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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
NASA NTRS1978

Some modern control techniques for human operator modeling and identification

Some recent results in quantitative modeling of the human operator are reviewed as well as a method for processing his input-output data. Modern control techniques are applied to the pilot operating in a dynamic compensatory tracking task. Trained pilots are known to behave in an optimal manner under fully stressed conditions. This has led to the development of an optimal control model of the human operator which takes his limitations into account. A rationale for determining the appropriate cost functions is determined. Experimental results indicate that the model can closely predict pilot performance in one- or two-axis tracking tasks. The model predicts pilot remnant (noise), error scores, and frequency response in both single-axis and multi-axis tracking tasks.

Shirley, R. S.↗
NASA NTRS2001

Empirical Analysis of EEG and ERPs for Psychophysiological Adaptive Task Allocation

The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocation decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a tracking task was switched between task modes based upon the participant's EEG. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower RMSE and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of the implications for adaptive automation design.

Prinzel, Lawrence J., III↗
NASA NTRS2004

The EOS-Aura Mission

The EOS-Aura atmospheric chemistry mission is scheduled for launch in June 2004. Aura is the third of the large EOS observatories. The spacecraft carries an international instrument payload that has a planned six year lifetime. The Aura mission will collect data to help answer stratospheric and tropospheric atmospheric chemistry questions. The mission has the following four major objectives: 1) Track the ozone layer to determine if it is recovering as predicted. The four Aura instruments, HIRDLS, OMI, MLS, and TES will measure ozone, key source, radical, reservoir, tracer gases, and aerosols. Aura's unique design allows for major ozone controlling gases to be measured within the same air mass within a few minutes. The OMI instrument will continue the trends fiom NASA's TOMS series. 2) Track tropospheric pollutant sources and measure tropospheric ozone precursors. Major pollution sources include urban, industrial and biomass burning regions. Tropospheric trace gases will be measured, using TES and OMI, at an average spatial resolution of about approximately 15 km with near global coverage. 3) Measure key upper tropospheric atmospheric constituents that influence climate. The Aura instruments will monitor O3, H2O, CO, cirrus ice, and aerosols. EOS-Aura will fly in a sun-synchronous polar orbit about 15 minutes behind Aqua and make near coincident and synergistic measurements with the EOS- Aqua, Cloudsat, OCO, PARASOL and Calipso missions. Aura launched July 15,2004.

Schoeberl, Mark R.↗
NASA NTRS2005

The EOS-Aura Mission

The EOS-Aura atmospheric chemistry mission is scheduled for launch in June 2004. Aura is the third of the large EOS observatories. The spacecraft carries an international instrument payload that has a planned six year lifetime. The Aura mission will collect data to help answer stratospheric and tropospheric atmospheric chemistry questions. The mission has the following four major objectives: 1) Track the ozone layer to determine if it is recovering as predicted. The four Aura instruments, HIRDLS, OMI, MLS, and TES will measure ozone, key source, radical, reservoir, tracer gases, and aerosols. Aura's unique design allows for major ozone controlling gases to be measured within the same air mass within a few minutes. The OMI instrument will continue the trends from NASA's TOMS series. 2) Track tropospheric pollutant sources and measure tropospheric ozone precursors. Major pollution sources include urban, industrial and biomass burning regions. Tropospheric trace gases will be measured, using TES and OMI, at an average spatial resolution of about approx. 15 km with near global coverage. 3) Measure key upper tropospheric atmospheric constituents that influence climate. The Aura instruments will monitor O3, H2O, CO, cirrus ice, and aerosols. EOS-Aura will fly in a sun-synchronous polar orbit about 15 minutes behind Aqua and make near coincident and synergistic measurements with the EOS-Aqua, Cloudsat, OCO, PARASOL and Calipso missions. Aura launched July 15,2004.

