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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRSDate not supplied

Extreme Precipitation in the Southern US Great Plains in the Spring of 2015: Mechanisms and Prediction

During May of 2015, the southern US Great Plains and adjacent Gulf Coast region experienced more than twice the long-term mean precipitation, making it the wettest May since 1895. We investigate the physical mechanisms associated with this event using a suite of large-ensemble regional replay AGCM simulations from the NASA-GEOS model. In these simulations, certain regions of the globe are constrained to closely follow observations while the remainder of the domain is free running, allowing for the isolation of the remote regions that were important for the event. Preliminary analysis provides evidence that the extreme southern US precipitation was linked in part to positive precipitation anomalies in the central and eastern tropical Pacific via a wave train, which ultimately caused anomalous moisture flux from the Gulf of Mexico. An analysis of Subseasonal Experiment (SubX) model output was conducted to explore the subseasonal prediction skill of the event. Several models are able the predict the presence of positive precipitation anomalies in or near the southern US at lead times exceeding 10 days, albeit with errors in the locations and magnitude of the heaviest precipitation anomalies. A more thorough investigation with version 2 of NASA’s GEOS-S2S model shows that the prediction skill stems from the model’s ability to reasonably predict the positive tropical Pacific precipitation anomalies and the initiation of the Rossby wave train that is believed to be linked to the event. The potential causes for limitations in the prediction skill of this event will be explored.

Great Plains↗
NASA NTRS2023

An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature

In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.

environmental barrier coatings↗
NASA NTRS2021

Applying Machine Learning to Predict Alaskan Ionospheric Irregularities

In this work several machine-learning (ML) techniques for predicting ionospheric irregularities in the northern auroral zone were tested. The techniques include Ridge Regression, Long Short-Term Memory Neural Network (LSTM), Classification Neural Network (CNN), Autoencoder Classification Neural Network (ACNN), and LSTM Autoencoder Classification Neural Network (LACNN). These techniques were tested with the rate of total electron content (TEC) index (ROTI) data collected during 2008 and 2009 from a geodetic station in Fairbanks, Alaska (64.98°N, 147.50°W), which is in the auroral zone. Using ROTI data with the ML techniques, experiments were conducted to reach two goals: (1) examine what space weather measurements present good correlation with ROTI so that they may be helpful in ML-based prediction of ionospheric irregularities in the polar region; (2) predict ROTI hours and days ahead by training the neural network models with historical ROTI data alone. The Ridge Regression experiments indicate that a combination of measurements of local geomagnetic horizontal components, geomagnetic SYM-H index, 3-hour Kp and ap indices, and F10.7 solar flux index appears to be more correlated to the single-site ROTI measurements than other parameters. The neural network (NN) experiments show that although the LACNN model allows for predictions of non-irregularity and irregularity conditions defined by ROTI levels up to 3 hours in advance, with an overall accuracy ≥ 92%, a number of irregularity events can still be missed. Hence, further development is needed to reduce the number of missed events. In this paper, the models, data processing, model performance, prediction results, and potential applications are presented.

Pi, Xiaoqing↗
NASA NTRS2022

Balancing Predictive and Reactive Science Planning for Mars 2020 Perseverance

The design of the science planning process for a space science mission needs to find a balance between operational and resource constraints and scientific decision-making. Science planning has previously been characterized as either predictive or reactive. Predictive science planning is needed when constraints drive science activities to be planned far in advance. For example, a combination of long one-way light time plus high-stakes science decisions drove the Cassini-Huygens mission to Saturn to have an extremely predictive planning process. On the other extreme, reactive science planning is needed when constraints drive science activities to be planned based on the results of the previous plan. For example, the Mars Exploration Rover mission interacted with the surface of Mars, and so the planning team needed to know the state of the rover at the end of each planning cycle before starting the next cycle. Operational and resource constraints that require management on intermediate timescales has led to the development of a science planning process between these two extremes. For example, the Mars Science Laboratory is a technically complex rover and has a parallel predictive process that allows the operations team to manage engineering constraints several days in advance while maintaining the reactive tactical planning process similar to that of MER. The Mars 2020 Perseverance rover is a technically complex rover in the MSL style, but has an added layer of science complexity: it is tasked with collecting a returnable cache of scientifically valuable samples of Mars within prime mission. Thus, the science planning process also needs to accommodate high-stakes longer-term science decisions in the style of Cassini. In order to balance the push-pull of these constraints, we have developed a science campaign-focused operational paradigm for Mars 2020 Perseverance that allows for both predictive planning to accommodate technological complexity and high-stakes science decisions as well as reactive planning to accommodate the realities of interacting with the martian surface. This paradigm influenced the design of operational processes and operational tools.

