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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

ALESQP: An Augmented Lagrangian Equality-Constrained SQP Method for Optimization with General Constraints

Here we present a new algorithm for infinite-dimensional optimization with general constraints, called ALESQP. In short, ALESQP is an augmented Lagrangian method that penalizes inequality constraints and solves equality-constrained nonlinear optimization subproblems at every iteration. The subproblems are solved using a matrix-free trust-region sequential quadratic programming (SQP) method that takes advantage of iterative, i.e., inexact linear solvers, and is suitable for large-scale applications. A key feature of ALESQP is a constraint decomposition strategy that allows it to exploit problem-specific variable scalings and inner products. We analyze convergence of ALESQP under different assumptions. We show that strong accumulation points are stationary. Consequently, in finite dimensions ALESQP converges to a stationary point. In infinite dimensions we establish that weak accumulation points are feasible in many practical situations. Under additional assumptions we show that weak accumulation points are stationary. We present several infinite-dimensional examples where ALESQP shows remarkable discretization-independent performance in all of its iterative components, requiring a modest number of iterations to meet constraint tolerances at the level of machine precision. Also, we demonstrate a fully matrix-free solution of an infinite-dimensional problem with nonlinear inequality constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Performance Portability of HPC Discovery Science Software: Fusion Energy Turbulence Simulations at Extreme Scale

As HPC R&D moves forward on a variety of “path to exascale” architectures today, an associated objective is to demonstrate performance portability of discovery-science-capable software. Important application domains, such as Magnetic Fusion Energy (MFE), have improved modelling of increasingly complex physical systems -- especially with respect to reducing “time-to-solution” as well as “energy to solution.” The emergence of new insights on confinement scaling in MFE systems has been aided significantly by efficient software capable of harnessing powerful supercomputers to carry out simulations with unprecedented resolution and temporal duration to address increasing problem sizes. Specifically, highly scalable particle-in-cell (PIC) programing methodology is used in this paper to demonstrate how modern scientific applications can achieve efficient architecture-dependent optimizations of performance scaling and code portability for path-to-exascale platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Distribution System V1G PEV Charging Impacts Report

Plug-in Electric Vehicles (PEVs) are a clean form of transportation that will require a new control paradigm between the energy supply and the transportation sectors as PEV adoption continues to grow. Meeting the projected PEV energy demands will require implementing PEV / grid integration methods beyond charging PEVs at incentivized times. MATLAB was used to simulate the daily vehicle trip demands and charging processes of hundreds of PEVs. The National Household Travel Survey (NHTS) data was used as the source of vehicle travel data, and EV Project provided measured PEV charging data to calibrate the PEV population models. GridLAB-D analyses used a prototypical feeder and PEV population models to evaluate residential PEV V1G (unidirectional) charging in multiple locations, V1G PEV distribution feeder impacts, Time-Of-Use (TOU) rate effects on PEV charging, and charging control methods. The key findings were: optimized charging using California TOU rates could result in savings of over $20 per month for non-TOU program participants; non-TOU participants consume 30-40% of PEV charging energy; about 50% of PEV drivers have their transportation fuel bill reduced by 50 dollars per month; 6.6kW charging will cause distribution transformer power limits to be exceeded on prototypical feeders if the household PEV adoption rate reaches 0.75 PEVs per home; distribution feeder loading is proportional to PEV charging rate; a method was devised to remotely identify when residential transformer overloads occur using changes in PEV charging rate; and the low-cost TOU period is composed of a distribution of PEVs needing small, medium and larger energy needs that can be more effectively shifted as groups to mitigate the TOU peak power and enable a higher PEV adoption rate per household without overloading distribution transformers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Hydrogen Diffusivity Measurements of YH 1.87 Moderator Material with Incoherent Quasielastic Neutron Scattering

