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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection

Abstract The next generation of telescopes will yield a substantial increase in the availability of high-quality spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the development of new, fast, and efficient methods for flagging interesting planets for reobservation and detailed analysis. We advocate the application of machine learning (ML) techniques for anomaly (novelty) detection to exoplanet transit spectra, with the goal of identifying planets with unusual chemical composition and even searching for unknown biosignatures. We successfully demonstrate the feasibility of two popular anomaly detection methods (local outlier factor and one-class support vector machine) on a large public database of synthetic spectra. We consider several test cases, each with different levels of instrumental noise. In each case, we use receiver operating characteristic curves to quantify and compare the performance of the two ML techniques.

Astronomy & Astrophysics↗
DOE OSTI2023

Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers

Abstract. Wind plants slow down the approaching wind, a phenomenon known as blockage. Wind plant blockage undermines turbine performance for front-row turbines and potentially for turbines deeper into the array. We use large-eddy simulations to characterize blockage upstream of a finite-size wind plant in flat terrain for different atmospheric stability conditions and investigate the physical mechanisms modifying the flow upstream of the turbines. To examine the influence of atmospheric stability, we compare simulations of two stably stratified boundary layers using the Weather Research and Forecasting model in large-eddy simulation mode, representing wind turbines using the generalized actuator disk approach. For a wind plant, a faster cooling rate at the surface, which produces stronger stably stratified flow in the boundary layer, amplifies blockage. As a novelty, we investigate the physical mechanisms amplifying blockage by evaluating the different terms in the momentum conservation equation within the turbine rotor layer. The velocity deceleration upstream of a wind plant is caused by an adverse pressure gradient and momentum advection out of the turbine rotor layer. The cumulative deceleration of the flow upstream of the front-row turbines instigates vertical motions. The horizontal flow is diverted vertically, reducing momentum availability in the turbine rotor layer. Although the adverse pressure gradient upstream of the wind plant remains unchanged with atmospheric stability, vertical advection of horizontal momentum is amplified in the more strongly stable boundary layer, mainly by larger shear of the horizontal velocity, thus increasing the blockage effect.

17 WIND ENERGY↗
DOE OSTI2024

MSD CoP Webinar: Applied Science for Decision-Making at the Water-Energy Nexus

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: The energy transition is a game-changer across multiple sectors, requiring researchers and practitioners to re-evaluate the evolving connections between climate, water, and power systems, as well as the associated co-management of resources and decision-making. In anticipation of new technologies, policies, business models, and other solutions, applied science for decision-making at the climate-water-energy nexus is needed to support transitions with the expected climate resilience, maintained and enhanced energy security, thriving economies, and equity considerations. Through panelists' applied research and operational experience, the webinar will provide lesson-learnt in how to increase the readiness of water-energy research and become actionable in the context of energy transitions. A number of themes will be discussed, including the connection between growing computational resources and complexity of models, and the implications on the intended users, the actual actionable products, the availability of data for validation of the models, uncertainty characterization and decision-making under uncertainty, and the communication of the novelty to decision-makers and the public. Presenters : David McCollum (Oak Ridge National Laboratory; Co-Chair), and Gokul Iyer (Pacific Northwest National Laboratory; Co-Chair), Curt Jawdy (Tennessee Valley Authority), Nathalie Voisin (Pacific Northwest National Laboratory), and Andrew D. Jones (Lawrence Berkeley National Laboratory) Moderator: Pat M. Reed (MSD CoP Facilitation Team) This webinar was held on: April 30, 2024 from 1-2 PM ET

Energy↗
DOE OSTI2024

Design and Optimization of Processes for Recovering Rare Earth Elements from End-of-Life Hard Disk Drives

In this conference paper, we propose a superstructure-based approach to finding the optimal pathways for recovering rare earth elements in their commercialized rare earth oxide form from end-of-life HDDs. The proposed superstructure was modeled as a MILP optimization problem, selecting the net present value as the objective function. Whenever possible, costing data taken from the literature was used to inform this mode. However, due to the novelty of this research area data were often not available thus requiring the generation of flowsheets that were implemented in Aspen Plus. To establish the base case optimal result, projections for the number of EOL HDDs in the U.S. available for recycling and estimates of the projected rare earth oxide prices over the lifetime of the plant were used to inform the model. The model was then expanded to include the recycling of EOL HDDs generated prior to the beginning of plant production (period ranging from 2006 through 2024).