Schoeberl, Mark R.↗
DOE OSTI2021

AlphaBuilding ResCommunity: A multi-agent virtual testbed for community-level load coordination

Training and validating algorithms in a simulation testbed can accelerate research and applications of optimal control of residential loads to improve energy flexibility and grid resilience. We developed an open-source simulation environment, AlphaBuilding ResCommunity, that can be used to train and validate algorithms to control a single thermostatically controlled load (TCL) or coordinate a group of TCLs. We used reduced-order models to simulate the thermodynamics of TCLs, and the parameter values were determined from the connected smart thermostat data of real households. The environment was built upon the standardized OpenAI Gym interface. Ancillary functions, such as retrieving the parameters and weather forecasts, are provided to facilitate control strategies that require predictive information. Compared with existing efforts, AlphaBuilding ResCommunity has three advantages: (1) more realistic model settings because the parameter values are identified from actual household operating data, and modelling and measurement uncertainty are considered; (2) passive thermal storage control; and (3) ease of use due to a simple software dependency and standardized interface. We demonstrated the applications of the environment by implementing a Kalman Filter and Model Predictive Control on a single TCL and a Priority-Stack-Based Control and Alternating Direction Method of Multipliers to coordinate multiple TCLs for load tracking.

Wang, Z↗
NASA NTRS2014

Output Feedback M-MRAC Backstepping With Aerospace Applications

The paper presents a certainty equivalence output feedback backstepping adaptive control design method for the systems of any relative degree with unmatched uncertainties without over-parametrization. It uses a fast prediction model to estimate the unknown parameters, which is independent of the control design. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters. The approach is applied to aerospace control problems and tested in numerical simulations.

Output feedback adaptive control↗
NASA NTRS2015

M-MRAC Backstepping for Systems with Unknown Virtual Control Coefficients

The paper presents an over-parametrization free certainty equivalence state feedback backstepping adaptive control design method for systems of any relative degree with unmatched uncertainties and unknown virtual control coefficients. It uses a fast prediction model to estimate the unknown parameters, which is independent of the control design. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters. The benefits of the approach are demonstrated in numerical simulations.

Certainty equivalence control↗
NASA NTRS2007

Coronas-F Orbit Monitoring and Re-Entry Prediction

Russian scientific satellite CORONAS-F was launched on July, 31, 2001. The object was inserted in near-circular orbit with the inclination 82.5deg and a mean altitude approx. 520 km. Due to the upper atmosphere drag CORONAS-F was permanently descended and as a result on December, 6, 2005 it has finished the earth-orbital flight, having lifetime in space approx. 4.5 years. The satellite structural features and its flight attitude control led to the significant variations of its ballistic coefficient during the flight. It was a cause of some specific difficulties in the fulfillment of the ballistic and navigation support of this space vehicle flight. Besides the main mission objective CORONAS-F also has been selected by the Inter-Agency Space Debris Coordination Committee (IADC) as a target object for the next regular international re-entry test campaign on a program of surveillance and re-entry prediction for the hazard space objects within their de-orbiting phases. Spacecraft (S/C) CORONAS-F kept its working state right up to the end of the flight - down to the atmosphere entry. This fact enabled to realization of the additional research experiments, concerning with an estimation of the atmospheric density within the low earth orbits (LEO) of the artificial satellites, and made possible to continue track the S/C during final phase of its flight by means of Russian regular command & tracking system, used for it control. Thus there appeared a unique possibility of using for tracking S/C at its de-orbiting phase not only passive radar facilities, belonging to the space surveillance systems and traditionally used for support of the IADC re-entry test campaigns, but also more precise active trajectory radio-tracking facilities from the ground control complex (GCC) applied for this object. Under the corresponding decision of the Russian side such capability of additional high-precise tracking control of the CORONAS-F flight in this period of time has been implemented. The organizing of the CORONAS-F ballistic and navigational support (BNS) and solving its main tasks (such as S/C orbit determination (OD) and its motion prediction and connected with them) both for regular mission stage and for additional flight program were realized by the group of specialists from the Mission Control Center (MCC). MCC was also assigned as a principal organization from the Russian side for participation in the 7th IADC re-entry test campaign on CORONAS-F. The CORONAS-F flight features and space environments circumstances during its flight as well as a methodology and technology of spacecraft ballistic and navigational support are given below. The BNS results for different phases of S/C flight, including the results of its re-entry predictions, obtained during the realization of the 7th IADC test campaign are submitted. The accuracy of space vehicle re-entry prediction and its dependence on various factors are analyzed in more details.

