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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 793 records · Page 44

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NASA NTRS1982

Pilot model hypothesis testing

The aircraft control time history predicted by the optimal control pilot model and actual pilot tracking data obtained from NASA Langley's differential maneuvering simulator (DMS) are analyzed. The analysis is performed using a hypothesis testing scheme modified to allow for changes in the true hypothesis. A finite number of pilot models, each with different hypothesized internal model representations of the aircraft dynamics, are constructed. The hypothesis testing scheme determines the relative probability that each pilot model best matches the DMS data. By observing the changes in probabilities, it is possible to determine when the pilot changes control strategy and which hypothesized pilot model best represent's the pilot's control behavior.

Broussard, J. R.↗

NASA NTRS1989

Evaluation of a technique for predicting longitudinal pilot-induced-oscillations

A technique for predicting the susceptibility of an aircraft to longitudinal pilot-induced-oscillations (PIO's) is evaluated using 62 configurations from a pair of flight tests involving the NT-33 variable stability aircraft. The technique is based upon the characteristics of the open-loop pilot/vehicle system for attitude control as predicted by the Optimal Control Model (OCM) of the human pilot. The OCM is simplified so that only the index of performance weighting coefficients need to be considered as problem variables and a simple technique for generating these coefficients is reviewed.

Hess, R. A.↗

NASA NTRS1992

Loop transfer recovery for general nonminimum phase discrete time systems. I - Analysis

A complete analysis of loop transfer recovery (LTR) for general nonstrictly proper, not necessarily minimum phase discrete time systems is presented. Three different observer-based controllers, namely, `prediction estimator' and full or reduced-order type `current estimator' based controllers, are used. The analysis corresponding to all these three controllers is unified into a single mathematical framework. The LTR analysis given here focuses on three fundamental issues: (1) the recoverability of a target loop when it is arbitrarily given, (2) the recoverability of a target loop while taking into account its specific characteristics, and (3) the establishment of necessary and sufficient conditions on the given system so that it has at least one recoverable target loop transfer function or sensitivity function. Various differences that arise in LTR analysis of continuous and discrete systems are pointed out.

Chen, Ben M.↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

DOE OSTI2022

Tunable interaction between excitons and hybridized magnons in a layered semiconductor

The interaction between distinct excitations in solids is of both fundamental interest and technological importance. One example of such interactions is coupling between an exciton, a Coulomb bound electron-hole pair, and a magnon, a collective spin excitation. The recent emergence of van der Waals magnetic semiconductors1 provides a powerful platform for exploring these exciton-magnon interactions and their fundamental properties, such as strong correlation2, as well as their photo-spintronic and quantum transduction3 applications. Here we demonstrate precise control of coherent exciton-magnon interactions in the layered magnetic semiconductor CrSBr. We show that by controlling the direction of applied magnetic fields relative to the crystal axes, and thus the rotational symmetry of the magnetic system4, we can tune not only the exciton coupling to the bright magnon, but also to an optically dark mode via magnon hybridization. The exciton-magnon coupling and associated magnon dispersion curves can be further modulated by applying a uniaxial strain. At the critical strain, a dispersionless dark magnon band emerges. Our results demonstrate unprecedented control of the opto-mechanical-magnonic coupling, and a step towards the predictable and controllable implementation of hybrid quantum magnonics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

We present the development of a machine learning (ML) based regulation system for third-order resonant beam extraction in the Mu2e experiment at Fermilab. Classical and ML-based controllers have been optimized using semi-analytic simulations and evaluated in terms of regulation performance and training efficiency. We compare several controller architectures and discuss the integration of neural control into an adaptive framework. We also present progress on surrogate models that predict the controller response given a spill intensity and controller action history. To enable real-time deployment, we report progress on implementing low-latency, edge-based inference suitable for hardware-constrained environments. Our results demonstrate the feasibility and advantages of ML-based control in managing complex, time-varying physical systems, with broader implications for accelerator operations and other domains requiring fast, adaptive regulation.