Laliwala, Chris↗
DOE OSTI2020

Remote Hardware-in-the-Loop Approach for Microgrid Controller Evaluation

Utilities have been installing microgrids because of the increased resilience and reliability advantages they may provide to the distribution system. A microgrid controller is a critical component in microgrids. It is of great benefit to derisk the installation of microgrid controllers before field deployment. Hardware-in-the-loop (HIL) testing is used by controller developers and utilities to evaluate the controllers under stressful conditions. In this work, a microgrid control function developed by the Synchrophasor Grid Monitoring and Automation (SyGMA) laboratory at the University of California, San Diego is tested in a remote HIL (RHIL) setup. The digital real-time simulation of the detailed microgrid system was operated at the National Renewable Energy Laboratory's Energy Systems Integration Facility. Under such RHIL setup, successful controller operation is contingent on understanding and characterizing the communications channel and in particular network latencies. The novelty of this paper is the proposed use of a RHIL setup that leverages existing power system communications protocols to evaluate the controller in conjunction with the simulation capabilities of a remote facility. The work presented here will provide the complete setup of the HIL evaluation platform, the details of the communications protocols used by the setup for data transfer between the two organizations, test cases developed to evaluate the controller, and the results from the experiments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Probing for Artifacts: Detecting Imagenet Model Evasions

While deep learning models have made incredible progress across a variety of machine learning tasks, they remain vulnerable to adversarial examples crafted to fool otherwise trustworthy models. In this work we approach this problem through the lens of a detection framework. We propose a classification network that uses the hidden layer activations of a trained model as inputs to detect adversarial artifacts in an input. We train this classification network simultaneously against multiple adversarial algorithms to create a more robust detector and show higher detection rates than several alternatives. The novelty of our approach is in the scale and scope of probing Imagenet models for adversarial artifacts. In addition, we propose an improvement to feature squeezing, another common adversarial example detection method.

Rounds, Jeremiah↗
DOE OSTI2020

Readying for a Color-Mixed Future Luminous Efficacy on the Horizon

If history repeats itself, lighting 20 years from now will be substantially different than what it is today. Twenty years ago, LEDs were a novelty, not quite ready for use in architectural lighting systems. A little more than 20 years before that, rare-earth tri-phosphor T8 fluorescent lamps were beginning to replace broadband halophosphate T12 fluorescent lamps, which themselves became the predominant source of lumens in the United States around the middle of the last century. At each step in this evolution, new lighting metrics have been developed to address differences in performance, and they in turn guided technology development. For example, in the 1960s, it was CRI and CCT, and more recently it has been LM-79 and TM-30.

Royer, Michael P.↗
DOE OSTI2020

Real-Time Distributed Control of Smart Inverters for Network-level Optimization

The limitations of centralized optimization methods in managing electric power distribution systems operations have led to the distributed paradigm of computing and decision-making. Unfortunately, the existing distributed optimization algorithms are limited in their applicability to managing fast varying phenomena such as those resulting from highly variable Distributed Energy Resource (DER) generation patterns. They require a large number of communication rounds (in the order of 10 2 to 10 3 ) among the computing agents to solve one instance of the optimization problem. Related real-time distributed control methods are equally limited in their applications to power distribution systems with fast-changing DER generation; they require hundreds of rounds of communication and thus are slow in tracking the network-level optimal solutions. In this paper, we propose a novel distributed voltage controller that provides a fast-tracking of rapidly varying DER generation profiles while simultaneously converging to network-level optimal solutions within a few communication rounds. The proposed control algorithm leverages the radial topology of the system, which reduces the required communication rounds to reach the network-level optimum solution by order of magnitude. The novelty lies in carefully reducing the electrical network model from the perspective of each distributed controller and enabling appropriate data sharing among upstream and downstream nodes to achieve fast convergence. The simulation results demonstrate the effectiveness of the proposed approach in minimizing the feeder losses while maintaining the node voltage within the pre-specified limits.

