Results for “programming systems” — Page 4

DOE OSTI2020

The Ristra Project: FY20/21 Milestone Report

The ASC Advanced Technology Development and Mitigation (ATDM) sub-program was established in 2014 to develop new simulation tools operating on exascale-class computers to serve NNSA (see Appendix B). Over the course of ATDM, LANL management have set a strategy for exascale-class application codes that follows two supportive and mutually risk-mitigating paths: evolution for established production integrated design codes (IDCs) – with a strong pedigree within the user community – based upon existing programming paradigms(MPI+X); and a new start ATDM project, Ristra, a high-risk/high-reward push for a next-generation multi-physics, multi-scale simulation toolkit based on emerging advanced programming systems(with an initial focus on data-flow task-based models exemplified by Legion). The role of Ristra as the high-risk/high-reward path for LANL’s codes was fully consistent with the goals of ATDM as described in Appendix B, in particular its emphasis on evolving ASC capabilities through novel computing programming models and computing technologies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Enhanced Simulation Tools for Scheduling Solar Plus Storage Power Plants

This is the final technical report for the Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technology's Office Balance of Systems program, Award Number 38429. This report summarizes the key outputs and findings of the project, which was focused on long-duration energy storage (LDES) and solar. In particular, one of the most significant challenges of maximizing the value of variable solar energy is the timing of charging and discharging stored energy from LDES devices. Power systems often fail to capture the benefits of LDES and can undervalue the role of storage in providing energy arbitrage, reliability support, and resiliency. This project examined those challenges and proposed alternative methodologies that better capture the value that LDES can provide.

14 SOLAR ENERGY↗

DOE OSTI2026

Aligning NASA Earth Science Data Stewardship with FAIR Principles: Outcomes, Recommendations, and Future Directions

The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—offer a widely accepted framework for improving the sharing and reuse of digital scientific data by both human and machine users. Following these principles is critical for effective scientific data stewardship, broader scientific collaboration, and compliance with federal and agency data policies. This paper, based on the work of NASA’s Open, Free, and FAIR Working Group (O’FAIR WG) under the Earth Science Data Systems Program, presents an overview of how FAIR is being applied within NASA’s Earth science data landscape. It highlights ongoing progress and challenges, identifies FAIR-enabling resources, and offers recommendations and strategic actions to enhance the FAIRness of NASA-funded open and free Earth science data products. The FAIR-enabling resources identified underscore the vital role of NASA's existing enterprise processes, standards, tools, and infrastructures in supporting FAIR implementation. Our findings show strong performance in making NASA Earth science data more findable and accessible. However, further work is needed—especially in enhancing interoperability, so that different systems and tools can better understand and exchange data. This is especially important for enabling machine-driven discovery and analysis. We emphasize the importance of a balanced strategy that combines a centralized, top-down approach—focused on building enterprise-level capabilities and processes—with a decentralized, bottom-up approach driven by discipline-specific needs and community practices. We advocate for coordinated efforts to enhance (meta)data interoperability to facilitate seamless data and information sharing and exchange of Earth science data both within NASA and across other agencies managing Earth science data.

Data Product↗

DOE OSTI2020

SMART Mobility. Mobility Decision Science Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Mobility Decision Science Pillar. The Mobility Decision Science Pillar sought to fill gaps in existing knowledge about the human role in the mobility system including travel decision-making and technology adoption in the context of future mobility. The objective was to study how underlying preferences, needs, and contextual factors might constrain or hasten future transportation system scenarios. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

PotLib 2023: New version of a potential energy surface library for chemical systems

