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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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NASA NTRS2012

Analysis of Vegetation Index Variations and the Asian Monsoon Climate

Vegetation growth depends on local climate. Significant anthropogenic land cover and land use change activities over Asia have changed vegetation distribution as well. On the other hand, vegetation is one of the important land surface variables that influence the Asian Monsoon variability through controlling atmospheric energy and water vapor conditions. In this presentation, the mean and variations of vegetation index of last decade at regional scale resolution (5km and higher) from MODIS have been analyzed. Results indicate that the vegetation index has been reduced significantly during last decade over fast urbanization areas in east China, such as Yangtze River Delta, where local surface temperatures were increased significantly in term of urban heat Island. The relationship between vegetation Index and climate (surface temperature, precipitation) over a grassland in northern Asia and over a woody savannas in southeast Asia are studied. In supporting Monsoon Asian Integrated Regional Study (MAIRS) program, the data in this study have been integrated into Giovanni, the online visualization and analysis system at NASA GES DISC. Most images in this presentation are generated from Giovanni system.

Shen, Sunhung↗
DOE OSTI2019

Physics based Reliability Models for Supercritical CO 2 Turbomachinery Components

CSP plants using sCO 2 power cycle can potentially achieve high thermal conversion efficiency at low capital cost due to compact turbo-machinery and other components. An sCO 2 expander and improved heat exchanger is expected to provide a major stepping stone for achieving CSP power at 0.06/kW-hr LCOE, energy conversion efficiency > 50 % ,and total power block cost< $1,200/kW installed. Hybrid gas bearings (HGB) and Dry gas seals (DGS) are two key components that are critical for robust and efficient operation of future sCO 2 power cycles. From a system perspective, HGBs can provide substantial benefits (~3 to 5%) to the modular CSP operation, while use of DGSs limits the leakage losses as well as the windage losses in the generator and can increase overall generator efficiency by ~ 8%. The life limiting mechanisms of HGBs and DGSs in high pressure, high temperature sCO 2 environment were not well understood and hence this program aims at developing physics-based performance prediction and life prediction models for these two components. The output of the performance models serves as input to the life models. The performance models were validated using experimental data and using data from literature. DGS and HGB models have shed light on some risks associated with its operation in sCO 2 expander and will guide more robust designs tailored towards sCO 2 cycle applications. A DGS was tested in 1MWe sCO 2 turbo expander to study its performance and validate the performance models. To understand the effect of high pressures, high temperatures and sCO 2 environment on crack initiation, crack propagation and LCF/HCF life of these components, a novel experimental setup was developed. The LCF test data was used to calibrate and validate the life prediction models. The effect of sCO 2 corrosion on the life of Ni-based super-alloys is quantified using long duration experiments. Advanced microstructure and spectroscopic analyses conducted shed light on some key differences between IN718 and IN617 in terms of oxidation morphology, chemical species diffusion and trapping, the formation of protective corrosion resistant layers and changes in surface properties. A novel crack initiation model was developed which takes into account strain distribution, temperature, hold times and activation energy at the gas-metal interface and predicts the number of cycles to initiate the crack in the Inconel alloy. An advanced 3D fracture mechanics-based crack propagation model was also developed which takes into account the transient loading mission of the component and predict how fast the crack will grow and what will be its final shape and size. High energy X-Ray tomography measurements were conducted to study and validate 3D morphology of crack growth inside the specimen. These two models along with chemical kinetics models obtained from corrosion tests can be combined to predict the total life of the component under given long term operating conditions. A Bayesian Hybrid Modeling (BHM) probabilistic framework was developed to quantify uncertainty in physics-based performance models and in crack initiation and crack propagation models, to calibrate the models and to validate the models with statistical confidence. Advanced coupled physics multi-phase flow models were developed to study condensation, boiling and phase change behavior of CO 2 under various sCO 2 turbomachinery and heat exchanger conditions. A novel experimental setup was also designed to observe phase change of CO 2 during its transition across liquid-gas-supercritical state lines.

