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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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At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

In-Flight Thermal Performance of the Lidar In-Space Technology Experiment

The Lidar In-Space Technology Experiment (LITE) was developed at NASA s Langley Research Center to explore the applications of lidar operated from an orbital platform. As a technology demonstration experiment, LITE was developed to gain experience designing and building future operational orbiting lidar systems. Since LITE was the first lidar system to be flown in space, an important objective was to validate instrument design principles in such areas as thermal control, laser performance, instrument alignment and control, and autonomous operations. Thermal and structural analysis models of the instrument were developed during the design process to predict the behavior of the instrument during its mission. In order to validate those mathematical models, extensive engineering data was recorded during all phases of LITE's mission. This inflight engineering data was compared with preflight predictions and, when required, adjustments to the thermal and structural models were made to more accurately match the instrument s actual behavior. The results of this process for the thermal analysis and design of LITE are presented in this paper.

Roettker, William↗
NASA NTRS2017

Characterization of Pilot Technique

Skilled pilots often use pulse control when controlling higher order (i.e. acceleration-command) vehicle dynamics. Pulsing does not produce a stick response that resembles what the human Crossover Model predicts. The Crossover Model (CM) assumes the pilot provides compensation necessary (lead or lag) such that the suite of display-human-vehicle approximates an integrator in the region of crossover frequency. However, it is shown that the CM does appear to drive the pilots pulsing behavior in a very predictable manner. Roughly speaking, the pilot generates pulses such that the area under the pulse (pulse amplitude multiplied by pulse width) is approximately equal to area under the hypothetical CM output. This can allow a pilot to employ constant amplitude pulsing so that only the pulse duration (width) is modulated a drastic simplification over the demands of continuous tracking. A pilot pulse model is developed, with which the parameters of the pilots internally-generated CM can be computed in real time for pilot monitoring and display compensation. It is also demonstrated that pursuit tracking may be activated when pulse control is employed.

rotorcraft↗
DOE OSTI2023

Controls and variability of soil respiration temperature sensitivity across China

Understanding the temperature sensitivity (Q 10 ) of soil respiration is critical for benchmarking the potential intensity of regional and global terrestrial soil carbon fluxes-climate feedbacks. Although field observations have demonstrated the strong spatial heterogeneity of Q 10 , a significant knowledge gap still exists regarding to the factors driving spatial and temporal variabilities of Q 10 at regional scales. Here, therefore, we used a machine learning approach to predict Q 10 from 1994 to 2016 with a spatial resolution of 1 km across China from 515 field observations at 5 cm soil depth using climate, soil and vegetation variables. Predicted Q 10 varied from 1.54 to 4.17, with an area-weighted average of 2.52. There was no significant temporal trend for Q 10 (p = 0.32), but annual vegetation production (indicated by normalized difference vegetation index, NDVI) was positively correlated to it (p < 0.01). Spatially, soil organic carbon (SOC) was the most important driving factor in 62 % of the land area across China, and varied greatly, demonstrating soil controls on the spatial pattern of Q 10 . These findings highlighted different environmental controls on the spatial and temporal pattern of soil respiration Q 10 , which should be considered to improve global biogeochemical models used to predict the spatial and temporal patterns of soil carbon fluxes to ongoing climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Robust and real-time control of magnetic bearings for space engines

Currently, NASA Lewis Research Center is developing magnetic bearings for Space Shuttle Main Engine (SSME) turbopumps. The control algorithms which have been used are based on either the proportional-intergral-derivative control (PID) approach or the linear quadratic (LQ) state space approach. These approaches lead to an acceptable performance only when the system model is accurately known, which is seldom true in practice. For example, the rotor eccentricity, which is a major source of vibration at high speeds, cannot be predicted accurately. Furthermore, the dynamics of a rotor shaft, which must be treated as a flexible system to model the elastic rotor shaft, is infinite dimensional in theory and the controller can only be developed on the basis of a finite number of modes. Therefore, the development of the control system is further complicated by the possibility of closed loop system instability because of residual or uncontrolled modes, the so called spillover problem. Consequently, novel control algorithms for magnetic bearings are being developed to be robust to inevitable parametric uncertainties, external disturbances, spillover phenomenon and noise. Also, as pointed out earlier, magnetic bearings must exhibit good performance at a speed over 30,000 rpm. This implies that the sampling period available for the design of a digital control system has to be of the order of 0.5 milli-seconds. Therefore, feedback coefficients and other required controller parameters have to be computed off-line so that the on-line computational burden is extremely small. The development of the robust and real-time control algorithms is based on the sliding mode control theory. In this method, a dynamic system is made to move along a manifold of sliding hyperplanes to the origin of the state space. The number of sliding hyperplanes equals that of actuators. The sliding mode controller has two parts; linear state feedback and nonlinear terms. The nonlinear terms guarantee that the systems would reach the intersection of all sliding hyperplanes and remain on it when bounds on the errors in the system parameters and external disturbances are known. The linear part of the control drives the system to the origin of state space. Another important feature is that the controller parameter can be computed off-line. Consequently, on-line computational burden is small.