voltage control, optimization, reactive power, inv↗
DOE OSTI2021

A MIMO AAA Agorithm for Frequency Dependent Line Modeling

Modeling of power distribution system components that are valid for a wide range of frequencies are crucial for highly accurate modeling of electromagnetic transient (EMT) events. This has recently become of interest due to the improvements needed for the resilient operation of distribution systems. Vector fitting (VF) is a very popular and commonly used algorithm for wide band representations of power system components in EMT simulations. In this research, we present a new multi-input rational approximation algorithm (MIAAA) and illustrate its advantages with respect to VF using examples of approximations of admittance matrices discussed in the literature. We show that MIAAA not only outperforms VF in terms of achieving better accuracy using lesser number of poles, but also has no numerical issues achieving convergence. In contrast to VF, MIAAA is not sensitive to the location of input sample points and it does not require good estimates for the location of the desired approximation poles. The novelty of this research work is the use of recent mathematical results to solve existing challenges in distribution system modeling and to develop rational approximations for power system models that intend to be optimal in terms of accuracy and performance.

AAA algorithm↗
DOE OSTI2022

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗
DOE OSTI2023

Development, Characterization, and Testing of Solid-State Electrolytes for Batteries

Traditional liquid electrolytes used in lithium (Li) metal batteries (LMBs) have severe safety issues, poor power density, as well as thermal and electrochemical instability that prevents their scaling to newer applications, such as electric vehicles. Using sulfide-based solid-state electrolytes (SSEs) is a viable method to address many of the issues that plague LMBs. However, the key issue preventing the expansion of sulfide-based SSEs is an interplay between poor interfacial stability with the electrodes and massive external stack pressure required to maintain electrode-electrolyte contact. The need for extensive pressure requires large system-level (i.e., battery pack used in an electric vehicle) housing that causes a disconnect between performance at a smaller scale and actual application. Secondary prevention to sulfide-based SSE adoption are difficulties in manufacturing, as toxic deterioration occurs when these materials are exposed to humidity, as well as drastic reduction in SSE performance at low temperature. This work hypothesizes that unique interfacial designs of electrodes and the modification of the sulfide-based SSE will enable reduction of necessary external cell pressure, and therefore support the design of practical LMBs. This will be done through collaboration within Idaho National Laboratory (INL), where manufacturing practices will be parameterized to produce low defect, low porosity SSEs with slight modifications. Next, artificial interfaces will be produced on electrodes to observe the conformality and mass transport properties between the modified SSE and pre-treated anode. External pressure on Li-metal cells will also be parameterized to reduce interfacial impedance while a reduction in the pressure will allot reasonable housing design for larger scale applications. Focus on novelty in this work occurs by modifications to the solid-solid interfaces between electrolyte and electrode, done through doping of the electrolyte and production of artificial interfaces on the Li-metal anode. The modifications at these interfaces, in addition to modified external pressure could identify and address the poor interfacial impedance that hinders high performance SSEs. Fundamental understanding on how to reduce the key hindrances of all-solid-state batteries (ASSBs) will support the advancement for larger scale production and performance, of which will be held in perspective for future works. Beyond battery development, the knowledge gained from process control and defect analysis is applicable to other ceramic systems, such as nuclear fuel coatings and high temperature electrolyzers.

