Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “expertise”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Solar Energy Innovation Network 2017-2024: Abbreviated Final Technical Report

This material is based upon work supported by the U.S. Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Solar Energy Technologies Office under the Agreement/Award Number 32954 for Solar Energy Innovation Network (SEIN) Project, 2017-2024. SEIN is a dynamic program that assembles diverse teams of stakeholders to research solutions to real-world challenges associated with solar energy adoption. In conjunction with its partner organizations, NREL implemented the program by providing research, analysis, and technical expertise directly to project teams and groups of teams (cohorts), by facilitating networked learning through cohorts and peer exchange, and by facilitating dissemination and replication of solutions and lessons learned among stakeholders across the U.S. with similar challenges.

14 SOLAR ENERGY↗
DOE OSTI2024

FY2023 NSTTF Annual Report

The National Solar Thermal Test Facility (NSTTF) is a DOE Core Capability and Technology Deployment Center located in Albuquerque, NM. It is operated by Sandia National Laboratories (Sandia) for the U.S. Department of Energy (DOE). The NSTTF is the only multi-mission, multi-use, multi-story test facility of its type in the United States. The NSTTF was founded in 1978 and began testing with high heat flux that same year. Over the past 45 years, the NSTTF has been at the forefront of the research, design, fabrication, and testing of many of the critical Concentrating Solar Power (CSP) technologies. These technologies have allowed costs to be dramatically reduced from over $\$ $0.40 /kWh to $\$$0.12 /kWh since the conception of this renewable energy technology. The NSTTF has worked to make the Solar Energy Generating Systems (SEGS) parabolic trough plants successful, while also working with the Solar One and Solar Two facilities for successful implementation. Over the four decades since its founding, the mission of the NSTTF has grown to include new receiver technologies, like our generation 3 falling particle system (G3P3 Tower), optical metrology techniques like SOFAST, molten salt testing, thermal energy storage, solar thermal chemistry, and more. We continue to expand our capabilities in pursuit of the DOE SETO mission and the DOE SunShot 2030 goals: unsubsidized LCOE of $\$$0.05/kWh for CSP that includes 12 or more hours of thermal energy storage. To support both the DOE SETO mission and support the CSP sector as a whole, we are working to develop our operations and maintenance framework to provide a world class testing facility in support of our technological achievements. To accomplish both of these missions, the NSTTF draws on the decades of experience and expertise of our staff along with the world-class facilities at Sandia National Laboratories to further the science of concentrated solar thermal technologies in diverse applications. We remain a trusted partner for high-quality and impactful research in both fundamental and applied arenas. We are able to provide our partners with both one-of-a-kind testing platforms as well as world-class analytics.

14 SOLAR ENERGY↗
DOE OSTI2021

Novel subnanosecond rise, flattop, 100 kV high voltage pulse generator (ASAP-LDRD-22-0122)

We looked to build a new type of 100 kV pulse generator that we hope will avoid many of the problems seen with other commercially available pulse generators. We looked to exploit recent improvements in both lasers and solid-state pulse charge units along with an alternative triggering layout to build something new. For the pulse charging unit, we collaborated with Texas Tech to leverage their knowledge and expertise in pulse transformers and solid-state switching. The final result of the LDRD looks promising. Texas Tech was able to build a pulse charge unit that meets our specifications regarding output, and we were able to integrate it with a switch and a laser to create a device that meets the goals of the LDRD. There is still much work to do to advance the design further and improve reliability, but the proto-type design satisfies the goals we set for this LDRD.

42 ENGINEERING↗
DOE OSTI2023

Computational and Experimental Investigation of Thermal-Mechanical- Chemical Mechanisms of High-burnup Spent Nuclear Fuel (SNF) Processes at Elevated Temperatures and Degradation Behavior in Geologic Repositories