Yttrium hydride is an optimal choice of moderator material for thermal neutron spectrum reactors requiring small core volumes. The Transformation Challenge Reactor (TCR) program at Oak Ridge National Laboratory (ORNL) aims to develop an additively manufactured microreactor core by leveraging recent advances in materials, manufacturing, data analytics, and high-fidelity modeling and simulation. Yttrium hydride was selected as the moderator material for TCR due to its high moderating power, which enables the spatially efficient reactor design and thermal stability that is superior when compared with zirconium hydride. Hydrogen desorption from yttrium hydride is expected at elevated temperatures, posing safety and performance concerns. As a lifetime component in advanced reactors, appropriate strategies to mitigate hydrogen release from yttrium hydride over long-term reactor operation are required. Understanding hydrogen mobility in yttrium hydride provides the scientific basis for developing a hydrogen barrier for hydride moderator and predicting the hydrogen redistribution within the material during various operational conditions. This study investigated the hydrogen motion in YH 1.87 as a function of temperature ranging from 200 to 1,173 K using incoherent quasielastic neutron scattering (IQNS) at the Spallation Neutron Source (SNS). The results are presented and discussed in terms of hydrogen self-diffusion coefficients, activation energy for translational hydrogen motion, and hydrogen jump distances. Based on the IQNS data, YH 1.87 is found to be stable at high temperatures with no translational H motion below 1,023 K within the IQNS detection limits. In the temperature range of 1,073–1,173 K, hydrogen diffusivity in YH 1.87 is $D = 4.57 X 10^{⁻3} [m^2/s] exp (⁻\frac{1.73[eV]}{RT}$). The hydrogen concentration’s dependence on hydrogen diffusivity is also discussed in this report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Facilitated Direct Liquid Fuel Cells with High Temperature Membrane Electrode Assemblies

Dimethyl ether (DME) is a liquid fuel of great potential impact due to its exceptionally high energy density. However, it has received minimal prior investigation as an alternative to either purified hydrogen or other liquid fuels, including methanol (MeOH). In the limited published literature work on direct dimethyl ether fuel cells, regardless of operating temperature, PtRu (either supported or unsupported on carbon) has been established as the standard catalyst of choice. The majority of the work in this program also utilized a Johnson Matthey (JM) HiSPEC ® 12100 PtRu/C (nominally 50% Pt, 25% Ru) while looking at electrode optimizations, beginning of life (BoL) performance, pressure- and temperature-dependent studies to look at the effect of binding affinity of DME oxidation intermediates, mass transport effects, crossover studies, and durability. However, it does also investigate some promising alternatives to PtRu/C as well, which should be investigated in more detail in further work. Those catalysts include a pair of ternary PtRuPd/C catalysts (from Los Alamos National Laboratory (LANL) and Pajarito Powder, LLC. (PP)) as well as a Pt 2 Bi Black catalyst from Professor Anastasios Angelopoulos of the University of Cincinnati (UC). This work achieved several project objectives, including an optimization of the membrane electrode assembly (MEA) process using PtRu/C anode catalyst. Additionally, these direct dimethyl ether fuel cells (DDFCs) were able to match or exceed many performance metrics for the state-of-the-art (SOA) direct methanol fuel cells (DMFCs), a primary and more evolved competitor to direct dimethyl ether fuel cells. This included peak specific power, total platinum group metal (PGM) loading, crossover current, degradation rate, start/stop cycling losses, and anode specific current.

09 BIOMASS FUELS↗
DOE OSTI2021

Secure Communications Concept and API Concept for Integrating XENDEE Positronix with TESLA PowerPack System at Site 300 (Final Deliverable)

The CleanStart DERMS project focuses on the management of Distributed Energy Resources (DER) for enhanced distribution grid resilience. The demonstration site has changed from Riverside Public Utility to the LLNS Site 300 DERS demonstration site. This project has so far focused only on device level controllers and local area controllers. These controllers potentially lack the ability to perform supervisory control and grid interactive control functions, essential for grid-level optimal DER management. This project seeks to close that gap in development of secure communication concept and appropriate Application Programming Interfaces (API) to enable integration with DERs, device level and local area controllers, such as Distributed Energy Resources Management System (DERMS).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Best Practices for Equitable Solar Workforce Development

The Midwest Renewable Energy Association (MREA) was selected to serve as a lead organization for the U.S. Department of Energy Solar Energy Technology Office’s Equitable Solar Communities of Practice initiative. This project, facilitated through a partnership with ENERGYWERX, aimed to develop strategies to support the expansion of equitable benefits in solar adoption across the U.S. Specifically, the MREA was chosen to lead the solar workforce development community of practice, focusing on scaling the U.S. solar workforce, to meet growing industry demands and ensure that these opportunities are accessible and beneficial to all communities. For the purpose of this initiative, we define the solar workforce in line with the National Solar Jobs Census, which defines a solar worker as someone who spends a majority of their time on solar-related work. This also includes workers who spend a plurality of their time on solar tasks. It’s important to note that manufacturing jobs were not included in this research, as the focus is primarily on solar installation, development, and related roles. To achieve the goals of the Equitable Solar Communities of Practice initiative, the MREA leveraged existing resources and engaged a diverse core team and group of stakeholders including industry professionals, educators, policymakers, and community leaders. The MREA began with a literature review and gap analysis to identify existing best practices and gaps in the solar workforce. This was followed by a community convening to gather insights from a wide range of stakeholders. The findings informed the best practices and pathways to scale the benefits of solar workforce development, focusing on training programs, workforce services, apprenticeship, and justice, inclusion, and sustainability. This report outlines the background, methodology, findings, and conclusions drawn from the landscape and gap analysis, providing valuable insights into workforce needs and training program capacities across the U.S. The outcomes of this research are presented in this report and contain recommendations for optimizing workforce development and training funding to support the equitable growth of the solar industry, ensuring that the transition to solar energy is inclusive and beneficial for all communities.

