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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
DOE OSTI2022

Training reinforcement learning models via an adversarial evolutionary algorithm

When training for control problems, more episodes used in training usually leads to better generalizability, but more episodes also requires significantly more training time. There are a variety of approaches for selecting the way that training episodes are chosen, including fixed episodes, uniform sampling, and stochastic sampling, but they can all leave gaps in the training landscape. In this work, we describe an approach that leverages an adversarial evolutionary algorithm to identify the worst performing states for a given model. We then use information about these states in the next cycle of training; this process can be repeated until the desired level of model performance is met. We demonstrate this approach with the OpenAI Gym cart-pole problem. With this problem, we show that the adversarial evolutionary algorithm did not reduce the number of episodes required in training needed to attain model generalizability when compared with stochastic sampling, and actually performed slightly worse.

Coletti, Mark↗
DOE OSTI2021

pnnl/FlexyAirDeepMPC

This is a trained machine learning model written in Pytorch for automated control of the FlexyAir (https://ocl.sk/hardware/flexyair/) dynamical system control educational device. This software is to be released to support our demonstration of our deepMPC methods on embedded hardware to control a real (albeit tutorial) physical system with control actuators.

Tuor, Aaron↗
DOE OSTI2022

The L-CAPE Project at FNAL

The controls system at FNAL records data asynchronously from several thousand Linac devices at their respective cadences, ranging from 15Hz down to once per minute. In case of downtimes, current operations are mostly reactive, investigating the cause of an outage and labeling it after the fact. However, as one of the most upstream systems at the FNAL accelerator complex, the Linac’s foreknowledge of an impending downtime as well as its duration could prompt downstream systems to go into standby, potentially leading to energy savings. The goals of the Linac Condition Anomaly Prediction of Emergence (L-CAPE) project that started in late 2020 are (1) to apply data-analytic methods to improve the information that is available to operators in the control room, and (2) to use machine learning to automate the labeling of outage types as they occur and discover patterns in the data that could lead to the prediction of outages. We present an overview of the challenges in dealing with time-series data from 2000+ devices, our approach to developing an ML-based automated outage labeling system, and the status of augmenting operations by identifying the most likely devices predicting an outage.

43 PARTICLE ACCELERATORS↗
DOE OSTI2011

Intergovernmental Advanced Stationary PEM Fuel Cell System Demonstration (Final Report)

A program to complete the design, construction and demonstration of a PEMFC system fuelled by Ethanol, LPG or NG for telecom applications was initiated in October 2007. Early in the program the economics for Ethanol were shown to be unfeasible and permission was given by DOE to focus on LPG only. The design and construction of a prototype unit was completed in Jun 2009 using commercially available PEM FC stack from Ballard Power Systems. During the course of testing, the high pressure drop of the stack was shown to be problematic in terms of control and stability of the reformer. Also, due to the power requirements for air compression the overall efficiency of the system was shown to be lower than a similar system using internally developed low pressure drop FC stack. In Q3 2009, the decision was made to change to the Plug power stack and a second prototype was built and tested. Overall net efficiency was shown to be 31.5% at 3 kW output. Total output of the system is 6 kW. Using the new stack hardware, material cost reduction of 63% was achieved over the previous Alpha design. During a November 2009 review meeting Plug Power proposed and was granted permission, to demonstrate the new, commercial version of Plug Power's telecom system at CERL. As this product was also being tested as part of a DOE Topic 7A program, this part of the program was transferred to the Topic 7A program. In Q32008, the scope of work of this program was expanded to include a National Grid demonstration project of a micro-CHP system using hightemperature PEM technology. The Gensys Blue system was cleared for unattended operation, grid connection, and power generation in Aug 2009 at Union College in NY state. The system continues to operate providing power and heat to Beuth House. The system is being continually evaluated and improvements to hardware and controls will be implemented as more is learned about the system's operation. The program is instrumental in improving the efficiency and reducing costs of PEMFC based power systems using LPG fuel and continues to makes steps towards meeting DOE's targets. Plug Power would like to thank DOE for their support of this program.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Grid-Scale Electricity Storage at Lowest Possible Cost Enabled by Pumped Heat Electricity Storage (Final Report)

Grid-scale energy storage technologies that can provide greater than 10 hours of electricity at full power address a critical need for full penetration of renewables on electricity grids worldwide, minimize renewable curtailment, and maximize grid security and reliability. Pumped Heat Energy Storage (PHES) systems can store energy thermally in any location at the low costs necessary for displacing fossil fuels, reducing foreign fuel imports, and reducing emissions to maintain the energy security of the United States. The award allowed Southwest Research Institute® (SwRI®) and Malta, Inc. (Malta) to advance the PHES concept to a proof-of-concept demonstration for a kW-scale system. The project focused on system operation and integration. The main project objectives were: 1. Address first implementation challenges 2. Explore system control strategies and operation 3. Translate lessons learned to the full-scale system Through these objectives the project completes a first demonstration of this technology and reduces the technical risk associated with a full-scale system.