14 SOLAR ENERGY↗
DOE OSTI2017

FY17 ISCR Scholar End-of-Assignment Report - Robbie Sadre

Throughout this internship assignment, I did various tasks that contributed towards the starting of the SASEDS (Safe Active Scanning for Energy Delivery Systems) and CES-21 (California Energy Systems for the 21st Century) projects in the SKYFALL laboratory. The goal of the SKYFALL laboratory is to perform modeling and simulation verification of transmission power system devices, while integrating with high-performance computing. The first thing I needed to do was acquire official Online LabVIEW training from National Instruments. Through these online tutorial modules, I learned the basics of LabVIEW, gaining experience in connecting to NI devices through the DAQmx API as well as LabVIEW basic programming techniques (structures, loops, state machines, front panel GUI design etc).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Further, negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1988

Galileo Earth-Venus trajectory correction maneuver design

As a result of delays in the Space Shuttle program and a re-evaluation of safety concerns which resulted in cancellation of the Shuttle Centaur Upper Stage program, the Galileo mission to Jupiter has undergone a substantial redesign effort. Use of the Inertial Upper Stage (IUS) for injection has necessitated a complicated Venus-Earth-Earth gravity assist trajectory in order to reach Jupiter. One flyby of Venus and two flybys of Earth are required to compensate for the lower injection energy available from the IUS. As the spacecraft was not originally designed for the environment within 1 AU of the Sun, modifications to the spacecraft and operating procedures have been developed. This paper describes some of these changes and how they have affected the design and implementation of trajectory correction maneuvers on the Earth-Venus leg of the mission. A strategy of biasing the IUS target at injection to control the statistical velocity distribution of a subsequent trajectory correction maneuver is discussed.

Wilson, M. G.↗
DOE OSTI2024

HiFiPDV2 v. 2

SAND2024-01061O The HiFiPDV-2 software is similar to other photonic Doppler velocimetry (PDV) analysis codes in that it converts signals from intensity-time space to frequency-time space using a short-time Fourier transform (STFT). It then analyzes peaks of the resulting power spectral density information to extract the velocity-time history. The unique aspects of HiFiPDV and HiFiPDV-2 are that this reduction process is repeated for an array of reasonable STFT input variables. The resulting population distribution of output velocity-time histories is evaluated to determine the most likely velocity history and its associated uncertainty. This uncertainty is defined as the systematic uncertainty of the PDV signal as it is a function of input values to the reduction process. The HiFiPDV-2 program brings new functionality and efficiency to these calculations—most notably significant reductions in memory usage and calculation times, the ability to evaluate frequency-shifted signals, and the ability to filter out baseline signals. Developers recommend that users have at least 2 GB of RAM per CPU core in their system. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Voorhees, Travis↗
DOE OSTI2018

Tracking the Sun: Installed Price Trends for Distributed Photovoltaic Systems in the United States - 2018 Edition

Berkeley Lab’s Tracking the Sun report series is dedicated to summarizing installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The present report, the 11th edition in the series, focuses on systems installed through year-end 2017, with preliminary trends for the first half of 2018. As in years past, the primary emphasis is on describing changes in installed prices over time and variation in pricing across projects based on location, project ownership, system design, and other attributes. New to this year, however, is an expanded discussion of other project characteristics in the large underlying data sample. Future editions will include more of such material, beyond the report’s traditional focus on installed pricing. The trends described in this report derive primarily from project-level data reported to state agencies and utilities that administer PV incentive programs, solar renewable energy credit (SREC) registration systems, or interconnection processes. In total, data were collected and cleaned for more than 1.3 million individual PV systems, representing 81% of U.S. residential and non-residential PV systems installed through 2017. The analysis of installed pricing trends is based on a subset of roughly 770,000 systems with available installed price data. Key findings from this year’s report include the following: -Installed Prices Continued to Fall through 2017 and into 2018. National median installed prices in 2017 were $3.7 per watt (W) for residential systems (a $0.2/W or 6% decline from the prior year), $3.1/W for small non-residential systems (a $0.4/W or 11% decline), and $2.2/W (a $0.1/W or 5% decline) for large non-residential systems. Data for the first half of 2018 show an additional drop of $0.1/W for residential and small non-residential systems, and effectively no change for large non-residential systems. These recent trends are generally consistent with the pace of price declines since 2014, and mark a slowing from the years immediately preceding (2009-2013) when prices fell by roughly $1/W per year. -Installed Prices Vary Widely Across Individual Projects. Among residential systems installed in 2017, 20% were priced below $3.0/W (the 20th percentile value) while another 20% were above the 80th percentile at $4.5/W. Non-residential systems also exhibit wide spreads, albeit shifted downward, from $2.4/W to $4.1/W for small non-residential and from $1.8/W to $2.8/W for large non-residential projects. These pricing spreads have persisted over time and are both reflective of the heterogeneity in PV systems and markets, as well as suggestive of the potential for further price declines. -Pronounced Pricing Disparities Exist Across States. State-level median installed prices in 2017 ranged from $2.6/W to $4.5/W for residential systems, from $2.2/W to $4.0/W for small non-residential systems, and from $2.1/W to $2.4/W for large non-residential systems. Three of the largest state markets (California, Massachusetts, and New York) are relatively high-priced, pulling overall U.S. median prices upward. These cross-state pricing differences reflect both idiosyncratic features of particular states as well as more-fundamental differences related to market and policy conditions. -Installed Pricing Variation Also Reflects Differences in System Design, Installer, and Customer Characteristics (among other factors). Significant pricing differences were also observed across system sizes within each customer segment (with median prices ranging from $3.2/W to $4.5/W across residential systems of varying size, and from $2.1/W to $3.7/W across non-residential systems), between third-party owned (TPO) and host-owned residential systems ($3.3/W vs. $3.8/W), across residential installers ($2.1/W to $9.6/W for host-owned systems and $1.1/W to $5.1/W for TPO systems), between residential new construction and retrofit systems in California ($2.3/W vs. $3.9/W), between large non-residential systems at commercial sites and tax-exempt customer sites ($2.1/W vs. $2.6/W), and between systems with standard vs. premium efficiency modules ($3.6/W vs. $4.2/W among residential systems). A public version of the underlying dataset can be downloaded at https://emp.lbl.gov/tracking-the-sun.