Spanovich, Nicole↗
NASA NTRS2023

Is the Global MHD Modeling of the Magnetosphere Adequate for GIC Prediction: the May 27–28, 2017 Storm

Practical steps taken by the international community to reduce the damage to technological systems from space weather include the development of numerical models capable of real-time predictions of electromagnetic disturbances at the Earth’s surface. Here we examine the feasibility of a version of the Space Weather Modeling Framework (SWMF) global MHD simulation code similar to that used by the NOAA Space Weather Prediction Center to predict the level of geomagnetic field variability, and consequently geomagnetically induced currents (GICs). We consider the contribution of geomagnetic disturbances to the bursts of GIC in the electric power line of the Kola Peninsula during the May 27–28, 2017 storm and compare the observations with results of the global MHD model. During the maximal disturbance magnetic field variations at East Scandinavian stations become more irregular, as intense Pi3 pulsations are superposed on the magnetic bay. These pulsations are not quasi-sinusoidal waves like typical Pc5 pulsations, but they are rather a quasi-periodic sequence of magnetic impulses with time scales ~5–15 min. During this period with elevated Pi3 activity very high values of GIC were recorded (variations >100 A) in the electric power transmission line. The SWMF modeling reasonably well reproduces the global magnetospheric parameters, such as SYM-H index or cross-polar potential. However, the magnetic field variability dB/dt in the East Scandinavia predicted by the modeling has turned out to be more than order of magnitude less than that observed. Thus, the version of SWMF with the grid used by NOAA SWPC still cannot adequately predict for the May 27–28 event the fine structure of the storm/substorm—Pi3 geomagnetic disturbances, and consequently the magnitude of the GIC that they drive.

geomagnetically induced currents↗
NASA NTRS2024

Core Body Temperature Predictions Using Metabolic Energy Expenditure and Heart Rate During Simulated Extravehicular Activity

Long duration spaceflight missions will require crew to become more autonomous in conducting extravehicular activities (EVA) without direct communication with Mission Control for biomedical support. To enable such autonomy, we are developing a Crew State and Risk Model (CSRM) as a collection of key physiology domains that drive EVA crew capabilities and workloads. One model component of CSRM is human thermal regulation. In this paper, customized development of a baseline model to predict core body temperature is presented using physiology inputs of heart rate and metabolic rate. The model development dataset included a baseline study where participants (n=6, equal male and female) performed a 5-hour EVA in a two-part session while wearing a hybrid space suit simulator (HS3). The first session included an end-to-end EVA traversing 1500 meters to a geology site and traversing back to a habitat conducting geology, payload relocation, and maintenance operations every 500 meters. The second session included standalone tasks of a 2000-meter traverse followed by geology tasks. Thermal measures of core body temperature, local skin temperature, liquid cooling garment temperature, heart rate, and metabolic rate were collected through the test duration. Multiple regression was used to build a linear equation to predict core body temperature from inputs of heart rate and metabolic rate. Heat storage was calculated via predicted core body temperature plus LCG and skin temperatures. The model was tested against a dataset from a pressurized suited test (n=6, equal male and female) in the NASA Active Response Gravity Offload System (ARGOS) conducting similar end-to-end EVA and standalone tasks. Predicted error of the model against the raw test cases was 0.2±0.15 °C. The baseline prediction of core body temperature using heart rates and metabolic rates allows for simple real-time tracking from data collected in-flight to monitor crew consumables and thermal flight limits during EVA.

