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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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DOE OSTI2015

NREL Highlight: Truck Platooning Testing; NREL (National Renewable Energy Laboratory)

NREL's fleet test and evaluation team assesses the fuel savings potential of semi-automated truck platooning of line-haul sleeper cabs with modern aerodynamics. Platooning reduces aerodynamic drag by grouping vehicles together and safely decreasing the distance between them via electronic coupling, which allows multiple vehicles to accelerate or brake simultaneously. In 2014, the team conducted track testing of three SmartWay tractor - two platooned tractors and one control tractor—at varying steady-state speeds, following distances, and gross vehicle weights. While platooning improved fuel economy at all speeds, travel at 55 mph resulted in the best overall miles per gallon. The lead truck demonstrated fuel savings up to 5.3% while the trailing truck saved up to 9.7%. A number of conditions impact the savings attainable, including ambient temperature, distance between lead and trailing truck, and payload weight. Future studies may look at ways to optimize system fuel efficiency and emissions reductions.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2018

When Data Analytics Meet Site Operation: Benefits and Challenges

Demand for using data analytics for energy management in buildings is rising. Such analytics are required for advanced measurement and verification, commissioning, automated fault-detection and diagnosis, and optimal control. While novel analytics algorithms continue to be developed, bottlenecks and challenges arise when deploying them for demonstration, for a number of reasons that do not necessarily have to do with the algorithms themselves. It is important for developers of new technologies to be aware of the challenges and potential solutions during demonstration. Therefore, this paper describes a recent deployment of an automated, physical model-based, FDD and optimal control tool, highlighting its design and as-operated benefits that the tool provides. Furthermore, the paper presents challenges faced during deployment and testing along with solutions used to overcome these challenges. The challenges have been grouped into four categories: Data Management, Physical Model Development and Integration, Software Development and Deployment, and Operator Use. The paper concludes by discussing how challenges with this project generalize to common cases, how they could compare to other projects in their severity, and how they may be addressed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Electric and Conventional Vehicle Performance over Eco-Driving Cycles: Energy Benefits and Component Loss

Battery electric vehicles (BEVs) are expected to be a pathway towards meeting greenhouse and criteria pollutant gas reductions in the current and future transportation sector. However, BEV technologies are still evolving, including efficiency optimization and enhancement. Emerging connected and automated vehicle (CAV) technologies provide an in-depth insight for developing innovative applications and systems to leverage BEV energy efficiency and substantially transform transportation systems. Therefore, we present simulation studies of various BEV types and compare the performance between measured, real-world drive cycles and highly optimized eco-driving cycles that are representative of trajectory modifications using advanced CAV technologies. The investigated vehicles include a compact car and a Class 7 delivery truck. The results demonstrate that eco-driving has a high potential to reduce energy consumption for the BEVs considered. As part of the study, a comprehensive EV powertrain model was developed to account for key EV components and powertrain configurations. The impact of eco-driving was further evaluated for conventional vehicles with characteristics that are comparable to the modeled BEVs.

Gao, Zhiming↗

DOE OSTI2019

Electric and Conventional Vehicle Performance over Eco-Driving Cycles: Energy Benefits and Component Loss

Gao, Zhiming↗

DOE OSTI2021

MADE3D: Enabling the Next-Generation High-Torque- Density Wind Generators by Additive Design and 3D Printing

Direct-drive wind turbine generators are increasing in popularity, thanks to recent project developments - especially offshore, where reliability and efficiency are major cost drivers. Yet, high capital costs are forcing many original equipment manufacturers to consider lightweight, high-torque density generators for next-generation multi-megawatt turbines that may be difficult to realize by traditional design or manufacturing methods. In this study, we present a new design framework enabled by advanced machine learning and multimaterial additive manufacturing to perform a magnetic topology optimization that maximizes the torque per rotor active mass for a 15-megawatt direct-drive permanent magnet wind generator. A comparison of the proposed approach against conventional topology optimization demonstrated a significant increase in computational efficiency and accuracy in performance predictions. Results using single and multimaterial compositions for rotor core and magnets identify a wider choice of 3D printable designs for a given specification. A hybrid combination of sintered and dysprosium-free polymer-bonded magnets shows good potential for torque performance by saving material costs up to 8.75%. More than 30% improvement in rotor torque densities is identified which can marginally improve the overall generator torque density. With the rapid evolution of multipowder deposition technologies, this study can greatly inspire a new paradigm for design-driven manufacturing with novel material compositions and lightweight, low-cost, high-strength multimaterial geometries that were previously unexplored for direct-drive generators.

