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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Network-cognizant voltage droop control

The present disclosure provides techniques for network-cognizant droop control in power systems, such as a power distribution system. An example device includes a processor configured to determine, based on (i) a model representing a structure of a power system that includes a plurality of energy resources and (ii) an indication of predicted uncontrollable power injections in the power system, for each controllable energy resource in the plurality of energy resources, a respective value of a first droop coefficient and a respective value of a second droop coefficient. The processor may be further configured to cause at least one controllable energy resource in the plurality of energy resources to modify an output power of the at least one energy resource based on the respective value of the first droop coefficient and the respective value of the second droop coefficient.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2006

Software for Simulating Air Traffic

Future Air Traffic Management Concepts Evaluation Tool (FACET) is a system of software for performing computational simulations for evaluating advanced concepts of advanced air-traffic management. FACET includes a program that generates a graphical user interface plus programs and databases that implement computational models of weather, airspace, airports, navigation aids, aircraft performance, and aircraft trajectories. Examples of concepts studied by use of FACET include aircraft self-separation for free flight; prediction of air-traffic-controller workload; decision support for direct routing; integration of spacecraft-launch operations into the U.S. national airspace system; and traffic- flow-management using rerouting, metering, and ground delays. Aircraft can be modeled as flying along either flight-plan routes or great-circle routes as they climb, cruise, and descend according to their individual performance models. The FACET software is modular and is written in the Java and C programming languages. The architecture of FACET strikes a balance between flexibility and fidelity; as a consequence, FACET can be used to model systemwide airspace operations over the contiguous U.S., involving as many as 10,000 aircraft, all on a single desktop or laptop computer running any of a variety of operating systems. Two notable applications of FACET include: (1) reroute conformance monitoring algorithms that have been implemented in one of the Federal Aviation Administration s nationally deployed, real-time, operational systems; and (2) the licensing and integration of FACET with the commercially available Flight Explorer, which is an Internet- based, real-time flight-tracking system.

Sridhar, Banavar↗
NASA NTRS2022

Accurate Modeling of Lyα Profiles and their Impact on Photolysis of Terrestrial Planet Atmospheres

Accurately measuring and modeling the Lyα (Lyα; λ1215.67 Å) emission line from low-mass stars is vital for our ability to build predictive high energy stellar spectra, yet interstellar medium (ISM) absorption of this line typically prevents model-measurement comparisons. Lyα also controls the photodissociation of important molecules, like water and methane, in exoplanet atmospheres such that any photochemical models assessing potential biosignatures or atmospheric abundances require accurate Lyα host star flux estimates. Recent observations of three early M and K stars (K3, M0, M1) with exceptionally high radial velocities (>100 km s −1 ) reveal the intrinsic profiles of these types of stars as most of their Lyα flux is shifted away from the geocoronal line core and contamination from the ISM. These observations indicate that previous stellar spectra computed with the PHOENIX atmosphere code have underpredicted the core of Lyα in these types of stars. With these observations, we have been able to better understand the microphysics in the upper atmosphere and improve the predictive capabilities of the PHOENIX atmosphere code. Since these wavelengths drive the photolysis of key molecular species, we also present results analyzing the impact of the resulting changes to the synthetic stellar spectra on observable chemistry in terrestrial planet atmospheres.

Sarah Peacock↗
NASA NTRS2023

An Investigation into the Effect of Liquid Accumulation on Thermo-Physiologic State using an Advanced Moisture Model Coupled with a High Resolution Human Thermal Model

A physics-based mathematical model for simulating heat and moisture transport on the skin and within clothing was developed to improve the accuracy of human thermal modeling predictions. In a previous study, a set of sweating hotplate experiments was specifically created to obtain test data for model validation. The clothing moisture model was subsequently coupled with a human thermal model and tested by simulating human experiments described in the open literature, which served to verify the model’s ability to predict whole body thermo-physiologic state in work-rest cycles during which sweating was known to occur. In this study, a human subject test was specifically designed to validate the model’s ability to predict local and whole body sweat evaporation and accumulation on the surface of the body. The human subject test incorporated twelve clothed male subjects of moderate physical fitness, ages 18-30, which were prescribed a work-rest cycle in a climate-controlled chamber (25 °C, 50% RH). Two activity levels were considered during the active stage (4 MET vs. 6.5 MET), and two sets of ending ambient conditions were considered for the final rest stage (25 °C vs. 15 °C, 50% RH). Model predictions were compared to the measured local skin temperatures, core temperatures, and whole body sweat losses. In general, the model was able to reproduce the trends observed in the human subject trial, but more importantly, provided insight into how dynamic changes in liquid content can impact the evolution of local and whole body thermo-physiologic state. This study demonstrates the ability of an advanced moisture model to capture distinct physics attributable to moisture accumulation, allowing for increased accuracy in predicting human thermal state and for interpreting the results of measurements derived from human subject tests.