Bradley Hoffmann↗
NASA NTRSDate not supplied

Higher-Order Statistical Moments of Predicted Sonic Boom Waveforms Through Turbulence

Sonic booms generated by supersonic aircraft are affected by turbulence in the atmospheric boundary layer through which they propagate. Turbulence effects lead to random variability of the sonic boom waveforms measured on the ground, complicating the prediction of such waveforms. As an initial effort to predict the waveform variability, the solution of the coherent or mean sonic boom waveform has previously been formulated by the author. The current paper extends the formulation to derive an expression of the second-order statistical moment necessary to calculate the variance of the spectral amplitudes. Since the derivation uses a full wave equation, results using the derived expression will be compared with those obtained using a parabolic approximation. In order to fully quantify the uncertainty of our predictions, the higher order statistical moments are also formulated and are shown to be approximately zero. Consequently, the probability density function (pdf) of the spectral amplitudes is predicted to be Gaussian. The formulation is further extended to determine the pdf of the loudness of sonic booms and quantify the uncertainties associated with the loudness prediction. Results from the formulation are compared with available flight test data.

sonic booms↗
NASA NTRSDate not supplied

Predicting Air Traffic Management Initiatives Using Supervised Learning

Terminal Traffic Management Initiatives (TMIs) such as Ground Stops (GS) and Ground Delay Programs (GDP) are implemented to manage excess demand or lowered capacity at an airport. Air Traffic Flow Management (TFM) specialists identify situations such as aviation constraints, current and forecasted weather conditions, airport demand and capacity, and initiate TMIs for safe and orderly movement of air traffic. In this paper, we outline supervised learning techniques that can be used to predict and recommend TMIs at an airport based on current weather and airport conditions. Our research involves building classic Machine Learning (ML) models such as Logistic Regression, K-Nearest Neighbor, Random Forest and XGBoost, as well as Long short-term memory (LSTM) networks. We trained the models on 3-year historical data (weather, airport demand, capacity and TMIs) from Newark (EWR) airport which was selected based on its higher TMI implementation rates and varied weather conditions. Although Random Forest and XGBoost algorithms are able to predict if a TMI is needed or not, they have difficulty in predicting specific program type. For this purpose, we found that LSTM time-series forecasting models performed better as they also learn from past TMI program type sequences. This study also lays down the foundation for advanced modeling techniques and architectures to predict TMIs in advance for future periods. The ability to predict TMIs in advance will be highly beneficial to the traffic controllers and managers as this will help them to prepare for and manage TMIs more efficiently.

Manoj Agrawal↗
NASA NTRSDate not supplied

NASA/ONERA Collaboration on Small Hovering Rotor Broadband Noise Prediction using Lattice-Boltzmann and Structured Navier-Stokes Solvers

This work compares two lattice-Boltzmann method solvers, PowerFLOW and ProLB, and two structured Navier-Stokes solvers, OVERFLOW2 and FAST, used by NASA and ONERA, respectively, for the broadband noise prediction of an ideally twisted rotor as part of Implementing Arrangement number FR-0685-0, ‘Comparing Computational Fluid Dynamics Solvers for Broadband Noise Prediction.’ Predicted results are evaluated against measured data from both smooth and rough blade sets acquired in the Small Hover Anechoic Chamber at the NASA Langley Research Center. Aerodynamic thrust predictions are seen to agree more favorably with the rough-blade measurements, whereas torque predictions agree better with the smooth-blade measurements. A tonal noise comparison shows better agreement to the measured data with the two structured Navier-Stokes solvers than with the two lattice-Boltzmann solvers, which is thought to be caused by the different geometric discretization associated with each solver paradigm. Broadband noise comparisons show that both lattice-Boltzmann method solvers trend well with the smooth-blade measurements, with the exception of an overprediction by ProLB between 4 kHz and 15 kHz. OVERFLOW2 is seen to capture the measured nondeterministic tonal content between 3 kHz and 8 kHz on a narrowband spectral basis and FAST agrees well with the rough blades on a one-third octave band basis.

