic System for Space Station Freedom Electrical Power System Testbed

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control to the Space Station Freedom Electrical Power System (SSF/EPS) testbed being developed and demonstrated at NASA Lewis Research Center. The objectives of the program are to establish artificial intelligence technology paths, to craft knowledge-based tools with advanced human-operator interfaces for power systems, and to interface and integrate knowledge-based systems with conventional controllers. The Autonomous Power EXpert (APEX) portion of the APS program will integrate a knowledge-based fault diagnostic system and a power resource planner-scheduler. Then APEX will interface on-line with the SSF/EPS testbed and its Power Management Controller (PMC). The key tasks include establishing knowledge bases for system diagnostics, fault detection and isolation analysis, on-line information accessing through PMC, enhanced data management, and multiple-level, object-oriented operator displays. The first prototype of the diagnostic expert system for fault detection and isolation has been developed. The knowledge bases and the rule-based model that were developed for the Power Distribution Control Unit subsystem of the SSF/EPS testbed are described. A corresponding troubleshooting technique is also described.

Long V Truong↗
NASA NTRS2026

Polymer Composite Material Testing for a Cryotank Application

Composite cryotanks will play a key role in enabling the next generation of efficient aircraft. Carbon fiber reinforced polymer (CFRP) composites have the benefits of reduced weight and potentially higher structural strength compared to traditional metallic fuel tanks. A material screening study was conducted to inform material selection for liquid hydrogen (LH2) fuel storage. Three composite materials were considered because of their aerospace grade toughness, strength, and existing data to compare against. These materials were a thermoplastic low-melt polyaryletherketone (LM-PAEK)/carbon fiber (CF), thermoset/CF, and hybrid thermoset/thermoplastic polyurethane (TPU) veil/CF composite. Mechanical screening tests included tension, compression, in-plane shear (IPS), and tensile-tensile fatigue (TTF). Each material was tested at both a baseline (no liquid nitrogen/LN2 cycling) and 100 LN2-cycled conditions to determine the knockdown factor, if any, of each material when exposed to environmental loading effects in a cryotank. Results show minimal effects of the LN2-cycling compared against baseline values. The three materials behaved similarly in tension; however, the thermoplastic/CF had the highest IPS toughness. The hybrid thermoset/TPU/CF composite had the lowest IPS strength, compressive strength, and toughness. LN2-cycling had minimal effects on tensile-tensile fatigue performance of the thermoplastic/CF material. Mechanical data was captured to guide material down selection for future commercially viable hydrogen aircraft design. In its current state, there does not exist a consolidated, publicly available database for CFRP composite material performance data at cryogenic temperatures. The next step in this work is to test the thermoplastic and thermoset CFRP composites, as well as the neat resins, at LH2 relevant temperature (20 K) to capture this crucial material property data. These results are essential to inform cryotank design and modeling efforts. The process to start this next round of testing has begun. Planned mechanical tests include toughness (single-edge notched beam), tension (unidirectional and quasi-isotropic), thermal expansion, and thermal conductivity. Some tests will also be conducted at an intermediate temperature of 111 K relevant to liquid natural gas (LNG), another attractive fuel choice. The ultimate goal is to manufacture a sub-scale cryotank part that can pass relevant burst, fatigue, permeation, and thermal cycling tests. This work is a part of NASA’s Commercially viable Hydrogen Aircraft for Robust Growth in Efficiency (CHARGE) Project under the larger NASA Subsonic Vehicle Technologies and Tools (SVTT) Project.

composites↗
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↗