3D printing↗

DOE OSTI2024

Fracture Networks Imaging in CO2 Injection Zones in IBDP Site: An Unsupervised Machine Learning Application with Multiple Datasets

This is the conference paper accompanying a poster presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. This work highlights the integration of unsupervised machine learning (ML) techniques as a transformative tool for advancing understanding of CO2 injection into reservoirs that could potentially contribute to optimizing injection strategies and reservoir management, ultimately bolstering the efficacy and sustainability of CO2 storage.

Kumar, Abhash↗

DOE OSTI2022

Risk-Informed Operations and Maintenance Decision Making Using Deep Reinforcement Learning

A challenge for operating nuclear power plants is the significant cost of operations and maintenance, at times consuming up to 66% of annual operating costs. This project aims to build a framework for a risk-informed asset-management tool that integrates inspections, repairs, spare-part inventory, supply chain, and business choices to lower overall O&M costs. Our approach uses a combination of data-driven modeling and deep reinforcement learning to create and implement optimal maintenance policies for the existing nuclear fleet, as well as new advanced reactors. The creation of an asset management tool that uses these advanced methods will give operators new capabilities to help reduce the burden of O&M spending in nuclear power plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2013

Final Report: Development of a CW NCRF Photoinjector using Solid Freeform Fabrication

The development of very high duty cycle, high gradient photoinjectors is critical for the next generation of accelerator systems, such as X-ray free-electron lasers (FELs), inverse Compton scattering (ICS) sources, energy recovery linacs (ERLs), injectors for linear colliders, as well as a variety of industrial systems for homeland security applications. NCRF photoinjectors have a proven track record at generating the high-quality beams necessary for these applications, however they are limited in large part to relatively low duty cycles due to ohmic wall looses. Thus a key issue for high average power, normal conducting, photoinjectors is effective structure cooling [9]. The fabrication of current high average-power photoinjectors relies on conventional design and fabrication techniques with many limitations. Advances in Solid Freeform Fabrication (SFF) technology may make it possible to design and produce near net-shape copper structures for the next generation of very high rep rate, high gradient radio frequency (RF) photoinjectors. RF and thermal-management optimized geometries could be fully realized without the usual constraints and compromises of conventional machining techniques*. Further advances in SFF and powder metallurgy (P/M) techniques, with the ability to fine tune material properties, may soon make it possible to produce monolithic, thermally robust RF structures faster and cheaper than ever before.

Frigola, Pedro↗

DOE OSTI2012

Radiotherapy Technical Considerations in the Management of Locally Advanced Pancreatic Cancer: American-French Consensus Recommendations

Summary: Pancreatic carcinoma is a leading cause of cancer-related mortality. Approximately 30% of pancreatic cancer patients present with locally advanced, unresectable nonmetastatic disease. For these patients, two therapeutic options exist: systemic chemotherapy or chemoradiotherapy. Within this context, the optimal technique for pancreatic irradiation is not clearly defined. A search to identify relevant studies was undertaken using the Medline database. All Phase III randomized trials evaluating the modalities of radiotherapy in locally advanced pancreatic cancer were included, as were some noncontrolled Phase II and retrospective studies. An expert panel convened with members of the Radiation Therapy Oncology Group and GERCOR cooperative groups to review identified studies and prepare the guidelines. Each member of the working group independently evaluated five endpoints: total dose, target volume definition, radiotherapy planning technique, dose constraints to organs at risk, and quality assurance. Based on this analysis of the literature, we recommend either three-dimensional conformal radiation therapy or intensity-modulated radiation therapy to a total dose of 50 to 54 Gy at 1.8 to 2 Gy per fraction. We propose gross tumor volume identification to be followed by an expansion of 1.5 to 2 cm anteriorly, posteriorly, and laterally, and 2 to 3 cm craniocaudally to generate the planning target volume. The craniocaudal margins can be reduced with the use of respiratory gating. Organs at risk are liver, kidneys, spinal cord, stomach, and small bowel. Stereotactic body radiation therapy should not be used for pancreatic cancer outside of clinical trials. Radiotherapy quality assurance is mandatory in clinical trials. These consensus recommendations are proposed for use in the development of future trials testing new chemotherapy combinations with radiotherapy. Not all of these recommendations will be appropriate for trials testing radiotherapy dose or dose intensity concepts.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