25 ENERGY STORAGE↗
DOE OSTI2024

Management and Operation of the Lawrence Livermore National Laboratory (Final Report)

Optimize existing characterization and control methods by developing methods for rapid synthesis of multi-qubit control for error mitigation and complex gate design using optimal control theory. The Subcontractor shall develop Hamiltonian learning methods that can be used for characterizing super-conducting quantum devices, in particular develop strategies for effective probing of such devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Final Technical Report

This research project aims to understand the structure and properties of hidden intermediates in amorphous oxides grown by atomic layer deposition (ALD), and apply what is learned to achieve predictive phase and property control in the ALD synthesis of amorphous oxide thin films. The one-year renewal is to further this understanding to understand how amorphous TiO2 thin films by ALD turn into crystalline phase in correlation to the presence of medium range ordering (MRO) and other impurities. This fundamental study is expected to produce a predictive synthesis science for amorphous thin films with substantially improved uniformity and thereby achieve higher stability as a new surface coating strategy used for electrochemical catalysis. In this final technical report, we summarize the key achievements in this project to meet the research goals. This includes (1) a new approach to achieve homogeneous nanometer-scale amorphous coating leading to a new record of lifetime for photoelectrochemical hydrogen fuel generation; (2) A advancement in scanning transmission electron microcopy (STEM) that led to new understanding of intermediate phase in amorphous films; and (3) a computational model for understanding the local structure ordering in amorphous TiO2 films. In addition, this project also yielded relevant technical innovations including a development of bioresorbable zinc primary batteries that can self-degrade after depletion; and a ferroelectric membrane that provides active dendrites suppression to substantially improve the lifetime of rechargeable batteries. Collaboration with Lam Research also yield a new understanding of how built-in strain influences the amorphous films’ quality, bringing direct impacts to semiconductor manufacturing industry.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Optimization of Variable Speed Chiller Plants: Frank M. Johnson Jr. Federal Building and U.S. Courthouse, Montgomery, Alabama

This report is divided into five sections. The first section describes the background and opportunity for the Control Optimization System for Chiller Plant technology to reduce space cooling energy consumption at U.S. General Services Administration (GSA) facilities with centrifugal chiller plants containing multiple water-cooled chillers. The second section discusses the new technology, how it may reduce energy consumption, and introduces the demonstration location. The third section provides a detailed description of the demonstration facility and the configuration of the technology at the demonstration facility. The third section also provides a detailed overview of the approach used to assess the performance of the technology and how the chiller plant was monitored. The fourth section presents the results of the monitoring activity, documents performance and resulting energy savings, and presents the results of a life-cycle cost analysis. The fourth section also presents additional opportunities to further improve the performance of the chiller plant with control optimization, based on observations and lessons learned. The final section draws conclusions from the demonstration results and projects how GSA may best benefit from the technology’s targeted deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Induced microearthquakes predict permeability creation in the brittle crust

Predicting the evolution of permeability accurately during stimulation at the reservoir scale and at the resolution of individual fractures is essential to characterize the fluid transport and the reactive/heat-transfer characteristics of reservoirs where stress exerts significant control. Here, we develop a hybrid machine learning (ML) model to visualize in situ permeability evolution for an intermediate-scale (~10 m) hydraulic stimulation experiment. This model includes an ML model that was trained using the well history of flow rate and wellhead pressure and MEQ data from the first three stimulation episodes to predict average permeability from the statistical features of the MEQs alone for later episodes. Moreover, a physics-inspired model is integrated to estimate in situ fracture permeability spatially. This method relates fracture permeability to fracture dilation and scales dilation to the equivalent MEQ magnitude, according to laboratory observations. The seismic data are then applied to define incremental changes in permeability in both space and time. Our results confirm the excellent agreement between the ground truth and model-predicted permeability evolution. The resulting permeability map defines and quantifies flow paths in the reservoir with the averaged permeability comparing favorably with the ground truth of permeability.