Ivanov, N. M.↗
NASA NTRSDate not supplied

Modeling Self-Pressurization and Spray Bar Pressure Control of A Cryogenic Storage Tank in Normal Gravity

This paper presents computational fluid dynamics (CFD) models for simulating self-pressurization and spray-bar pressure control processes in a large-scale liquid hydrogen storage tank under normal gravity conditions. For self-pressurization, the model employs the kinetics-based Schrage equation alongside the volume-of-fluid (VOF) method to account for interfacial mass transfer. The CFD predictions of pressure and temperature are compared with experimental data from the Multipurpose Hydrogen Test Bed (MHTB) experiment, and the predicted interfacial mass transfer rates are also presented. A CFD model simulating pressure control using a spray bar has also been developed. An Eulerian-Lagrangian approach models the interactions between discrete droplets and the continuous ullage (vapor) phase. The spray model is coupled with the VOF method by tracking droplets in the ullage and removing them when they reach the liquid interface. The T-sat model calculates droplet-ullage heat and mass transfer, where droplets warm up to the saturation temperature corresponding to the ullage vapor pressure before evaporating while remaining at the saturation temperature. The evolution of tank pressure, vapor temperature, and liquid temperature predicted by the CFD model is validated against data from the MHTB spray-bar mixing experiment. Overall, the CFD models agree with experimental data, demonstrating their capability to simulate self-pressurization and pressure control processes in large-scale cryogenic storage tanks. These models can be valuable tools for designing and optimizing cryogenic fluid management systems in future applications.

Computational Fluid Dynamics↗
NASA NTRSDate not supplied

Modeling Self-Pressurization and Spray Bar Pressure Control of A Cryogenic Storage Tank in Normal Gravity

This paper presents computational fluid dynamics (CFD) models for simulating self-pressurization and spray-bar pressure control processes in a large-scale liquid hydrogen storage tank under normal gravity conditions. For self-pressurization, the model employs the kinetics-based Schrage equation alongside the volume-of-fluid (VOF) method to account for interfacial mass transfer. The CFD predictions of pressure and temperature are compared with experimental data from the Multipurpose Hydrogen Test Bed (MHTB) experiment, and the predicted interfacial mass transfer rates are also presented. A CFD model simulating pressure control using a spray bar has also been developed. An Eulerian-Lagrangian approach models the interactions between discrete droplets and the continuous ullage (vapor) phase. The spray model is coupled with the VOF method by tracking droplets in the ullage and removing them when they reach the liquid interface. The T-sat model calculates droplet-ullage heat and mass transfer, wherein droplets warm up to the saturation temperature corresponding to the ullage vapor pressure before evaporating while remaining at the saturation temperature. The evolution of tank pressure, vapor temperature, and liquid temperature predicted by the CFD model is validated against data from the MHTB spray-bar mixing experiment. Overall, the CFD models agree with experimental data, demonstrating their capability to simulate self-pressurization and pressure control processes in large-scale cryogenic storage tanks. These models can be valuable tools for designing and optimizing cryogenic fluid management systems in future applications.

Self-Pressurization↗
NASA NTRS1988

Prediction and measurement of human pilot dynamic characteristics

An analytical and experimental study of human pilot control strategies in a manned rotorcraft simulation is described. The task simulated involves a low-speed, constant-altitude maneuvering task in which a head-down display is utilized to allow the pilot to track a moving hover point. The efficacy of the display law driving an 'acceleration symbol' is determined and the manner in which the prediction and measurement of pilot/vehicle dynamics can be made part of man/machine system evaluations is demonstrated.

Hess, Ronald A.↗
NASA NTRS1991

Sensitivity of Space Station alpha joint robust controller to structural modal parameter variations

The photovoltaic array sun tracking control system of Space Station Freedom is described. A synthesis procedure for determining optimized values of the design variables of the control system is developed using a constrained optimization technique. The synthesis is performed to provide a given level of stability margin, to achieve the most responsive tracking performance, and to meet other design requirements. Performance of the baseline design, which is synthesized using predicted structural characteristics, is discussed and the sensitivity of the stability margin is examined for variations of the frequencies, mode shapes and damping ratios of dominant structural modes. The design provides enough robustness to tolerate a sizeable error in the predicted modal parameters. A study was made of the sensitivity of performance indicators as the modal parameters of the dominant modes vary. The design variables are resynthesized for varying modal parameters in order to achieve the most responsive tracking performance while satisfying the design requirements. This procedure of reoptimization design parameters would be useful in improving the control system performance if accurate model data are provided.

