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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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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]↗

NASA NTRS2020

Shape Validation and RF Performance of Inflatable Antennas

Inflatable aperture antennas are an emerging technology that is being investigated for potential use in science and exploration missions. In particular, for missions to Mars and beyond, large deployable aperture antennas can provide the antenna gain required for high data rate communications, where the necessary antenna diameter exceeds the available volume of typical launch vehicle platforms. As inflatable aperture antennas have not been proven fully qualified for space missions, the author's Master's Thesis assessed the Ruze equation in characterizing this antenna technology. Inflatable aperture antennas do not follow a parabolic shape, and so the Ruze equation is not applicable due to the macroscopic shape errors of this technology. Therefore, geometric evaluations of the surface profile cannot simply correlate antenna gain degradation with the root-mean square shape error with a parabolic surface. Consequently, the focus of this work was to derive an accurate mathematical model of an inflatable aperture antenna to in order to characterize its Radio Frequency (RF) performance. Calculus of Variations methodologies were used to derive the surface profile shape of the inflatable aperture antenna. Physical Optics techniques were used to generate the antenna pattern profile. Validation testing of the predicted inflatable antenna shape model was performed through use of Laser Radar metrology measurements on an inflatable test article. Assessments of the RF performance of the inflatable aperture antenna, compared with nominally shaped paraboloidal antennas, were obtained through simulations of both technologies using a common diameter, depth, and arc length. Assessments of the RF performance of the inflatable aperture antenna was also performed against itself for changes in distance of the antenna feed location in the axial direction. Whereas the Ruze equation is limited to assessing gain reduction, this effort will also assess beam spreading and first side lobe angle and magnitude. The ability to characterize the RF response of this antenna will provide for an improved understanding of this technology. The accurate representation of the shape of this type of antenna technology will help to identify the most appropriate ways in which this technology could be utilized in planning future communication architectures for NASA missions to Mars and beyond.

Welch, Bryan↗

NASA NTRS2015

Validating and Verifying Biomathematical Models of Human Fatigue

Airline pilots experience acute and chronic sleep deprivation, sleep inertia, and circadian desynchrony due to the need to schedule flight operations around the clock. This sleep loss and circadian desynchrony gives rise to cognitive impairments, reduced vigilance and inconsistent performance. Several biomathematical models, based principally on patterns observed in circadian rhythms and homeostatic drive, have been developed to predict a pilots levels of fatigue or alertness. These models allow for the Federal Aviation Administration (FAA) and commercial airlines to make decisions about pilot capabilities and flight schedules. Although these models have been validated in a laboratory setting, they have not been thoroughly tested in operational environments where uncontrolled factors, such as environmental sleep disrupters, caffeine use and napping, may impact actual pilot alertness and performance. We will compare the predictions of three prominent biomathematical fatigue models (McCauley Model, Harvard Model, and the privately-sold SAFTE-FAST Model) to actual measures of alertness and performance. We collected sleep logs, movement and light recordings, psychomotor vigilance task (PVT), and urinary melatonin (a marker of circadian phase) from 44 pilots in a short-haul commercial airline over one month. We will statistically compare with the model predictions to lapses on the PVT and circadian phase. We will calculate the sensitivity and specificity of each model prediction under different scheduling conditions. Our findings will aid operational decision-makers in determining the reliability of each model under real-world scheduling situations.

fatigue↗

DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗

DOE OSTI2024

WEC-Sim Modeling of Laminar Scientific's Patented Seesaw Wave Energy Converter: Preprint