Sinha, Alok↗
DOE OSTI2022

Intra-Specific Variability in Plant Hydraulic Parameters Inferred From Model Inversion of Sap Flux Data

Understanding plant hydraulic regulation is critical for predicting plant and ecosystem responses to projected increases in drought stress. Plant hydraulic regulation is controlled by observable, diverse plant hydraulic traits that can vary as much across individuals of the same species as they do across different species. Direct measurements of plant hydraulic traits from a range of ecosystems remain limited in comparison to other, more readily measured traits (e.g., specific leaf area). Furthermore, plant hydraulic trait measurements, often made at leaf or branch levels, are not easily scaled to whole-plant values that are typically used to predict plant and ecosystem fluxes. In this study, multiple whole-plant hydraulic parameters are inferred from observations of plant water use (i.e., sap flow), soil properties, and meteorological data. We use a Markov Chain Monte Carlo model inversion approach to obtain the best estimates and uncertainty of plant hydraulic parameters that capture whole-plant effective embolism resistance and stomatal sensitivity to decreasing plant water potential. We then use the inferred values in the model to estimate whole-tree water use and isohydricity. This approach reliably infers whole-plant parameter values with enough specificity to resolve inter- and intra-specific differences, and thus supplements time- and labor-intensive direct measurements of traits.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Status of two studies on active control of aeroelastic response

The application of active control technology to the suppression of flutter has been successfully demonstrated during two recent studies in the Langley transonic dynamics tunnel. The first study involved the implementation of an aerodynamic-energy criterion, using both leading- and trailing-edge controls, to suppress flutter of a simplified delta-wing model. Use of this technique has resulted in an increase in the flutter dynamic pressure of approximately 12 percent for this model at a Mach number of 0.9 Analytical methods used to predict the open- and closed-loop behavior of the model are also discussed. The second study, was conducted to establish the effect of active flutter suppression on a model of the Boeing B-52 Control Configured Vehicle (CCV). Some preliminary results of this study indicate significant improvements in the damping associated with the critical flutter mode.

Abel, I.↗
NASA NTRS1970

Stability and control derivatives of the lifting body vehicles

Predictions of the flight characteristics of lifting bodies have been based almost exclusively on wind-tunnel data for small-scale models. A need thus exists to compare the results from small-scale and full-scale wind-tunnel tests with results from flight tests to establish some measure of the accuracy of the predictions and to assess the sensitivity of the vehicle's handling qualities to typical discrepancies between flight and wind-tunnel results. Comparisons of these types have been one of the primary objectives of the M2-F2, HL-10, and X-24A flight programs. In this paper the more important longitudinal and lateral-directional aerodynamic stability and control derivatives obtained from flight are compared with small- and full-scale wind-tunnel results where applicable. Significant trends and important differences are pointed out, and the implications discussed.

Robert W Kempel↗
DOE OSTI2023

Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4-5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1997

