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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Accelerating technology development to monitor and minimize effects from land‐based wind energy on birds and bats

While wind energy is a key sector of domestic energy production for the United States, operation of wind turbines directly and indirectly adversely affects certain species of birds and bats. The cumulative effect of wind turbine strikes can have both biological and regulatory consequences, and, in some cases, delay permitting and construction or affect ongoing operations. Technology can help quantify and minimize these effects, but the pace of development, acceptance, and adoption of technological solutions is slow. Although adopting cost‐effective technologies may reduce negative effects on wildlife and help achieve both energy production and conservation goals, consensus is lacking among developers, regulators, and the conservation community regarding how to define technology effectiveness and acceptance and how to develop a standardized process for doing so. Removing barriers to technology advancement requires deviating from the status quo. Changes include 1) creating incentives to mitigate impacts, 2) establishing options for research as mitigation, 3) rethinking how research is funded, 4) increasing stakeholder coordination, and 5) increasing the efficiency of research and development. We recommend the creation of a national framework to establish clear criteria and protocols for technology evaluation and adoption.

17 WIND ENERGY↗
DOE OSTI2025

Possible Observation of Quadrupole Waves in Spin Nematics

Discovery of new states of matter is a key objective in modern condensed matter physics, which often leads to revolutionary technological advancements such as superconductivity. Quantum spin nematic, a “hidden order” that evades conventional magnetic probes, is one such state. Na 2 ⁢BaNi⁢(PO 4 ) 2 is a potential spin nematic material, suggested by the observation of a two-magnon Bose-Einstein condensation from above the saturation field. However, direct confirmation of the spin nematicity remains elusive. Here, this Letter presents inelastic neutron scattering spectra from the putative spin nematic phases of Na 2⁢ BaNi⁢(PO 4 ) 2 , revealing low-energy quadrupole waves that are absent in the neighboring conventional magnetic phases. A spin-one model quantitatively captures the full details of the spin excitation spectra across all low-temperature phases, providing direct evidence of the spin nematic orders. Additionally, we show evidence of the three-magnon continuum and two-magnon bound states in the 1/3-magnetization plateau, revealing condensation of the two-magnon bound state as the origin of the low-field spin nematic supersolid phase.

Sheng, Jieming [Southern University of Science and↗
DOE OSTI2020

Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses -A review

Heating, ventilation and air-conditioning (HVAC) systems have been adopted to create comfortable, healthy and safe indoor environments. In the control loop, the technical feature of the human demand-oriented supply can help operate HVAC effectively. Among many technical options, real time monitoring based on feedback signals from end users has been frequently reported as a critical technology to confirm optimizing building performance. Recent studies have incorporated human thermal physiology signals and thermal comfort/discomfort status as real-time feedback signals. A series of human subject experiments used to be conducted by primarily adopting subjective questionnaire surveys in a lab-setting study, which is limited in the application for reality. With the help of advanced technologies, physiological signals have been detected, measured and processed by using multiple technical formats, such as wearable sensors. Nevertheless, they mostly require physical contacts with the skin surface in spite of the small physical dimension and compatibility with other wearable accessories, such as goggles, and intelligent bracelets. Most recently, a low cost small infrared camera has been adopted for monitoring human facial images, which could detect the facial skin temperature and blood perfusion in a contactless way. Also, according to latest pilot studies, a conventional digital camera can generate infrared images with the help of new methods, such as the Euler video magnification technology. Human thermal comfort/discomfort poses can also be detected by video methods without contacting human bodies and be analyzed by the skeleton keypoints model. Here, new sensing technologies were summarized, their cons and pros were discussed, and extended applications for the demand-oriented ventilation were also reviewed as potential development and applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Module-OT: A Hardware Security Module for Operational Technology

Increased penetration levels of renewable energy and other types of distributed energy resources (DERs) on the modern electric grid-combined with technological advancements for electric system monitoring and control-introduce new cyberattack vectors and increase the cyberattack surface of energy systems. According to the IEEE Std. 1547-2018, DERs must use Modbus, Distributed Network Protocol 3 (DNP3), or Smart Energy Profile 2.0 (SEP2) as their communication protocol. Previous research identified several vulnerabilities and security breaches in each one of these communication protocols; despite this, existing standards for DERs do not recommend cybersecurity measures. In order to reduce vulnerabilities in power distribution systems, this paper presents a novel open-source hardware security module that improves both information and operational security to better protect data and communications on the distribution grid. The security hardware is called “module for operational technology,” or simply Module-OT, and it has been validated and tested in an emulated distribution system application. Module-OT is integrated within a communication system in the transport layer of the Open Systems Interconnection (OSI) model. It improves system security through encryption, authentication, authorization, certificate management, and user access control. The main advancement of Module-OT is the addition of hardware cryptographic acceleration that improves the overall communication performance in terms of end-to-end latency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

