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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Advances in Carbon Capture and Use (CCU) Technologies: A Comprehensive Review and CO2 Mitigation Potential Analysis

One of society’s major current challenges is carbon dioxide emissions and their consequences. In this context, new technologies for carbon dioxide (CO2) capture have attracted much attention. One of these is carbon capture and utilization (CCU). This work focuses on the latest trends in a holistic approach to carbon dioxide capture and utilization. Absorption, adsorption, membranes, and chemical looping are considered for CO2 capture. Each CO2 capture technology is described, and its benefits and drawbacks are discussed. For the use of carbon dioxide, various possible applications of CCU are described, starting with the utilization of carbon dioxide in agriculture and proceeding to the conversion of CO2 into fuels (catalytic processes), chemicals (photocatalytic processes), polymers, and building supplies. For decades, carbon dioxide has been used in industrial processes, such as CO2-enhanced oil recovery, the food industry, organic compound production (such as urea), water treatment, and, therefore, the production of flame retardants and coolants. There also are several new CO2-utilization technologies at various stages of development and exploitation, such as electrochemical conversion to fuels, CO2-enhanced oil recovery, and supercritical CO2. At the end of this review, future opportunities are discussed regarding machine learning (ML) and life cycle assessment (LCA).

Peres, Christiano B.↗
DOE OSTI2025

Wave energy in season: a comparative approach to feasibility of seasonal deployments for remote coastal communities

Remote coastal communities, which could be early adopters of wave energy projects, have concerns over costs, conflicts, and potential risks of development. Designers and developers are challenged to address these community concerns as they continue to develop wave energy technologies. One potential means of reducing costs, conflicts, and risks, especially for demonstration and pilot-scale projects, could be planning a deployment that operates for only a portion of the year—a seasonal deployment. Here, in this paper, we examine the impacts of a seasonal deployment in terms of cost, electricity production, operations and maintenance, environmental impacts, and community benefits. We take a holistic, comparative approach to feasibility that can be replicated for other comparative studies. We estimate electricity production using a point absorber WEC modeled near Sitka, AK, USA and optimized for the given sea conditions. We determine that, for remote community sized projects, seasonal deployments could result in small cost savings (less than 10 %), but larger decreases in annual energy production (around 30 % for our case study area). Seasonal deployments could be preferable in places with seasonal energy needs, if failures and device access become a major hindrance to wave energy technology development, or as a cautionary approach to introducing new technology to the oceans. We also determine that a highly seasonal wave resource is not necessarily a requirement for seasonal deployments to be considered. Seasonal deployments are an alternative to year-round deployments that can be considered in places where marine spatial conflict is a seasonal concern.

WEC optimizations↗
DOE OSTI2022

Techno-economic analyses of several redox flow batteries using levelized cost of energy storage

Development of inexpensive long-duration energy storage supports widespread deployment of variable renewable energy resources onto the electricity grid. Flow batteries are a promising class of devices for long-duration energy storage. Techno-economic modeling is needed to evaluate commercial feasibility of existing technologies and to help guide research and development of new technologies. Levelized cost of storage is a useful metric that accounts for capital and operating costs and energy throughput over the life of a project. This metric is used to compare the economic prospects of lithium ion to eight aqueous and two hypothetical nonaqueous flow batteries in four use cases. Finally, flow batteries with inexpensive active materials are favored in long-duration applications.

25 ENERGY STORAGE↗
DOE OSTI2021

Programming Abstractions for Managing Workflows on Tiered Storage Systems

Scientific workflows in High Performance Computing (HPC) environments are processing large amounts of data. The storage hierarchy on HPC systems is getting deeper, driven by new technologies (NVRAMs, SSDs, etc.) There is a need for new programming abstractions that allow users to seamlessly manage data at the workflow level on multi-tiered storage systems, and provide optimal workflow performance and use of storage resources. In previous work, we introduced a software architecture Managing Data on Tiered Storage for Scientific Workflows (MaDaTS) that used a Virtual Data Space (VDS) abstraction to hide the complexities of the underlying storage system while allowing users to control data management strategies. In this article, we detail the data-centric programming abstractions that allow users to manage a workflow around its data on the storage layer. The programming abstractions simplify data management for scientific workflows on multi-tiered storage systems, without affecting workflow performance or storage capacity. We measure the overheads and effectiveness introduced by the programming abstractions of MaDaTS. Our results show that these abstractions can optimally use the storage capacity in lesser capacity storage tiers, and simplify data management without adding any performance overheads.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Hacking Kilometer-Scale Models: A Participative Model for Climate Information