Heterogeneous Combustion of Porous Graphite Particles in Microgravity

Recent theoretical investigations on graphite particle combustion have employed several levels of heterogeneous reaction models, ranging from global to elementary models, to describe the oxidation of carbon to gaseous products. Unlike the counterpart homogeneous reaction models, these heterogeneous reaction models are not well developed because of the difficulties associated with decoupling the physical characteristics of the solid (e.g. surface area taking part in combustion) from the chemical kinetic data. This is certainly true for porous graphite particle combustion, where heterogeneous and homogeneous reactions occur within the pores and play an important role in the overall oxidation process. As a result, there are considerable uncertainties of physical phenomena predicted using different heterogeneous kinetic models available in the literature. A good example, discussed later in this paper, is the predicted critical particle size below which the mass burning rate becomes exponentially small. The main goal of this study is to understand the basic mechanism controlling such rapid changes in burning rates, by developing a model where physical contributions are decoupled from chemical rate constants in a consistent manner. Another important goal of the proposed study is to develop a truly intrinsic, detailed heterogeneous reaction model for porous graphite combustion at high-temperatures, and to derive a systematically reduced heterogeneous reaction model in terms of the elementary reaction rate constants of the detailed model. The validation of chemical kinetic models describing the heterogeneous and homogeneous combustion in and around a spherically symmetric porous graphite particle can be considerably simplified by experimental measurements obtained under microgravity conditions. A vital component of this study is to conduct such supporting experiments on particle burning rate and surface temperature using NASA microgravity facilities, in close coordination with the theoretical effort. The basic understanding obtained and models developed as part of this project will be useful for optimal design of coal combustion devices. These models can also be extended to investigate the role of heterogeneous chemistry on pollutant formation pathways in combustion devices. The theoretical approach developed here, with pore diffusion effects decoupled from the chemical effects, can also be extended to understand the heterogeneous combustion of other porous fuels, for example, combustion of magnesium in a CO2 environment for propulsion in the Martian atmosphere.

Chelliah, Harsha K.↗
DOE OSTI2025

Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications

This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

UV-Vis spectrophotometric determination of rare earth elements (REE) speciation at near-neutral to alkaline pH. Part I: m-cresol purple properties from 25-75 °C and Er hydrolysis

The speciation and mobility of rare earth elements (REE) strongly depends on pH which controls the formation of charged aqueous hydroxyl species. The latter potentially play an important role in controlling heavy REE adsorption on clay minerals in near-neutral to alkaline waters such as in regolith-hosted REE mineral deposits. However, accurate REE hydrolysis constants are needed for developing geochemical models that can predict the role of these charged species in natural systems. Here, we develop a robust experimental UV-Vis spectrophotometric method using m-cresol purple to determine in situ pH from 25 to 75 °C. This method is used to derive the average ligand number and hydrolysis constants of erbium (Er) at 25 °C in aqueous solutions with low ionic strength (≤ 0.001 mol/L) at pH from ~7 to 9.5 and in the presence of Er concentrations from 0 to 0.057 mM. The average ligand number ranges between 1 and 3 indicating that Er(OH) 2+ , Er(OH) 2 + and Er(OH) 3 0 control speciation in the experiments. The logarithm of the Er hydrolysis constants (log*β n ° , n= 1 to 3) derived at infinite dilution for the reaction Er 3+ + nH 2 O = Er(OH) n 3-n + nH + are: *β 1 ° = –7.22 ± 0.10, *β 2 ° = –14.52 ± 0.08, *β 3 ° = –23.24 ± 0.04. Implementation of these experimental data into a geochemical model indicates that the Er(OH) 2 + and Er(OH) 3 0 species are both stable in a much wider pH range than previously predicted. Consequently, the positively charged REE hydroxyl complexes can potentially control the fractionation of light vs. heavy REE via adsorption as observed in the formation of certain regolith-hosted REE deposits.

58 GEOSCIENCES↗
DOE OSTI2026

Quantitative Analysis and Prediction of Thermal Runaway Metrics of High-Nickel Oxide Cathodes by Machine Learning Models

The pursuit of higher energy density in lithium-ion batteries has made high-nickel (Ni) layered oxides leading cathode candidates for next-generation electric vehicles. However, their poor thermal stability, particularly at Ni contents ≥ 90%, increases the risk of cathode-initiated thermal runaway. Furthermore, we present a data-driven framework combining linear and nonlinear machine learning models to predict key thermal runaway descriptors from a high-throughput differential scanning calorimetry database. With cathode composition and state of charge (SOC) as input features, the ensemble model accurately predicts peak temperature, heat release, and peak heat flow. SHAP analysis identifies Ni content and SOC as the dominant factors controlling thermal runaway temperature, while SOC primarily governs heat release and peak heat flow. Al, Mg, and Mn improve thermal stability by strengthening metal–oxygen bonding and delaying structural transformation, whereas B mainly reduces heat release through surface passivation. Validation with a new cathode composition confirms accurate prediction of SOC-dependent thermal runaway behavior and critical SOC.

