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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Impedance sources (Z sources) with inherent fault protection for resilient and fire-free electricity grids

Modern societies would not survive without electricity and at the same time electrical faults could cause and have caused many catastrophes—mainly deadly fires—to our societies. There are two types of electricity sources: the voltage source such as generators, charged batteries and capacitors, and the current source such as charged inductors, current-regulated rectifiers, and superconducting magnetic energy storage. An “ideal” voltage source—that is often-sought-or-intentionally engineered—generates a constant voltage irrespective of its load current, and an “ideal” current source injects a constant current irrespective of its load voltage. However, two problems exist: (1) voltage or current sources do not represent many emerging natural/renewable energy sources such as wind turbine generators, photovoltaic cells, and fuel cells, whose output voltage and current are strongly dependent on each other, and (2) a short-circuit fault to an artificially-made and controlled “ideal” voltage source or an open-circuit fault to an “ideal” current source can cause catastrophic failures of the source itself and its surrounding circuits due to large (theoretically infinite) short-circuit current or open-circuit voltage. Here we introduce an impedance source concept to represent, characterize, and model those electricity sources whose output voltage and current are strongly dependent on each other. First, we found that many electric sources with no feedback (or active) control of their output voltage and/or current are a natural impedance source with inherent fault protection at short-circuit or open-circuit faults. Second, any electrical source can be artificially controlled to mimic a natural impedance source. Finally, we show how to apply natural impedance sources and nature-mimicking artificially-controlled sources to the electricity grid—the most complex machine ever made by human beings—to realize electricity grids that are naturally stable, self-protected against electrical faults, and resilient to natural and human-made events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Feedback Control Strategy for Transient Stability Application

Power systems are subjected to a wide range of disturbances during daily operations. Severe disturbances, such as a loss of a large generator, a three-phase bolted fault on a generator bus, or a loss of a transmission line, can lead to the loss of synchronism of a generator or group of generators. The ability of a power system to maintain synchronism during the few seconds after being subjected to a severe disturbance is known as transient stability. Most of the modern methods of controlling transient stability involve special protection schemes or remedial action schemes. These special protection schemes sense predetermined system conditions and take corrective actions, such as generator tripping or generation re-dispatch, in real time to maintain transient stability. Another method is the use of a real-time feedback control system to modulate the output of an actuator in response to a signal. This paper provides a fundamental evaluation of the use of feedback control strategies to improve transient stability in a power system. An optimal feedback control strategy that modulates the real power injected and absorbed by distributed energy-storage devices is proposed. Its performance is evaluated on a four-machine power system and on a 34-machine reduced-order model of the Western North American Power System. The result shows that the feedback control strategy can increase the critical fault clearing time by 60%, thereby improving the transient stability of the power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

VisMetHack2022: Visualizing winds and surface variables from the ECMWF IFS 1-km nature run

This data collection was contributed to the Visualisation Hackathon 2022 (#VisMetHack2022), in conjunction with the Using ECMWF's Forecasts (UEF2022) workshop. The European Center for Medium-Range Weather Forecasts (ECMWF) and the Oak Ridge National Laboratory (ORNL) are pleased to announce access to the data collection from global 1-km nature run (NR) simulations using the Integrated Forecast System (IFS) with explicit convection. We invite you to join us in exploring this precursor to a digital twin of the earth! The NR simulations reveal unprecedented detail of the earth’s atmosphere, and the then outgoing Editor-in-Chief of AGU JAMES commended the project as one of “stunning ambitions,” enabled by computational capacity at scale. The project also won the 2020 HPCwire Readers Choice Award for Best Use of HPC in Physical Sciences. A set of two NR seasonal simulations have been completed, one corresponding to the northern hemispheric winter months (NDJF) and the other for the North Atlantic tropical cyclone season (ASO). The project used the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). The simulations were facilitated with an INCITE award from the US Department of Energy Office of Science. For the first seasonal run of four months (NDJF), the hydrostatic IFS model was initialized at 00Z on 1 November 2018. The NR for the TC season (AS) was initialized at 00Z on 1 August 2019. The NR simulations were constrained only by sea surface temperatures (SST) at the lower boundary. The IFS output was saved every 3 hours. After feedback and interest from the scientific community, the simulations were rerun for four specific extreme events, with output every 15 minutes. The special cases include a tropical cycle and three severe storm events over the continental USA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Nuclear imaging to diagnose and correct target-driver registration at high-repetition-rate for improved reactor efficiency