Christopher S. Thurman↗
NASA NTRSDate not supplied

Variation in Predicted Orbital Lifetime Due to Launch Year

We present trends in predicted orbital lifetimes of CubeSats based not only on orbital parameters, but also launch year and the Area-to-Mass (AtM) ratio of the CubeSat. Determining the orbital lifetime variation of CubeSats in low-Earth orbit (LEO) is an important aspect of mission planning because of two competing factors: (1) the maximum orbital lifetime for orbital debris mitigation requirements, and (2) the minimum orbit duration necessary to accomplish the spacecraft mission requirements. The orbital lifetime is a function of orbital parameters, the AtM ratio of the CubeSat, and date of orbit insertion. Solar flux varies with time, peaking and declining across the 11-year solar cycle and affecting the amount of atmospheric drag on the CubeSat. This results in large variations in orbital lifetime dependent on the mission's launch date. We calculated the variation of orbital lifetime for multiple commonly used 1U to 6U CubeSat mission types across the two upcoming solar cycles. For any given AtM ratio and altitude combination in this analysis, the predicted orbital lifetime varies up to a factor of five due to orbit insertion occurring in a different year. Some examples of orbital lifetime spreads are 5 months to 2 years, and 1.5 to 7.5 years. While orbital lifetimes correlate to the solar cycle, the phasing of the maximum values varies based on a given AtM ratio and altitude combination. Different combinations of these two factors will result in the maximum predicted orbital lifetime occurring at different launch years throughout the solar cycle. Therefore, there is not a specific year within a solar cycle which can be used to calculate the maximum predicted orbital lifetime for all CubeSats. Since CubeSats are typically flown as a rideshare payload on a launch vehicle, mission planners must account for launch date variation in their orbital lifetime predictions. We recommend calculating orbital lifetime for a range of dates to allow for risk planning due to launch date slips, and other mission planning best practices.

CubeSat↗
NASA NTRSDate not supplied

Using Long-Short Term Memory Models to Predict Solar Active Regions Emergence

We train Long Short-Term Memory (LSTM) models that predict the formation of active regions (ARs), the main source of eruptive solar activity. Using the Doppler shift velocity, the continuum intensity and the magnetic field full-disk maps from SDO/HMI we have created time-series datasets of acoustic power and magnetic flux which are used to train Long Short-Term Memory (LSTM) models on predicting decreases in continuum intensity 12 hours in advance. Testing of the models' performance was done on data from 5 ARs, unseen from the model during training. The model predicted the emergence of AR11726, AR13165 and AR13179, 10, 29 and 5 hours in advance, and variations of this model achieved average RMSE values of 0.11 for both active and quiet parts of the solar disc, showing the ability of the model to capture acoustic power anomalies and predict continuum intensity variations. This work sets the foundations for the very first ML-aided prediction of solar ARs.

SMD↗
NASA NTRSDate not supplied

EVA Planning: Using Neutral Buoyancy Laboratory (NBL) Training to Predict in-Flight Energy Expenditure