14 SOLAR ENERGY↗
DOE OSTI2021

Numerical Investigation of Enhanced Dehumidification Processes By Using Dielectrophoresis Principles in Moist Airflows

Dispersed particle-laden flows are encountered in many building and industrial applications, such as flow in a fluidized bed, hydrocarbon transportation in pipelines, and the fouling of air-cooled heat exchangers (Kuruneru et al., 2016; Ray et al., 2019; Wang et al., 2019). Computational fluid dynamic (CFD) models have been developed in recent years to depict particle-fluid and particle-particle interactions in laminar or turbulent flows with increasing accuracy and stability. One particular particle-laden system of interest for moisture control is electrically-enhanced condensation in air and water droplet flows. Electrically-enhanced condensation consists of the use of highly charged water droplets injected in the moist air. The droplets become electric seeds that attract polar water vapor molecules to their surfaces and promote condensation. The nucleation and growth of the charged droplets deplete the vapor phase near a droplet, which is compensated for by the dielectrophoresis flow and diffusion. Dielectrophoresis flow involves surrounding vapor at a distance of about 10 to 100 nm for droplets charged by an electrospray compared to ~2 nm for a single electron charge in a droplet. As the vapor molecules collapse on the surface of the droplets, their initial electrical charge decreases with time due to the neutralization of the ions. While the physics of this phenomena is well known, engineering models for predicting the condensation rates are not available. This work computationally investigates dehumidification of moist airflow in a converging rectangular duct. The objective is to develop an engineering model that predicts water vapor condensation by employing dielectrophoresis principles. We construct a Computational Fluid Dynamics (CFD) model of the duct with electrically-enhanced condensation. The model is implemented in the open-source software OpenFOAM. We utilize the Multi-Phase Particle-In-Cell (MP-PIC) method coupled with a Population Balance Equation (PBE) approach to simulate the particle-laden system. This methodology is an Eulerian-Lagrangian approach used to simulate the droplets' behavior in the humid air. The MP-PIC approach (Andrews and O'Rourke, 1996) mitigates the computational cost by parceling several fundamental particles with similar properties (such as types, sizes, and temperature) into one computational particle. Thus, the billions of particles can be substituted by millions of computational particles without significant loss of information. The PBE was considered with the Lagrangian frame to combine the particle distribution function used in MP-PIC (Kim et al., 2020). This approach preserves mass and energy conservation between the phases in the Eulerian and Lagrangian structures. The PBE in this procedure was directly linked to the discrete parcels, making the simulation of the particle distribution computationally efficient and robust. The MP-PIC-PBE approach used in the present work was applied to the dehumidification of air. Water droplets were injected in the air stream and forced to grow according to experimentally derived correlation. The experiments were conducted on a converging duct with the same geometry and boundary conditions used to build the CFD model. This approach enabled us to approximate the effect of dielectrophoresis phenomena on the droplet and air interface. This presentation will discuss the details of the new CFD model built for the duct, the implementation of the model in OpenFOAM CFD programming language, and the experimental validation of the newly developed model. The results revealed a moderate yet measurable increase in droplet diameter due to water vapor condensation at the vapor-liquid interface of the electrically charged droplets' surface. The seed water droplet particles grew in size by capturing the water vapor in the surrounding air. The OpenFOAM model predicted reductions of humidity in the air from 5 to 10 percent.