Inertial Confinement Fusion produces energy from a burning plasma lasting a fraction of a nanosecond. Power plant designs based on Inertial Fusion Energy (IFE) will need to ignite targets 1-10 times a second, fired as projectiles into a chamber and delivering the driver to the target location. Driver asymmetry is known to impact ICF experiments at gain near unity and remains a candidate for primary yield degradation, and therefore fusion power plant energy output, for high-gain target designs. For a Fusion Power Plant (FPP), continuous and real-time monitoring of target performance provides an opportunity to stabilize or correct the target-driver registration. This requires x-ray and neutron imaging with a large field-of-view, sufficiently high resolution, fast analysis and to subtend a minimal solid angle. We introduce design criteria for such an imaging system that uses a coded aperture and time-gated, lens-coupled scintillators as a viable solution and outline the research steps required to field such a system. Integrating the imaging system into an IFE power plant as part of an active feedback loop could increase average power output by reducing the failure rate due to mis-aligned drivers with respect to the target.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

An adaptive approach to machine learning for compact particle accelerators

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow: Preprint

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Online Control of Process Variance Using Feedback

For many stochastic control problem formulations, design objectives typically include constraining the covariance of the outputs of the system. Recent engineering challenges have demonstrated an interest in controlling system output covariances to a setpoint. However, open-loop control of system output covariance can be exceptionally challenging when the system has significant plant uncertainties. This paper explores closed-loop control of the system’s output covariance using a windowed variance function. The stochastic nature of the windowed variance feedback means that root locus and other classical control design techniques cannot be immediately used. Expected value analysis is used to generate transfer functions for the average response of such systems, and the results are used to design controllers. Stochastic simulations are presented to verify the analytical results and make other observations concerning closed-loop variance control.

Bieniek, Mitchell↗
DOE OSTI2021

Optical current monitor

An optical current monitor for detecting a current traveling through conductive material. The optical current monitor comprises a light source for emitting light at an output level; a lens configured to receive the light; Faraday material positioned near the conductive material and configured to receive light that has passed through the lens, thereby producing rotated light; a polarizer configured to polarize the rotated light; a photodetector configured to receive the rotated light and output a signal as a function of the rotated light; and a feedback system. The feedback system is configured to receive the signal from the photodetector and modify the output level of the light source based on the signal so that the signal remains at a reference level when the current is not traveling through the conductive material.

Laughlin, Daric Gray↗
DOE OSTI2023

Flash radiotherapy accelerator

Methods, devices and systems for ultra-high dose radiotherapy are disclosed. The described techniques rely in-part on active switching control of a photoconductive switch during the time the accelerator is accelerating charged particles to produce the output radiation at the desired dose rates. One flash radiotherapy system includes an induction accelerator, and a controllable switch coupled to the induction accelerator. The switch is operable to produce a plurality of voltage pulses to drive the induction accelerator. The radiotherapy system also includes a radiation measurement device to measure output radiation produced by the radiotherapy system and provide feedback to the controllable switch. The controllable switch is operable to, based on the received feedback, modify an amplitude, shape, spacing, number or width of the voltage pulses that are supplied to the particle accelerator to deliver the desired output radiation.

