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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

Matthews, Bryan L.↗
NASA NTRS2018

Occluded Object Reconstruction for First Responders with Augmented Reality Glasses Using Conditional Generative Adversarial Networks

Firefighters suffer a variety of life-threatening risks, including line-of-duty deaths, injuries, and exposures to hazardous substances. Support for reducing these risks is important. We built a partially occluded object reconstruction method on augmented reality glasses for first responders. We used a deep learning based on conditional generative adversarial networks to train associations between the various images of flammable and hazardous objects and their partially occluded counterparts. Our system then reconstructed an image of a new flammable object. Finally, the reconstructed image was superimposed on the input image to provide "transparency". The system imitates human learning about the laws of physics through experience by learning the shape of flammable objects and the flame characteristics.

Chow, Edward↗
DOE OSTI2022

Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection Processes

The current interconnection process and hosting capacity analysis for distributed energy resources (DERs), such as photovoltaics (PV) and battery energy storage systems, are based on analyzing grid network constraints (voltage and thermal) using only medium-voltage distribution network models. This is because most utilities do not have secondary low-voltage system models that connect service transformers and residential customers. This is important because in many cases the main impact of interconnecting DERs could occur on the low-voltage distribution systems. This paper proposes a supervised learning method to approximate local secondary models to improve the interconnection process. The proposed supervised learning method includes a decision tree model that predicts the secondary topology and a logistic regression model that predicts conductor types. The case studies demonstrate the benefits of including secondary low-voltage circuits in the interconnection process. We report the proposed modeling methodology is readily scalable and thus can reduce the cost and effort of PV interconnection for the industry and stakeholders.

14 SOLAR ENERGY↗
NASA NTRS2022

Spatial Grid-Based Object Localization from A Single Passive Sensor: A Deep Learning-Integrated Approach

Ongoing efforts at NASA’s Langley Research Center have produced a single passive sensor system for detecting ground objects and pinpointing their real-world location to a desired level of precision. The Langley center serves as a test range for unmanned aerial systems (UAS) and real-time knowledge about the location of people on campus is needed to inform least-risk UAS flight operations. The proposed system provides this knowledge through a camera combined with a convolutional neural network and an algorithm that projects an imaginary grid of square cells from the ground plane onto the perspective view of the camera. The position of detected objects on the camera’s projected grid determines their location in the real-world. The imaginary grid is easily mapped to a universal coordinate system, such as longitude and latitude, to provide both relative and absolute positional information of the detected objects. This simple system is shown to be accurate and effective, with decisive advantages over alternative multi-sensor and active sensor approaches. Extensions to the system are described to allow adaptation to a variety of other use cases.

object localization↗
DOE OSTI2022

On-policy learning-based deep reinforcement learning assessment for building control efficiency and stability

Artificial intelligence technologies have emerged as a game changer not only in specific applications such as image recognition and machine translation but also in many scientific domains. In particular, as deep reinforcement learning (DRL) has shown great success in complex control problems, DRL-based control has been considered as a potential solution to efficiently control and manage building systems. However, broad assessment of DRL-based building control is still required to characterize their pros and cons in comparison with conventional building control methods (e.g., rule-based feedback controls). In this paper, we assessed DRL-based controls with on-policy learning-based algorithms and continuous control actions for cooling control of large office buildings in the summer season to minimize whole-building energy use and occupant discomfort. We compared DRL-based control methods with two baseline control methods: (1) a pre-determined schedule with supply temperature and static pressure setpoints, and (2) advanced reset method that adjusts setpoints based on heuristic rules, i.e., ASHRAE Guideline 36. We also tested the DRL algorithms to evaluate their performances in multiple climate locations. We found that DRL-based control methods outperformed the baseline control methods in terms of energy savings while maintaining a thermal comfort. DRL reduced energy use between ~4%–22% on average compared to the baseline methods, depending on climate location. We also evaluated DRL-based control in terms of control stability and showed that DRL-based methods should address the span of hardware lifetimes in practical operations.

control stability↗
DOE OSTI2024

Enhancing Lattice Kinetic Schemes for Fluid Dynamics with Lattice-Equivariant Neural Networks