POTLIB is a library of global and semiglobal potential energy surface subprograms. The library currently features 410 entries, including both single-state entries and multi-state entries. When one calls the routine of a single-state entry, it returns the ground-electronic-state adiabatic potential energy surface at the input geometry. In addition, some entries also return the gradient of the surface. Further, when one calls a multi-state entry, it returns a diabatic potential energy matrix (DPEM). If the entry also has the gradient of the DPEM, one can compute adiabatic surfaces, their gradients, and the nonadiabatic coupling vectors (NACs) from the DPEM and its gradient by diagonalization. Some but not all the routines conform to one of a set of standard interfaces. The goal is to facilitate chemical dynamics research by collecting and disseminating a comprehensive collection of state-of-the-art potential energy routines (developed by a wide, international group of researchers) with systematic and well-defined interfaces for use with chemical dynamics programs. Systems in the library include CHArO 2 , CHN 2 O + , CH 2 O, CH 2 O 2 , CH 3 N 2 , CH 3 O, CH 4 , CH 4 Br, CH 4 Cl, CH 4 F, CH 4 O, CH 4 OCl, CH 4 OF, CH 4 OH, CH 5 , CH$^{+}_{5}$, CH 5 N, CH 5 O 2 , CH 8 O 2 , C 2 H 2 N 2 O, C 2 H 2 O, C 2 H 4 N, C 2 H 4 O 4 , C 2 H 6 Cl, C 2 H 6 F, C 2 H 6 H, C 2 H 6 O, C 2 H 6 OH, C 2 H 7 , C 2 O 2 , C 3 H 4 O 2 , C 3 H 7 NO, C 6 H 6 O, C 6 H 6 S, C 7 H8S, HBrCl, HCl 2 , HF 2 , HI 2 , HLiF, HNaF, HO 2 , HO 3 , HOBr, HSiO, H 2 Br, H 2 ClO, H 2 F, H 2 F 2 , H 2 FO, H 2 Na, H 2 O, H 2 O 2 , H 2 OBr, H 3 , H 3 Cl, H 3 ClN, H 3 ClO, H 3 N, H 3 O, H 3 O 2 , H$^{+}_{3}$, H 3 S, H 4 ClSi, H 4 N, H 4 NO, H 4 O 2 , H 5 GeO, H 5 Si, H$^{+}_{7}$, Al m H n , Al n , Ar N O, K 2 Rb 2 , NO 2 , N 2 O, N 2 O 2 , N 3 , N 4 , O 3 , and O 4 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

SMART Mobility. Modeling Workflow Development, Implementation, and Results Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the SMART Mobility Modeling Workflow effort. The SMART Mobility Modeling Workflow was developed to evaluate new transportation technologies such as connectivity, automation, sharing, and electrification through multi-level systems analysis that captures the dynamic interactions between technologies. By integrating multiple models across different levels of fidelity and scale, the Workflow yields insights about the influence of new mobility and vehicle technologies at the system level. For information about the other Pillars, please refer to the relevant pillar’s Capstone Report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

SMART Mobility. Urban Science Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The Urban Science (US) Pillar focuses on maximum-mobility and minimum-energy opportunities associated with emerging transportation and transportation-related technologies specifically within the urban context. Such technologies, often referred to as automated, connected, efficient (or electrified), and shared (ACES), have the potential to greatly improve mobility and related quality of life in urban areas. Although all the SMART Mobility research pillars share some commonalities, Urban Science strives to model, analyze, and gain insights from the perspective of human settlements (the “city”) as a living organism. This is especially critical as the United States is one of the most urbanized countries, and as more and more of the global population migrates to urban areas.1 The urban mobility system consists of a complex network that reaches well beyond roads and vehicles and includes significant investments in public transit, private mobility services (such as taxis and transportation network companies, or TNCs), significant parking reserves, and curb management practices, not to mention the abundance of emerging on-demand micromobility services for the movement of people and goods such as e-bikes and scooters, which make the urban space a dynamic laboratory for mobility. Urban spaces also concentrate employment, markets, services, and attractions, which are the destinations for most trips. The concentration of human activities and ensuing density also creates the need and emphasis for space efficiency in urban environments, which is often less of a constraint in suburban or rural contexts. This mixture of transportation and mobility infrastructure and practice, combined with global urbanization trends, make urban spaces a critical focus of research for developing energy-efficient mobility systems (EEMS).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