14 SOLAR ENERGY↗
DOE OSTI2017

How Many Kilonovae Can Be Found in Past, Present, and Future Survey Data Sets?

The discovery of a kilonova (KN) associated with the Advanced LIGO (aLIGO)/Virgo event GW170817 opens up new avenues of multi-messenger astrophysics. Here, using realistic simulations, we provide estimates of the number of KNe that could be found in data from past, present, and future surveys without a gravitational-wave trigger. For the simulation, we construct a spectral time-series model based on the DES-GW multi-band light curve from the single known KN event, and we use an average of BNS rates from past studies of ${10}^{3}\,{\mathrm{Gpc}}^{-3}\,{\mathrm{yr}}^{-1}$, consistent with the one event found so far. Examining past and current data sets from transient surveys, the number of KNe we expect to find for ASAS-SN, SDSS, PS1, SNLS, DES, and SMT is between 0 and 0.3. We predict the number of detections per future survey to be 8.3 from ATLAS, 10.6 from ZTF, 5.5/69 from LSST (the Deep Drilling/Wide Fast Deep), and 16.0 from WFIRST. The maximum redshift of KNe discovered for each survey is $z=0.8$ for WFIRST, $z=0.25$ for LSST, and $z=0.04$ for ZTF and ATLAS. This maximum redshift for WFIRST is well beyond the sensitivity of aLIGO and some future GW missions. For the LSST survey, we also provide contamination estimates from Type Ia and core-collapse supernovae: after light curve and template-matching requirements, we estimate a background of just two events. Finally, more broadly, we stress that future transient surveys should consider how to optimize their search strategies to improve their detection efficiency and to consider similar analyses for GW follow-up programs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

TMS 2014 143rd Annual Meeting & Exhibition, Annual Meeting Supplemental Proceedings (ISBN: 978-1-118-88972-5)

The Magnetic Materials for Energy Applications IV, held at the 2014, 143rd Annual Meeting of The Minerals, Metals, and Materials Society (TMS), brought together experts, young investigators, and students from this sub-discipline of materials science in order for them to share their latest discoveries and develop collaborations. This symposium, which is organized by The Minerals, Metals, and Materials Society, is an important event for this community of scientists. This year, over 50 high-level technical talks were planned over the course of the event. In addition, the students and young investigators in attendance ensured the maximum benefit to the next generation’s work force in this area of study. Meeting global energy needs in a clean, efficient, secure and sustainable manner is arguably the greatest challenge facing mankind today. Magnetic materials play a critical role in myriad devices for the collection, conversion, transmission and storage of energy. For example, high performance permanent magnets are currently in great demand for the generators in wind turbines and electric motors in hybrid vehicles. Other classes of advanced magnetic materials are essential for efficient inductors/transformers and motors. Energy efficient cooling based on the magnetocaloric effect is another exciting possibility which is rapidly becoming industrially viable. The potential energy savings related to refrigeration and air conditioning based on magnetocaloric materials are highly attractive. This symposium allowed experts in areas relating to the advanced characterization, simulation, and optimization of magnetic materials to convene and present their latest research. The types of interactions afforded by this event are beneficial to society at large primarily because they provide opportunities for the leaders within this field to learn from one another and thus improve the quality and productivity of their investigations. Additionally, the presence of young investigators and students with technical interests in this field provides promise that the United States will continue to be a leader in this area. The support provided by the Department of Energy for this event directly enhanced its impact on the field by helping a number of students, young investigators, and technical experts attend and participate in this event. This symposium brought together research experts and students in the field to present the latest developments on the science surrounding advanced characterization, simulation, and optimization of magnetic materials and their use in energy applications such as generation and magnetic cooling. A strong technical program, containing more than 50 presentations was developed and organized around the following technical sessions. A strong list of invited speakers contributed to the high quality of technical content. Rare Earth Permanent Magnets: Processing, Characterization and Modeling Rare Earth Free Permanent Magnets Fundamentals of the Magnetocaloric Effect and Current Status of Magnetic Cooling Technology Magnetocaloric Materials High Performance Soft Magnets I High Performance Soft Magnets II