In May 2025, nearly 700 participants from all around the world coalesced at 10 regional nodes and a few satellite nodes to take part in a global hackathon of kilometer-scale (horizontal grid spacing < 10 km) regional and global Earth system models. Exciting science is emerging from these efforts, ranging across novel model analysis, new ways of integrating with satellite data, and emulation with machine learning. New technologies were trialed that enable the community to work in new and complementary ways to democratize access to global information at a local scale from a set of the world’s highest-resolution climate models. The hackathon demonstrated how exascale data can be organized to be accessible to anyone. Fundamentally, the community could apply these techniques and technologies to move toward more participative models for coproduction and delivery of diverse sources of climate information for climate scientists and citizens alike.

Climate models↗
DOE OSTI2022

PARETO 0.5.0 Release

PARETO 0.5.0 Release. Highlights: Treatment Center Modeling - New case study with desalination, clean brine, and evaporation treatment technologies - New option to reduce disposal capacity due to Seismic Response Areas (SRA) - New option to consider water sharing outside of system General Updates - New features for Sankey Diagram visualization (multiple regions, filtered time periods) - Introduce badges to README.md - Convert documentation to ASCII LaTeX - Code cleaning and maintenance - Correct strategic model documentation typo - Update and format treatment demo input spreadsheet Bug Fixes - Fix water quality operational model results printing bug - Piping and trucking variables are now built only over defined arcs instead of all possible connections - Revise pipeline expansion cost constraints, ensure that all pipelines built incur cost

PARETO,PARETO-UI,PSE,Process Systems Engineering,P↗
DOE OSTI2021

Science & Technology Review - July 2021 Issue

In the early weeks of the COVID-19 pandemic, Lawrence Livermore researchers mobilized to address emerging challenges and develop capabilities for detecting the SARS-CoV-2 virus and fighting COVID-19, the diseased caused by the virus. As described in the article beginning on p. 4, the Laboratory’s Innovation and Partnerships Office provided technology transfer expertise to deploy existing, in-development, and new technologies to help detect SARS-CoV-2 and treat COVID-19.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Barriers to adopting artificial intelligence and machine learning technologies in nuclear power

Artificial intelligence and machine learning (AI/ML) technologies offer unique opportunities to transform nuclear plant operations and power generation. Benefits will be felt not only within existing analog and digital instrumentation and control, but also within work processes, the integration of people with technology and most importantly, the business case. The application of this new technology can help simplify complex problems and produce more effective decision-making, making nuclear power safer, more efficient, and more economically viable in the current energy market. Nonetheless, there are potential barriers to its adoption that must be overcome. The purpose of this paper is to categorize, review, and discuss barriers to AI/ML adoption within the nuclear power industry, with a focus on existing commercial reactors. Unique considerations for advanced reactors are also offered. Here we provide a comprehensive overview of the historical, technical, and business barriers that the industry faces, as well as stakeholder readiness, and end-user acceptance. We underscore the importance of user experience and offer potential solutions in overcoming each barrier. These include provisions for easier plant data access, a friendly regulatory environment, and investment in user trust and explainable AI.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Computationally Efficient Control Co-Design Optimization Framework with Mixed-Fidelity Fluid and Structure Analysis: OpenTurbineCoDe (OTCD) - ARPA-E ATLANTIS Phase I Project (Final Technical Report)

The goal of the Department of Energy (DOE) Advanced Research Projects Agency-Energy (ARPA-E) Aerodynamic Turbines Lighter and Afloat with Nautical Technologies and Integrated Servo-control (ATLANTIS) Program is to develop new technologies for floating offshore wind turbines, or wind farms, using the discipline of control co-design (CCD.) In this context, our goal is to develop a computationally efficient optimization framework for design of floating offshore wind turbines. Our specific aim is to utilize high-fidelity structural, aerodynamic, aero-structural tools, and to derive control-oriented reduced- or low-order models directly from the high-fidelity tools. We proposed a mixed-fidelity modeling approach which means that we are also using low- and mid-fidelity tools when necessary. This research is conducted by a multidisciplinary team consisting of Rutgers University, University of Michigan, Brigham Young University, and the National Renewable Energy Laboratory (NREL). The computational framework, called OpenTurbineCoDe, is designed to integrate, where possible, traditional structural, aerodynamic, aeroelastic models (e.g., OpenFAST) and advanced control algorithms with higher fidelity simulation tools including Reynolds-averaged Navier–Stokes (RANS) solvers, and three-dimensional structural finite element solvers. All the high-fidelity tools used in this research provide numerically exact gradients to facilitate both efficient optimization and local linearization for control implementation.