The overarching goal of the combined computational and experimental R&D activities proposed in this project is to enhance understanding of the mechanisms and thermal-mechanical-chemical (TMC) parameters controlling the instant release fraction (IRF) and matrix dissolution of high-burnup (HB; burnup) spent nuclear fuels (SNFs) and the subsequent formation, stability, and phase transformations of SNF alteration products under long-term storage and geological disposal conditions. Uranium dioxide may undergo oxidative corrosion/alteration, and the IRF may be increased for HB SNF, both of which may affect environmental systems associated with SNF long-term storage and disposal. The oxidative matrix dissolution may form various complex uranyl-based phases, including a rich variety of oxides, silicates, carbonates and other secondary minerals in varied geological environments (e.g., studtite, metastudtite, amorphous uranyl peroxide, uranium trioxide, triuranium octoxide, schoepite, dehydrated schoepite, metaschoepite, becquerelite, soddyite, rutherfordine,...). These uranyl phases generally have higher mobility UO 2 +2 species than less soluble U 4+ phases. However, limited information on the thermodynamic properties and formation kinetics of these uranyl-bearing phases is available to predict explicitly paragenesis under the conditions relevant to long-term storage or disposal. The proposed project draws on complementary expertise and research backgrounds from the team members: (i) to apply a combined ab initio modeling (UNLV/UTEP and SNL) and experimental (UNLV) strategy investigating the high-temperature TMC mechanisms of alteration of SNF under α-radiolysis conditions; (ii) to investigate the mechanistic of phase transformations in UNF degradation products under various conditions expected in long-term storage systems (e.g. (UO 2 )O 2 (H 2 O) 4 → (UO 2 )O 2 (H 2 O) 2 → U 2 O 7 → UO 3 → U 3 O 8 ); (iii) to determine high-accuracy TMC parameters for complex uranyl-based phases formed in storage or geological disposal environments (e.g. UO 3 (H 2 O) 2 , Ca[(UO 2 ) 6 O 4 (OH) 8 ] 8 H 2 O, (UO 2 ) 2 (SiO 4 ) 3 2H 2 O,…). The unforeseen COVID-19 pandemic led to the laboratory/campus closure since March 2020, that resulted in a significant delay in reaching milestones in a satisfactory manner, due to (i) the statewide recommendation from stop-working to later limited work in the lab and work-from-home (WFH), (ii) no in-person interactions, and (iii) a hiring freeze at UNLV. Therefore, a no cost extension (10/01/2021- 9/30/2022) was requested to help make up the time we lost during the global pandemic in 2020-2021, leading to paradigm shifts in the focus of the project in the following three main tasks: Task 1 (Computational), Task 2 (Experimental), and Task 3 (Final report, due on 12/29/2022).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An Atlas of Current Customer-Facing Utility Program Designs and Pricing

The National Renewable Energy Laboratory (NREL) and Kevala conducted a targeted literature review and leveraged extensive expertise, including team members with four decades of experience in the utility sector, to characterize utility programs and rate designs. This analysis delves into the motivations behind these programs and the various hurdles they face. Through this in-depth study, the team provides critical insights into the operational dynamics of utility programs, identifying key drivers and barriers that influence their effectiveness and implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

U.S. DOE Midwest Combined Heat and Power Technical Assistance Partnership (Midwest CHP TAP) (Final Technical Report)

The University of Illinois Chicago (UIC) was awarded the U.S. DOE Midwest Combined Heat and Power Technical Assistance Partnership (Midwest CHP TAP) program to provided technical and policy expertise to support local opportunities to promote CHP, waste heat to power, district energy, and microgrids in the Midwest and Central regions of the U.S. that included the states of Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, Ohio, and Wisconsin. During this contract period, UIC, through the Midwest CHP TAP activities, provided significant regional support to the U.S. strategic goals of: 1) Stimulating America’s Energy Revolution and Promoting Energy Independence; 2) Promoting Jobs and Manufacturing Competitiveness; 3) Supporting a Positive Trade Balance; 4) Enhancing Energy System Resiliency and Energy Security; and 5) Improving the Nation’s Infrastructure. UIC met the annual go/no-go metrics each year and either met and/or exceeded the milestones each year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

