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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 1,063 records · Page 59

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NASA NTRS1991

Neural network application to aircraft control system design

The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

Troudet, Terry↗

NASA NTRS1991

Neural network application to aircraft control system design

The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

Troudet, Terry↗

NASA NTRS1992

Improved computed torque control for industrial robots

The authors examine the computed torque control problem for a robot arm with flexible, geared, joint drive systems which are typical in many industrial robots. The standard computed torque algorithm is not directly applicable to this class of manipulators due to the dynamics introduced by the joint drive systems. The proposed approach overcomes this problem by combining a novel computed torque algorithm with simple torque controllers at each joint of the robot. The control scheme is applied to a seven degree-of-freedom industrial manipulator, and the system performance in standard tasks is evaluated using both dynamic simulation and actual experiments. The results show that the proposed controller leads to improved tracking performance over a conventional PD (proportional plus derivative) controller.

Uebel, Mark↗

NASA NTRS2015

Biomimetic Optical Sensor for Real-Time Aircraft Wing Deflection Measurement

Reducing the environmental impact of aviation is a primary goal of NASA aeronautics research. One approach to achieve this goal is to build lighter weight aircraft, which presents complex challenges due to a corresponding increase in structural flexibility. Wing flexibility can adversely affect aircraft performance from the perspective of aerodynamic efficiency and safety. Knowledge of the wing position during flight can aid active control methods designed to mitigate problems due to increased wing flexibility. Current approaches to measuring wing deflection, including strain measurement devices, accelerometers, or GPS solutions, and new technologies such as fiber optic strain sensors, have limitations for their practical application to flexible aircraft control. Hence, it was proposed to use a bio-mimetic optical sensor based on the fly-eye to track wing deflection in real-time. The fly-eye sensor has several advantages over conventional sensors used for this application, including light weight, low power requirements, fast computation, and a small form factor. This paper reports on the fly-eye sensor development and its application to real-time wing deflection measurement.

Frost, Susan A.↗

DOE OSTI2021

Inpainting radar missing data regions with deep learning

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1975

Accuracy of estimating the masses of Phobos and Deimos from multiple Viking orbiter encounters

The problem was investigated of estimating the masses of Phobos and Deimos from Doppler and onboard optical measurements during the Viking extended mission. A Kalman filter was used to analyze the effects of gravitational uncertainties and nongravitational accelerations. These accelerations destroy the dynamical integrity of the orbit, and multibatch or limited memory filtering is preferred to single batch processing. Optical tracking is essential to improve the relative orbit geometry. The masses can be determined to about 10% and 25% respectively for Phobos and Deimos, assuming satellite densities of about 3 gr/cu cm.

Tolson, R. H.↗

NASA NTRS1976

An analytical and experimental evaluation of a Fresnel lens solar concentrator

An analytical and experimental evaluation of line focusing Fresnel lenses with application potential in the 200 to 370 C range was studied. Analytical techniques were formulated to assess the solar transmission and imaging properties of a grooves down lens. Experimentation was based on a 56 cm wide, f/1.0 lens. A Sun tracking heliostat provided a nonmoving solar source. Measured data indicated more spreading at the profile base than analytically predicted, resulting in a peak concentration 18 percent lower than the computed peak of 57. The measured and computed transmittances were 85 and 87 percent, respectively. Preliminary testing with a subsequent lens indicated that modified manufacturing techniques corrected the profile spreading problem and should enable improved analytical experimental correlation.

Hastings, L. J.↗

NASA NTRS1975

Accuracy of estimating the masses of Phobos and Deimos from multiple Viking orbiter encounters

This paper addresses the problem of estimating the masses of Phobos and Deimos from Doppler and onboard optical measurements during the Viking extended mission. A Kalman filter is used to analyze the effects of gravitational uncertainties and nongravitational accelerations. These accelerations destroy the dynamical integrity of the orbit and multi-batch or limited memory filtering is preferred to single batch processing. Optical tracking is essential to improve the relative orbit geometry. The masses can be determined to about 10% and 25% respectively for Phobos and Deimos, assuming satellite densities of about 3 g per cu cm.

Tolson, R. H.↗

NASA NTRS1980

Wallops waveform analysis of SEASAT-1 radar altimeter data

Fitting a six parameter model waveform to over ocean experimental data from the waveform samplers in the SEASAT-1 radar altimeter is described. The fitted parameters include a waveform risetime, skewness, and track point; from these can be obtained estimates of the ocean surface significant waveheight, the surface skewness, and a correction to the altimeter's on board altitude measurement, respectively. Among the difficulties encountered are waveform sampler gains differing from calibration mode data, and incorporating the actual SEASAT-1 sampled point target response in the fitted wave form. There are problems in using the spacecraft derived attitude angle estimates, and a different attitude estimator is developed. Points raised in this report have consequences for the SEASAT-1 radar altimeter's ocean surface measurements are for the design and calibration of radar altimeters in future oceanographic satellites.