IRIS Reimagined: Advancements in Intelligent Runtime System for Task-Based Programming

Task-based programming models are gaining traction in scientific computing. IRIS is a portable runtime system that exploits multiple heterogeneous programming systems and can discover available resources and manage multiple diverse programming systems (e.g., CUDA, Hexagon, HIP, Level Zero, OpenCL, and OpenMP) simultaneously. It accounts for the constraints of task dependencies and provides customizable scheduling policies to map those tasks to heterogeneous devices. In this paper, we present new capabilities added to IRIS to improve its portability for heterogeneous programming, build-friendliness, and performance efficiency. The new additions include vendor-specific kernel support, a runtime system with a foreign function interface to eliminate writing wrapper or boilerplate code for heterogeneous kernels, an easy-to-use and configurable CMake-based build environment, automatic and efficient data transfers and orchestration, and the Hunter and DAGGER toolchains to evaluate IRIS’s task scheduling algorithms.

Miniskar, Narasinga Rao↗

DOE OSTI2020

SMART Mobility. Multi-Modal Freight Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Multi-Modal Freight Pillar. The Multi Modal Freight Pillar’s objective is to assess the effectiveness of emerging freight movement technologies and understand the impacts of the growing trends in consumer spending and e-commerce on parcel movement considering mobility, energy, and productivity. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

SMART Mobility. Connected and Automated Vehicles Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Connected and Automated Vehicles (CAVs) Pillar. This Pillar investigated the energy, technology, and usage implications of vehicle connectivity and automation and identified efficient CAV solutions. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

SMART Mobility. Advanced Fueling Infrastructure Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Advanced Fueling Infrastructure Pillar. This Pillar investigated the charging infrastructure needs of electric ride-hailing and car-sharing vehicles, automated shuttle buses, and freight-delivery truck fleets. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Self-Propulsion by Directed Explosive Emulsification

An active droplet system, programmed to repeatedly move autonomously at a specific velocity in a well-defined direction, is demonstrated. Coulombic energy is stored in oversaturated interfacial assemblies of charged nanoparticle-surfactants by an applied DC electric field and can be released on demand. Spontaneous emulsification is suppressed by an increase in the stiffness of the oversaturated assemblies. Rapidly removing the field releases the stored energy in an explosive event that propels the droplet, where thousands of charged microdroplets are ballistically ejected from the surface of the parent droplet. The ejection is made directional by a symmetry breaking of the interfacial assembly, and the combined interaction force of the microdroplet plume on one side of the droplet propels the droplet distances tens of times its size, making the droplet active. The propulsion is autonomous, repeatable, and agnostic to the chemical composition of the nanoparticles. The symmetry-breaking in the nanoparticle assembly controls the microdroplet velocity and direction of propulsion. Finally, this mechanism of droplet propulsion will advance soft micro-robotics, establishes a new type of active matter, and introduces new vehicles for compartmentalized delivery.

36 MATERIALS SCIENCE↗

DOE OSTI2025

COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin–Orbit Couplings and Dynamics

The COLUMBUS program system provides the tools for performing high-level multireference (MR) computations, including the multireference configuration interaction (MRCI) method and its multireference averaged quadratic coupled cluster (MR-AQCC) extension, allowing computations on a wide range of fascinating atomic and molecular systems, including the treatment of open-shells and complicated excited state phenomena. The inclusion of spin−orbit coupling (SOC) directly within the MRCI step enables the description of systems containing heavy elements, such as lanthanides and actinides, whose properties are strongly influenced by SOC. Analytic energy gradients and nonadiabatic couplings at the correlated MRCI level provide the foundation for a variety of dynamics studies, giving insight into ultrafast photochemistry. New and ongoing method developments in COLUMBUS include the computation of spin densities, improved descriptions of ionic states, enhancements to the AQCC method, and the porting of COLUMBUS to graphical processing units (GPUs). New external interfaces enable an enhanced description of electronic resonances and molecules in strong laser fields. This work highlights these new developments while providing a detailed account of the diverse applications of COLUMBUS in recent years.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Wootz: a compiler-based framework for fast CNN pruning via composability

