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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Effects of modeling errors on trajectory predictions in air traffic control automation

Air traffic control automation synthesizes aircraft trajectories for the generation of advisories. Trajectory computation employs models of aircraft performances and weather conditions. In contrast, actual trajectories are flown in real aircraft under actual conditions. Since synthetic trajectories are used in landing scheduling and conflict probing, it is very important to understand the differences between computed trajectories and actual trajectories. This paper examines the effects of aircraft modeling errors on the accuracy of trajectory predictions in air traffic control automation. Three-dimensional point-mass aircraft equations of motion are assumed to be able to generate actual aircraft flight paths. Modeling errors are described as uncertain parameters or uncertain input functions. Pilot or autopilot feedback actions are expressed as equality constraints to satisfy control objectives. A typical trajectory is defined by a series of flight segments with different control objectives for each flight segment and conditions that define segment transitions. A constrained linearization approach is used to analyze trajectory differences caused by various modeling errors by developing a linear time varying system that describes the trajectory errors, with expressions to transfer the trajectory errors across moving segment transitions. A numerical example is presented for a complete commercial aircraft descent trajectory consisting of several flight segments.

Jackson, Michael R. C.↗
NASA NTRS1990

Commercial applications

Viewgraphs on commercial applications of fuzzy logic in Japan are presented. Topics covered include: suitable application area of fuzzy theory; characteristics of fuzzy control; fuzzy closed-loop controller; Mitsubishi heavy air conditioner; predictive fuzzy control; the Sendai subway system; automatic transmission; fuzzy logic-based command system for antilock braking system; fuzzy feed-forward controller; and fuzzy auto-tuning system.

Togai, Masaki↗
NASA NTRS1991

Prediction of forces and moments for hypersonic flight vehicle control effectors

Developing methods of predicting flight control forces and moments for hypersonic vehicles, included a preliminary assessment of subsonic/supersonic panel methods and hypersonic local flow inclination methods for such predictions. While these findings clearly indicate the usefulness of such methods for conceptual design activities, deficiencies exist in some areas. Thus, a second phase of research was proposed in which a better understanding is sought for the reasons of the successes and failures of the methods considered, particularly for the cases at hypersonic Mach numbers. To obtain this additional understanding, a more careful study of the results obtained relative to the methods used was undertaken. In addition, where appropriate and necessary, a more complete modeling of the flow was performed using well proven methods of computational fluid dynamics. As a result, assessments will be made which are more quantitative than those of phase 1 regarding the uncertainty involved in the prediction of the aerodynamic derivatives. In addition, with improved understanding, it is anticipated that improvements resulting in better accuracy will be made to the simple force and moment prediction.

Maughmer, Mark D.↗
DOE OSTI2021

Co-Optimization Scheme for Hybrid Electric Vehicles Powertrain and Exhaust Emission Control System Using Future Speed Prediction

Hybrid electric vehicles (HEVs) have been an effective solution for improved vehicle fuel efficiency and reduced emission pollution. In this paper, a co-optimization scheme is proposed to optimize fuel efficiency for HEVs. Here, the proposed optimization scheme uses obtainable future speed prediction as the basis to optimally tune control parameters for the existing powertrain control system. The ramp-up time of the catalyst temperature to reach its light-off level in the exhaust emission system is also considered as an additional optimization constraint to reduce emission. The Toyota Prius Hybrid Simulink model which is an integrated model for a powertrain and exhaust emission system is validated using real data from several real driving cycle scenarios. Then, to simplify the formulation of the proposed algorithm, the model for the optimization for powertrain and exhaust emission systems is represented by a set of equivalent neural network (NN) models learned using the data generated from the well-validated Toyota Prius Hybrid Simulink model. Using NN models, a co-optimization algorithm is established that provides an optimal tuning of some fuel-sensitive powertrain control parameters using future speed prediction, leading to a novel co-optimization algorithm, achieving on average a further 9.22% fuel savings for the Toyota Prius Hybrid Simulink model.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2003

Correlation of the Hubble Space Telescope (HST) Space Telescope Imaging Spectrometer (STIS) On-Orbit Data with Pre-launch Predictions and Ground Contamination Controls

The Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) was deployed on-orbit in February 1997. The contamination program for STIS was stringently controlled as the five-year end-of-life deposition was set at 158, per optical element. Contamination was controlled through materials selection, extensive vacuum outgassing certifications, cleaning techniques, and environmental controls. In addition to ground contamination controls, on-orbit contamination controls were implemented for both the HST servicing mission activities and early post-servicing mission checkout. The extensive contamination control program will be discussed and the STIS on-orbit data will be correlated with the prelaunch analytical predictions.