induced seismicity↗
DOE OSTI2023

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

47 OTHER INSTRUMENTATION↗
DOE OSTI2011

Ionic Liquids: Radiation Chemistry, Solvation Dynamics and Reactivity Patterns

Ionic liquids (ILs) are a rapidly expanding family of condensed-phase media with important applications in energy production, nuclear fuel and waste processing, improving the efficiency and safety of industrial chemical processes, and pollution prevention. ILs generally have low volatilities and are combustion-resistant, highly conductive, recyclable and capable of dissolving a wide variety of materials. They are finding new uses in chemical synthesis, catalysis, separations chemistry, electrochemistry and other areas. Ionic liquids have dramatically different properties compared to conventional molecular solvents, and they provide a new and unusual environment to test our theoretical understanding of primary radiation chemistry, charge transfer and other reactions. We are interested in how IL properties influence physical and dynamical processes that determine the stability and lifetimes of reactive intermediates and thereby affect the courses of reactions and product distributions. We study these issues by characterization of primary radiolysis products and measurements of their yields and reactivity, quantification of electron solvation dynamics and scavenging of electrons in different states of solvation. From this knowledge we wish to learn how to predict radiolytic mechanisms and control them or mitigate their effects on the properties of materials used in nuclear fuel processing, for example, and to apply IL radiation chemistry to answer questions about general chemical reactivity in ionic liquids that will aid in the development of applications listed above. Very early in our radiolysis studies it became evident that the slow solvation dynamics of the excess electron in ILs (which vary over a wide viscosity range) increase the importance of pre-solvated electron reactivity and consequently alter product distributions and subsequent chemistry. This difference from conventional solvents has profound effects on predicting and controlling radiolytic yields, which need to be quantified for the successful use under radiolytic conditions. Electron solvation dynamics in ILs are measured directly when possible and estimated using proxies (e.g. coumarin-153 dynamic emission Stokes shifts or benzophenone anion solvation) in other cases. Electron reactivity is measured using ultrafast kinetics techniques for comparison with the solvation process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

FInd Gas Source

FIGS is a neural network software that ingests real time synchronized field data on environmental flow fields and turbulence and gas concentration variations at high frequency and uses an error minimization algorithm to locate the gas source and quantify its strength. The software can be interfaced with atmospheric, oceanic and subsurface instruments in a variety of platforms stationary or mobile (e.g. cars, UAVs, submersible vehicles or boreholes) and used to find gas sources by smart use of data and phenomenology. FIGS can be trained by phenomenological model of the flow fields in the environment of interest and/or be calibrated by controlled release. After initial deployment the FIGS learning will grow with time as it accumulates data on source quantification. FIGS can be installed on any computer from small beagle-bones for field deployment/end-use to PC/MACs/main-frame for training/analysis. FIGS has been trained (using LANL's high resolution atmospheric simulations) and calibrated, tested and evaluated in the field and shown to perform well in finding and quantifying methane leaks at 10-100m scales at well pads by ingesting atmospheric measurements. The code is applicable to gas and particle source location at large scales.

Travis, Bryan↗
DOE OSTI2023

Measurement and Control of Beam Energy at the Fermilab 400 MeV Transfer Line

Linac is the first machine in the Accelerator chain at Fermilab where particles are accelerated from 35 keV to 400 MeV and travel to the Booster where they are stripped of the extra electrons to become protons. Tuning Linac is performed using diagnostics to ensure stable intensity and energy while minimizing uncontrolled particle loss. I have been revisiting diagnostics in the Linac in order to understand their signals and to ensure their data is reliable. I revisited Beam Loss Monitors (BLMs) for the loss data confidence. For the confidence of energy data there were two approaches. The first approach was time-of-flight measurements using Beam Position Monitors (BPMs) and beam velocity stripline pick-up that provides beam phase data. The second approach used the relation between beam position data from BPMs and dispersion values from MAD-X simulation to calculate energy. Our goal after understanding the data from the Linac diagnostics and finding the data reliable is to control the L inac parameters using Machine Learning techniques to increase the reliability and quality of beam delivered from Linac.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Adaptive PV Frequency Control Strategy Based on Real-time Inertia Estimation

The declining cost of solar Photovoltaics (PV) generation is driving its worldwide deployment. As conventional generation with large rotating masses is being replaced by renewable energy such as PV, the power system’s inertia will be affected. As a result, the system’s frequency may vary more dramatically in the case of a disturbance, and the frequency nadir may be low enough to trigger protection relays such as under-frequency load shedding. The existing frequency-watt function mandated in power inverters cannot provide grid frequency support in a loss-of-generation event, as PV plants usually do not have power reserves. Here, a novel adaptive PV frequency control strategy is proposed to reserve the minimum power required for grid frequency support. A machine learning model is trained to predict system frequency response under varying system conditions, and an adaptive allocation of PV headroom reserves is made based on the machine learning model as well as real-time system conditions including inertia. Case studies show the proposed control method meets the frequency nadir requirements using minimal power reserves compared to a fixed headroom control approach.

14 SOLAR ENERGY↗
DOE OSTI2022

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions.

Pinto, G↗
DOE OSTI2025

LLM-Based Adaptive Distribution Voltage Regulation Under Frequent Topology Changes: An In-Context MPC Framework

This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model

Abstract Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization.

36 MATERIALS SCIENCE↗