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

Projecting California Light-Duty Vehicle Attributes (2019-2035)

In this report, we estimated a set of future vehicle attribute scenarios for new light-duty vehicles for the California market using the Automotive Deployment Options Projection Tool (ADOPT). ADOPT starts simulations with all existing vehicle makes and models and endogenously creates new vehicle models over time based on assumed technology improvements and market conditions. For this study, we simulated four scenarios with varied technology, policy, and electric vehicle infrastructure assumptions. We aggregated simulation results into up to 30 vehicle classes (covering size and price classifications) for each powertrain, for six powertrains. We simulated model years 2019 to 2035. We present results for four vehicle attributes: vehicle acceleration, fuel economy (including electric and gasoline for plug-in hybrid electric vehicles [PHEVs]), vehicle range, and vehicle purchase price. We also present results showing the estimated number of vehicle models available in each vehicle class over time. In the Mid scenario, which contains conditions between our most conservative and most optimistic assumptions for emerging electric and hydrogen technologies, we observe improvements in acceleration, range (for battery-electric vehicles [BEVs]), and vehicle purchase price for electric and hydrogen powertrains. In some cases, we project that consumer preferences lead to trade-offs between vehicle attributes, such as reduced fuel economy in exchange for improved acceleration. Conventional vehicles show modest improvements in fuel economy in these scenarios due to the high numbers of BEV and PHEV sales, which reduce the improvements required in conventional vehicles to meet fleet fuel economy standards. In scenarios with advanced technology assumptions (including reduced battery price and improved energy density), we observe further improvements in BEV range and price relative to the improvements in the Mid scenario.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles

Off-road vehicles, such as wheel loaders, excavators, and harvesters, are extensively utilized across a wide range of industries, including construction, agriculture, and mining. These machines have become indispensable in supporting the day-to-day operational needs of a nation, playing a critical role in various sectors' infrastructure and productivity. However, despite their utility, off-road vehicles are significant consumers of fossil fuels, resulting in substantial emissions that contribute to environmental degradation. This highlights the pressing need for research and technological advancements aimed at improving their energy efficiency and reducing their carbon footprint. There are, however, two primary challenges that must be addressed to achieve these goals. First, off-road vehicles typically perform both driving and working tasks simultaneously, which introduces a high level of complexity into their overall dynamic systems. Analysis the interactions between these functions is challenging. Second, research into off-road vehicles is inherently interdisciplinary, demanding expertise across several domains such as fluid power systems, vehicle dynamics, control theory, optimization techniques, and real-world implementation. Recognizing these challenges, we proposed the project titled "Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles" as a comprehensive solution to enhance fuel efficiency while simultaneously improving productivity. This project specifically focuses on autonomous off-road vehicles, with particular attention to wheel loaders, and seeks to develop novel methods to optimize energy consumption without sacrificing operational performance. The project integrates real-time control algorithms, vehicle dynamics modeling, and co-optimization of powertrain system and vehicle system to achieve these goals. Our optimization strategy dynamically co-optimizes critical parameters at both the powertrain and vehicle levels, including vehicle speed, working tool movements, powertrain dynamics, and engine operations in real-time. To streamline this optimization process, we developed a vehicle model that captures the key dynamics while significantly enhancing computational efficiency. This allows the system to intelligently minimize fuel consumption, all while maintaining or even improving productivity through real-time calculations during various off-road operations. To validate the effectiveness of this energy optimization method, we introduced a state-of-the-art Hardware-in-the-Loop (HIL) testbed. This reconfigurable testbed seamlessly integrates the actual engine with virtual models of the wheel loader's subsystems, allowing for accurate emulation of real-world operational loads and environments. By simulating these conditions, the HIL testbed enables us to evaluate the wheel loader’s performance under diverse working scenarios, ensuring the developed solution is applicable in real-world operations. This testbed proved to be instrumental in validating the optimization algorithms and demonstrating the system's practical effectiveness. During the evaluation and testing phase, we employed the HIL testbed to rigorously assess the energy savings and productivity improvements generated by the optimized system. The results were highly encouraging, revealing that the automated wheel loader achieved over 30% fuel savings compared to traditional, human-operated cycles, with comparable or even enhanced levels of productivity. The insights gained from this HIL-based testing provided critical validation of our approach and highlighted the potential for deploying these optimized autonomous technologies in real-world off-road vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