36 MATERIALS SCIENCE↗
DOE OSTI2016

A Distributed OpenCL Framework using Redundant Computation and Data Replication

Applications written solely in OpenCL or CUDA cannot execute on a cluster as a whole. Most previous approaches that extend these programming models to clusters are based on a common idea: designating a centralized host node and coordinating the other nodes with the host for computation. However, the centralized host node is a serious performance bottleneck when the number of nodes is large. In this paper, we propose a scalable and distributed OpenCL framework called SnuCL-D for large-scale clusters. SnuCL-D's remote device virtualization provides an OpenCL application with an illusion that all compute devices in a cluster are confined in a single node. To reduce the amount of control-message and data communication between nodes, SnuCL-D replicates the OpenCL host program execution and data in each node. We also propose a new OpenCL host API function and a queueing optimization technique that significantly reduce the overhead incurred by the previous centralized approaches. To show the effectiveness of SnuCL-D, we evaluate SnuCL-D with a microbenchmark and eleven benchmark applications on a large-scale CPU cluster and a medium-scale GPU cluster.

Kim, Junghyun↗
DOE OSTI2017

DOE Zero Energy Ready Home Case Study: United Way of Long Island, United Veterans Beacon House

United Way of Long Island’s Housing Development Corporation built this 3,719-ft2 two–story, 5-bedroom home in Huntington Station, New York, to the rigorous performance requirements of the U.S. Department of Energy’s Zero Energy Ready Home Program. The home is packed with high-performance features like LED lighting and ENERGY STAR appliances. The asymmetrical, optimally angled roof provides plenty of space for roof-mounted solar panels for electric generation and hot water.

Building America↗
DOE OSTI2019

First Test Results of Superconducting Twin Axis Cavity for ERL Applications

Superconducting cavities with two beam pipes had been proposed in the past for energy recovery linac applications. The relatively complex geometry of those cavities presented a serious challenge for fabrication and surface processing. Main concerns have now been overcome with the production and successful RF testing of a new elliptical twin-axis cavity proposed by Jefferson Lab and optimized by the Center for Accelerator Science at Old Dominion University in the frame of a DoE accelerator stewardship program. The cavity design provides uniform accelerating or decelerating fields for both beams. This paper describes the cavity design, fabrication experience, and the first cold RF test results and explores potential applications especially for Jefferson Lab s EIC (JLEIC).

Park, H.-K.↗
DOE OSTI2020

Examining the Net Revenue and Downstream Flow Impact Trade-Offs for a Network of Cascading, Small-Scale Hydropower Facilities: Preprint

In this work, we used a price-taker model to investigate the trade-offs between net revenue and downstream flow impacts for a network of small, cascading hydro facilities. The network consisted of 36 facilities, each with a small amount of local storage (between 2 and 45 minutes). Generator sizes ranged between 0.5 and 1 MW, nominal, and the total capacity of the network was 33.5 MW. We used a multi-integer linear programing model to maximize the net revenue of the combined network subject to operating and environmental constraints. Net revenue optimizations relied on historic price data, and dry, typical, and wet years were studied to help ensure robustness. Energy and ancillary service sales were included in the net revenue calculations, and both unit commitment and dispatch simulations were performed. We found that limiting the downstream flows to ±50% of the river’s natural flows had a negligible impact on net revenues (<1% reduction), irrespective of hydrologic conditions, and even when downstream flows were limited to ±5%, net revenues were only impacted by 4%. These outcomes are significant because they demonstrate how an array of small-scale hydropower facilities can be operated to have minimal impact on natural stream flows—addressing a critical environmental concern.

downstream flow↗
DOE OSTI2018

Assessment of Optimal Flexibility in Ensemble of Frequency Responsive Loads

Potential of electrical loads in providing grid ancillary services is often limited due to the uncertainties associated with the load behavior. A knowledge of the expected uncertainties with a load control program would invariably yield to better informed control policies, opening up the possibility of extracting the maximal load control potential without affecting grid operations. In the context of frequency responsive load control, a probabilistic uncertainty analysis framework is presented to quantify the expected error between the target and actual load response, under uncertainties in the load dynamics. A closed-form expression of an optimal demand flexibility, minimizing the expected error in actual and committed flexibility, is provided. Analytical results are validated through Monte Carlo simulations of ensembles of electric water heaters.