Biorefinery siting and sizing to achieve the US Billion‐Ton Bioeconomy vision: A case study using a gasification–Fischer–Tropsch process

Achieving a secure, abundant, and affordable energy future requires a robust and adaptable energy strategy, with bioenergy playing a pivotal role. Biomass-based energy presents a promising pathway to use domestic resources while fostering economic opportunities in rural areas. Despite the potential to source more than 1 billion dry short tons of biomass annually in the US, significant infrastructure and economic barriers hinder full utilization for energy production. This study used the Biofuel Infrastructure, Logistics, and Transportation (BILT) model to assess biorefinery siting and scale and determine the number and size of facilities required to maximize use of the US biomass potential. A spatially agnostic approach first assessed the effects of facility capacity and transportation constraints on biomass use. Then, a spatially explicit analysis integrated county-level biomass availability from the US Department of Energy's 2023 Billion-Ton Report and technoeconomic assessments to evaluate different biorefinery deployment scenarios. The results indicate that an optimized mix of facility sizes is essential to leverage biomass resources fully across varying regional production densities to maximize use of the US biomass potential. Larger biorefineries or co-located smaller facilities significantly enhance biomass use while reducing costs through economies of scale. These findings underscore the importance of strategically balancing facility capacity and spatial distribution to optimize the bioenergy supply chain. In conclusion, this study provides critical insights for advancing the US bioenergy economy by aligning biorefinery deployment with biomass resource availability and economic viability.

BILT Model↗

DOE OSTI2024

Fast ion studies in the extended high-performance high β P plasma on EAST

Comprehending and optimizing fast ion behaviors is critical for the enhancement of performance in Experimental Advanced Superconducting Tokamak (EAST). This study explores the potential benefits of several factors that can improve the fast ion confinement. First, experiments show the change in the direction of the NBI2 from counter-I p to co-I p leads to a significant reduction in fast ion losses. TRANSP/NUBEAM simulation and tomography results based on fast-ion D-alpha measurements reveal that after the neutral beam injection (NBI) upgrade, the beam ion prompt loss is reduced by approximately 50%. Second, the upgraded ion cyclotron resonant frequency (ICRF) antenna at the N-port features twice the coupling resistance of the original antennas at EAST. This improved ICRF power coupling has enhanced the synergistic heating effect of NBI + ICRF, where the ICRF wave field accelerates beam ions at the harmonics. Experiments demonstrate that NBI + ICRF synergistic not only enhances plasma neutron yield and β P , but also accelerates beam ions to hundreds of keV. Further, the electron density and the neutral beam voltage have been optimized to reduce the fast ion slowing-down time and beam ion losses. Experimental and simulation results indicate that increasing the electron density reduces beam ion losses and enhances the bootstrap current fraction. While higher beam voltage results in a slight decrease in beam power absorption, it can increase the fraction of bootstrap current. With the understanding of these optimization of fast ion confinement, experiments have demonstrated fully non-inductive operation at high density (n e /n G ∼ 0.67, β P ∼ 3.1, β N ∼ 2.1, H 98,y2 ∼ 1.2) even without the support of co-I p beam NBI2. This investigation presents a potential regime to enhance fast ion confinement and extend performance in the high β P plasma for future experiments.