Metabolic rate (“met rate”) is the amount of energy expended over a period of time and is influenced by many factors including body composition, level of physical activity, resting metabolic rate, sex, age, and food intake. Met rate is measured during Extravehicular Activity (EVA) training at the Neutral Buoyancy Laboratory (NBL) and during in-flight EVAs through indirect calorimetry, calculating energy expenditure from respiratory measurements of O 2 consumption and/or CO 2 production. During Extravehicular Activity (EVA) planning, metabolic cost is important to consider and is used to inform EVA duration based on spacesuit consumables associated with life support systems. Currently, NBL and previous ISS EVA met rate data for specified crewmembers are utilized to predict in-flight EVA metabolic costs based on a proposed EVA timeline. Timeline data collected during training is used to relate met rates to specific EVA activities, which are in turn assigned to more generalized EVA task categories, categorizing by both task type and restraint type. EVA task categories include EVA Setup/Cleanup, Worksite Setup/Cleanup, Cable Routing, Bolts, Fluid Connectors, Electrical Connectors, R&R Work, Miscellaneous Work, Incapacitated Crew Rescue (being rescued or performing), Assisted Crew Rescue (being assisted or performing), and Translation. Restraint types consist of Free-Float, Body Restraint Tether (BRT), Articulating Portable Foot Restraint (APFR), and Space Station Remote Manipulator System (SSRMS). From a crewmember’s historical data, individualized 10th, 50th and 90th percentile met rate estimates are generated for each task category and used to estimate the proposed EVA timeline metabolic cost. In-flight metabolic data (“As-Executed”) from recent ISS US EVAs 85-88 (totaling eight EVA crewmember met rates) was compared with their predicted metabolic cost (“As-Planned”) to evaluate the accuracy of the current met rate estimation method. Across the four EVAs, As-Executed Cumulative EVA Total Metabolic Cost (M = 5893.78 BTU, SD = 816.60) was not significantly different compared to As-Planned Cumulative EVA Total Metabolic Cost (M = 6106.60 BTU, SD = 826.62; t(7) = 0.751 , p = .477). Though not a significant difference, generally, As-Planned total estimates were slightly higher than As-Executed total metabolic cost. Relative Error for Cumulative EVA Total Metabolic Cost ranged from -33% to 14.7%, depending on the crewmember and EVA. When comparing As-Planned to A-Executed EVA task categories for Bolts, Electrical Connectors, EVA Cleanup, EVA Setup, Miscellaneous Work, Translation, Worksite Cleanup, and Worksite Setup during these EVAs, no significant differences were observed, however, there was a significant difference in As-Planned (M = 783.71 BTU, SD = 408.88) compared to As-Executed (M = 608.87 BTU, SD = 425.29) metabolic cost for the task category of Repair-and-Replace (R&R) Work (t(17) = 3.21 , p = .005). Looking closer within the R&R Work task category, As-Executed R&R Work with Free-Float restraint type (M = 706.17 BTU, SD = 352.18) was significantly less than As-Planned R&R Work with Free-Float restraint type (M= 887.96 BTU, SD = 381.42; t(12) = 2.59, p < .024). As-Executed R&R Work with SSRMS Restraint type (M = 355.89 BTU, SD = 534.65) was not significantly different from As-Planned values (M = 512.69 BTU, SD = 383.34; t(4) = 1.88, p = 0.132). These findings suggest that the energy expended performing R&R Work (Free-Float) is lower in flight than predicted. Accurate predictions of the metabolic cost of EVA are essential for planning and executing successful ISS EVAs. Overall, the current met rate prediction method is similar to actual in-flight values, slightly erring on the side of overestimation. Future work includes analysis of more historical in-flight EVA data to increase the power of the analysis, evaluating the NBL-ISS met rate conversion factor between NBL and ISS tasks, as well as exploring methods of substitution when crewmembers are missing prior task category data.

Lauren Cox↗
NASA NTRS2024

Soil Salinity Level Assessment and Prediction Integrating UAV-borne Hyperspectral Imaging and Machine Learning Algorithms to Combat Desertification

In response to the ongoing global food crisis, the United Nations has identified “Zero Hunger” as one of its Sustainable Development Goals. A central contributor to the crisis is the process in which agricultural lands go through desertification. Research has shown a direct correlation between soil salinity and desertification - increased salinity levels indicate a higher risk for desertification. Furthermore, researchers have explored various techniques to map soil salinity, but these methods are oftentimes inefficient and don’t address future salinity predictions. To improve desertification monitoring, soil salinity can be observed via hyperspectral imaging on unmanned aerial vehicles (UAVs) to predict the risk of agricultural desertification using artificial intelligence (AI) and machine learning (ML) techniques. A significant gap exists in past research that applies ML and imaging techniques to soil salinity: convolutional neural networks (CNNs) and regression models are rarely leveraged together, despite the efficiency and accuracy of these models. To compensate for this gap, the proposed system leverages the use of these AI and ML models to improve soil assessment and prediction techniques. This approach involves three steps - data collection, image analysis, and future prediction. Using hyperspectral cameras on UAVs to collect the data from the region, a trained CNN model will output estimated soil salinity levels at a specific time. The estimations will then be analyzed by a regression model to assess the accuracy of future soil salinity predictions. The proposed system will identify regions at risk of desertification to help farmers mitigate agricultural loss, in turn helping alleviate the food crisis.