Sampayan, Stephen E.↗
DOE OSTI2026

Feedforward-feedback ammonia control at a water resource recovery facility based on a digital twin with hybrid model

Ammonia-based aeration control (ABAC) at full-scale Water Resource Recovery Facilities (WRRFs) can be challenged by diurnal loading and transport delays. This work addressed these challenges using a hybrid feedforward–feedback controller built on Activated Sludge Model 1 (ASM1), marking the first full-scale deployment to pair a mechanistic feedforward core with data-driven corrections. The objectives were to improve ammonia setpoint tracking, assess performance of the mechanistic model when enhanced with data-driven corrections, and document full-scale operation. The hybrid model incorporates two data-driven components: (1) a Mechanistic Error Forecasting Engine (MEFE), consisting of a multivariate linear regressor and a long short-term memory (LSTM) ensemble. Defying expectations, low-parameter models outperformed more complex alternatives, reducing the mechanistic error by 71%. (2) A Residual Oscillation Forecasting Engine (ROFE), based on Fast Fourier Transform, reduced the remaining error by another 35%. Two proportional–integral (PI) feedback loops further (i) trim the feedforward output and (ii) eliminate residual controller error in the final aerobic zone. In full-scale operation, the controller reduced mean-squared error (MSE) by 94% over the baseline and produced more stable dissolved oxygen (DO) setpoints. Overall, it was proven that layering multi-timescale data-driven models on a mechanistic core can yield reliable ABAC performance at WRRFs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia's Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia's ElectroMagnetic Plasma In Realistic Environments ( EMPIRE ) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia's HPC users.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability (Final)

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Control of atomic Bose – Einstein condensate with interferometric feedback probing

We consider the problem of decoherence in a Bose – Einstein condensate (BEC) of noninteracting atoms during interferometric probing with a classical monochromatic external field. The condensate is located in one of the arms of the Mach – Zehnder interferometer, while part of the radiation from the interferometer output is fed back to the input, thereby closing the coherent feedback loop. A more general setting of the problem is also considered, in which the condensate is located in a system of interferometers ‘nested’ into each other, while part of the output radiation of each of them is also fed back to its input, closing the so-called multiloop feedback. The possibility of effective control of decoherence rates of various matrix elements is shown. The application of feedback of the proposed type to BEC in a double-well potential is investigated. It is found that this feedback allows efficient control of the distribution of atoms between the wells. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Investigation of Superluminescent Diodes for Smart Lighting Systems. Final Report

The solid-state lighting ecosystem has evolved very rapidly over the last few years, with significant improvements in the technical performance of light-emitting diodes (LEDs) and the commoditization of LED-based lighting fixtures. As the performance of conventional lighting products begins to saturate, there is growing market interest in “Lighting as a Service” applications that will leverage advanced systems to impart new functionalities to lighting and improve energy efficiency, human health, and productivity. These “Smart Lighting” systems will include high-performance light sources, specialized sensors, and dynamic controls to deliver high quality, energy efficient, color tunable lighting with customized spatial light delivery and integrated visible light communication capability. To achieve these capabilities, smart lighting systems will place greater demands on the performance of light sources. Highly efficient sources with additional functionalities compared to conventional LEDs, such as small form factor, large modulation bandwidth, and spatially coherent output beams, will be required. While laser diodes have been proposed as potential sources for smart lighting systems, they also exhibit several properties that pose challenges, such as temporal coherence, extremely high spatial coherence, and ultra-narrow linewidths. To address these issues, we propose an investigation of an alternative device architecture known as a superluminescent diode (SLD). SLDs are similar in form to ridge laser diodes and share many of the same characteristics, such as stimulated emission operation, spatially coherent output, small form factor, and the potential for large modulation bandwidth. However, the operating principle for SLDs is distinct from laser diodes in that SLDs lack a strong cavity feedback mechanism, resulting in spatially coherent but temporally incoherent light output. Thus, SLDs may address the issues with laser diodes for lighting, while simultaneously maintaining some of the desirable characteristics of both laser diodes and LEDs. The objectives of this proposal are to design, grow, and fabricate blue (450 nm) SLDs on polar c-plane and nonpolar m-plane free-standing GaN substrates, and to evaluate their potential as sources in smart lighting systems through basic device characterization and detailed investigations of their efficiency droop and modulation bandwidth. The primary scientific aims of this work are to understand the fundamental role of optical gain in the superluminescent (non-lasing) regime on efficiency droop and modulation bandwidth in III-nitride emitters and to understand the effects of higher optical gain on SLD performance by comparing polar c-plane and nonpolar m-plane SLDs. The University of New Mexico (UNM) will collaborate with Sandia National Laboratories (SNL) and the Center for Integrated Nanotechnologies to design, fabricate, grow, and characterize the SLDs. UNM will perform the design and epitaxial growth, while SNL will focus on the fabrication and device characterization. The novelty of the proposed work includes the first comprehensive theoretical and experimental investigations of efficiency droop and the first investigation of modulation bandwidth in GaN-based emitters operating in the superluminescent regime. Moreover, the comparison of c-plane and m-plane SLDs will enable the first analysis of the effects of higher optical gain on the device performance. The development of high-performance GaN-based SLDs may enable efficiency gains and improved functionality in next-generation smart lighting systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