EAST tokamak↗

DOE OSTI2013

Evaluation of the applicability of existing nuclear power plant regulatory requirements in the U.S. to advanced small modular reactors.

The current wave of small modular reactor (SMR) designs all have the goal of reducing the cost of management and operations. By optimizing the system, the goal is to make these power plants safer, cheaper to operate and maintain, and more secure. In particular, the reduction in plant staffing can result in significant cost savings. The introduction of advanced reactor designs and increased use of advanced automation technologies in existing nuclear power plants will likely change the roles, responsibilities, composition, and size of the crews required to control plant operations. Similarly, certain security staffing requirements for traditional operational nuclear power plants may not be appropriate or necessary for SMRs due to the simpler, safer and more automated design characteristics of SMRs. As a first step in a process to identify where regulatory requirements may be met with reduced staffing and therefore lower cost, this report identifies the regulatory requirements and associated guidance utilized in the licensing of existing reactors. The potential applicability of these regulations to advanced SMR designs is identified taking into account the unique features of these types of reactors.

LaChance, Jeffrey L.↗

DOE OSTI2021

Tailored Fiber Placement for Complex Preforms

Tailored Fiber Placement (TFP) offers a novel approach to optimize fiber architecture for the fabrication of complex, structural parts not traditionally suitable for advanced composites. This technology not only offers new routes for weight reduction via metal substitution, it also offers cost reduction through minimization of material scrap and reduced labor. This reduction in component weight leads to increased fuel efficiency, and reduced production energy consumption, thereby, helping to achieve the stated IACMI technical goals. This technology leverages centuries of manufacturing development in support of the textile and embroidery industry. One major drawback to this technology is the lack of commercial or non- proprietary structural performance data and robust analytical tools used to optimize fiber architecture and predict performance. This project was structured to utilize common sub-element features to validate analytical performance tools, generate performance data, and gather cost and performance data on components of interest. This project was designed to give industry sponsors the confidence and ability to take full advantage of TFP to fabricate primary, highly loaded structure and integrate features such as metallic fasteners. The project focused principally on the use of high strength carbon fiber, such as T700, and the use of aerospace epoxy resin matrix to primarily support development of new composite applications in vehicle, aerospace, and industrial markets. This project applied previously developed analytical tools to predict the performance of TFP produced parts. This work focused on developing the pipeline to characterize material in order to accurately predict component performance when modifying the TFP print paths and stitch density. This focused on experimental characterization via standardized ASTM testing, alongside experimental testing of more representative service components by testing curved beam strength, beam shear performance, a large scale TFP lug, and ultimately designing a fully TFP clip bracket that reduced weight and cost compared to a traditional metallic component. The new knowledge gained from this program included: 1) development and demonstration of novel analytical tools applied to analysis of TFP preforms; 2) development and demonstration of a building block approach using coupons and sub-elements to optimize the design of a more complex component; 3) demonstration that optimized fiber orientation using TFP can exceed performance of conventional textile composite materials and can open new applications currently limited to metallic components; 4) Demonstration of performance and cost benefits of the TFP process as compared to metallic and conventional textile composites. Recommendations for follow-on work include development of design allowables to assess the impact of high temperature/moisture exposure or saturation during loading, tracking the impact of stitching needle wear on the performance of parts and ability to stitch thicker preforms, using TFP preforms as local reinforcement at areas of bearing or complex loading, and topology optimization of components by tow steering. The expertise developed during the course of this project can be leveraged to provide commercial engineering design and fabrication services using TFP. UDRI is in the process of formalizing their partnership with Spintech, who will serve as the commercialization partner for this technology and provide molding services and deliver finished components to the end user. UDRI will continue to produce the preforms until the economics allow Spintech to procure its own TFP equipment or lease UDRI equipment, at which point UDRI will step away from manufacture and serve as the engineering and design lead on product development.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Linac_Gen: Integrating Machine Learning and Particle-in-Cell Methods for Enhanced Beam Dynamics at Fermilab

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in Linacs over traditional methods, through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of Linac systems, as evidenced in its application to Fermilab's PIP-II Linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in linacs over traditional methods through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of linac systems, as evidenced in its application to Fermilab's PIP-II linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.