TRANSPORTATION CONSIDERATIONS FOR USED FUEL AND ADVANCED NUCLEAR ENERGY TECHNOLOGIES

This study focuses on requirements and challenges pertaining to advanced transportable nuclear energy systems, which are currently in development and feature varying degrees of mobility. These reactors mostly use high-assay low-enriched uranium (HALEU) fuel, and only limited refueling is required during their lifecycles. Due to the smaller source terms, higher burnup fuel, and potentially higher thermal efficiency involved, the waste inventory will be less than that generated by conventional light-water reactors. However, the relevant regulatory requirements must be met when transporting HALEU fuel (whether fresh or used) inside the reactor vessel—mainly the fuel material transportation, siting, and physical protection requirements outlined in US Nuclear Regulatory Commission (NRC) regulations 10 CFR 71 and 73. Spent nuclear fuel (SNF), as well as irradiated fuel (used during any interval of reactor operation), contains highly radioactive materials that generate heat and necessitate special precautions during transportation. This study addresses the challenges that this incurs, and outlines potential solutions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

2024 IUFRO Tree Biotechnology Conference (Aug 4-8, 2024)

The 2024 IUFRO Tree Biotechnology Conference is the biennial meeting on genomics, molecular biology, and biotechnology of forest trees, associated with the IUFRO Working Party 2.04.06. This year's meeting was held in Annapolis, MD, USA from August 4th to 8th and was hosted by Yiping Qi (University of Maryland), Edward Eisenstein (University of Maryland), Gary Coleman (University of Maryland), and Heather Coleman (Syracuse University). The conference covered seven topics over the course of five days: 1) Biological and ecological insights from OMICS, 2) Advancing technologies for targeted trait manipulation and acceptability to diverse tree species, 3) Genes, development, and physiology, 4) Translating genomics and biotechnology to practice, 5) Trees in a changing world, 6) Genetic and phenotypic diversity for breeding and genomic selection, and 7) Biotechnology for biomaterials and bioeconomy. In addition to the sessions, there were two plenary sessions, provided by John Ralph (University of Wisconsin) and Tanja Pyrhäjärvi (University of Helsinki). The meeting celebrated the second awardees of the newly created IUFRO WG 2.04.06 Award: Excellence in Forest Molecular Biology and Genomics, which was presented to Chung-Jui (C.J.) Tsai (University of Georgia). Greg Goralogia (Oregon State University) was the recipient of the associated Early Career Award. The scientific presentations at the conference highlighted cutting-edge advancements in many facets of forest biotechnology research, including applications of genomic selection in forest genetics and breeding, the use of genetic editing, tree physiology, stress response, molecular breeding, wood development, "omics" technologies, and the social and economic impacts of genetically modified (GM) trees. Scientific take homes from the meeting include the power of NMR to dissect the composition of lignin, the genomic diversity of forest trees that has enormous potential for tree improvement and the integration of systems biology with climate and geographical data. The conference attracted a mix of students (25), postdoctoral fellows (32), and scientists from academia (66) and industry (18). In all, the conference was attended by 141 registered participants, representing 20 countries that participated in 23 invited lectures (including 6 'early-career' keynotes), 27 voluntary talks and 61 poster presentations. Support for the conference was drawn from a wide variety of Academia, Industry, and Government sources, and included financial support from several tree improvement companies. Overall, the conference was a great success, providing an exceptional mix of science and social activities in a relaxed and collegial atmosphere. More information about the meeting can be found at treebiotech.org. The next meeting will be held in Stellenbosch, South Africa, in 2026, hosted jointly by Zander Myburg, Dave Drew (University of Stellenbosch,) and Sanushka Naidoo (University of Pretoria, FABI).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Life Cycle Inventory Availability: Status and Prospects for Leveraging New Technologies