Laminar Scientific's patented seesaw wave energy converter was modeled in WEC-Sim to predict performance. The device operates by utilizing ocean surface waves to rotate a truss in pitch about a pivot. The pivot is located at the top of two pylons, which are embedded in the seafloor. The seesaw has a float on either end, and the buoyancy forces from each float cause the system to rise or fall with passing waves. Device performance relies upon seesaw length and ocean wavelength creating an antiphase effect. The seesaw truss has an adjustable length intended to achieve this effect. The operation method enforces a narrow band of wavelengths which induce the largest rotational motion from the device. The hydrodynamic analysis of the device was performed using Capytaine, and the results confirmed that the device operates best in a narrow frequency band. Four float-to-float spacing cases and three pylon radii were examined. The hydrodynamic results indicate a match between the model and the physical expectations for the device, and that varying the pylon radii by 0.1-m increments for three instances creates minimal changes in hydrodynamic properties. Power matrices for three float spacing cases of the device were simulated with Joint North Sea Wave Project spectra waves and optimal power take-off damping in WEC-Sim. The maximum average power production for the 15-m spacing case was 14.1 kW with a 5.0-s peak wave period and 4-m significant wave heights. Plots of capture-width ratios indicated that the device performance was linear and confirmed that the device is optimal in a narrow frequency band. The maximum percentage of the available wave power produced by the 15-m device was approximately 16%. Simulations of the device in regular waves were used to produce plots of average power compared to a ratio of float spacing to wavelength. These plots indicate that the power production is maximized at a ratio of 0.5, and further confirm that the device has a narrow frequency response. The device was simulated at an example field location, where the device produced an annual average power rating of 1.6 kW given an average omnidirectional wave climate of 10.3 kW m-1 and an optimal, linearized power take-off model. While the maximum predicted device performance is reliant upon a narrow band of wave frequencies, the conducted analysis provides an opportunity to improve device design prior to prototyping and testing. Modifying the design to respond to a broader frequency range would improve device performance.

numerical modeling↗

NASA NTRS2000

Theta-Pinch Thruster for Piloted Deep Space Exploration

A new high-power propulsion concept that combines a rapidly pulsed theta-pinch discharge with upstream particle reflection by a magnetic mirror was evaluated under a Phase 1 grant awarded through the NASA Institute for Advanced Concepts. Analytic and numerical models were developed to predict the performance of a theta-pinch thruster operated over a wide range of initial gas pressures and discharge periods. The models indicate that a 1 m radius, 10 m long thruster operated with hydrogen propellant could provide impulse-bits ranging from 1 N-s to 330 N-s with specific impulse values of 7,500 s to 2,500 s, respectively. A pulsed magnetic field strength of 2 T is required to compress and heat the preionized hydrogen over a 10(exp -3) second discharge period, with about 60% of the heated plasma exiting the chamber each period to produce thrust. The unoptimized thruster efficiency is low, peaking at approximately 16% for an initial hydrogen chamber pressure of 100 Torr. The specific impulse and impulse-bit at this operating condition are 3,500 s and 90 N-s, respectively, and the required discharge energy is approximately 9x10(exp 6) J. For a pulse repetition rate of 10 Hz, the engine would produce an average thrust of 900 N at 3,500 s specific impulse. Combined with the electrodeless nature of the device, these performance parameters indicate that theta-pinch thrusters could provide unique, long-life propulsion systems for piloted deep space mission applications.

LaPointe, Mike R.↗

NASA NTRS2005

An information maximization model of eye movements

We propose a sequential information maximization model as a general strategy for programming eye movements. The model reconstructs high-resolution visual information from a sequence of fixations, taking into account the fall-off in resolution from the fovea to the periphery. From this framework we get a simple rule for predicting fixation sequences: after each fixation, fixate next at the location that minimizes uncertainty (maximizes information) about the stimulus. By comparing our model performance to human eye movement data and to predictions from a saliency and random model, we demonstrate that our model is best at predicting fixation locations. Modeling additional biological constraints will improve the prediction of fixation sequences. Our results suggest that information maximization is a useful principle for programming eye movements.

NASA Discipline Neuroscience↗

NASA NTRSDate not supplied

Validation of Transient Spacecraft Refueling Model with Gateway Breadboard Test Data

The ability to refuel spacecraft on-orbit is a key enabling technology for deep space human exploration. On-orbit refueling requires priming a pressurized liquid propellant from a source tank into fluid transfer lines at vacuum conditions, which could result in excessive surge pressures beyond the system’s qualified operating limits and potentially damage hardware. Therefore, it is imperative to employ numerical models during the development of flight hardware to parametrically characterize system performance over a wide range of operating conditions. Since priming transients are difficult to model accurately, it is important to validate those models with test data. As part of the Gateway bipropellant refueling system development, a simplified fluidic breadboard system was created using water as a simulant for fuel to support early program risk reduction of refueling operations. The objectives of breadboard testing were to gather performance data to characterize and demonstrate critical refueling operations and to validate numerical models that can be extended to predicting flight system performance. A description of the breadboard test system and the results of numerical model validation is presented. The model is shown to have very good agreement with test data.