17 WIND ENERGY↗
DOE OSTI2025

Regulatory Sandboxes and Other Processes to Expedite Utility Adoption of Advanced Grid Technologies

Advanced grid technologies are increasingly important to enable electric transmission and distribution systems to meet growing demands. However, traditional regulatory processes typically lag technological advancements. Regulatory sandboxes, which provide a structured environment for testing new technologies and business approaches under modified rules to increase the speed of adoption, aim to bridge the gap between grid needs and opportunities to deliver solutions at scale. This report examines the role of regulatory sandboxes in promoting utility innovation, highlights examples of successful sandbox mechanisms, and provides emerging best practices for designing and implementing regulatory sandboxes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

MORPHOLOGICAL AND RADIATION DAMAGE INFORMED THERMAL PROPERTY PREDICTION IN SCALED GEOMETRIC DOMAINS

This proposed work has the potential to rewrite the way the nuclear industry investigates new fuel and nuclear material designs. The current rubric of nuclear material design has myriad steps in the process, and while certain physics are modeled accurately, each step must be connected in order to obtain an entire description of the process. At present, neutronic, thermal, microstructural, fission product chemistry and migration, and radiation defect analysis (hereafter referred to together as “combined analysis”) are performed, albeit separately. There is no existing method which combines these physics in an attempt to understand the natural interactions between these phenomena. Consequently, the timeline for design, fabrication, experiment, validation, and licensing can take years. A disruptive approach is required to accelerate the development of new technology. This proposed undertaking creates a validated computational framework, generating a new microscopic-to-macroscopic methodology yielding thermal property predictions for nuclear fuels and materials at an engineering spatial scale.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Design and implementation of the new scintillation light detection system of ICARUS T600

ICARUS T600 is the far detector of the Short Baseline Neutrino program at Fermilab (U.S.A.), which foresees three Liquid Argon Time Projection Chambers along the Booster Neutrino Beam line to search for LSND-like sterile neutrino signal. The T600 detector underwent a significant overhauling process at CERN, introducing new technological developments while maintaining the already achieved performances. The realization of a new liquid argon scintillation light detection system is a primary task of the detector overhaul. As the detector will be subject to a huge flux of cosmic rays, the light detection system should allow the 3D reconstruction of events contributing to the identification of neutrino interactions in the beam spill gate. The design and implementation of the new scintillation light detection system of ICARUS T600 is described.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

New high-throughput endstation to accelerate the experimental optimization pipeline for synchrotron X-ray footprinting

Synchrotron X-ray footprinting (XF) is a growing structural biology technique that leverages radiation-induced chemical modifications via X-ray radiolysis of water to produce hydroxyl radicals that probe changes in macromolecular structure and dynamics in solution states of interest. The X-ray Footprinting of Biological Materials (XFP) beamline at the National Synchrotron Light Source II provides the structural biology community with access to instrumentation and expert support in the XF method, and is also a platform for development of new technological capabilities in this field. Hee, the design and implementation of a new high-throughput endstation device based around use of a 96-well PCR plate form factor and supporting diagnostic instrumentation for synchrotron XF is described. This development enables a pipeline for rapid comprehensive screening of the influence of sample chemistry on hydroxyl radical dose using a convenient fluorescent assay, illustrated here with a study of 26 organic compounds. The new high-throughput endstation device and sample evaluation pipeline now available at the XFP beamline provide the worldwide structural biology community with a robust resource for carrying out well optimized synchrotron XF studies of challenging biological systems with complex sample compositions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Scalable Wind Turbine Generator Bearing Fault Prediction Using Machine Learning: A Case Study

Operation and maintenance (O&M) costs for wind turbines pose a risk to competitiveness and asset owners. With machine-learning technologies and digitalization rapidly maturing, the wind industry is actively investigating these new technologies to optimize O&M practices and reduce costs. This paper reviews recent work on machine-learning approaches to generator bearing failure prediction and presents a relevant real-world case study through a collaboration between the National Renewable Energy Laboratory and Envision Digital Corporation. In the case study, we evaluate the performance of representative machine-learning algorithms for predicting wind turbine generator bearing failures. Operational supervisory control and data acquisition data from one wind power plant was used to train and test the machine-learning models. The investigated data channels are chosen based on whether physically they reflect the failed generator bearing conditions and the component historical usage, including both environmental and operational conditions. Benefits and drawbacks of different methods are identified.