The demand for life cycle assessments (LCA) is growing rapidly, which leads to an increasing demand of life cycle inventory (LCI) data. While the LCA community has made significant progress in developing LCI databases for diverse applications, challenges still need to be addressed. This perspective summarizes the current data gaps, transparency, and uncertainty aspects of existing LCI databases. Additionally, we survey and discuss novel techniques for LCI data generation, dissemination, and validation. We propose key future directions for LCI development efforts to address these challenges, including leveraging scientific and technical advances such as the Internet of Things (IoT), machine learning, and blockchain/cloud platforms. Adopting these advanced technologies can significantly improve the quality and accessibility of LCI data, thereby facilitating more accurate and reliable LCA studies.

blockchain platforms↗
DOE OSTI2025

ChargeX OCPI Recommendations

The Open Charge Point Interface (OCPI) is an open protocol that enables electric vehicle (EV) charging systems to work together across networks. It supports communication and data sharing between Charge Point Operators (CPOs), who manage charging stations, and e-Mobility Service Providers (eMSPs), who provide charging services to EV drivers. OCPI facilitates functions like user authorization, remote charge point control, charging session data exchange, and billing through Charge Detail Records (CDRs). This allows EV roaming, so drivers can charge at different networks without multiple accounts. As the EV market grows due to increased adoption and technological advancements, OCPI faces higher demands. This has revealed issues with CDR format consistency, timestamp standardization across regions, transmission of EV-side error codes for troubleshooting, and support for new use cases. These challenges can affect operations and user experience, particularly as the industry starts considering Vehicle-to-Grid (V2G) systems, where EVs supply energy to the grid, and Vehicle-to-Everything (V2X) technologies for broader energy interactions. Using feedback from the ChargeX Diagnostics taskforce discussions, industry 1-on-1 meetings, technical standards, and OCPI’s evolution through versions (e.g., OCPI 2.1.1, 2.2, and 2.2.1), this report identifies these issues and suggests practical recommendations. These aim to improve interoperability, streamline operations, and prepare OCPI for future trends in the EV charging ecosystem.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2025

Sustainable recovery of Rare Earth Elements (REEs) from coal and coal ash through urban mining: A Nature Based Solution (NBS) for circular economy

The demand for rare earth elements (REEs) has surged in recent years, driven by their crucial role in various industrial applications and their uneven geological distribution. As a result, urban mining from secondary resources, particularly coal and coal ash, has gained traction as a sustainable solution within a circular economy framework. This study highlights the significant presence of REEs in coal and coal ash, revealing that certain samples contain REE concentrations that rival traditional ores. Notably, coal ash has the potential to yield approximately 312,000 tons of REEs annually, far exceeding global demand. The research delves into advanced techniques for analyzing REEs, including elemental, isotopic, and mineralogical studies. Additionally, it explores innovative extraction methods such as the use of green solvents, nature-based solutions, and bioleaching and biosorption. By leveraging coal and its byproducts as secondary resources, this study underscores the opportunity to reduce dependence on conventional mining, enhancing the sustainability of REE recovery. Here, a comprehensive literature review was conducted to highlight technological advancements and emerging opportunities that can address current challenges in this field.

Circular economy↗
DOE OSTI2024

Early Detection of Li-Ion Battery Thermal Runaway Using Commercial Diagnostic Technologies

The rate of electric vehicle (EV) adoption, powered by the Li-ion battery, has grown exponentially; largely driven by technological advancements, consumer demand, and global initiatives to reduce carbon emissions. As a result, it is imperative to understand the state of stability (SoS) of the cells inside an EV battery pack. That understanding will enable the warning of or prevention against catastrophic failures that can lead to serious injury or even, loss of life. The present work explores rapid electrochemical impedance spectroscopy (EIS) coupled with gas sensing technology as diagnostics to monitor cells and packs for failure markers. These failure markers can then be used for onboard assessment of SoS. Experimental results explore key changes in single cells and packs undergoing thermal or electrical abuse. Rapid EIS showed longer warning times, followed by VOC sensors, and then H 2 sensors. While rapid EIS gives the longest warning time, with the failure marker often appearing before the cell vents, the reliability of identifying impedance changes in single cells within a pack decreases as the pack complexity increases. This provides empirical evidence to support the significant role that cell packaging and battery engineering intricacies play in monitoring the SoS.