Yel Mahi, Maliha↗
DOE OSTI2023

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2000

Theoretical Technology Research for ISTP/SOLARMAX

During the last decade, we have been developing theoretical tools to support the scientific objectives of the International Solar Terrestrial Physics (ISTP) program. Results from our mission-oriented theory program have contributed significantly to the development of predictive capabilities by using real upstream solar wind conditions as input to our models and forecasting events observed downstream near Earth. We also developed the capability to unravel the complex information contained in ion velocity distribution functions measured near the Earth to determine their origin and energization process. During solar maximum, solar flares and coronal mass ejections (CMEs) dominate the sun's activity. It is now widely accepted that the impact of CMEs (or magnetic clouds) with the Earth's magnetosphere is the cause of most magnetic storms during solar maximum. One important aspect of a CME is the occurrence of solar energetic particle (SEP) events. During these events, protons, electrons, and heavy ions of solar origin are accelerated to very high energies by shock waves driven out from the sun. We carried out a series of large-scale kinetic (LSK) simulations to model the effect of SEPs on the near-Earth environment and the accessibility of these high-energy particles to the inner magnetosphere. We present the results of these studies.

Ashour-Abdalla, Maha↗
DOE OSTI2020

Single-Step Conversion of Ethanol to n-Butene over Ag-ZrO2/SiO2 Catalysts

Ethanol is a promising platform molecule for production of a variety of fuels and chemicals. Of particular interest is producing middle distillate fuels (i.e., jet and diesel blendstock) from renewable ethanol feedstock. State-of-the-art alcohol-to-jet technology requires multiple process steps based on catalytic dehydration of ethanol to form ethylene, followed by sometimes a multi-step oligomerization, and then hydrotreatment and distillation. Here we report on a new catalytic route in which ethanol is directly converted to n-butene (1- and 2-butene mixtures) over Ag-ZrO2/SBA-16, thus offering the potential for a reduction in the number of required processing steps versus conventional alcohol-to-jet technology. This catalyst system provides the balanced metal and Lewis acid sites required to selectively facilitate a cascading sequence of reactions that includes dehydrogenation, aldol condensation, Meerwein–Ponndorf–Verley reduction, dehydration, and hydrogenation. High conversion and selectivity toward either n-butene or 1,3-butadiene is achieved by tuning the hydrogen feed partial pressure and other process/catalyst parameters. With sufficient hydrogen partial pressure 1,3-butadiene is completely and selectively hydrogenated to form n-butene. The reaction mechanism was elucidated through operando-nuclear magnetic resonance investigations coupled with reactivity measurements. Combined experimental-computational investigation reveals how changes in silver and zirconium composition and the silver oxidation state affects reactivity under controlled hydrogen partial pressures and after prolonged run times. Finally, catalyst effectiveness also was demonstrated when using wet ethanol feed, thus highlighting process flexibility in terms of feedstock purity requirements. This work was financially supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, and was performed at the Pacific Northwest National Laboratory (PNNL) under Contract No. DE-AC05-76RL01830 and the National Renewable Energy Laboratory under Contract No. DE-AC36- 08GO28308. Part of the work conducted by S. A. Akhade was performed under the auspices of the U.S. DOE at Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344. This work was partly supported through the PNNL-WSU Distinguished Graduate Research Program for ADW. NMR and XPS experiments were performed using EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research

Dagle, Vanessa↗
DOE OSTI2021

Extension of the Hauser-Feshbach fission fragment decay model to multichance fission

The Hauser-Feshbach fission fragment decay model, HF 3 D, which calculates the statistical decay of fission fragments, has been expanded to include multichance fission, up to neutron incident energies of 20 MeV. The deterministic decay takes as input prescission quantities—fission probabilities and the average energy causing fission—and postscission quantities—yields in mass, charge, total kinetic energy, spin, and parity. From these fission fragment initial conditions, the full decay is followed through both prompt and delayed particle emissions, allowing for the calculation of prompt neutron and γ properties, such as multiplicity and energy distributions, both independent and cumulative fission yields, and delayed neutron observables. Here, we describe the implementation of multichance fission in the HF 3 D model, and show an example of prompt and delayed quantities beyond first-chance fission, using the example of neutron-induced fission on 235 U. This expansion represents significant progress in consistently modeling the emission of prompt and delayed particles from fissile systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2014