25 ENERGY STORAGE↗
DOE OSTI2023

Development, Characterization, and Testing of Solid-State Electrolytes for Batteries

Traditional liquid electrolytes used in lithium (Li) metal batteries (LMBs) have severe safety issues, poor power density, as well as thermal and electrochemical instability that prevents their scaling to newer applications, such as electric vehicles. Using sulfide-based solid-state electrolytes (SSEs) is a viable method to address many of the issues that plague LMBs. However, the key issue preventing the expansion of sulfide-based SSEs is an interplay between poor interfacial stability with the electrodes and massive external stack pressure required to maintain electrode-electrolyte contact. The need for extensive pressure requires large system-level (i.e., battery pack used in an electric vehicle) housing that causes a disconnect between performance at a smaller scale and actual application. Secondary prevention to sulfide-based SSE adoption are difficulties in manufacturing, as toxic deterioration occurs when these materials are exposed to humidity, as well as drastic reduction in SSE performance at low temperature. This work hypothesizes that unique interfacial designs of electrodes and the modification of the sulfide-based SSE will enable reduction of necessary external cell pressure, and therefore support the design of practical LMBs. This will be done through collaboration within Idaho National Laboratory (INL), where manufacturing practices will be parameterized to produce low defect, low porosity SSEs with slight modifications. Next, artificial interfaces will be produced on electrodes to observe the conformality and mass transport properties between the modified SSE and pre-treated anode. External pressure on Li-metal cells will also be parameterized to reduce interfacial impedance while a reduction in the pressure will allot reasonable housing design for larger scale applications. Focus on novelty in this work occurs by modifications to the solid-solid interfaces between electrolyte and electrode, done through doping of the electrolyte and production of artificial interfaces on the Li-metal anode. The modifications at these interfaces, in addition to modified external pressure could identify and address the poor interfacial impedance that hinders high performance SSEs. Fundamental understanding on how to reduce the key hindrances of all-solid-state batteries (ASSBs) will support the advancement for larger scale production and performance, of which will be held in perspective for future works. Beyond battery development, the knowledge gained from process control and defect analysis is applicable to other ceramic systems, such as nuclear fuel coatings and high temperature electrolyzers.

25 ENERGY STORAGE↗
DOE OSTI2023

On Soft Mathematical Models of Subjective Time Acceleration with Age

It is a commonplace perception that speed of time subjectively experienced by humans significantly differs from chronological (objective) time and shows a great deal of variability. An often cited example is the phenomenon of the time acceleration with age - subjectively, the time passes faster as we get older. While the exact mechanisms behind it are not yet fully established, here we consider three 'soft' (conceptual) mathematical models that might be applicable to the speeding time phenomenon: two proportionality theories widely discussed in the past and the original model that takes into account the novelty of experience effect. Furthermore, the latter is found the most plausible, as not only it satisfactorily describes the decadal subjective time acceleration, but also offers a reasonable explanation of the human life experience accumulation with age.

acceleration↗
DOE OSTI2022

Techno-economic Analysis for a Low Cost and Recyclable Oxygen Carrier

The novelty of a chemical looping combustion (CLC) process is its ability to oxidize solid or gaseous fuels in a nitrogen-free environment eliminating the need for post combustion carbon dioxide capture. The technology is based on the oxygen carrier (OC); a metal oxide that at high temperatures can undergo a reduction reaction with a fuel, followed by an oxidation reaction with air. Successful development of CLC processes is dependent on the OC having: low cost, high attrition resistance under CLC conditions, and high reactivity for coal. In this paper, we discuss the development of a novel iron-based OC formulation produced from low cost and abundant raw materials. The active OC components are recyclable, with reformulated material performance comparable to the virgin OC, even when reformulated with ash contents of up to 10%. Furthermore, we discuss a techno-economic analysis for a centrally located OC facility for the novel OC, with a production capacity of 1 million metric tonnes per year. The facility serves a hypothetical market comprising 25 chemical looping combustion facilities, averaging 5 tonnes per hour OC replacement per facility. Estimates for the cost of the OC are around $150/tonne, and include an energy-to-power option to improve process economics.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

RADIANCE Executive Summary

Microgrids are gaining attention from organizations and cities because of their potential reliability and resilience benefits, especially in remote geographic areas: Microgrids can provide local power during emergencies, and they can reduce the costs of imported fuel by reducing overall fuel use. But the complexities and novelties of microgrid technologies are often barriers to deployment that require testing and validation to overcome. This report describes the testing, validation, and deployment of microgrid technologies in Cordova, Alaska, completed through the U.S. Department of Energy (DOE) Grid Modernization Laboratory Consortium (GMLC) project Resilient Alaskan Distribution system Improvements using Automation, Network analysis, Control, and Energy storage (RADIANCE).