25 ENERGY STORAGE↗
DOE OSTI2021

Off-Road Vehicle Energy-Saving Potential

The U.S. Department of Energy’s (DOE’s) Vehicle Technology Office (VTO) has played a critical role in enabling electrification and reducing fuel consumption in the automotive sector. However, the off-road vehicle sector accounts for 8% of transportation fuel, and that share is likely to increase over time as onroad vehicles move towards electrification. DOE can again play a key role in the off-road vehicle market by providing tools that can be used by academia and the industry to evaluate the impact of advanced technologies. The need for a pre-competitive simulation tool was also identified during a 2019 DOE-led workshop at Argonne National Laboratory that included OEMs and suppliers to this market. Autonomie, a powerful and robust system simulation tool for vehicle energy consumption and performance analysis, was developed by Argonne National Laboratory in collaboration with General Motors. Its application covers energy consumption and performance analysis throughout the entire vehicle development process by leveraging its plug-and-play powertrain and vehicle model architecture and development environment

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

The 2020 photovoltaic technologies roadmap

Over the past decade, the global cumulative installed photovoltaic (PV) capacity has grown exponentially, reaching 591 GW in 2019. Rapid progress was driven in large part by improvements in solar cell and module efficiencies, reduction in manufacturing costs and the realization of levelized costs of electricity that are now generally less than other energy sources and approaching similar costs with storage included. Given this success, it is a particularly fitting time to assess the state of the photovoltaics field and the technology milestones that must be achieved to maximize future impact and forward momentum. This roadmap outlines the critical areas of development in all of the major PV conversion technologies, advances needed to enable terawatt-scale PV installation, and cross-cutting topics on reliability, characterization, and applications. Each perspective provides a status update, summarizes the limiting immediate and long-term technical challenges and highlights breakthroughs that are needed to address them. In total, this roadmap is intended to guide researchers, funding agencies and industry in identifying the areas of development that will have the most impact on PV technology in the upcoming years.

14 SOLAR ENERGY↗
DOE OSTI2021

Transforming Energy through Computational Science: Computing for Clean Energy

This fact sheet discusses the opportunity space for NREL's computational science capabilities to address national clean energy objectives. Achieving a carbon-free power sector by 2035 as a step towards a decarbonized U.S. energy economy in 2050 will require major advances in power generation, autonomous energy systems, transportation, and buildings/communities. Development of integrated modeling approaches for complex energy systems will be essential for deployment. Success requires developments in optimization and control theory, complemented by machine learning (ML) and artificial intelligence (AI), all of which in turn need targeted investments in breadth and scale of computing. This document defines an opportunity space where: embracing computing can link established research and development (R&D) to a decarbonization agenda; pursuing emerging approaches can accelerate the pace of technology advancement across the portfolio; and leading by example could reduce the carbon footprint of computing worldwide.

advanced computing↗
DOE OSTI2022

H2@Scale Program Multi-Party Cooperative Research and Development Agreement: California Hydrogen Infrastructure Research Consortium Task (Cooperative Research and Development Final Report, CRADA Number CRD-18-00754)

Many stakeholders are working on hydrogen and fuel cell products, markets, requirements, mandates, and policies. California has been leading the way for hydrogen infrastructure and fuel cell electric vehicle (FCEV) deployment. This leadership has advanced a hydrogen network that is not duplicated anywhere in the United States and is unique in the world for its focus on providing a retail fueling experience. In addition, the advancements have identified many lessons learned for hydrogen infrastructure development, deployment, and operation. Other interested states and countries are using California's experience as a model case, making success in California paramount to enabling market acceleration and uptake in the United States. The technical research capability of the national laboratories can be used to assist California in decisions and evaluations, as well as to verify solutions to problems impacting the industry. Because these challenges cannot be addressed by one agency or one laboratory, a hydrogen research consortium has been organized to combine and collaborate. The collaboration aims to ensure that data are available to evaluate projects and inform decision makers, independently verify and validate component solutions, provide experimental results for future hydrogen infrastructure, increase the availability of technical experts for quick-need issues for California hydrogen infrastructure development, deployment, operation, and technology advances. The proposed tasks include data collection from operational stations, component failure fix verification (i.e. nozzle freeze lock), new fueling methods for medium and heavy duty applications, and ensuring hydrogen quality is maintained. U.S. leadership for hydrogen technologies is rooted in California, a location for implementing many H2@Scale pathways such as reducing curtailment and stranded resources, reducing petroleum use and emissions, and developing and creating jobs.

08 HYDROGEN↗
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↗