UAV systems↗
NASA NTRSDate not supplied

Jet Noise Modeling Using an Acoustic Analogy: From Theoretical Formulation to Practical Noise Predictions

In this presentation an overview of the development of an acoustic analogy formulation, and its implementation into a reduced-order prediction method for turbulent jet mixing noise, will be given. The definition and rationale of an acoustic analogy approach to the prediction of flow-generated noise will be provided. A high-level description of the mathematical processes involved will be outlined in the context of a particular formulation developed at NASA Glenn Research Center. The modelling approximations needed to implement the general theoretical formulation into a practical noise prediction tool will be discussed. Sample results obtained using a noise prediction code based on the NASA Glenn formulation, and comparisons with experimental data, will be presented for a series of turbulent jet test cases. A view on the prospects for continued use of acoustic analogy-based methods for the prediction of flow-generated noise and the assessment of noise-reduction technologies will conclude the presentation.

Jet noise↗
NASA NTRSDate not supplied

USM3D-ME Contributions to the 5th AIAA High Lift Prediction Workshop

This paper presents the results of Reynolds-averaged Navier-Stokes (RANS) simulations conducted by NASA’s flow solver, mixed-element USM3D (USM3D-ME), for the 5th AIAA High-Lift Prediction Workshop. As part of the Fixed-Grid RANS Technology Focus Group (TFG), these simulations were performed to assess the accuracy and efficiency of the USM3D-ME solutions in predicting high-lift flows. The High-Lift Common Research Model (CRM-HL) served as the primary geometry. Several CRM-HL configurations were used for three case studies: a verification study (Case 1), a configuration buildup study (Case 2), and a Reynolds-number variation study (Case 3). Overall, USM3D-ME RANS results aligned with the solutions selected by the Fixed-Grid RANS TFG and available wind tunnel data Simulations for Cases 1 and Configuration 2.1 achieved machine-zero residual convergence, with aerodynamic coefficients converging to steady-state values. However, Configurations 2.2-2.4 and Case 3 encountered iterative- and grid-convergence challenges, particularly at high angles of attack. Compared with the experimental data available for Configurations 2.2-2.4, close agreement was demonstrated at low angles of attack. However, for angles of attack approaching the maximum lift conditions, the predicted lift coefficient and pitching moment deviated from experimental values. The drag-coefficient predictions were in a relatively good agreement, however, slight overpredictions were observed at the highest angle of attack corresponding to the maximum-lift condition. Although iterative convergence for Configurations 2.2-2.4 at high angles of attack remains a persistent challenge, averaging aerodynamic coefficients over the last 5000 iterations yielded satisfactory agreement with the available wind tunnel experimental data. During the workshop, the lack of iterative convergence was attributed to the vortex structures emanating from the slat brackets. To investigate this issue further, post-workshop simulations were conducted on Configuration 2.2. In one study, RANS simulations were performed on a simplified geometry with the slat brackets removed. The second study focused on performing URANS simulations on the original Configuration 2.2 geometry. Preliminary results from both studies are presented and compared with wind tunnel data for Configuration 2.2. Consistent with the findings of other participants in the Fixed-Grid RANS TFG, this study emphasizes the necessity for further exploration and advancement in RANS technology for predicting high-lift flows.