43 PARTICLE ACCELERATORS↗

DOE OSTI2018

Xenon Gas Separation and Storage using Metal-Organic Frameworks

The global demand for Xe, a noble gas with applications in electronics, lighting, and the medical industry, is expected to rise significantly over the coming decades. However, the low abundance of Xe in the earth’s atmosphere and the costly cryogenic distillation process that is used to obtain Xe commercially via air separation have limited the scale of applications of Xe. A physisorption-based separation using porous materials may be a viable and cost-effective alternative to cryogenic distillation. In particular, metal-organic frameworks (MOFs) have shown promise as highly Xe-selective, porous solids. In this review, we detail the recent advances of MOFs as adsorbents for noble gas adsorption/separation and the role of computer simulation in finding optimal materials for Xe adsorption.

MOFs, Xenon, Krypton↗

DOE OSTI2026

A Discrete Hankel Transform Approach to Nuclear Data Processing for Fusion Applications

This study introduces advancements to the numerical solutions employed in the processing of nuclear data for fusion applications. It leverages the convolution theorem and Fourier transform techniques to enhance computational efficiency and broaden applicability. Building upon a previously reported discrete Hankel transform approach for Doppler broadening, this work refines the solution of convolution integrals central to these applications. The methodology provides a general and unified framework for evaluating any convolution operation, regardless of whether the underlying problem involves temperature effects in nuclear reactions. The applicability to the nuclear data processing for fusion is demonstrated by deriving the convolution integrals for some of the fusion-related quantities. As before, the convolution operation utilizes a Gaussian-based kernel; however, the discrete Hankel transform of order $𝛼$ = $\frac{1}{2}$ is now applied to the forward Fourier transform of the nonkernel argument, rather than the inverse Fourier transform. This modification eliminates the need for the integration of the nonkernel, cross section–based function, which is a step that posed challenges for certain pointwise cross-section representations. It also removes the requirement for cross-section linearization. Optimized for graphics processing unit architectures, the approach significantly improves computational performance. These advancements are currently under evaluation as the foundation for the next-generation thermonuclear data file processing codes being developed at Lawrence Livermore National Laboratory.

Nuclear science and engineering↗

DOE OSTI2019

PREDICTION OF CYCLIC VARIABILITY AND KNOCK-LIMITED SPARK ADVANCE (KLSA) IN SPARK-IGNITION (SI) ENGINE

Engine knock remains one of the major barriers to further improve the thermal efficiency of spark-ignition (SI) engines. SI engine is usually operated at knock-limited spark advance (KLSA) to achieve possibly maximum efficiency with given engine hardware and fuel properties. Co-optimization of fuels and engines is promising to improve engine efficiency, and predictive computational fluid dynamics (CFD) models can be used to facilitate this process. However, cyclic variability of SI engine demands that multi cycle results are required to capture the extreme conditions. In addition, Mach Courant Friedrichs-Lewy (CFL) number of 1 is desired to accurately predict the knock intensity (KI), resulting in unaffordable computational cost. In this study, a new approach to numerically predict KLSA using large Mach CFL of 50 with ten consecutive cycle simulation is proposed. This approach is validated against the experimental data for a boosted SI engine at multiple loads and spark timings with good agreements in terms of cylinder pressure, combustion phasing, and cyclic variation. Engine knock is predicted with early spark timing, indicated by significant pressure oscillation and end-gas heat release. Maximum amplitude of pressure oscillation analysis is performed to quantify the KI, and the slope change point in KI extrema is used to indicate the KLSA accurately. Using a smaller Mach CFL number of 5 also results in the same conclusions, thus demonstrating that this approach is insensitive to the Mach CFL number. The use of large Mach CFL number allows us to achieve fast turn-around time for multicycle engine CFD simulations.

CCV↗