A new class of equivariant neural networks is presented, hereby dubbed lattice-equivariant neural networks (LENNs), designed to satisfy local symmetries of a lattice structure. The approach develops within a recently introduced framework aimed at learning neural network-based surrogate models’ lattice Boltzmann collision operators. Whenever neural networks are employed to model physical systems, respecting symmetries and equivariance properties has been shown to be key for accuracy, numerical stability, and performance. Here, hinging on ideas from group representation theory, trainable layers are defined whose algebraic structure is equivariant with respect to the symmetries of the lattice cell. In this work, the presented method naturally allows for efficient implementations, in terms of both memory usage and computational costs, supporting scalable training/testing for lattices in two spatial dimensions and higher (in which the size of symmetry group grows). The approach is validated and tested considering 2D and 3D flowing dynamics, both in laminar and turbulent regimes. It is compared with group-averaged-based symmetric networks and with plain, nonsymmetric, networks, showing how the presented approach unlocks the (a posteriori) accuracy and training stability of the former models and the train/inference speed of the latter networks. (LENNs are about one order of magnitude faster than group-averaged networks in 3D.) The work in this paper opens toward practical use of machine learning-augmented lattice Boltzmann CFD in real-world simulations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

Using Model-Based Reasoning for Autonomous Instrument Operation

Multiprobe missions are an important part of NASA's future: Cluster, Magnetospheric Multi Scale, Global Electrodynamics and Magnetospheric Constellation are representatives from the Sun-Earth Connections Theme. To make such missions robust, reliable, and affordable, ideally the many spacecraft of a constellation must be at least as easy to operate as one spacecraft is today. To support this need for scalability, science instrumentation must become increasingly easy to operate, even as this same instrumentation becomes more capable and advanced. Communication and control resources will be at a premium for future instruments. Many missions will be out of contact with ground operators for extended periods either to reduce operations cost or because of orbits that limit communication to weekly perigee transits. Autonomous capability is necessary if such missions are to effectively achieve their operational objectives. An autonomous system is one that acts given its situation in a mission appropriate manner without external direction to achieve mission goals. To achieve this capability autonomy must be built into the system through judicious design or through a built-in intelligence that recognizes system state and manages system response. To recognize desired or undesired system states, the system must have an implicit or explicit understanding of its expected states given its history and self observations. The systems we are concerned with, science instruments, can have stringent requirements for system state knowledge in addition to requirements driven by health and safety concerns. Without accurate knowledge of the system state, the usefulness of the science instrument may be severely limited. At the same time, health and safety concerns often lead to overly conservative instrument operations further reducing the effectiveness of the instrument. These requirements, coupled with overall mission requirements including lack of communication opportunities and tolerance of environmental hazards, frame the problem of constructing autonomous science instruments. we are developing a model of the Low Energy Neutral Atom instrument (LENA) that is currently flying on board the Imager for Magnetosphere-to-Aurora Global Exploration (IMAGE) spacecraft. LENA is a particle detector that uses high voltage electrostatic optics and time-of-flight mass spectrometry to image neutral atom emissions from the denser regions of the Earth's magnetosphere. As with most spacecraft borne science instruments, phenomena in addition to neutral atoms are detected by LENA. Solar radiation and energetic particles from Earth's radiation belts are of particular concern because they may help generate currents that may compromise LENA's long term performance. An explicit model of the instrument response has been constructed and is currently in use on board IMAGE to dynamically adapt LENA to the presence or absence of energetic background radiations. The components of LENA are common in space science instrumentation, and lessons learned by modelling this system may be applied to other instruments. This work demonstrates that a model-based approach can be used to enhance science instrument effectiveness. Our future work involves the extension of these methods to cover more aspects of LENA operation and the generalization to other space science instrumentation.

