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Results for “data predictive control”

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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
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 NTRS1983

DC-10 winglet flight evaluation

The results of a flight evaluation of winglets on a DC-10 Series 10 aircraft are presented. For sensitive areas of comparison, effects of winglets were determined back to back with and without winglets. Basic and reduced span winglet configurations were tested. After initial encounter with low speed buffet, a number of acceptable configurations were developed. For maximum drag reduction at both cruise and low speeds, lower winglets were required, having leading edge devices on upper and lower winglets for the latter regime. The cruise benefits were enhanced by adding outboard aileron droop to the reduced span winglet aircraft. Winglets had no significant impact on stall speeds, high speed buffet boundary, and stability and control characteristics. Flutter test results agreed with predictions and ground vibration data. Flight loads measurement also agreed with predictions.

Source record↗
NASA NTRS1992

Navier-Stokes computations for oscillating control surfaces

Unsteady Navier-Stokes computations have been performed for simulating transonic flows over wings with oscillating control surfaces using a locally moving grid and a stationary-mismatched zoning scheme. An F-5 wing and a clipped delta wing are chosen for the present study. The computed unsteady pressures and the response characteristics to the control surface motions are compared with experimental data. The results successfully predict main features of the unsteady pressure profiles, such as the double peaks at the shock wave and at the hinge line.

Obayashi, Shigeru↗
NASA NTRS2010

Monitoring System for Storm Readiness and Recovery of Test Facilities: Integrated System Health Management (ISHM) Approach

Severe weather events are likely occurrences on the Mississippi Gulf Coast. It is important to rapidly diagnose and mitigate the effects of storms on Stennis Space Center's rocket engine test complex to avoid delays to critical test article programs, reduce costs, and maintain safety. An Integrated Systems Health Management (ISHM) approach and technologies are employed to integrate environmental (weather) monitoring, structural modeling, and the suite of available facility instrumentation to provide information for readiness before storms, rapid initial damage assessment to guide mitigation planning, and then support on-going assurance as repairs are effected and finally support recertification. The system is denominated Katrina Storm Monitoring System (KStorMS). Integrated Systems Health Management (ISHM) describes a comprehensive set of capabilities that provide insight into the behavior the health of a system. Knowing the status of a system allows decision makers to effectively plan and execute their mission. For example, early insight into component degradation and impending failures provides more time to develop work around strategies and more effectively plan for maintenance. Failures of system elements generally occur over time. Information extracted from sensor data, combined with system-wide knowledge bases and methods for information extraction and fusion, inference, and decision making, can be used to detect incipient failures. If failures do occur, it is critical to detect and isolate them, and suggest an appropriate course of action. ISHM enables determining the condition (health) of every element in a complex system-of-systems or SoS (detect anomalies, diagnose causes, predict future anomalies), and provide data, information, and knowledge (DIaK) to control systems for safe and effective operation. ISHM capability is achieved by using a wide range of technologies that enable anomaly detection, diagnostics, prognostics, and advise for control: (1) anomaly detection algorithms and strategies, (2) fusion of DIaK for anomaly detection (model-based, numerical, statistical, empirical, expert-based, qualitative, etc.), (3) diagnostics/prognostics strategies and methods, (4) user interface, (5) advanced control strategies, (6) integration architectures/frameworks, (7) embedding of intelligence. Many of these technologies are mature, and they are being used in the KStorMS. The paper will describe the design, implementation, and operation of the KStorMS; and discuss further evolution to support other needs such as condition-based maintenance (CBM).

Figueroa, Fernando↗
NASA NTRS2018

Survey of Methods to Predict Controller Workload for Real-Time Monitoring of Airspace Safety

The Real Time Safety Monitoring (RTSM) approach allows for assessment and prediction of the safety margin in the National Airspace System (NAS) to help preempt incidents and accidents, rather than having to reactively mitigate them. In RTSM, the NAS is modeled using state variables, and safety metrics are defined in terms of these state variables. The safety metrics have been classified as weather-related, airspace-related, and human-related. Many of the formulated human-related safety metrics need an estimate of the controller workload for their computation. However, this computation is not trivial. Hence, in this report, we perform a literature survey to identify the different factors that enable the computation of controller workload and categorize these factors. Next, we describe studies undertaken to determine a minimal set of factors that pro- vide a correct assessment of controller workload. Lastly, we survey approaches for evaluating how well the selected factors correlate with the controllers' subjective assessment of their workload. Based on this survey, we present factors beneficial to computing and predicting controller workload in real time, and discuss the status of data sources necessary for these computations.