Hansen, Patricia A.↗
DOE OSTI2022

Instantiation of HCML Demonstrating Bayesian Predictive Modeling for Attentional Control

The research team developed models of Attentional Control (AC) that are unique to existing modeling approaches in the literature. The goal was to enable the research team to (1) make predictions about AC and human performance in real-world scenarios and (2) to make predictions about individual characteristics based on human data. First, the team developed a proof-of-concept approach for representing an experimental design and human subjects data in a Bayesian model, then demonstrated an ability to draw inferences about conditions of interest relevant to real-world scenarios. Ultimately, this effort was successful, and we were able to make reasonable (meaning supported by behavioral data) inferences about conditions of interest to develop a risk model for AC (where risk is defined as a mismatch between AC and attentional demand). The team additionally defined a path forward for a human-constrained machine learning (HCML) approach to make predictions about an individual's state based on performance data. The effort represents a successful first step in both modeling efforts and serves as a basis for future work activities. Numerous opportunities for future work have been defined.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

Some recent developments in predicting unsteady loadings caused by control surface motions

Solution instabilities are identified within the procedures used to predict unsteady loadings due to motions of swept hingeline control surfaces. A preferred solution process that demonstrates the need of calculating smooth residual downwash distributions is described. Numerical results displaying erratic solution behavior when use is made of various forms of pressure expressions currently employed in control surface analyses are presented. A new expression of the asymptotic pressure function is derived, which exactly satisfies the change in boundary conditions around the boundary of the control surface. Results of applying the new pressure function are presented and indicate that stable converged solutions may be achieved for predicting the unsteady loadings caused by motions of swept hingeline control surfaces.

Rowe, W. S.↗
DOE OSTI2024

Robust Data-Driven Predictive Run-to-Run Control for Automated Serial Sectioning

This letter presents a one-step predictive run-to-run controller (R2R-MPC) for the automation of mechanical serial sectioning (MSS), a destructive material analysis process. To address the inherent uncertainty and disturbances in the MSS process, a robust closed-loop approach is presented. Here, the robust R2R-MPC models the uncertainty of the MSS process using a linear differential inclusion. As an analytical model of the MSS process is unavailable, the differential inclusion is identified from historical data. The R2R-MPC is posed as an optimization problem that computes incremental changes to the control input which minimize the worst-case material removal errors. This optimization-based controller is combined with a run-to-run controller to provide integral action that rejects constant disturbances and tracks constant reference removal rates. To demonstrate the efficacy of our robust R2R-MPC, we present simulation results which compare the presented controller with a conventional non-robust R2R.

42 ENGINEERING↗
DOE OSTI2025

Machine Learning for Real-time Fusion Plasma Behavior Prediction and Manipulation (Final Report)

The goal of this project is to implement real-time analysis of 2D Beam Emission Spectroscopy (BES) data to predict and control transient and high-bandwidth events at DIII-D. In essence, we wish to bring high-bandwidth fluctuation diagnostics into the realm of real-time measurements and control. The BES ML models will necessarily be deep neural networks (DNN) with a “data flow” architecture for compatibility with high-throughput, low-latency evaluation on a field-programmable gate array (FPGA) or other emerging processor technologies. The real-time output will be fed to the plasma control system (PCS) for real-time control tasks, specifically for ELM control and avoidance and for QH-mode access and sustainment. We anticipate that the real-time analysis of fluctuation diagnostics will create new enabling technologies to predict and control transient events such as confinement mode transitions, edge-localized modes, Alfven eigenmode events, and disruptions. The proposed research is aligned with ITER research needs and DIII-D programmatic goals. For instance, the prediction and avoidance of ELM events is critical for ITER machine safety. Also, H-mode access with RMP ELM suppression in ITER is an active research area due to high separatrix density, narrow SOL width, and elevated LH transition power threshold.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Packetized Energy Management: Coordinating Transmission and Distribution (Final Report)

The project Packetized Energy Management (PEM): Coordinating Transmission and Distribution was part of the ARPA-E NODES program from 2015 to 2023. The high-level goal of the project was to develop and demonstrate novel, scalable, and impactful technologies related to the coordination of networked distributed energy resources (DERs). By demonstrating responsive means by which fleets of DERs could be coordinated to enhance grid operation and reliability, the U.S. could accelerate renewable integration and electrification efforts and meet decarbonization goals.