Hayne, G. S.↗

NASA NTRS1989

Expert system development methodology and the transition from prototyping to operations: FIESTA, a case study

A major barrier in taking expert systems from prototype to operational status involves instilling end user confidence in the operational system. The software of different life cycle models is examined and the advantages and disadvantages of each when applied to expert system development are explored. The Fault Isolation Expert System for Tracking and data relay satellite system Applications (FIESTA) is presented as a case study of development of an expert system. The end user confidence necessary for operational use of this system is accentuated by the fact that it will handle real-time data in a secure environment, allowing little tolerance for errors. How FIESTA is dealing with transition problems as it moves from an off-line standalone prototype to an on-line real-time system is discussed.

Happell, Nadine↗

NASA NTRS1989

Structural stereo matching of Laplacian-of-Gaussian contour segments for 3D perception

The stereo correspondence problem is solved using Laplacian-of-Gaussian zero-crossing contours as a source of primitives for structural stereopsis, as opposed to traditional point-based algorithms. Up to 74 percent matching of candidate zero crossing points are being achieved on 240 x 246 images at small scales and large ranges of disparity, without coarse-to-fine tracking and without precise knowledge of the epipolar geometry. This approach should prove particularly useful in recovering the epipolar geometry automatically for stereo pairs for which it is unavailable a priori. Such situations occur in the extraction of terrain models from stereo aerial photographs.

Boyer, K. L.↗

NASA NTRS1989

Expert system development methodology and the transition from prototyping to operations - Fiesta, a case study

Happell, Nadine↗

NASA NTRS2010

Resource Balancing Control Allocation

Next generation aircraft with a large number of actuators will require advanced control allocation methods to compute the actuator commands needed to follow desired trajectories while respecting system constraints. Previously, algorithms were proposed to minimize the l1 or l2 norms of the tracking error and of the control effort. The paper discusses the alternative choice of using the l1 norm for minimization of the tracking error and a normalized l(infinity) norm, or sup norm, for minimization of the control effort. The algorithm computes the norm of the actuator deflections scaled by the actuator limits. Minimization of the control effort then translates into the minimization of the maximum actuator deflection as a percentage of its range of motion. The paper shows how the problem can be solved effectively by converting it into a linear program and solving it using a simplex algorithm. Properties of the algorithm are investigated through examples. In particular, the min-max criterion results in a type of resource balancing, where the resources are the control surfaces and the algorithm balances these resources to achieve the desired command. A study of the sensitivity of the algorithms to the data is presented, which shows that the normalized l(infinity) algorithm has the lowest sensitivity, although high sensitivities are observed whenever the limits of performance are reached.

Frost, Susan A.↗

NASA NTRS2006

Cassini Archive Tracking System

The Cassini Archive Tracking System (CATS) is a computer program that enables tracking of scientific data transfers from originators to the Planetary Data System (PDS) archives. Without CATS, there is no systematic means of locating products in the archive process or ensuring their completeness. By keeping a database of transfer communications and status, CATS enables the Cassini Project and the PDS to efficiently and accurately report on archive status. More importantly, problem areas are easily identified through customized reports that can be generated on the fly from any Web-enabled computer. A Web-browser interface and clearly defined authorization scheme provide safe distributed access to the system, where users can perform functions such as create customized reports, record a transfer, and respond to a transfer. CATS ensures that Cassini provides complete science archives to the PDS on schedule and that those archives are available to the science community by the PDS. The three-tier architecture is loosely coupled and designed for simple adaptation to multimission use. Written in the Java programming language, it is portable and can be run on any Java-enabled Web server.