Kumar, Renjith R.↗
DOE OSTI2022

Reinforcement learning for online adaptation of model predictive controllers: Application to a selective catalytic reduction unit

Here we present a novel application of reinforcement learning (RL) for online dynamic tuning of model predictive controllers (MPC). Applying a state-action-reward-state-action (SARSA) algorithm for temporal difference learning with a control-specific reward function improves the error tracking performance of a standard MPC formulation. The proposed RL approach is also readily adaptable to other MPCs, or entirely different control approaches. Practical details for the implementation of the RL-MPC algorithm are also presented. The proposed algorithm is applied to a case study of controlling nitrogen oxide (NO x ) emissions in an industrial selective catalytic reduction (SCR) unit, a control problem characterized by significant nonlinearity and time delay. Along with an RL-MPC formulation for NOx control, another MPC is proposed to mitigate ammonia slip and decrease ammonia consumption in the SCR. Results showing the efficacy of the RL-MPC for NO x control through learning and implementation on the nonlinear SCR dynamic model are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Kinetics of moisture-induced phase transformation in inorganic halide perovskite

The high-temperature (high-T) phase of cesium lead iodide (CsPbI 3 ) presents great promise for photovoltaic applications; however, exposure to ambient moisture at room temperature transforms it into its less-desirable low-temperature (low-T) phase with a larger band gap. While there have been theoretical predictions on the influence of moisture level on the phase transformation kinetics, the corresponding quantitative experimental evidence has remained limited. Tracking CsPbI 3 phase transformation under controlled relative humidity (RH), we find that rising RH increases the nucleation rate of low-T CsPbI 3 exponentially, but has a weak effect on its growth. The overall transformation is nucleation limited, with higher RH leading to a lower nucleation barrier. Finally, we find that heating between 40°C and 80°C facilitates water desorption and suppresses phase transformation. Our findings elucidate the relationship between moisture and the phase energetics of CsPbI 3 , which can serve as references for thin film applications of CsPbI 3 and future designs of stable photovoltaics systems.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Adaptive Data-based Predictive Control for Short Take-off and Landing (STOL) Aircraft

Data-based Predictive Control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. The characteristics of adaptive data-based predictive control are particularly appropriate for the control of nonlinear and time-varying systems, such as Short Take-off and Landing (STOL) aircraft. STOL is a capability of interest to NASA because conceptual Cruise Efficient Short Take-off and Landing (CESTOL) transport aircraft offer the ability to reduce congestion in the terminal area by utilizing existing shorter runways at airports, as well as to lower community noise by flying steep approach and climb-out patterns that reduce the noise footprint of the aircraft. In this study, adaptive data-based predictive control is implemented as an integrated flight-propulsion controller for the outer-loop control of a CESTOL-type aircraft. Results show that the controller successfully tracks velocity while attempting to maintain a constant flight path angle, using longitudinal command, thrust and flap setting as the control inputs.

Barlow, Jonathan Spencer↗
DOE OSTI2020

A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination

Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While granulomas serve to physically contain and immunologically restrain bacteria growth, some granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the granuloma and disseminating, either resulting in additional granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB lung granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple granulomas in an entire lung. MultiGran follows cells, cytokines, and bacterial populations within each lung granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, granuloma, and whole-lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

The Hubble Space Telescope fine guidance system operating in the coarse track pointing control mode

The Hubble Space Telescope (HST) Fine Guidance System has set new standards in pointing control capability for earth orbiting spacecraft. Two precision pointing control modes are implemented in the Fine Guidance System; one being a Coarse Track Mode which employs a pseudo-quadrature detector approach and the second being a Fine Mode which uses a two axis interferometer implementation. The Coarse Track Mode was designed to maintain FGS pointing error to within 20 milli-arc seconds (rms) when guiding on a 14.5 Mv star. The Fine Mode was designed to maintain FGS pointing error to less than 3 milli-arc seconds (rms). This paper addresses the HST FGS operating in the Coarse Track Mode. An overview of the implementation, the operation, and both the predicted and observed on orbit performance is presented. The discussion includes a review of the Fine Guidance System hardware which uses two beam steering Star Selector servos, four photon counting photomultiplier tube detectors, as well as a 24 bit microprocessor, which executes the control system firmware. Unanticipated spacecraft operational characteristics are discussed as they impact pointing performance. These include the influence of spherically aberrated star images as well as the mechanical shocks induced in the spacecraft during and following orbital day/night terminator crossings. Computer modeling of the Coarse Track Mode verifies the observed on orbit performance trends in the presence of these optical and mechanical disturbances. It is concluded that the coarse track pointing control function is performing as designed and is providing a robust pointing control capability for the Hubble Space Telescope.

Whittlesey, Richard↗