25 ENERGY STORAGE↗
DOE OSTI2021

Surrogate Model of Flexible Research Platform EnergyPlus Models to Enable Sensitivity Analysis

This letter report describes the surrogate models developed from the EnergyPlus model of Oak Ridge National Laboratory’s Flexible Research Platform. Two data-driven black-box models were developed, and the outputs of the surrogate models were compared with the EnergyPlus model. The two models developed are a multilayer perceptron deep learning model, and a long short-term memory (LSTM) neural network model. The three factors for selecting the black-box models are scalability, computation time, and accuracy. A total of 107 input variables were the dominant variables in determining the outputs of building energy consumptions and thermal comfort. A total of 54 output variables were identified as the prediction targets, including the system- and zone-level outputs. The large set of the simulation cases were generated by integrating sensor errors into an emulator based on EnergyPlus and Python EMS, which includes advanced control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. The surrogate models were developed based on a set of large-scale simulation runs (i.e., 4,000 runs) on a cloud platform. The comparison analysis shows that the two black-box models had good accuracy for predicting new outputs for sensitivity analysis using the root mean square error metric. As a next step, the developed surrogate models will be used to perform sensitivity analysis for different sensor impacts (e.g., sensor types, sensor locations).

42 ENGINEERING↗
NASA NTRS1976

Aeroelastic stability and control of an oblique wing - Wind tunnel experiments

Results are presented for wind tunnel tests of an elastic wing model to verify the theoretical predictions for the aeroelastic instability of an oblique wing. The model wing has an elliptic planform of 10 to 1 axis ratio and a symmetrical airfoil section of 7-1/2% thickness/chord ratio. The wing is of wood and as may be seen in the photographs presented, slack wires are used to limit the amplitude of unstable motions. The fuselage is mounted on bearings permitting freedom of roll, but provision is made to clamp the fuselage for some of the tests. It is found that freedom in roll increases the dynamic pressure at which aeroelastic instability first appears. With the model free in roll, the effectiveness of the ailerons in maintaining trim is not noticeably affected by passage through the speed at which the wing would become unstable if clamped.

Jones, R. T.↗
DOE OSTI2026

Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management

Accurate subsurface reservoir pressure control is extremely challenging due to geological heterogeneity and multiphase fluid-flow dynamics. Predicting behavior in this setting relies on high-fidelity physics-based simulations that are computationally expensive. Yet, the uncertain, heterogeneous properties that control these flows make it necessary to perform many of these expensive simulations, which is often prohibitive. To address these challenges, we introduce a physics-informed machine learning workflow that couples a fully differentiable multiphase flow simulator, which is implemented in the DPFEHM framework with a convolutional neural network (CNN). The CNN learns to predict fluid extraction rates from heterogeneous permeability fields to enforce pressure limits at critical reservoir locations. By incorporating transient multiphase flow physics into the training process, our method enables more practical and accurate predictions for realistic injection-extraction scenarios compared to previous works. To speed up training, we pretrain the model on single-phase, steady-state simulations and then finetune it on full multiphase scenarios, which dramatically reduces the computational cost. We demonstrate that high-accuracy training can be achieved with fewer than three thousand full-physics multiphase flow simulations – compared to previous estimates requiring up to ten million. This drastic reduction in the number of simulations is achieved by leveraging transfer learning from much less expensive single phase simulations.

25 ENERGY STORAGE↗
NASA NTRS1984

Dependence of the spectrum of Pc 3-4 pulsations on the interplanetary magnetic field

Dependence of the power spectrum of Pc 3-4 magnetic pulsations observed at the ATS 6 geosynchronous satellite on the interplanetary magnetic field (IMF) has been studied. Pulsation events that were observed near noon and exhibited harmonic structure are chosen for analysis. Further selected are pulsation events with identical fundamental frequency to study dependence of the power of pulsations at different harmonic bands on the IMF. A weak negative correlation is observed between the IMF cone angle theta-XB and the power of pulsations in the frequency range 20-70 mHz. Also, a positive correlation between the intensity of the IMF B(IMF) and the power of pulsations at 50-70 mHz is found. This B(IMF) control is present at all ranges of the cone angle. A comparison is conducted of this observation with the frequency of bow shock associated upstream waves predicted from a model of wave generation by a cyclotron resonance of ions reflected at the bow shock. The predicted frequency depends on the IMF as B(IMF) (cos theta-XB)-squared. Although this relation gives a proportionality between the frequency and B(IMF) qualitatively consistent with the observation, it does not explain the most obvious IMF control of the spectrum of the pulsations.