Conner, Diane↗

DOE OSTI2023

Implementation of a High-Fidelity Interface Resolving Method in Nek5000

The development and utilization of computational fluid dynamics (CFD) models for large, high-temperature electric melters in the Waste Treatment and Immobilization Plant (WTP) in eastern Washington State have proven to be valuable for various purposes. These models allow for a better understanding of the physio-chemical processes occurring within the melter vessels and can contribute to improving operational efficiency, throughput, and addressing operational issues related to vitrification. The CFD models employed for these melter vessels incorporate multiphase fluid flow and heat transfer simulations in different regions, including the plenum, cold cap, and molten glass regions. As the tank waste and glass formers are introduced into the melter, a reacting batch layer known as the cold cap forms on top of the molten glass. To enhance the melt rate, forced convection bubblers located at the bottom of the melters generate convection currents that help homogenize the molten glass and provide heat to the cold cap. As the bubbles rise through the highly viscous glass, they adopt a spherical-cap shape [1]. Meanwhile, the conversion of the batch to glass generates significant amounts of gases (such as water vapor, carbon dioxide, sulfur dioxide, and NOx) due to thermal decomposition [2]. These gases become trapped between the cold cap and molten glass, forming a foam layer [3]. For modeling multiphase flow in CFD and heat transfer simulations of waste glass melters at different scales, efforts are underway to augment the capabilities of the Nek5000 [4] and NekRS [5] open-source codes [6]. Nek5000/NekRS is a scalable and efficient spectral element code that has been successfully applied to a wide range of fluid dynamics problems. By leveraging the Nek5000/NekRS software, it becomes possible to model the melter systems more affordably and with lower computational requirements compared to currently utilized commercial CFD software. The specific objectives of this ongoing effort include: 1. Implementation of a level set method in Nek5000/NekRS: The level set method is a numerical technique commonly employed in CFD simulations to track and represent the interface between different phases or materials accurately. By incorporating this method into Nek5000/NekRS, the ability to simulate multiphase flows in waste glass melters at a high level of fidelity can be achieved. 2. Demonstration of capability for air bubbling through molten glass: As part of the development process, a specific case of air bubbling through molten glass will be simulated using the augmented Nek5000/NekRS code. This demonstration aims to showcase the ability of the software to accurately capture and analyze the complex phenomena involved in the multiphase flow within waste glass melters. By achieving these objectives, the improved Nek5000/NekRS code will offer a powerful computational tool for simulating and analyzing waste glass melter systems, enabling better understanding, optimization, and troubleshooting of these vitrification processes. The ability to accurately model and simulate multiphase flows has broad relevance across many industries and scientific domains, and the improved functionality can contribute to advancements in various fields beyond waste glass melter simulations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

A Reinforcement Learning Approach to Augment Conventional PID Control in Nuclear Power Plant Transient Operation

The ability of nuclear reactors to operate their power conversion cycles more flexibly will enhance their value to energy grids with variable pricing. Current nuclear control systems are typically classical controllers that are often based on proportional-integral-derivative (PID) control. This paper presents a method of augmenting the existing PID control for difficult transient operations in nuclear power plants using a reinforcement learning–derived feedforward signal applied in real time. The agents, which are trained on a test thermal load-following problem, are designed to improve steam generator outlet temperature control for a range of fast load-following scenarios covering ramp rates from 9%/min to 15%/min. Several reinforcement learning algorithms were initially investigated for the training of the feedforward agents with deep Q-learning (DQN) and proximal policy optimization (PPO) networks, which were found to be the most promising. The DQN controllers utilize discrete actions, giving them a better disturbance rejection at steady state but inconsistent response to initial temperature deviations. In contrast, PPO-trained agents, which take continuous actions except for a dead zone around zero, were shown to have the best combination of high disturbance rejection at steady state and good tracking of the desired temperature value. The ability of the PPO agent was also examined, with the average time of decision making found to be on the order of 1 ms. The fault properties of the controller under the loss of the reinforcement learning agent feedforward signal were also examined. The controller showed strong performance in situations of “no-signal” faults. but was less good at handling “stuck-at” faults, where the feedforward signal remains at a set value. In both cases, however, the PID was able to successfully maintain stability, eventually returning the system to a steady state. It is hoped that this work will allow for the proposed control architecture to be examined for more difficult control problems such that it may eventually be used to adapt existing nuclear plants for more aggressive load-following on grids of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Subnanometer Visualization of Spatially Varying Local Field Resonances that Drive Tip-Enhanced Optical Spectroscopy

Our knowledge of the electromagnetic fields that power modern nanoscale optical measurements, including (non)linear tip-enhanced Raman and photoluminescence, chiefly stems from numerical simulations. Aside from idealized in silico vs heterogeneous (nano)structures in the laboratory, challenges in quantitative descriptions of nanoscale light–matter interactions more generally stem from the very nature of the problem, which lies at the interface of classical and quantum theories. This is particularly the case in ultrahigh spatial resolution measurements that are sensitive to local optical field variations that take place on subnanometer length scales. Here this work approaches this challenge through extinction-based spectral nanoimaging experiments. We demonstrate <1 nm spatial resolution in hyperspectral extinction measurements that track spatially varying plasmon resonances. We describe the principles behind our experiments and highlight more general implications of our observations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Gradient-Based Multi-Area Distribution System State Estimation

The increasing distributed and renewable energy resources and controllable devices in distribution systems make fast distribution system state estimation (DSSE) crucial in system monitoring and control. We consider a large multi-phase distribution system and formulate DSSE as a weighted least squares (WLS) problem. We divide the large distribution system into smaller areas of subtree structure, and by jointly exploring the linearized power flow model and the network topology, we propose a gradient-based multi-area algorithm to exactly and efficiently solve the WLS problem. The proposed algorithm enables distributed and parallel computation of the state estimation problem without compromising any performance. Numerical results on a 4,521-node test feeder show that the designed algorithm features fast convergence and accurate estimation results. Comparison with traditional Gauss-Newton method shows that the proposed method has much better performance in distribution systems with a limited amount of reliable measurement. The real-time implementation of the algorithm tracks time-varying system states with high accuracy.

41 EE - Solar Energy Technologies Office (EE-4S)↗