generator bearing failures↗
DOE OSTI2023

Centering Energy and Environmental Justice in the Buildings Energy Sector

We face incredible challenges for decarbonizing our economy and raising the standard of living for all members of our society at the same time. Historical energy efficiency efforts have been effective in making small steps, but they fall far short of the massive changes we need to make, and they completely miss helping communities of disadvantage (e.g. low-income, African American, Hispanic American, Native American and tribal nations, etc). Business as usual efforts do not take the time to connect with and understand the challenges of these historical underinvested communities and therefore have not been effective at helping these communities. The Biden Harris Administration has set ambitious goals for decarbonization of our economy that include a requirement that 40% of efforts support energy and environmental justice communities. If we are to meet our decarbonization goals, it is imperative that we change our approach to research, development, and deployment of new technologies. The Department of Energy has set energy justice as a priority and is working with the national laboratories to change our approaches. This paper starts with definitions of what we mean by energy and environmental justice and how they apply to building technologies and deployment efforts. We provide several examples of how historical efforts have succeeded and how they have failed to account for challenges of communities of disadvantage. We identify market and technology barriers to decarbonization and energy efficiency for specific technologies and how these barriers are exacerbated for disadvantaged communities. From these examples, we propose a new framework for integrating energy and environmental justice into all aspects of technology development, deployment, and policy efforts within the building energy sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Process Intensification for Nanostructure Aluminum Extrusions

A new technology called Shear Assisted Processing and Extrusion (ShAPE™), pioneered at the Pacific Northwest National Laboratory (PNNL), has the potential to revolutionize the US extrusion industry. Maturation of the ShAPE™ process would create a new cross cutting US manufacturing technology that advances three key the objectives within AMO; namely, nanomaterials processing, process intensification, and materials for extreme and harsh environments. PNNL and SCM Metals Products, Inc. (SCM), a division of Kymera International, will collaborate to mature the ShAPE™ extrusion process beyond TRL 3 to increase the likelihood of its adoption by industry. PNNL’s custom (one-of-a-kind in the world) ShAPE™ machine will be used to extrude round rods of high strength aluminum alloy feedstock powder provided by SCM. As proof-of-concept, PNNL has previously extruded 5 mm diameter rods with SCM aluminum alloy powder AL-12.4TM. This project aims to scale up the process to a diameter of 25 mm while retaining the excellent mechanical properties achieved for the 5 mm diameter rods. The degree of process intensification with ShAPE™ will also be estimated. The primary objectives of this project are as follows. 1) Fabricate 25 mm diameter nanostructured extrusions in bulk, directly from AL-12.4TM aluminum alloy powder without the need for canning, degassing, compaction, and other intermediate steps typical of powder metallurgy extrusion. 2) Achieve a microstructure with an ultrafine grained (UFG <1 µm) aluminum matrix having refined and homogenously distributed nanoscale second phases. 3) Show that the ram force can be significantly reduced compared to conventional powder metallurgy extrusion of the same material and estimate the extent of process intensification that may be possible with ShAPE™. 4) Demonstrate improved high-temperature mechanical properties compared to conventional extrusion of the same material. The following tasks will be undertaken to achieve these objectives. PNNL will develop the die set and process parameters necessary to fabricate Al-12.4TM extrusions with a 25 mm diameter. Microstructural analysis will be performed by PNNL while mechanical testing will be conducted by SCM. With information learned from the process development and characterization, PNNL will perform an estimate of the degree of process intensification that may be possible with ShAPE™.

42 ENGINEERING↗
DOE OSTI2025

Chemically Enhanced Electrodialysis (CEED) for Recovery of Rare Earth Elements

Rare-earth elements are crucial for numerous technologically relevant industries including energy, electronics, and aerospace; however, both the supply and separation of rare earths are pressing challenges. The long-term goal of the collaborative project is to develop a novel approach to existing extraction behaviors. To achieve this goal, a class of electrodialysis membranes with novel ion-gating functionality will be developed and paired with a conventional electrodialysis strategy. The hypothesis of the work is that incorporating sequestrants of suitable structures at the surface of the electrodialysis membrane will enhance extraction efficiency for ions of interest. Our ability to prepare sequestrants of diverse conformations and to adapt their molecular structure for binding lanthanides renders the proposed technology a versatile method for selective ion separation. Our approach will enable an entirely new technological approach to the separation of valuable rare-earth elements.

36 MATERIALS SCIENCE↗