CFD↗
NASA NTRSDate not supplied

USM3D-ME Contributions to the 5th AIAA High Lift Prediction Workshop

This paper presents the results of Reynolds-averaged Navier-Stokes (RANS) simulations conducted by the NASA flow solver, mixed-element USM3D (USM3D-ME), for the 5th AIAA High-Lift Prediction Workshop. As part of the Fixed-Grid RANS Technology Focus Group (TFG), these simulations were performed to assess the accuracy and efficiency of the USM3D-ME solutions in predicting high-lift flows. The High-Lift Common Research Model (CRM-HL) served as the primary geometry. Several CRM-HL configurations were used for three case studies: a verification study (Case 1), a configuration buildup study (Case 2), and a Reynolds-number variation study (Case 3). Overall, USM3D-ME RANS results aligned with the solutions selected by the Fixed-Grid RANS TFG and available wind tunnel data. Simulations for Case 1 and Configuration 2.1 achieved machine-zero residual convergence, with aerodynamic coefficients converging to steady-state values. However, Configurations 2.2-2.4 and Case 3 encountered iterative- and grid-convergence challenges, particularly at high angles of attack. Compared with the experimental data available for Configurations 2.2-2.4, close agreement was demonstrated at low angles of attack. However, for angles of attack approaching the maximum lift conditions, the predicted lift coefficient and pitching moment deviated from experimental values. The drag-coefficient predictions were in relatively good agreement, however, slight overpredictions were observed at the highest angle of attack corresponding to the maximum-lift condition. Although iterative convergence for Configurations 2.2-2.4 at high angles of attack remains a persistent challenge, averaging aerodynamic coefficients over the last 5000 iterations yielded satisfactory agreement with the available wind tunnel experimental data. During the workshop, the lack of iterative convergence was attributed to the vortex structures emanating from the slat brackets. To investigate this issue further, post-workshop simulations were conducted on Configuration 2.2. In one study, RANS simulations were performed on a simplified geometry with the slat brackets removed. The second study focused on performing unsteady RANS (URANS) simulations on the original Configuration 2.2 geometry. Preliminary results from both studies are presented and compared with wind tunnel data for Configuration 2.2. Consistent with the findings of other participants in the Fixed-Grid RANS TFG, this study emphasizes the necessity for further exploration and advancement in RANS technology for predicting high-lift flows.

Aerodynamics↗
NASA NTRSDate not supplied

Predicting Team Functioning in Long Term Space Missions Using Acoustic and Linguistic Measures

Maintaining optimal team functioning is critical for long-duration space exploration missions, yet traditional monitoring methods, such as self-reports and wearable sensors, often impose operational burdens or suffer from bias. This paper investigates a non-intrusive speech-based artificial intelligence (AI) framework to predict degradations in team functioning using data from the Human Exploration Research Analog (HERA) of the U.S. National Aeronautics and Space Administration (NASA). Using acoustic features, linguistic descriptors, and semantic embeddings, we evaluate static non-linear and temporal machine learning models to predict both objective (task accuracy) and subjective (self-reported efficacy and cohesion) team functioning outcomes. Results indicate that temporal models outperform static approaches, with prediction of objective task accuracy in Team Interaction Battery (TIB) improving from near chance to 71%. Self-reported outcomes, including team efficacy and cohesion, are predicted more reliably than task performance, achieving balanced accuracies of up to 85.56% and 78.12%, respectively, and are found to be most strongly associated with acoustic features. In a second interdependent task, the MMSEV–EVA, accuracies of up to 78% are achieved using temporal models with acoustic features. Furthermore, incorporating just 1–2 days of team-specific historical data systematically improved performance, and acoustic markers from informal pre-task interactions provided modest predictive gains. Finally, while automated preprocessing yielded viable accuracy, humancorrected data provided moderate performance gains, though transcription error rates did not significantly correlate with model performance. These findings highlight the potential of speech as a passive, high-fidelity monitoring tool for autonomous habitats.

Temporal modeling↗
DOE OSTI2025

Airport Delay Prediction with Temporal Fusion Transformers

Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the novel Temporal Fusion Transformer model and predict numerical airport arrival delays at quarter hour level for U.S. top 30 airports. Inputs to our model include airport demand and capacity forecasts, historic airport operation efficiency information, airport wind and visibility conditions, as well as en-route weather and traffic conditions. The results show that our model achieves satisfactory performance measured by small prediction errors on the test set. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction.

Liu, Ke [University of California Berkeley]↗