Berlioz, Jose Rene [Fermilab]↗

DOE OSTI2023

Peak Reduction Using Mode Adjustment of Heat Pump Water Heaters in a Residential Neighborhood

Building electrification is putting pressure on distribution grid worldwide. Peak reduction is an important concern that can help reduce the growing stress and allow to defer investments in new capacity. Water heaters represent a convenient way of reducing peak because they are less dependent on weather, and their storage volume allows for asynchronous water heating and hot water use. Previous empirical studies investigated the ability of water heaters to reduce peak through the adjustment of the temperature setpoint. However, not all equipment vendors offer this option. This study aims at understanding the feasibility of peak reduction with an alternative configuration available in the market - by adjusting the device mode rather than temperature setpoint. The peak reduction methodology is tested in an occupied 46-townhome neighborhood located in Atlanta, GA. We find that peak shifting is possible with the adjustable mode approach, with the change in the peak load by 30-60%.

demand response↗

DOE OSTI2022

Adaptive Urban Traffic Signal Control for Multiple Intersections: An LQR Approach

Traffic congestion leads to severe problems especially in urban traffic networks. It increases the chance of accidents, energy waste, and social costs. In order to address these problems, an adaptive linear quadratic regulator (LQR) approach is developed for traffic signal control at multiple intersections in an urban area. The proposed method controls the green time of the traffic signals to reduce traffic congestion and smooth traffic flow. Real-world data from vision-based traffic sensors are used to build the traffic network model, which mimics the real-world traffic behavior. In addition, the proposed control utilizes recursive least square parameter estimation, which is capable of tracking dynamic changes in traffic conditions. Simulation of Urban MObility (SUMO) is used to analyze the efficacy of the proposed method. Results of the simulation show that the proposed method outperforms pretimed control in various aspects.

adaptive LQR control↗

DOE OSTI2024

Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Assessment of Microstructure Prediction Capabilities for Powder Bed Fusion Stainless Steel 316

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the characteristic process-structure-property relationships of additive manufacturing (AM) materials pose challenges for the qualification and certification of AM nuclear components. In particular, component-scale variations in microstructure and properties can be driven by localized changes in melt pool dynamics due to how process parameters interact with different part geometries. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents final results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing identified challenges and future opportunities for connecting the developed simulation workflow with mechanics simulations for prediction of part performance.

36 MATERIALS SCIENCE↗

NASA NTRS1980

Reduction of computer usage costs in predicting unsteady aerodynamic loadings caused by control surface motion. Addendum to computer program description

Changes to be made that provide increased accuracy and increased user flexibility in prediction of unsteady loadings caused by control surface motions are described. Analysis flexibility is increased by reducing the restrictions on the location of the downwash stations relative to the leading edge and the edges of the control surface boundaries. Analysis accuracy is increased in predicting unsteady loading for high Mach number analysis conditions through use of additional chordwise downwash stations. User guideline are presented to enlarge analysis capabilities of unusual wing control surface configurations. Comparative results indicate that the revised procedures provide accurate predictions of unsteady loadings as well as providing reductions of 40 to 75 percent in computer usage cost required by previous versions of this program.

Rowe, W. S.↗

NASA NTRSDate not supplied

A CFD Validation Study using Artemis 1 Orbital Slosh Test Data

Advancements in liquid propellant management science and technologies are key to increasing safety, decreasing cost, and increasing payload mass of NASA space missions. Liquid propellant usually comprises a large portion of the total mass of launch vehicles and spacecraft, so predicting and controlling the motion of it are important. Computational fluid dynamics (CFD) programs are critical to predicting slosh dynamics, but CFD programs require experimental validation with physically relevant test data before the results can be trusted. Low-gravity slosh test data is lacking, and most of what exists is inadequate for CFD validation. A potential slosh risk for Orion, and the lack of validated low-gravity slosh models, led to performing on-orbit slosh tests during the Artemis 1 mission to assess the impacts of slosh on the guidance, navigation, and control of Orion. This work details the validation of a coupled slosh-motion CFD model using the Orion orbital slosh test data set.

CFD↗

NASA NTRSDate not supplied

A CFD Validation Study Using Artemis 1 Orbital Slosh Test Data

CFD↗

NASA NTRS2012

Prediction of Traffic Complexity and Controller Workload in Mixed Equipage NextGen Environments