Brian S Lusby↗

NASA NTRSDate not supplied

Validation of Transient Spacecraft Refueling Model with Gateway Breadboard Test Data

The ability to refuel spacecraft on-orbit is a key enabling technology for deep space human exploration. On-orbit refueling requires priming a pressurized liquid propellant from a source tank into fluid transfer lines at vacuum conditions, which could result in excessive surge pressures beyond the system’s qualified operating limits and potentially damage hardware. Another source of potentially damaging surge pressures is the water hammer transient that occurs when the isolation valves are closed to pause or end the refueling operation. Therefore, it is imperative to employ numerical models during the development of flight hardware to parametrically characterize system performance over a wide range of operating conditions to minimize the risk of exceeding pressure limits during flight. Since rapid pressure transients can be difficult to model accurately, it is important to validate those models with test data. As part of the Gateway bipropellant refueling system development, a simplified fluidic breadboard system was created using water as a simulant for fuel to support early program risk reduction of refueling operations. The objectives of breadboard testing were to gather performance data to characterize and demonstrate critical refueling operations and to validate numerical models that can be extended to predicting flight system performance. A description of the breadboard test article and the results of numerical model validation is presented. The model is shown to have very good agreement with test data.

Refueling↗

NASA NTRSDate not supplied

Initial Validation of a Gas-Granular Flow Solver Using a Subscale, Reduced Pressure Plume Surface Interaction Ground Test

With NASA’s goal to land the next humans on the lunar surface in the next few years, it is vitally important to have a better understanding of the plume surface interaction (PSI) between the landing vehicles and the lunar regolith. The Fluid Dynamics Branch at NASA/MSFC has previously used the gas-granular flow solver Loci/GGFS to qualitatively predict crater formation due to PSI effects in a lunar (near vacuum) ambient environment. In this paper, validation of Loci/GGFS crater width and depth predictions in ambient near-lunar conditions are provided using experimental data collected at MSFC during the Physics-Focused Ground Test 1 (PFGT-1) campaign in 2022. To observe sensitivity to soil models, simulations were conducted with both monodisperse glass bead (MGB) and BP-1 lunar regolith simulant soil models in Loci/GGFS. Crater depth and width comparisons are made with PFGT-1 Run 56, which used BP-1 soil. The Loci/GGFS BP-1 soil model performed slightly better with a mean predicted crater depth within 10% of the experiment. Both soil models predicted crater width within 10%. Cratering occurred more quickly with the MGB soil model. Mesh and spatial order sensitivity are also examined for the BP-1 soil model.

Validation↗

DOE OSTI2025

Benchmark of numerical modeling approaches on the systematic performance evaluation of wave energy converters

Different numerical modeling methods have been developed and applied to evaluate a variety of performance indicators of wave energy converters (WECs), including the power performance, structural loads, levelized cost of energy, etc. Based on the modeling fidelity, the commonly used numerical modeling approaches can be classified as linear modeling, weakly nonlinear modeling and fully nonlinear modeling approaches. Each method differs in accuracy and computational efficiency, making them suitable for different stages of WEC design. However, the selection of modeling approach could significantly impact evaluation outcomes. For instance, simplified linear models may underestimate structural loads or overestimate energy production in some operational conditions, potentially leading to less cost-effective designs. Given the widespread utilization of these models, it is essential to understand the uncertainties brought by them in performance evaluations. This work is dedicated to benchmarking different linear-potential-flow-based numerical models for evaluating the systematic performance of WECs. Three representative numerical modeling approaches are considered in this work, including linear frequency-domain modeling, statistically linearized spectral-domain modeling and Cummins equation-based nonlinear time-domain modeling. A generic point absorber WEC is considered as the research reference in this work, and different sea sites are taken into account. The numerical models are utilized to predict critical performance indicators, including power performance, the annual energy production, the capacity factor, the levelized cost of energy and the PTO fatigue loads. By comparing the results, this work identifies the uncertainties associated with different modeling approaches in evaluating WEC performance.