National Airspace System↗
NASA NTRS2019

Management by Trajectory: Improving Predictability for Airspace Operations

In the present-day National Airspace System, the air traffic management system attempts to predict the trajectory for each flight based on the flight plan and scheduled or controlled departure time. However, gaps in trajectory data and models, coupled with tactical control actions that are not communicated to automation systems or other stakeholders, lead to trajectory predictions that are less accurate than they could be. This affects traffic flow management performance. Management by Trajectory (MBT) is a NASA concept for air traffic management in which every flight operates in accordance with a 4D trajectory that is negotiated between the airspace user and the FAA to account for the airspace user’s goals while complying with NAS constraints. The primary benefit of MBT is an improvement in system performance due to increased trajectory predictability and stability, which result from managing traffic in all four dimensions (2D route, vertical, and time), ensuring that changes to the flight’s trajectory are incorporated into the assigned trajectory, and utilizing improved time or arrival control standards. Importantly, the performance improvements support increasing efficiency without increasing collision risk. This paper provides an overview of MBT and describes fast-time simulation results evaluating the safety, performance, and efficiency effects of MBT.

Fernandes, Alicia B.↗
DOE OSTI2023

Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood

Heating, ventilation, and air conditioning (HVAC) systems account for the highest share of home energy consumption in the United States. Optimized HVAC control can provide thermal improved comfort to the occupants, improve energy efficiency, reduce energy cost, and support grid services. In this paper, we discuss a multi-agent and cloud-based software framework that has been deployed in occupied residential neighborhood. This system enables automatic data collection, learning, optimization, and dispatches signals to neighborhood devices. HVAC optimization is based on model predictive control (MPC). Since the operational performance of MPC depends on model forecasting accuracy, it is crucial to evaluate the model continuously and modify or retrain it as necessary. In this research, we developed an automated workflow to evaluate the performance of temperature and power forecasts based on measured data in the real world. This will provide researchers with a deeper understanding of the model and how it can be improved.

Lebakula, Viswadeep↗
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↗
NASA NTRS2015

Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors: An Interrupted Time-Series Analysis of Controller Behavior

A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASAs Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlantas Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the studys 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 20-knot over-predictions and 20-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?

human-automation interaction↗
DOE OSTI2025

Machine learning modeling and model predictive control of a closed-circuit reverse osmosis system

Closed-circuit reverse osmosis (CCRO) offers a flexible and energy-efficient alternative to conventional reverse osmosis by operating in a semi-batch mode that recycles brine, enabling higher recovery rates and reduced specific energy consumption (SEC). However, developing accurate, system-level dynamic models for CCRO remains challenging due to its nonlinear, multi-phase operation and sensitivity to variable feed water conditions. Traditional modeling approaches, such as NARMAX (nonlinear autoregressive moving average with exogenous inputs), often struggle to generalize across varying inlet feed concentrations, necessitating frequent parameter re-estimation and limiting their utility for real-time control applications. To address these limitations, we developed a long short-term memory (LSTM) neural network model trained on an extensive experimental data set from a CCRO pilot plant. The model accepts three inputs, feed flow rate, recirculation flow rate, and initial feed conductivity, and predicts three key outputs: reject conductivity, feed pump power draw, and recirculation pump power draw. We validated the LSTM model against experimental data, demonstrating its ability to distinguish between different feed conductivities and adapt to variable flow rates. Subsequently, we incorporated the LSTM model within a nonlinear model predictive control (MPC) scheme and conducted closed-loop simulations to optimize the integrated SEC (iSEC). In conclusion, the results project up to a 6% reduction in iSEC by using MPC to optimize performance over the entire experiment duration, without requiring any random excitation for data collection or parameter re-estimation.

Desalination↗
DOE OSTI2025

Combining physics-based and data-driven models for quantitatively accurate plasma profile prediction that extrapolates well; with application to DIII-D, AUG, and ITER tokamaks

For design, scenario planning, and control, ITER and all other envisioned tokamaks rely on a variety of statistical and physics-based models to extrapolate to unseen regimes; most notably from low plasma current to high. A 'meta-learning' methodology for combining the accuracy of data-driven models with the generalizability of physics-based models is described and tested, yielding a 5–10 percent improvement in performance beyond either alone for the task of extrapolating time-dependent plasma profile prediction from low- to high- plasma current DIII-D tokamak discharges. Meanwhile, it is shown that both machine learning models extrapolated far-distribution and state-of-the-art 'physics-based' profile predictors fare worse than merely assuming plasma profiles do not change from their initial values. Finally, a variety of other mechanisms for helping data-driven models generalize—transfer learning, adding contextual information from physics simulators, and adding data from the ASDEX Upgrade tokamak—are attempted for similar extrapolation tasks but, in the methodology used in this paper, yield no significant improvement beyond simple data-driven models. Results are summarized in figures 15 and 16.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review