CERESMIP: a climate modeling protocol to investigate recent trends in the Earth's Energy Imbalance

The Clouds and the Earth's Radiant Energy System (CERES) project has now produced over two decades of observed data on the Earth's Energy Imbalance (EEI) and has revealed substantive trends in both the reflected shortwave and outgoing longwave top-of-atmosphere radiation components. Available climate model simulations suggest that these trends are incompatible with purely internal variability, but that the full magnitude and breakdown of the trends are outside of the model ranges. Unfortunately, the Coupled Model Intercomparison Project (Phase 6) (CMIP6) protocol only uses observed forcings to 2014 (and Shared Socioeconomic Pathways (SSP) projections thereafter), and furthermore, many of the ‘observed' drivers have been updated substantially since the CMIP6 inputs were defined. Most notably, the sea surface temperature (SST) estimates have been revised and now show up to 50% greater trends since 1979, particularly in the southern hemisphere. Additionally, estimates of short-lived aerosol and gas-phase emissions have been substantially updated. These revisions will likely have material impacts on the model-simulated EEI. We therefore propose a new, relatively low-cost, model intercomparison, CERESMIP, that would target the CERES period (2000-present), with updated forcings to at least the end of 2021. The focus will be on atmosphere-only simulations, using updated SST, forcings and emissions from 1990 to 2021. The key metrics of interest will be the EEI and atmospheric feedbacks, and so the analysis will benefit from output from satellite cloud observation simulators. The Tier 1 request would consist only of an ensemble of AMIP-style simulations, while the Tier 2 request would encompass uncertainties in the applied forcing, atmospheric composition, single and all-but-one forcing responses. We present some preliminary results and invite participation from a wide group of models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

HydroForecast Long-term: Improving hydropower’s resilience to climate change through accurate climate-scale

With hydrologic patterns and water availability across the globe shifting due to climate change, advancements in hydrologic prediction systems can help significantly reduce the uncertainties that utilities and water supply entities have in their decision making. Understanding and estimating hydrology at the climate scale is critical for managing water resources under changing climate scenarios. This project focuses on integrating state-of-the-art neural network modeling with downscaled climate projections to deliver the reliable water supply projections decades into the future to meet an urgent need from hydropower operators and water utilities. In this Phase 1 DOE SBIR proposal, we developed and validated a theory-guided neural network model, HydroForecast Long-term, for climate-scale hydrology and implemented the model within existing HydroForecast infrastructure. HydroForecast Long-term combines the most accurate streamflow modeling system with a flexible and scalable data architecture to generate water supply projections out to the year 2100. This report illustrates that we have achieved our four objectives: 1) create a prototype of HydroForecast Long-term, building the neural network prediction model, 2) build an automated data input pipeline that processes large amounts of data from the latest global temperature and precipitation climate models; 3) benchmark the accuracy of the hydrologic model over the recent two decades over a large set of diverse basins, and 4) create a set of output visuals and summary metrics informed by customer feedback that connect the data to critical decision points. This work empowers water users to make data-informed decisions supporting a resilient, renewable-powered grid and water system. The results advance the Department of Energy’s mission by addressing critical gaps in water supply planning under climate change.

13 HYDRO ENERGY↗