Smart Grid Development Issues for Terrestrial and Space Applications

The development of the so called Smart Grid has as many definitions as individuals working in the area. Based on the technology or technologies that are of interest, be it high speed communication, renewable generation, smart meters, energy storage, advanced sensors, etc. they can become the individual defining characteristic of the Smart Grid. In reality the smart grid encompasses all of these items and quite at bit more. This discussion attempts to look at what the needs are for the grid of the future, such as the issues of increased power flow capability, use of renewable energy, increased security and efficiency and common power and data standards. It also shows how many of these issues are common with the needs of NASA for future exploration programs. A common theme to address both terrestrial and space exploration issues is to develop micro-grids that advertise the ability to enable the load leveling of large power generation facilities. However, for microgrids to realize their promise there needs to a holistic systems approach to their development and integration. The overall system integration issues are presented along with potential solution methodologies.

Power Systems↗
DOE OSTI2026

Data for Resilience and Reliability [Slides]

The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Planning for Major Energy Disruptions in the Southeast" focuses on strategies for entities in the Southeast to quickly recover from impacts to energy infrastructure caused by extreme events, such as severe weather and natural disasters. This presentation focuses on the data available related to energy disruptions. The workshop is on April 23, 2026.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1998

Testing of the Geoscience Laser Altimeter System (GLAS) Prototype Loop Heat Pipe

This paper describes the testing of the prototype loop heat pipe (LHP) for the Geoscience Laser Altimeter System (GLAS). The primary objective of the test program was to verify the loop's heat transport and temperature control capabilities under conditions pertinent to GLAS applications. Specifically, the LHP had to demonstrate a heat transport capability of 100 W, with the operating temperature maintained within +/-2K while the condenser sink was subjected to a temperature change between 273K and 283K. Test results showed that this loop heat pipe was more than capable of transporting the required heat load and that the operating temperature could be maintained within +/-2K. However, this particular integrated evaporator-compensation chamber design resulted in an exchange of energy between the two that affected the overall operation of the system. One effect was the high temperature the LHP was required to reach before nucleation would begin due to inability to control liquid distribution during ground testing. Another effect was that the loop had a low power start-up limitation of approximately 25 W. These Issues may be a concern for other applications, although it is not expected that they will cause problems for GLAS under micro-gravity conditions.

Douglas, Donya↗
DOE OSTI2020

Effectively Using Remote I/O For Work Composition in Distributed Workflows

Distributed scientific workflows are becoming more important with the interest in incorporating AI into their loops. A critical programming and performance question is how to compose workflow tasks when data is produced on one system but must be consumed on another. Since the dominant technique is composition with remote I/O, this paper explores its performance expectations. We describe BigFlowSim, a workflow I/O simulator that captures key implementation choices for remote I/O, including intensity, reuse, locality, access pattern, and data movement.With BigFlowSim, we generate a synthetic benchmark. We quantify the effects of each parameter with a performance sensitivity study. We explain trends in terms of data movement reduction and show that, under certain conditions, it is possible to establish a total order among most parameters. We apply these insights to a high energy physics workflow, Belle II Monte Carlo and simulate several I/O optimizations. Speedups range from 5% to 2×, without changing compute time.

Friese, Ryan D.↗
DOE OSTI2021

Modeling the Strategic Behavior of an Active Distribution Network in the ISO Markets

With increasing integration of distributed energy resources (DERs), active distribution networks (ADNs) can actively participate in the electricity markets by dispatching their DERs, which can change the existing electricity market paradigm. It is essential to investigate the strategic behaviors of ADNs and their DER dispatch when they participate in the wholesale market as price-makers. This paper proposes a bi-level optimization model to study the strategic behavior of an ADN in both energy and reserve markets. The optimal scheduling of DERs in the ADN is modeled as the upper level problem and the joint energy and reserve market-clearing of the ISO is modeled as the lower-level problem. The two-level optimization models exchange bidding information and energy/reserve prices with each other. The proposed bi-Ievel optimization problem is converted to a mathematical programming with equilibrium constraints (MPEC) by using Karush-Kuhn Tucker (KKT) conditions and strong duality theory. Further, the MPEC problem is reformulated as a computationally-solvable mixed integer second order cone programming (MISOCP) model. The simulation results on an illustrative case demonstrate the impact of the strategic bidding of the ADN on the day-ahead energy and reserve market prices.

Xue, Yaosuo↗
DOE OSTI2023

Bi-Level Adaptive Storage Expansion Strategy for Microgrids Using Deep Reinforcement Learning

Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. Further, the lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.

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