Due to the fast advancement of communication and information technology, intelligent buildings have garnered great interest. These buildings can forecast weather, ambient temperature, and sun irradiation and can modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on current and previous data. This change is intended to reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. Although substantial research has been conducted on MPC in building HVAC systems, there is a shortage of critical reviews and a lack of a comprehensive framework that formulates and defines the applications. Here, this article provides a comprehensive state-of-the-art overview of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, occupancy behavior, building type, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. The primary objective of this work is to highlight critical design characteristics for the MPC control scheme and to give improved suggestions for future research. Moreover, numerous prospective scenarios have been suggested that might provide future research direction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1986

Flight research on natural laminar flow nacelles - A progress report

This paper presents a progress report on an ongoing flight experiment for natural laminar flow nacelles. The results given herein were obtained during the first phase of the experiment, in which an instrumented natural laminar flow nacelle fairing was flight tested in the presence of turbofan engine noise and a controlled noise source. The results indicate that with the controlled noise source off, natural laminar flow was measured as far aft as 37 percent of the fairing length. The transition front was irregular in contour, and the extent of natural laminar flow was significantly affected by the relative flow angle for the fairing. In addition to these test results, the paper discusses the results of some recent computational analyses to predict pressure distributions and transition location, and to explain some of the data trends. Comparisons between measured and predicted data indicate that the analytical methods successfully predicted trends for the baseline (no controlled noise source) studies.

Hastings, E. C., Jr.↗
NASA NTRS1986

Structural dynamics research in a full-scale transport aircraft crash test

A remotely piloted air-to-ground crash test of a full-scale transport aircraft was conducted for the first time for two purposes: (1) to demonstrate performance of an antimisting fuel additive in suppressing fire in a crash environment, and (2) to obtain structural dynamics data under crash conditions for comparison with analytical predictions. The test, called the Controlled Impact Demonstration (CID), was sponsored by FAA and NASA with cooperation of industry, the Department of Defense, and the British and French governments. The test aircraft was a Boeing 720 jet transport. The aircraft impacted a dry lakebed at Edwards Air Force Base, CA. The purpose of this paper is to discuss the structural aspects of the CID. The fuselage section tests and the CID itself are described. Structural response data from these tests are presented and discussed. Nonlinear analytical modeling efforts are described, and comparisons between analytical results and experimental results are presented.

Mccomb, H. G., Jr.↗
NASA NTRS1957

Loads Due to Controls at Transonic and Low Supersonic Speeds

Some results of recent experimental investigations at supersonic and transonic speeds are presented to show the present status in the estimation of load distributions on controls and adjacent wing surfaces resulting from the deflection of flap controls and spoiler controls. The results indicate that the development of methods for predicting loads associated with controls has not kept pace with the acquisition of experimental data. At low supersonic speeds sweeping the hinge line induces strong three-dimensional-flow characteristics which cannot be treated by the simplified methods previously developed for controls without sweep. At transonic speeds the estimation of loads associated with controls must usually be dependent upon experimental information inasmuch as the latest attempts to predict chordwise and spanwise loadings have met with only limited success.

West, F. E, Jr↗
NASA NTRS1980

Orbit Transfer Vehicle (OTV) advanced expander cycle engine point design study. Task 7: Engine data summary

A performance optimized engine system design for a man-rated advanced LOX/hydrogen expander cycle engine was investigated. The data are presented in tables, figures, and drawings. The following categories of data for the advanced expander cycle engine are presented: engine operating specification and pressure schedule; engine system layout drawing; major component layout drawings, including thrust chamber and nozzle, extendible nozzle actuating mechanism and seal, LOX turbopump, LOX boost pump, hydrogen turbopump, hydrogen boost pump, and propellant control valves; engine performance and service life prediction; engine weight; and engine envelope. The data represent updates based upon current results from the design and analyses tasks performed under contract. Futher iterations in the designs and data can be expected as the advanced expander cycle engine design matures.

Christensen, K. L.↗
DOE OSTI2021

Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations

This work derives a residual-based a posteriori error estimator for reduced models learned with non-intrusive model reduction from data of high-dimensional systems governed by linear parabolic partial differential equations with control inputs. It is shown that quantities that are necessary for the error estimator can be either obtained exactly as the solutions of least-squares problems in a non-intrusive way from data such as initial conditions, control inputs, and high-dimensional solution trajectories or bounded in a probabilistic sense. Here, the computational procedure follows an offline/online decomposition. In the offline (training) phase, the high-dimensional system is judiciously solved in a black-box fashion to generate data and to set up the error estimator. In the online phase, the estimator is used to bound the error of the reduced-model predictions for new initial conditions and new control inputs without recourse to the high-dimensional system. Numerical results demonstrate the workflow of the proposed approach from data to reduced models to certified predictions.

97 MATHEMATICS AND COMPUTING↗