Convolutional Neural Networks (CNN) are widely used for Deep Learning tasks. CNN pruning is an important method to adapt a large CNN model trained on general datasets to fit a more specialized task or a smaller device. The key challenge is on deciding which filters to remove in order to maximize the quality of the pruned networks while satisfying the constraints. It is time-consuming due to the enormous configuration space and the slowness of CNN training.The problem has drawn many efforts from the machine learning field, which try to reduce the set of network configurations to explore. This work tackles the problem distinctively from a programming systems perspective, trying to speed up the evaluations of the remaining configurations through computation reuse via a compiler-based framework. We empirically uncover the existence of composability in the training of a collection of pruned CNN models, and point out the opportunities for computation reuse. We then propose composability-based CNN pruning, and design a compression-based algorithm to efficiently identify the set of CNN layers to pre-train for maximizing their reuse benefits in CNN pruning. We further develop a compiler-based framework named Wootz, which, for an arbitrary CNN, automatically generates code that builds a Teacher-Student scheme to materialize composability-based pruning. Experiments show that network pruning enabled by Wootz shortens the state-of-art pruning process by up to 186X while producing significantly better pruning results.

Guan, Hui↗

DOE OSTI2024

Advancing South American Water and Climate Science Through Multi-Decadal Convection-Permitting Modeling

The South America Affinity Group (SAAG) was established in early 2019 by the National Center for Atmospheric Research (NCAR) Water Systems Program as a community effort focused on improving hydroclimate science over South America. SAAG supports large research efforts such as the ANDEX Regional Hydroclimate Program as well as individual research groups. The group started with a dozen members and quickly grew to over 100 participants from more than ten countries. For the past four years, the SAAG has been meeting online every two weeks and has organized sessions at international conferences such as the American Geophysical Union Fall Meeting and the Convection-Permitting Climate Workshop. At the core of the SAAG effort are two multi-decadal convection permitting (CP) model simulations with 4-km grid spacing for historical and future climates over the South American continent. Additionally, a major observational data collection effort has been undertaken, including in-situ station data from South American meteorological and water services, gridded products, satellite-based observations, and field campaign data. In conclusion, this article discusses the research needs and scientific goals that drive this community of scientists with diverse backgrounds and interests.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Improving Energy Efficiency of Air Separation via Hollow Fiber Sorbents