human thermal model↗
NASA NTRS1989

Notched fatigue of single crystal PWA 1480 at turbine attachment temperatures

The focus is on the lower temperature, uncoated and notched features of gas turbine blades. Constitutive and fatigue life prediction models applicable to these regions are being developed. Fatigue results are presented which were obtained thus far. Fatigue tests are being conducted on PWA 1480 single crystal material using smooth strain controlled specimens and three different notched specimens. Isothermal fatigue tests were conducted at 1200, 1400, and 1600 F. The bulk of the tests were conducted at 1200 F. The strain controlled tests were conducted at 0.4 percent per second strain rate and the notched tests were cycled at 1.0 cycle per second. A clear orientation dependence is observed in the smooth strain controlled fatigue results. The fatigue lifes of the thin, mild notched specimens agree fairly well with this smooth data when elastic stress range is used as a correlating parameter. Finite element analyses were used to calculate notch stresses. Fatigue testing will continue to further explore the trends observed thus far. Constitutive and life prediction models are being developed.

Meyer, T. G.↗
NASA NTRS1998

Numerical Simulations of a Lobed Fuel Injector

Numerical modeling of the nonreactive mixing processes associated with a lobed fuel injector in a coflowing air stream is presented. The lobed fuel injector is a device which generates strong streamwise vorticity, producing locally high strain rates which can enhance the molecular mixing of reactants while delaying ignition in a controlled manner. Vortex element modeling is used to simulate flow field evolution and fuel element mixing characteristics for this lobed fuel injector. Quantitative predictions for vorticity generation and qualitative results for streamwise rollup compare well qualitatively with recent experimental investigations of this flow field [Smith et al, Phys. Fluids 9, 667 (1997)]. Parametric studies of the effects of lobe amplitude-to-wavelength ratio, lobe angle, and lobe shape for given flow conditions suggest that geometrical features may be optimized to enhance mixing and control reaction processes.

Strickland, J. H.↗
NASA NTRS2019

FUN3D and USM3D Analysis of the Propulsion Aerodynamic Workshop 2018 S-Duct Test Case

This work presents the results of Fun3D and USM3D analyses that were performed for the 4th AIAA Propulsion Aerodynamics Workshop (PAW). The PAW workshop is separated into three sections that focus on internal duct flows, nozzle flows and a special topic. This paper focuses on the internal duct flow section of PAW04 while an accompanying paper discusses the analyses performed for the nozzle portion. For the internal duct flow section, the PAW04 participants were provided with the two configurations consisting of an S-duct with and without aerodynamic interface plane (AIP) rake legs modeled. The participants were asked to perform a grid refinement study as well as a turbulence model study for the configuration with the rake legs. The analyses discussed here were performed on custom grids developed under the guidelines of the workshop. Additionally, the paper discusses the development and use of flow controllers for matching the desired flow characteristics. The results show that both solvers do well for predicting internal flow characteristics of the S-duct based on direct comparison with the experimental data. However, the CFD-to-CFD comparison proved to be more challenging due to the localized occurrence of supersonic flow near the rake legs when using the mass flow controller. A turbulence model study was performed to compare the two-equation SST model to the SA-QCR model. The results show that although the turbulence model does affect the solution, it makes a minimal impact on pressure recovery and inlet distortion intensity for this case. Suggestions for future workshops include gridding guidelines similar to those employed for the Drag Prediction Workshop series for the grid refinement study and a time accuracy study.

Carter, Melissa B.↗
DOE OSTI2025

Twinac: initiation of a community-driven accelerator digital twin framework

We present the initiation of a community-driven framework for the integration of accelerator digital twins into control systems: Twinac. Few facilities have fully integrated accelerator digital twins like at Cornell’s CHESS. Many facilities have active research to employ surrogate models to aid in operational decisions like at Argonne’s ALS, MSU’s FRIB, SLAC’s LCLS-II, and Fermilab’s FAST/IOTA, PIP-II, and main complex. To lower the barrier to entry for all accelerator facilities to build and benefit from a digital twin of their own accelerators, we propose the following software framework. Twinac will provide the capability to compose one’s own digital twin using reusable components engineered at other facilities. With this model in place, Twinac will also support tools for (1) predictive maintenance systems; (2) discovery of correlated but uncontrolled environmental factors, like seasonal temperature variations causing performance changes on power supplies, magnets, etc.; and (3) prototyping and updating sophisticated optimization and controls algorithms. The Twinac framework will enable sharing and simplified deployment of modeled components and control algorithms at all facilities. With an inter-facility team to build and support the Twinac framework, it will be easy to publish and try out the latest advancements at one’s own facility.