Fatigue↗

NASA NTRS2003

A Nonlinear Model for Fuel Atomization in Spray Combustion

Most gas turbine combustion codes rely on ad-hoc statistical assumptions regarding the outcome of fuel atomization processes. The modeling effort proposed in this project is aimed at developing a realistic model to produce accurate predictions of fuel atomization parameters. The model involves application of the nonlinear stability theory to analyze the instability and subsequent disintegration of the liquid fuel sheet that is produced by fuel injection nozzles in gas turbine combustors. The fuel sheet is atomized into a multiplicity of small drops of large surface area to volume ratio to enhance the evaporation rate and combustion performance. The proposed model will effect predictions of fuel sheet atomization parameters such as drop size, velocity, and orientation as well as sheet penetration depth, breakup time and thickness. These parameters are essential for combustion simulation codes to perform a controlled and optimized design of gas turbine fuel injectors. Optimizing fuel injection processes is crucial to improving combustion efficiency and hence reducing fuel consumption and pollutants emissions.

Liu, Nan-Suey↗

DOE OSTI2021

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. Here, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

The expansion of commercial building demand response as a demand-side management resource for the electric grid necessitates new decision support resources for customers seeking to assess the benefit–risk tradeoffs of possible strategies for energy flexible building operations. To address this need, we, in this study, develop surrogate models that predict the impacts of several load flexibility strategies on commercial building electricity demand and indoor temperature, focusing on offices and retail buildings at multiple scales. The surrogate models are fit to a synthetic database generated via whole building simulations, which establish the relationships between the key operational features of a given strategy and potential changes in building demand and temperature across a variety of contexts. The surrogate models are translated to a Bayesian framework to allow straightforward communication of uncertainty and parameter updating given new evidence. We find strong predictive performance across the suite of models, underscoring the usefulness of the approach in guiding decisions about implementing load flexibility strategies under a particular set of operational and environmental conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2003

Statistical Methods for Rapid Aerothermal Analysis and Design Technology: Validation

The cost and safety goals for NASA s next generation of reusable launch vehicle (RLV) will require that rapid high-fidelity aerothermodynamic design tools be used early in the design cycle. To meet these requirements, it is desirable to identify adequate statistical models that quantify and improve the accuracy, extend the applicability, and enable combined analyses using existing prediction tools. The initial research work focused on establishing suitable candidate models for these purposes. The second phase is focused on assessing the performance of these models to accurately predict the heat rate for a given candidate data set. This validation work compared models and methods that may be useful in predicting the heat rate.

DePriest, Douglas↗

DOE OSTI2024

Individual Data Sparsity in Smart Thermostat Big Data: Impacts on Modeling Thermostat Use Behavior Dynamics

This study explores the impacts of the sparsity of individual thermostat interaction data on modeling thermostat use behavior dynamics using a dataset of over 100,000 smart thermostats. In developing a data-driven model of Thermal Frustration Theory (TFT), we investigate the challenges and trade-offs in clustering occupant data to enhance predictive accuracy. Our findings reveal that a single, aggregated model fails to capture the diversity of occupant behaviors, resulting in extremely poor prediction performance. Conversely, excessive clustering exacerbates data sparsity, undermining model reliability. By identifying an optimal clustering strategy, we achieve a balance that significantly improves the prediction of manual setpoint changes during demand response (DR) events, enhancing energy management and occupant comfort

Fannon, David↗

DOE OSTI2024

Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-Based Battery Enclosures for Crashworthiness

Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. Here, in this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R 2 > 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling

Ten established, data-driven dynamic algorithms are surveyed and a practical guide for understanding these methods generated. Existing Python programming packages for implementing each algorithm are acknowledged, and the model equations necessary for prediction are presented. A case study on a coal-fired power plant’s NO x emission rates is performed, directly comparing each modeling method’s performance on a mutual system. Each model is evaluated by its root mean squared error (RMSE) on out-of-sample future horizon predictions. Optimal hyperparameters are identified using either an exhaustive search or genetic algorithm. The top five model structures of each method are used to recursively predict future NO x emission rates over a 60-step time horizon. The RMSE at each future timestep is determined, and the recursive output prediction trends compared against measurements in time. The GRU neural network is identified as the best candidate for representing the system, demonstrating accurate and stable predictions across the future horizon by all considered models, while satisfactory performance was observed in several of the ARX/NARX formulations. Finally, these efforts have contributed 1) a concise resource of multiple proven dynamic machine learning methods, 2) a practical guide explaining the use of these methods, effectively lowering the “barrier-to-entry” of deploying such models in control systems, 3) a comparison study evaluating each method’s performance on a mutual system, 4) demonstration of accurate multi-timestep emissions modeling suitable for systems-level control, and 5) generalizable results demonstrating the suitability of each method for prediction over a multi-step future horizon to other complex dynamic systems.

42 ENGINEERING↗