This report summarizes the work performed under the Department of Energy’s National Energy Technology Laboratory (DOE-NETL) Advanced Energy Systems Program award number DE-FE0026163 Improving Energy Efficiency of Air Separation via Hollow Fiber Sorbents. The overall objective of the project was to develop technology to produce oxygen for use in coal gasification processes at a significantly lower cost than that of the commercial state-of-the-art technology. The focus of the project was development and optimization of hollow fiber sorbents for use in a pressure swing adsorption (PSA) process for producing oxygen from air. The key technical advancement under this program was the development of a hollow fiber sorbent capable of producing oxygen in a PSA process. PSAs produce oxygen by using a beaded zeolite material (typically lithium exchanged zeolites or LiX) that selectively adsorb nitrogen over oxygen. For a fiber to adsorb nitrogen, LiX needed to be integrated into a polymer fiber and then activated at over 350C to remove residual moisture. After screening multiple materials and process techniques, Matrimid/LiX hollow fibers were produced that were able to withstand the activation and had crystalline nitrogen capacity close to pure LiX crystals. The fibers were dual layer fibers with a barrier layer isolating the hollow bore from the LiX embedded layer. The barrier layer was formed from an Ultem® based solution. The hollow bore was intended for encapsulating phase change materials to ensure isothermal PSA operation. Modeling work from the project suggested that the benefits of this additional complexity were marginal, thus the team fabricated non-hollow fibers comprised of Matrimid and LiX. The Matrimid/LiX fibers were loaded into a module and tested on a bench scale PSA unit. The fibers were able to separate oxygen from air, but the purity and recovery were lower than expected and lower than traditional beaded LiX zeolites, although modeling results suggested that the bed sizes for fiber sorbents could be half the size of a beaded LiX bed. Additional work will be needed to understand the shortfall and to produce fiber sorbent modules with performance equal to the targets set for this program. In addition to producing fibers, modeling work was carried out at both the fiber level and process level to guide both fiber sorbent development and to determine the optimal process conditions for a PSA using fiber sorbents. A 2D hollow fiber model was developed in gPROMS Model Builder. This model was based on literature data for mass transfer and other parameters. This model used a basic single bed PSA process to determine important fiber properties and process conditions for achieving high oxygen recovery. The preliminary 2D fiber model was a valuable tool, however, for step-out technologies, such as a PSA process with fiber sorbents, intelligent optimization and plant control was needed to effectively determine minimum power designs. Therefore, a gPROMS Process Builder-based optimization tool was developed with an “Automated Cycle Optimizer” model that was able to determine optimal cycle conditions for a fiber sorbent PSA. The results from the optimizer were used in the final power analysis. Flow sheet analysis and techno-economic analysis (TEA) was carried out to determine a lower power solution versus the baseline case. The original project scope suggested that the fiber sorbent PSA could be used to debottleneck a cryogenic air separation plant (cryo-ASU) with the combined plant having lower power than a standalone cryo-ASU. However, early flowsheet analysis showed that the PSA/cryo-ASU concept would not lead to a lower power solution. However, further flowsheet analysis suggested that an oxygen PSA combined with a nitrogen PSA could have lower power versus state-of-the-art air separation technology. Work concluded with the TEA showing a fiber sorbent oxygen PSA combined with an advanced nitrogen PSA could be up to 8% lower power versus the base case cryo-ASU. Overall, there is significant potential remaining for the zeolite hollow fiber sorbent concept, and this project successfully identified -- and ultimately remedied -- many technical challenges associated with scale-up.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2019

Grid Engineering for Accelerated Renewable Energy Deployment (GEARED): National Network Administrator (Final Report)

DOE developed the five-year, Grid Engineering for Accelerated Renewable Energy Deployment (GEARED) initiative and provided funding to create four Distributed Technology Training Consortia (DTTC) made up of universities, utilities, and organizations. These DTTCs supported the inclusion of power systems analysis, research and development, into training activities such as curriculum and short course development, internships & coops, and continuing education. Additionally, DOE aimed to build a national framework for power systems training and curriculum that accelerates the growth of power systems programs and human capacity. To that end, the GEARED initiative utilized a National Network Administrator (NNA) to manage and leverage the activities of the four DTTCs as well as that of others participating in distributed technology and engineering through research or practice. DOE chose the Interstate Renewable Energy Council (IREC) as the NNA.

14 SOLAR ENERGY↗

DOE OSTI2019

Grid Engineering for Accelerated Renewable Energy Deployment (GEARED) - Topic 2: National Network Administrator (Final Report)

DOE developed the five-year, Grid Engineering for Accelerated Renewable Energy Deployment (GEARED) initiative and provided funding to create four Distributed Technology Training Consortia (DTTC) made up of universities, utilities, and organizations. These DTTCs supported the inclusion of power systems analysis, research and development, into training activities such as curriculum and short course development, internships & coops, and continuing education. Additionally, DOE aimed to build a national framework for power systems training and curriculum that accelerates the growth of power systems programs and human capacity. To that end, the GEARED initiative utilized a National Network Administrator (NNA) to manage and leverage the activities of the four DTTCs as well as that of others participating in distributed technology and engineering through research or practice. DOE chose the Interstate Renewable Energy Council (IREC) as the NNA.

14 SOLAR ENERGY↗