Takahashi, K.↗
NASA NTRS2018

A Prognostics Framework Development for Swarm Satellite Formations

Prognostics is the science of predicting the failure(s) of a component or a system and understanding how the performance will change in the event of a failure or degradation mechanism. With accurate predictions of possible failures, autonomous mitigative actions can be taken to correct/repair any issues or alert human operators of a failure threshold exceedance requiring condition-based maintenance. Although there is extensive research on failure predictions for a component or a system, there are significantly more opportunities to foray into failure predictions and prognostics for a system of systems such as an airspace consisting of multiple aircraft, a fleet of unmanned aerial vehicles, and a swarm of intelligent satellite systems. Failure prediction and mitigation are particularly important in autonomous systems such as satellite swarm systems that need effective resource management and minimal human interactions. Based on NASA's decadal survey, there is a clear need to prioritize the development of satellite swarm technology for studies of space physics and Earth science. The science community will propose future missions that return in-situ measurements from a 3-D (three-dimensional) volume of space, with relative spacecraft motion and inter-satellite baselines controlled according to the mission objectives. For such multi-spacecraft missions, it is required that ground operations resources do not scale with the number of satellites, thus compromising the swarm or leading to inefficiencies in resource allocation. Swarms of tens or hundreds of small satellites will require autonomy in attitude control, navigation and failure. Although significant research has been conducted in the areas of autonomous formation flying algorithms, less attention has been given to the development of resilient systems robust to failures.The focus of this research paper is the integration of model-based prognostics into the swarm dynamics control and decision-making algorithms. We simulate swarm management strategies for a subsystem failure to demonstrate the importance of failure predictions by comparing two cases: (i) no health information is provided to the system and utilized in the decision-making process and (2) system health information is obtained using prognostics and employed by the control system. One example scenario presented is for the GPS (Global Positioning System) system of an individual satellite to perform off-nominally due to increasing estimated error. In this scenario, the keep-out zone for that satellite would become more conservative, thereby decreasing the risk of collision. This is achieved via tuning the individual artificial repulsive functions assigned to each satellite.This paper is structured as follows. First we provide an overview of current swarm technology development, where we specifically use the term swarm to define multiple satellites flying in formation in similar orbits, with cross-link communication and station-keeping capabilities. Second, we give an introduction to the Swarm Orbital Dynamics Advisor (SODA), a tool that accepts high-level configuration commands and provides the orbital maneuvers required to achieve the prescribed formation configuration. Third, we provide the details of the model-based prognostics algorithm implementation in SODA. Finally, we present different case studies for potential component/subsystem failures and the swarm responses based with and without failure prediction information.

prognostics↗
DOE OSTI2026

Attention-based 3D – convolutional neural network model for mechanical property predictions using visible light images in metal additive manufacturing

Additive manufacturing (AM), while commonly used for rapid prototyping and creating components with complex geometries, has not been widely adopted for critical applications across the aerospace, automotive, defense, energy, and medical industries. This is, in part, due to the challenges of controlling flaws and uncertainty in the mechanical behavior of additively manufactured components. In recent years, there has been an increase in research aimed at predicting the final mechanical properties of additively manufactured components during the printing process. To address these issues, a 3D-CNN model was trained using low-cost in situ visible-light camera data, anomaly classifications, and the chosen process parameters to predict the ultimate tensile strength (UTS), yield strength (YS), total elongation (TE), and uniform elongation (UE). The 3D-CNN layers of the model employed attention mechanisms to prioritize features in the data, thereby improving prediction accuracy. Furthermore, the effect of each process parameter and anomaly class is investigated using attention-based dynamic sigmoid weighted gates to interpret the influence each class has on the final prediction. Different combinations of the in situ data were fed into the 3D-CNN, with varying amounts of image layers, to determine the ideal combination for predicting mechanical properties in situ. Here, the 3D-CNN model achieved mean absolute percentage errors (MAPE) below 5% for both UTS and YS while using only a single camera input and under half of the available image layers.

36 MATERIALS SCIENCE↗