25 ENERGY STORAGE↗
NASA NTRS2013

The Impact of Trajectory Prediction Uncertainty on Air Traffic Controller Performance and Acceptability

A Human-In-The-Loop air traffic control simulation investigated the impact of uncertainties in trajectory predictions on NextGen Trajectory-Based Operations concepts, seeking to understand when the automation would become unacceptable to controllers or when performance targets could no longer be met. Retired air traffic controllers staffed two en route transition sectors, delivering arrival traffic to the northwest corner-post of Atlanta approach control under time-based metering operations. Using trajectory-based decision-support tools, the participants worked the traffic under varying levels of wind forecast error and aircraft performance model error, impacting the ground automations ability to make accurate predictions. Results suggest that the controllers were able to maintain high levels of performance, despite even the highest levels of trajectory prediction errors.

trajectory prediction uncertainty↗
DOE OSTI2018

Natural and Hydraulic Fracture Density Prediction and Identification of Controllers

Discrete Fracture Networks (DFN’s) incorporated into hydraulic fracture modeling and reservoir simulation are typically constructed and calibrated to all available high-quality natural fracture data from image logs and core, which generally results in an extremely limited calibration data set. To extrapolate these data over large areas, more broadly sampled data sets, such as discontinuity-related 3-D seismic attributes are often used. Broad spatial trending methodologies can potentially misrepresent natural fracture systems through over-reliance on seismic attributes that are commonly influenced by noise. The Hydraulic Fracture Test Site (HFTS) provides a rare insight of the subsurface natural fracture network and controlling factors on fracture distribution from a mechanical and lithological standpoint. The physical occurrence of hydraulic fractures and their interaction and relationship to preexisting natural fractures can be predicted using analytical models. Such model outputs can be applied to provide higher confidence when developing DFN’s.

Campbell, Whitney↗
DOE OSTI2025

A Sequential Model Predictive and Deep Reinforcement Learning-Based Controller for Distribution System Outage Mitigation under Hurricane Events

This paper proposes a proactive outage mitigation framework for power distribution networks to withstand hurricane-induced disruptions. It leverages Model Predictive Control (MPC) to identify safe lines for proactive switching during hurricanes, minimizing the risk of cascading failures and voltage violations. The switching strategies optimized by MPC are sequentially integrated with a Deep Reinforcement Learning agent using the Advantage Actor-Critic algorithm, enabling dynamic line switching to maximize connected buses and minimize voltage violations in real time. Using a probabilistic hurricane model, the framework predicts line failures and adapts to varying conditions to enhance grid resilience. Simulations on the IEEE 123-bus system demonstrate its effectiveness in maintaining high connectivity and minimizing disruptions. Real-time testing with an RTDS confirms the practicality and reliability of the proposed approach.

Selim, Alaa [University of Connecticut]↗
NASA NTRS1989

The insertion of human dynamics models in the flight control loops of V/STOL research aircraft. Appendix 2: The optimal control model of a pilot in V/STOL aircraft control loops

An overview is presented of research work focussed on the design and insertion of classical models of human pilot dynamics within the flight control loops of V/STOL aircraft. The pilots were designed and configured for use in integrated control system research and design. The models of human behavior that were considered are: McRuer-Krendel (a single variable transfer function model); and Optimal Control Model (a multi-variable approach based on optimal control and stochastic estimation theory). These models attempt to predict human control response characteristics when confronted with compensatory tracking and state regulation tasks. An overview, mathematical description, and discussion of predictive limitations of the pilot models is presented. Design strategies and closed loop insertion configurations are introduced and considered for various flight control scenarios. Models of aircraft dynamics (both transfer function and state space based) are developed and discussed for their use in pilot design and application. Pilot design and insertion are illustrated for various flight control objectives. Results of pilot insertion within the control loops of two V/STOL research aricraft (Sikorski Black Hawk UH-60A, McDonnell Douglas Harrier II AV-8B) are presented and compared against actual pilot flight data. Conclusions are reached on the ability of the pilot models to adequately predict human behavior when confronted with similar control objectives.

Zipf, Mark E.↗
DOE OSTI2022

Mobility and Energy Improvements Realized through Prediction-based Vehicle Powertrain Control and Traffic Management (Research Performance Final Report)

The goal of this project was to utilize individual vehicle and systems-level transportation big data to develop real-world implementable techniques for energy efficiency. We collected a real world dataset in Fort Collins, CO using technology that is currently available. This dataset was used to (1) create individual vehicle prediction models and emissions models using cutting-edge artificial intelligence (AI) techniques, (2) create traffic prediction models, (3) create boundary condition constraints for optimal trajectory derivation, and (4) develop a novel Mobility Energy Productivity (MEP) model for Fort Collins, CO at a fidelity and flexibility which did not previously exist. Each of these techniques is novel but the most interesting results occurred from the intersection of all techniques. It was found that when optimal vehicle control is combined with optimal traffic light control, that significant energy efficiency improvements that do not compromise travel time are available.

42 ENGINEERING↗
NASA NTRS2016

Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

trajectory prediction uncertainty↗