NREL Hydrogen Sensor Testing Laboratory

Relevance: Detection is recognized as a critical element for hydrogen facility safety design and supports risk mitigation. Detection methodologies will support validation of H2 behavior research. Hydrogen point sensors play a critical role for safety and process monitoring, but other methodologies can be developed. Approach: NREL Sensor Laboratory tests and verifies sensor performance for manufacturers, developers, end-users, regulatory agencies and SDOs/CDOs NREL deployment activity supports regulatory requirement verification, hydrogen behavior models, and method development for use by stakeholders. Accomplishments and Progress: NREL's R&D accomplishments have supported developers, industry, and SDOs by providing sensor performance and deployment expertise not otherwise available. Development of alternative detection strategies for hydrogen applications have been initiated. HyWAM and advanced detection methodologies deployments at H2@Scale Facilities are being implemented. Collaborations: Collaboration with government laboratories, universities, private organizations and regulatory agencies has leveraged the NREL Sensor Laboratory's success in advancing hydrogen safety sensors and process control. Proposed Future Work: NREL will support hydrogen deployment by the proper implementation of hydrogen sensors and advanced detection strategies. NREL will continue to support science-based codes and standards. This effort will be guided by the needs of the hydrogen community.

codes and standards↗
DOE OSTI2024

SQMS science advances impact on Rigetti commercial processors

The collaboration between the Superconducting Quantum Materials and Systems Center (SQMS) and Rigetti Computing produced several advancements in our understanding of the role of materials characteristics in quantum processor performance. This partnership leverages SQMS's extensive characterization infrastructure and cutting-edge research in materials, and Rigetti's expertise in quantum hardware and robust nanofabrication to improve precision and performance of Rigetti's test QPUs. Qubit frequency is determined in large part by the properties of Josephson junctions (JJs) made of amorphous oxide tunnel barriers; the Alternating-Bias Assisted Annealing (ABAA) process allows us to tune JJs to their desired frequency [1]. Work by SQMS researchers in characterizing high-precision JJs post-processed (using ABAA) have yielded crucial information on the nature of the structure and chemical bonding uniformity of the ABAA processed amorphous oxides. Performance has also been improved through a comprehensive series of experiments that tested encapsulation and surface treatment. Encapsulation of the niobium metal layer with tantalum resulted in an T1 improvement of 80%, experimentally confirming the role of Nb surface losses in qubit performance [2]. Pre-treatment of the underlying silicon surface prior to JJ fabrication by replacing a buffered oxide etch (BOE) with hydrofluoric acid (HF) followed by aqueous ammonium fluoride (NH4F) has shown a statistically significant improvement of T1 by 22%, and reduction in the number of strongly-coupled TLS [3]. These examples, as well as many other published and ongoing investigations, demonstrate the mutual benefits that come from Rigetti's involvement in the SQMS collaboration. [1] - Pappas, D.P., et al. (2024). https://doi.org/10.1038/s43246-024-00596-z [2] - Bal, M., et al. (2024). https://doi.org/10.1038/s41534-024-00840-x [3] Kopas, C. J. et al. Preprint at https://doi.org/10.48550/arXiv.2408.02863 (2024).

Lachman, Ella↗
DOE OSTI2024

Cyber-Physical System: Design for Sustainability and Resilience

When considering the design tools needed in the transition from numeric models to pilot plant, cyber-physical systems (CPS) come to the forefront as a method to model complex integrated energy systems. CPS approach has proven to be valuable to identify opportunities for economically viable early adoption of integrated energy technologies. This tutorial will introduce the concepts and the roles of CPS in co-design to minimize risks for pilot plant and technology deployment. This tutorial will also layout basic requirements for the CPS development, which requires a highly interdisciplinary effort with expertise in sensors, hardware testing, real-time modeling, controls, and system integration.

Harun, Nor Farida↗
DOE OSTI2024

Deep Learning for Full Waveform Inversion of Elastic Active-Source Seismic Data to Estimate P-Wave Velocity Models

Seismic imaging methods are critical for Global Security and Energy & Homeland Security missions and activities that rely on subsurface characterization, but traditional methods remain computationally expensive and require significant labor hours and expertise to execute. Within the past few years, machine learning (ML), namely deep learning (DL), has been used to develop data-driven end-to-end full waveform inversion (FWI) methods to estimate 2D P-wave velocity (Vp) models in a fraction of the time as conventional FWI. These methods, however, are trained on simplistic acoustic wave seismic data and Vp models that are not realistic nor representative of real-world observations, leaving a large gap between the state-of-the-art and deployable, feasible, and practical DL FWI methods. Here, we generate a synthetic active-source, 3D, elastic wave seismic data set and a variety of Vp models with realistic geologic structure for training DL FWI methods. We evaluate six different methods that have performed well for acoustic DL FWI or medical imaging tasks using our more realistic dataset. We find that these six trained models do not match the performance of published acoustic end-to-end DL FWI methods, indicating more training data may be needed, physics may need to be incorporated to achieve good accuracy at the sacrifice of the end-to-end advantage, and/or novel methods need to be developed to enable end-to-end DL FWI methods to perform well for real-world seismic data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Brine Availability Test in Salt (BATS) FY24 Update