Miceli, Tia [Fermilab]↗
DOE OSTI2025

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗
NASA NTRS1990

A tool for modeling concurrent real-time computation

Real-time computation is a significant area of research in general, and in AI in particular. The complexity of practical real-time problems demands use of knowledge-based problem solving techniques while satisfying real-time performance constraints. Since the demands of a complex real-time problem cannot be predicted (owing to the dynamic nature of the environment) powerful dynamic resource control techniques are needed to monitor and control the performance. A real-time computation model for a real-time tool, an implementation of the QP-Net simulator on a Symbolics machine, and an implementation on a Butterfly multiprocessor machine are briefly described.

Sharma, D. D.↗
NASA NTRS2024

Considerations for Thrust Vector Control (TVC) In-Flight Load Predictions

This presentation communicates an overview of a methodology developed by the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) Thrust Vector Control (TVC) Systems Integration & Components Branch (MSFC-ER63). The methodology is based on NASA heritage TVC design practices and is designed to analyze the summation of static and quasi-static environments acting on a gimbaled rocket engine/motor system. The analysis determines the maximum overall expected induced torque acting on the rocket engine/motor system about the gimbal center of rotation and assumes a mathematical 3-Dimensional (3D) 2-Degree-of-Freedom (DOF) rigid-body pendulum model over a predefined range of angular motion. Additionally, it calculates the associated reaction force acting along a TVC actuator line-of-action (LOA) to maintain thrust vector holding operations. This methodology is particularly useful for determining maximum overall design loads for TVC actuator design-space shaping and requirements determination.

Thrust Vector Control↗
NASA NTRS1992

An analytical and experimental investigation of flutter suppression via piezoelectric actuation

The feasibility of using piezoelectric material actuators to suppress flutter was investigated analytically and experimentally. For this purpose, a two-degree of freedom wind-tunnel model was designed, analyzed, and tested. A digital control law was designed and implemented based on a discretized model. Experiments consisted of open- and closed-loop flutter tests. It is shown that analytical predictions agree well with experimental results. The open-loop flutter velocity prediction was 3.5 percent conservative, while the closed-loop prediction was 7.6 percent conservative. Results of the analysis showed that the flutter velocity would be increased by 15.7 percent with the control law, while 20 percent improvement was observed experimentally.

Heeg, Jennifer↗
DOE OSTI2025

Divertor Plasma Detachment Control Neural Network

DivControlNN is a state-of-the-art software tool that leverages advanced machine learning techniques to predict and control divertor plasma behavior in fusion reactors. Plasma, a highly energetic and electrically charged gas, requires meticulous management to protect reactor components and maintain optimal energy production. Conventional simulation methods, although extremely detailed, typically demand extensive computational time-making them unsuitable for real-time control scenarios. DivControlNN addresses this challenge by learning from tens of thousands of high-fidelity simulations, thereby creating a rapid surrogate model that can deliver near-instantaneous predictions. At the core of its functionality is a sophisticated technique known as latent space mapping, which condenses complex, high-dimensional plasma data into a compact, lower-dimensional representation. This streamlined representation enables the system to quickly forecast essential plasma properties and determine the precise conditions required for effective detachment. Detachment is a crucial process in which the plasma is cooled before reaching the divertor plates, thereby reducing heat loads and mitigating material erosion. In recent experiments conducted on the KSTAR tokamak in South Korea, DivControlNN successfully guided the detachment process without any fine-tuning-even when applied to a new tungsten divertor configuration. By achieving a computational speed-up of over one hundred million times compared to traditional simulation methods while maintaining low prediction errors, DivControlNN stands to significantly enhance real-time control and diagnostic capabilities in future fusion reactors. This breakthrough paves the way for safer, more reliable reactor operation and represents a major advancement toward realizing fusion energy as a practical, sustainable, and clean power source.