ARIES↗
DOE OSTI2020

Design and Development of a Robotic Exoskeleton Device for use in Glove Boxes - 20374

Glovebox operators in nuclear facilities often operate under radioactive environment and typically perform monotonous, dull and repetitive work. This can result in muscle fatigue leading to increased levels of accidents. Current research work aims in assisting the glovebox operators by designing a novel upper limb exoskeleton device to reduce injuries and increase worker efficiency. Almost none of the commercially available hand and upper body exoskeletons are directly suitable for use in gloveboxes. This explains the novelty of the present work in developing a wearable powered robotic exoskeleton device. The device consists of electronics, motors and spring systems mounted on adjustable plates and is supported by a stationary frame behind the worker. The device is intended to be portable and adjustable according to the operator height. The elbow has a flexion movement assisted with motor control by way of a Bowden cable system. The system is floated on a spring and pulley combination that allows for extra safety and smoothness of operation. The end points are fixed near the pivot points to help minimize unwanted movement/torque. The extension movement of the upper and lower arm are assisted by gravity and a counter spring. The shoulder and upper arm movement are held in a neutral position with a spring system for counter balance allowing small movements within the glovebox. This initial concept considers only the vertical movement of the arm. The abduction and adduction movement will not be assisted or impeded by the over shoulder plate or at the forearm plate allowing for full range of motion. Several glovebox aspects such as the level of task assistance, ergonomic design, minimum hindrance in dexterity/operator movements and tear resistance for hazardous safety reasons will be considered in the design of the exoskeleton. Computer models will be simulated to test the load levels and stress conditions on the exoskeleton to enhance the designs. The exoskeleton focusses on major muscular involvement reduction through weight offset and active assistance with minimal parts and hence the device is derived with several joint links and contains both passive and active joints. The paper details the various stages in the design development and prototype testing of the exoskeleton device. Upon successful prototype device development, human testing will be conducted in a replica of the SRS-235F hot cell facility constructed at FIU-ARC. The final stage of the project will be potential testing/deployment of the exoskeleton device in one of the glovebox facilities in Savannah River Nuclear Waste site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

New modelling capabilities in IDT

This work concerns the enhanced modelling capabilities of the discrete ordinates transport solver IDT. The novelties introduced allow for modelling unstructured geometries composed by a collection of X/Y segments and circles, and the use of reciprocity and conservation relations reduce the memory imprint as well as the computational cost of the method. IDT decomposes geometries in modular Cartesian patterns, which are the so-called Heterogeneous Cartesian Cells (HCCs), containing a chunk of the original unstructured geometries. Each HCC can be then discretized by superimposing a XY grid to refine locally the HCC. Unlike the most popular MOC, IDT performs the spatial sweeping by directional collision probabilities instead of trajectories. The sources and interface angular fluxes are expanded up to linear order. The accuracy of ray-tracing, the memory imprints together with the novel mesh refinement capabilities have been verified. A first set of preliminary results on PWR lattice problems will be presented in this paper.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Recycling Rare Earth Elements from End-of-Life Electric and Hybrid Electric Vehicle Motors

In this paper, we propose a superstructure-based approach to finding the optimal pathways for recovering rare earth elements in their commercialized rare earth oxide form from end-of-life EV and HEV motors. The proposed superstructure was modeled as a MILP optimization problem, selecting the net present value as the objective function. Whenever possible, costing data taken from the literature was used to inform this mode. However, due to the novelty of this research area data were often not available thus requiring the generation of flowsheets that were implemented in Aspen Plus.

Laliwala, Chris↗