This report summarizes fiscal year 2024 (FY24) activities centered around a series of field tests in bedded salt at the Waste Isolation Pilot Plant (WIPP) funded by the Office of Spent Fuel and Waste Science and Technology in the Spent Fuel and Waste Disposition (SFWD) program of the US Department of Energy’s Office of Nuclear Energy (DOE-NE). High-level Purpose of Experiments: The Brine Availability Test in Salt (BATS) field tests are revealing both brine occurrence (i.e., where, and how much) and brine migration (i.e., how easily it moves) in the excavation damaged zone (EDZ). This understanding is foundational to develop a safety case for a future heat-generating waste repository in salt, and to starting up a generic repository program in salt to buy down risk. BATS seeks to predict how much brine can flow into both ambient and heated excavations (e.g., boreholes or rooms) in salt. This work is educating and empowering new repository scientists on two fronts: “design and execution of field tests” and “prediction and modeling of coupled processes.” DOE-NE capabilities in salt have grown and been tested through international modeling and benchmarking exercises (e.g., DECOVALEX, RANGERS, KOMPASS, and MEASURES; see Mills et al., 2024). The hands-on expertise we are building is a necessary step towards large-scale disposal demonstrations and eventual implementation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

BeyondFingerprinting: AI-guided discovery of robust materials & processes

BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject-matter expertise with algorithms enriched with physics-based constraints to unearth process-structure-property correlations. Such algorithms, trained on high-throughput experiments and simulations, are shown to serve as surrogate models that efficiently detect key “fingerprints” in materials data, prognose material performance, and guide effective process improvements. To accelerate broader adoption across mission areas, this AI-guided approach was demonstrated with three complex process-centric exemplars: electroplating, physical vapor deposition, and laser powder bed fusion. Together, these exemplars impact nearly every hardware component relevant to DOE and NNSA national security missions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Collaborative Research: Louis Stokes Regional Center of Excellence: Louis Stokes Midwest Regional Center of Excellence (LSMRCE) for Broadening Participation in STEM

The Louis Stokes Midwest Regional Center of Excellence (LSMRCE) for Broadening Participation in STEM, a partnership of Chicago State University (CSU), aimed to increase the number of underrepresented minority (URM) students graduating with science, technology, engineering, and math (STEM) degrees and matriculating into graduate STEM programs. As a member institution of the LSMRCE, Fermi Research Alliance, LLC, (Fermilab) looked to support its continued mission of increasing URM student participation in its Summer Internship in Science and Technology (SIST) internship program. The program provided URMs with access to research skills development, mentoring and professionalization activities via paid, summer research internships at Fermilab. Students received instruction and mentoring while gaining exposure to a global laboratory workforce and community with diverse academic and professional expertise. In addition, students established professional relationships and networked with senior researchers, early career scientists, technical professionals, post-docs and other undergraduate interns to help forge collaboration, innovation and mentoring opportunities at Fermilab and LSMRCE partner institutions

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Viscosity Measurements of the HLW APPS Glasses

The U.S. Department of Energy is responsible for building the Hanford Waste Treatment and Immobilization Plant (WTP) at the Hanford site in Washington to remediate 56 million gallons of radioactive waste historically stored in 177 underground tanks. The Office of River Protection has requested that the Savannah River National Laboratory (SRNL) contribute in areas of recognized capabilities and expertise for glass waste form development to support successful startup of the WTP. SRNL support of this work is defined in the Task Technical and Quality Assurance Plan.1 This report provides results from viscosity measurements on a series of simulated nuclear waste glasses designed and fabricated at Pacific Northwest National Laboratory (PNNL). The glasses were designated the high-level waste – Aspen Process Performance Simulation (HLW-APPS) study glasses. The data provided in this report is to be used in the development, validation, and implementation of enhanced property/composition models for nuclear waste glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Abstract for CRADA between NETL and the AZ Board of Regents on behalf of Arizona State University