Johnson, Mike↗
DOE OSTI2023

Learning continuous models for continuous physics

Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties. This results in models that often do not capture any underlying continuous dynamics—either of the system of interest, or indeed of any related system. To address this challenge, we develop a convergence test based on numerical analysis theory. Our test verifies whether a model has learned a function that accurately approximates an underlying continuous dynamics. Models that fail this test fail to capture relevant dynamics, rendering them of limited utility for many scientific prediction tasks; while models that pass this test enable both better interpolation and better extrapolation in multiple ways. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Learning Model Structural Uncertainty with Gaussian Processes

The advent of commercially available quantum computers has marked the beginning of quantum computing as a reality. Both quantum gate and annealing computers have been released by major computer hardware companies. In this work, the D-Wave 2XTM quantum annealing computer housed at the NASA Advanced Systems computational facility is investigated to accelerate Machine Learning (ML) for image registration. NASA collects large amounts of images over the globe remotely using space-based monitoring. Images of a fixed areas of the land surface are taken over time. Due to the orbit of the sensors, the viewing angles deviate slightly, and it is necessary to align or register the images precisely to create image time series over the land surface. Unaligned images can lead to substantial analysis errors. These time-series are then used in modeling Earth Systems models such as hydrological, weather, and carbon monitoring models. In this work, we consider the Moderate Resolution Image Spectrometer (MODIS) data collected by the NASA's terra satellite. Artificial Neural Networks (ANNs) is a natural fit for ML modelling of images. Several successes have been reported using machine learning related to image processing. We investigate the use of ML to register MODIS images. ANNs are investigated in combination with a Restricted Boltzmann Machines (RBM) as an auto-encoder. We will present results showing the accuracy and efficiency of this approach.The D-Wave 2XTM quantum annealer samples the ground-state wave-function of a spin-Ising systems with quadratic interactions between qubits and a Chimera connectivity. The system sits in a ~15 mK thermal bath. One can think of the system as being placed in the ground state initially and subject to thermal excitations governed by Boltzmann statistics. If this is assumed true, one can use the statistics from the D-Wave 2XTM to train RBMs. Generating statistics for training Boltzmann machines is an NP-hard problem and constitutes the largest compute cost. We investigate the use of the D-Wave 2XTM to accelerate the training of the RBMs in our ANNs and report on the results.

Kouatchou, Jules↗
DOE OSTI2022

Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Several recent technological advancements have made it possible to realize these capabilities. In this paper, we present our efforts at ORNL toward developing a secure computation platform. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL’s Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure collaboration capabilities within and outside of ORNL.

Yoginath, Srikanth↗
DOE OSTI2022

Transmission-distribution long-term volt-var planning considering reactive power support capability of distributed PV

High penetration of grid-edge, inverter-based photovoltaic (PV) can cause significant voltage fluctuations not only at the distribution but also at the sub-transmission levels due to PV output intermittency. Traditional reactive power planning approaches do not consider intermittency, nor the possibility of coordinating the control of existing and future volt-ampere reactive resources. This paper proposes a reactive power planning tool for sub-transmission systems to mitigate voltage violations and fluctuations caused by high PV penetration and intermittency with a minimum investment cost. The planning tool coordinates with an optimization-based volt-var operational tool for: a) modeling the coordination of all existing var assets in both sub-transmission and distribution systems to reduce the need of new equipment, and b)selecting a set of scenarios with voltage violations, derived from PV intermittency c) testing the final investment decision. The tool obtains an investment need for each intermittency scenario with a proposed optimal power-flow framework with efficient techniques to handle a high number of discrete variables. Two options are provided for final planning decision: i) a conservative direct combination of investment need solutions and ii) a machine learning-based selection of representative investment needs at most time steps. The final investment decision options are verified using a realistic large-scale sub-transmission system and 5-minute PV and load data. The results show a significant voltage performance improvement with a lower investment cost for additional var equipment compared to conventional approaches.

14 SOLAR ENERGY↗
DOE OSTI2018

SynthNotes: A Generator Framework for High-volume, High-fidelity Synthetic Mental Health Notes

One of the key, emerging challenges that connects the "Big Data" and the AI domain is the availability of sufficient volumes of training data for AI/Machine Learning tasks. SynthNotes is a framework for generating standards-compliant, realistic mental health progress report notes at the very large, population-level scale, and in a strict privacy-preserving manner. Our framework, inspired by the needs to explore, evaluate, and train computational methods for the emerging mental health crisis in the US, is useful for benchmarking, optimization, and training of biomedical natural language processing, information extraction, and machine learning systems intended to operate at "Big Data" scale (billions of notes). The free text notes generated by SynthNotes are based on the literature and public statistical models allowing for realistic, natural language representation of a patient, and his or her mental health characteristics. Additionally, SynthNotes can partially simulate stylistic, grammatical, and expressive characteristics of a licensed mental health professional. SynthNotes is modular and flexible, allowing for representation of variety of conditions, incorporation of alternative foundational models, and parametrization of the variability of the structure, content, and size of the synthetically generated corpus. In this paper, we report on the initial use and performance characteristics of our SynthNotes framework and on the ongoing work for inclusion of content planning and deep learning-based generative methods trained on real data.