Controller workload is a key factor in limiting en route air traffic capacity. Past efforts to quantify and predict workload have resulted in identifying objective metrics that correlate well with subjective workload ratings during current air traffic control operations. Although these metrics provide a reasonable statistical fit to existing data, they do not provide a good mechanism for estimating controller workload for future air traffic concepts and environments that make different assumptions about automation, enabling technologies, and controller tasks. One such future environment is characterized by en route airspace with a mixture of aircraft equipped with and without Data Communications (Data Comm). In this environment, aircraft with Data Comm will impact controller workload less than aircraft requiring voice communication, altering the close correlation between aircraft count and controller workload that exists in current air traffic operations. This paper outlines a new trajectory-based complexity (TBX) calculation that was presented to controllers during a human-in-the-loop simulation. The results showed that TBX accurately estimated the workload in a mixed Data Comm equipage environment and the resulting complexity values were understood and readily interpreted by the controllers. The complexity was represented as a "modified aircraft account" that weighted different complexity factors and summed them in such a way that the controllers could effectively treat them as aircraft count. The factors were also relatively easy to tune without an extensive data set. The results showed that the TBX approach is well suited for presenting traffic complexity in future air traffic environments.

Lee, Paul U.↗

NASA NTRS2015

Effect of Interfacial Turbulence and Accommodation Coefficient on CFD Predictions of Pressurization and Pressure Control in Cryogenic Storage Tank

Laminar models agree closely with the pressure evolution and vapor phase temperature stratification but under-predict liquid temperatures. Turbulent SST k-w and k-e models under-predict the pressurization rate and extent of stratification in the vapor but represent liquid temperature distributions fairly well. These conclusions seem to equally apply to large cryogenic tank simulations as well as small scale simulant fluid pressurization cases. Appropriate turbulent models that represent both interfacial and bulk vapor phase turbulence with greater fidelity are needed. Application of LES models to the tank pressurization problem can serve as a starting point.

Condensation Evaporation↗

DOE OSTI2020

Hydration Mechanisms in Nanoparticle Interaction and Surface Energetics

Work on the project advanced molecular level understanding, prediction and control of nanoscale hydration in salt solutions under electric stimuli or ionic patterning. Permeation of nanoporous electrodes at preset voltage underlies the function of ultracapacitors. Transitory regulation of wetting in nanoporous media by electric field spans an array of applications in materials, energy storage, and separation sciences. The physically related modulation of nanoparticle solubility by surface charges can significantly extend the range of the nanomaterial applications, improve processing techniques, and potentially alleviate environmental concerns. Pore/solution equilibria, and activated kinetics of liquid gating by nanoconfined electrolytes are challenging problems at the forefront of experimental and theoretical research. Addressing these problems from a molecular perspective required the development of state-of-the-art simulation algorithms in statistical mechanics to capture complex processes in open systems under electric control. Parallel studies of wetting and dispersibility of polar and ionizing particles aim to uncover predictive relations between electrowetting, chemical functionalization, and geometry of nanomaterial particles. By nonequilibrium dynamic modeling, we elucidated electrolyte flow in nanochanels and associated electrokinetic energy conversion. Research on the program provided training opportunities for the next generation of scientists in computational chemistry. Insights, and methods from the project implicate broad segment of researchers in energy and nanosciences, materials and surface chemistry.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRS2008

Overview of CFD Validation Experiments for Circulation Control Applications at NASA

Circulation control is a viable active flow control approach that can be used to meet the NASA Subsonic Fixed Wing project s Cruise Efficient Short Take Off and Landing goals. Currently, circulation control systems are primarily designed using empirical methods. However, large uncertainty in our ability to predict circulation control performance has led to the development of advanced CFD methods. This paper provides an overview of a systematic approach to developing CFD tools for basic and advanced circulation control applications. This four-step approach includes "Unit", "Benchmar", "Subsystem", and "Complete System" experiments. The paper emphasizes the ongoing and planned 2-D and 3-D physics orientated experiments with corresponding CFD efforts. Sample data are used to highlight the challenges involved in conducting circulation control computations and experiments.

Jones, G. S.↗

NASA NTRS2017

Tank System Integrated Model: A Cryogenic Tank Performance Prediction Program

Accurate predictions of the thermodynamic state of the cryogenic propellants, pressurization rate, and performance of pressure control techniques in cryogenic tanks are required for development of cryogenic fluid long-duration storage technology and planning for future space exploration missions. This Technical Memorandum (TM) presents the analytical tool, Tank System Integrated Model (TankSIM), which can be used for modeling pressure control and predicting the behavior of cryogenic propellant for long-term storage for future space missions. Utilizing TankSIM, the following processes can be modeled: tank self-pressurization, boiloff, ullage venting, mixing, and condensation on the tank wall. This TM also includes comparisons of TankSIM program predictions with the test data andexamples of multiphase mission calculations.

Bolshinskiy, L. G.↗