Arizona State University (ASU) and the National Energy Technology Laboratory (NETL) will collaborate on the development and scale up of sorbent composites that efficiently capture carbon dioxide (CO 2 ) directly from air under an awarded project from the Department of Energy’s Direct Air Capture (DAC) Pre-Commercialization Technology Prize. For DAC to be considered a viable technology for decarbonization, the cost of carbon removal needs to decrease below the proposed carbon tax incentive outlined in the recent Inflation Reduction Act (IRA) (Section 45 Q), which is set at $\$$180 per ton of CO 2 . Achieving this goal requires the development of a cost-effective, environmentally friendly sorbent with high CO 2 sorption capacity and efficient kinetics under DAC conditions as the overall cost of CO 2 captured is highly sensitive to factors such as sorbent cost and sorbent lifetime. ASU has developed a sorbent technology that can potentially reduce CO 2 removal costs by DAC. NETL has expertise in DAC TEA and LCA development and DAC sorbent testing. The collaboration between ASU and NETL aims to accelerate development and deployment of ASU’s technology by quantifying the performance, cost and lifecycle impacts of ASU’s technology and validating sorbent performance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Advancements in Development and Testing of Thermal Power Dispatch Simulators

Flexible plant operations and generation (FPOG) offer nuclear power plants (NPPs) the chance to leverage alternative, non-electric revenue streams while ensuring their continued role as reliable, clean, and constant sources of baseload electrical power. The excess thermal energy generated from NPPs during periods of low electricity demand can be channeled as raw materials to numerous industrial processes via a thermal power dispatch (TPD) system. Hydrogen production via high-temperature steam electrolysis (HTSE) is an optimal use case based on technical and economic feasibility. Researchers at Idaho National Laboratory (INL) have conducted previous works that developed and implemented TPD system models within the GSE Solutions Generic Pressurized Water Reactor (GPWR) simulator to support human-in-the-loop (HITL) scenario-based evaluations. The first part of this report documents modifications made to the GPWR TPD model and HMI from the previous iteration in line with a new Sargent and Lundy (S&L) TPD design with an automatic control system. The was done in collaboration with Westinghouse using their three-loop pressurizer water reactor (W3LPWR) simulator which contains an industrial grade automatic control system for the TPD. This was installed in the Human Systems Simulation Laboratory (HSSL) at INL. The second part of the report documents findings from an all-hands-on-deck integration and verification workshop that was conducted in the HSSL over several days. The research team comprised INL human factors and TPD experts, a nuclear engineer from GSE Solutions who implemented the revised TPD model for GPWR, the human-machine interface (HMI) prototyping and human factors team from the University of Idaho, and personnel with operations experience with pressurized water reactors. The workshop provided time and expertise to conduct the final activities to bring the operations, HMI, and simulator into a functional state. The goals of the integration and verification workshop were: 1. to install the revised GPWR TPD model into the HSSL 2. verify the TPD HMI prototype was functional 3. integrate the HTSE Simulink model to GPWR. 3. Issues were identified for resolution, but overall the workshop accomplished its goal to integrated and verify the majority of the intended functional. Future work will resolve the identified issues and use the integrated simulation to support an evaluation and demonstration in the next fiscal year.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Energy Transitions Initiative Partnership Project: Bainbridge Island, Washington: Cohort 2 Technical Assistance: Improving Resilience

The City of Bainbridge Island (COBI) applied for and received technical assistance from the Energy Transitions Initiative Partnership Program (ETIPP) in part to achieve their goal of 100% renewable electricity generation by 2040, five years ahead of the Washington State goal. Their second goal of increasing energy resilience is discussed in this report. Pacific Northwest National Laboratory (PNNL) completed the technical analysis aspects of the project, supported by the community partner Spark Northwest and the program administrator, the National Renewable Energy Laboratory (NREL). PNNL worked with the Community Lead and stakeholders to ensure selection of hazards and energy infrastructure of highest priority and integration of existing expertise into the analysis. We have also worked to align analysis with current activities conducted by Puget Sound Energy (PSE), and other efforts within the Bainbridge Island community around resilience. This report, conducted under the ETIPP project in partnership with COBI, aims to build on this existing knowledge base with a specific energy focus, to identify hazards and threats to critical energy infrastructure, summarize the risk, and identify and analyze remaining resilience gaps.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