Begoli, Edmon↗
DOE OSTI2023

Fast Frequency Response using Reinforcement Learning-Controlled Wind Turbines

To fulfill the auxiliary grid services such as load regulation, spin and non-spin reserve, and frequency support during emergencies, power system operators often require certain wind farms to operate in de-loaded modes. By leveraging the fast response capability of wind farms, the reserved power in deloaded modes can significantly enhance the stability and reliability of power grids. This paper presents a novel methodology that incorporates wind turbines into reinforcement learning-based solutions for frequency response. The proposed approach employs the state-of-the-art reinforcement learning algorithm, surrogategradient- based evolution strategy (GSES), for continuous control of the wind farm output. Our methodology is tested on a modified IEEE-39 bus system, and simulation outcomes demonstrate that the proposed approach can reliably support the frequency of the power system and prevent unnecessary load shedding.

Gao, Wei↗
NASA NTRS2019

A Survey of the Spacecraft Line-Of-Sight Jitter Problem

Predicting, managing, controlling, and testing spacecraft Line-of-Sight (LoS) jit- ter due to on-board internal disturbance sources is a challenging multi- disciplinary systems engineering problem, especially for those observatories hosting extremely sensitive optical sensor payloads with stringent requirements on allowable LoS jitter. Some specific spacecraft jitter engineering challenges will be introduced and described in this survey paper. Illustrative examples of missions where dynamic interactions have to be addressed to satisfy demanding payload instrument LoS jitter requirements will be provided. Some lessons learned and a set of recommended rules of thumb are also presented to provide guidance for analysts on where to initiate and how to approach a new spacecraft jitter design problem. These experience-based spacecraft jitter lessons learned and rules of thumb are provided in the hope they can be leveraged on new space system development projects to help overcome unfamiliarity with previously identified jitter technical pitfalls and challenges.

Dennehy, Cornelius J.↗
NASA NTRS1994

Fuzzy self-learning control for magnetic servo system

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

Tarn, J. H.↗
DOE OSTI2022

Machine learning reveals factors that control ion mobility in anti-perovskite solid electrolytes

Solid-state batteries are projected to exhibit improved energy densities and safety compared to liquid-electrolyte-based systems. Consequently, the development of solid electrolytes that can conduct cations at rates comparable to that of liquid electrolytes is an active area of research. To facilitate this search it is desirable to understand the chemical and structural features that control ion transport through a crystalline lattice. Here, machine learning (ML) is used to identify features that influence ion mobility in solids, quantify the relative importance of those features, and demonstrate how the variation of individual features alters ion mobility. Adopting the anti-perovskite lattice as a model system, ML algorithms were trained to predict migration barriers from a data set containing hundreds of barriers calculated using density functional theory. In this study, more than 10 6 feature sets of non-redundant descriptors were evaluated for their ability to predict migration barriers. The present analysis correctly reiterates the importance of several features that are known to influence mobility – for example, anion polarizability – but also identifies others whose importance is not widely recognized. Lattice properties such as hopping distance and channel width exhibit the greatest influence on cation mobility. Individual conditional expectation analyses performed on these features shows that barriers decrease as hopping distance decreases and as channel width increases. The defect formation energy – which is commonly associated with the concentration of defects, but not with their mobility – is identified as an important feature for interstitial migration. In sum, this analysis aids in the design of optimal solid electrolytes by simplifying the multi-dimensional design space to a sub-set of properties that are the most important.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

From neural-based object recognition toward microelectronic eyes

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

Sheu, Bing J.↗
NASA NTRS1993

Adaptive process control using fuzzy logic and genetic algorithms

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

Karr, C. L.↗