Xu, Xueqiao [Lawrence Livermore National Laborator↗
DOE OSTI2023

Multi-decadal variation of ENSO forecast skill since the late 1800s

Diagnosing El Niño-Southern Oscillation (ENSO) predictability within operational forecast models is hindered by computational expense and the need for initialization with three-dimensional fields generated by global data assimilation. We instead examine multi-year ENSO predictability since the late 1800s using the model-analog technique, which has neither limitation. We first draw global coupled model states from pre-industrial control simulations, from the Coupled Model Intercomparison Project Phase 6, that are chosen to initially match observed monthly sea surface temperature and height anomalies in the Tropics. Their subsequent 36-month model evolution are the hindcasts, whose 20th century ENSO skill is comparable to twice-yearly hindcasts generated by a state-of-the-art European operational forecasting system. Despite the so-called spring predictability barrier, present throughout the record, there is substantial second-year ENSO skill, especially after 1960. Overall, ENSO exhibited notably high values of both amplitude and skill towards the end of the 19th century, and again in recent decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The kinetics of static recovery by dislocation climb

Abstract The initial microstructure of a wide range of structural materials is conditioned by thermo-mechanical treatments such as hot-working, tempering, or solution annealing. At the elevated temperatures associated with these treatments the dislocation microstructure evolves, usually decreasing in density through a process known as static recovery. Despite its technological relevance, static recovery is not fully characterized from a theoretical standpoint, with even the controlling mechanisms subject to debate. In this study, a climb-enabled discrete dislocation dynamics (DDD) capability is leveraged to explore the kinetics of static recovery in pure Fe when controlled by dislocation climb. Quantitative data from these simulations is used to develop a revised static recovery law, and provides the parameters appropriate for predictive microstructure models in Fe. This law differs from previous analytical derivations invoking climb of dislocations, following the logarithmic trends typical of experimental observations where prior work did not. Direct comparison between the recovery law derived from DDD to experimental recovery data in alpha Fe shows strong agreement across a range of temperatures, and suggests that climb is the controlling mechanism for static recovery in pure metals.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Online Learning of Effective Turbine Wind Speed in Wind Farms

To develop better wind farm controllers that can meet more complex objectives, methods of modeling the wind turbine wakes at low computational expense are needed. Gaussian process (GP) regression offers a computationally inexpensive framework for learning complex functions from noisy measurements with very few datapoints. In this work, an online learning approach is presented to learn the rotor-averaged wind velocity at downstream wind turbines with GPs, using the available datastream of wind field measurements and wind turbine control set-points. This framework can readily be integrated into model-based controls methods because the model a) is updated online at low computational expense, b) assumes a mathematically favorable Gaussian form, and c) explicitly quantifies the stochastic nature of the wake field so that the trade-off between exploration and exploitation, and the uncertainty in the prediction, can be utilized. We show that a GP-learned model can match true values with errors within 0.5% on average, with as few as 5 training data points.

Gaussian process↗
DOE OSTI2025

Evaluating the limitations of Bayesian metabolic control analysis

AbstractBayesian Metabolic Control Analysis (BMCA) has emerged as a promising framework for inferring metabolic control coefficients in data-limited scenarios by integrating Bayesian inference with linlog rate laws. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCCs), and concentration control coefficients (CCCs) under varying data availability conditions using three synthetic metabolic network models. Our findings highlight the strengths and weaknesses of BMCA, guiding its application in metabolic engineering and emphasizing the need for methodological refinements.Author summaryUnderstanding how enzymes control metabolic pathways is crucial for optimizing biomanufacturing and synthetic biology applications. Bayesian Metabolic Control Analysis (BMCA) is a promising computational method that integrates Bayesian inference with metabolic control analysis to estimate key control parameters, even in cases with limited experimental data. However, the accuracy and limitations of BMCA remain unclear. In this study, we systematically evaluate BMCA using three synthetic metabolic networks to determine how different types of physiological data impact its predictive performance. We find that BMCA requires flux and enzyme concentration data for accurate predictions, while external metabolite concentrations contribute little. Additionally, BMCA fails to predict elasticity values beyond a magnitude of 1.5 and reliably infer allosteric regulation, even when strong regulatory interactions exist. In addition, BMCA does not accurately rank metabolic control points, which may limit its utility in identifying key enzymes in engineered pathways. Our work provides practical insights into when and how BMCA can be applied, guiding future research in metabolic modeling and control analysis.

Shin, Janis (ORCID:0000000216572455)↗
NASA NTRS1990

Verification of component mode techniques for flexible multibody systems

Investigations were conducted in the modeling aspects of flexible multibodies undergoing large angular displacements. Models were to be generated and analyzed through application of computer simulation packages employing the 'component mode synthesis' techniques. Multibody Modeling, Verification and Control Laboratory (MMVC) plan was implemented, which includes running experimental tests on flexible multibody test articles. From these tests, data was to be collected for later correlation and verification of the theoretical results predicted by the modeling and simulation process.

Wiens, Gloria J.↗