Kundu, Soumya↗
DOE OSTI2020

Benders Cut Classification via Support Vector Machines for Solving Two-Stage Stochastic Programs

In this work, we consider Benders decomposition for solving two-stage stochastic programs with complete recourse based on finite samples of the uncertain parameters. We define the Benders cuts binding at the final optimal solution or the ones significantly improving bounds over iterations as valuable cuts. We propose a learning-enhanced Benders decomposition (LearnBD) algorithm, which adds a cut classification step in each iteration to selectively generate cuts that are more likely to be valuable cuts. The LearnBD algorithm includes two phases: (i) sampling cuts and collecting information from training problems and (ii) solving testing problems with a support vector machine (SVM) cut classifier. We run the LearnBD algorithm on instances of capacitated facility location and multicommodity network design under uncertain demand. Our results show that SVM cut classifier works effectively for identifying valuable cuts, and the LearnBD algorithm reduces the total solving time of all instances for different problems with various sizes and complexities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Electric Grid Expansion Planning with High Levels of Variable Generation

Renewables are taking a large proportion of generation capacity in U.S. power grids. As their randomness has increasing influence on power system operation, it is necessary to consider their impact on system expansion planning. To this end, this project studies the generation and transmission expansion co-optimization problem of the US Eastern Interconnection (EI) power grid with a high wind power penetration rate. In this project, the generation and transmission expansion problem for the EI system is modeled as a mixed-integer programming (MIP) problem. This study analyzed a time series creation method to capture the diversity of load and wind power across balancing regions in the EI system. The obtained time series can be easily introduced into the MIP co-optimization problem and then solved robustly through available MIP solvers. Simulation results show that the proposed time series generation method and the expansion co-optimization model and can improve the expansion result significantly after considering the diversity of wind and load across EI regions. The improved expansion plan that combines generation and transmission will aid system planners and policy makers to maximize the social welfare. This study shows that modelling load and wind variations and diversities across balancing regions will produce significantly different expansion result compared with former studies. For example, if wind is modeled in more details (by increasing the number of wind output levels) so that more wind blocks are considered in expansion planning, transmission expansion will be larger and the expansion timing will be earlier. Regarding generation expansion, more wind scenarios will slightly reduce wind generation expansion in the EI system and increase the expansion of other generation such as gas. Also, adopting detailed wind scenarios will reveal that it may be uneconomic to expand transmission networks for transmitting a large amount of wind power through a long distance in the EI system. Incorporating more details of renewables in expansion planning will inevitably increase the computational burden. Therefore, high performance computing (HPC) techniques are urgently needed for power system operation and planning optimization. As a scoping study task, this project tested some preliminary parallel computation techniques such as breaking down the simulation task into several sub-tasks based on chronology splitting or sample splitting, and then assigning these sub-tasks to different cores. Testing results show significant time reduction when a simulation task is split into several sub-tasks for parallel execution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Surface‐modified Ag@Ru‐P25 for photocatalytic CO 2 conversion with high selectivity over CH 4 formation at the solid–gas interface

Systematic optimization of the photocatalyst and investigation of the role of each component is important to maximizing catalytic activity and comprehending the photocatalytic conversion of CO 2 reduction to solar fuels. A surface-modified Ag@Ru-P25 photocatalyst with H 2 O 2 treatment was designed in this study to convert CO 2 and H 2 O vapor into highly selective CH 4 . Ru doping followed by Ag nanoparticles (NPs) cocatalyst deposition on P25 (TiO 2 ) enhances visible light absorption and charge separation, whereas H 2 O 2 treatment modifies the surface of the photocatalyst with hydroxyl (–OH) groups and promotes CO 2 adsorption. High-resonance transmission electron microscopy, X-ray photoelectron spectroscopy, X-ray absorption near-edge structure, and extended X-ray absorption fine structure techniques were used to analyze the surface and chemical composition of the photocatalyst, while thermogravimetric analysis, CO 2 adsorption isotherm, and temperature programmed desorption study were performed to examine the significance of H 2 O 2 treatment in increasing CO 2 reduction activity. The optimized Ag 1.0 @Ru 1.0 -P25 photocatalyst performed excellent CO 2 reduction activity into CO, CH 4 , and C 2 H 6 with a ~95% selectivity of CH 4 , where the activity was ~135 times higher than that of pristine TiO 2 (P25). For the first time, this work explored the effect of H 2 O 2 treatment on the photocatalyst that dramatically increases CO 2 reduction activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