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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The CMS Phase-1 Pixel Detector Upgrade

The CMS detector at the CERN LHC features a silicon pixel detector as its innermost subdetector. The original CMS pixel detector has been replaced with an upgraded pixel system (CMS Phase-1 pixel detector) in the extended year-end technical stop of the LHC in 2016/2017. The upgraded CMS pixel detector is designed to cope with the higher instantaneous luminosities that have been achieved by the LHC after the upgrades to the accelerator during the first long shutdown in 2013–2014. Compared to the original pixel detector, the upgraded detector has a better tracking performance and lower mass with four barrel layers and three endcap disks on each side to provide hit coverage up to an absolute value of pseudorapidity of 2.5. This paper describes the design and construction of the CMS Phase-1 pixel detector as well as its performance from commissioning to early operation in collision data-taking.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Competitiveness Improvement Project (CIP)

Through the Competitiveness Improvement Project (CIP), the U.S. DOE supports small businesses who design and manufacture small or medium wind turbine technology through cost-shared subcontracts awarded via a competitive process. The goals of the CIP are to: improve cost competitiveness with other distributed generation technology through optimized designs and advanced manufacturing processes; increase the number of certified small and medium wind turbine designs across a wide range of sizes; and accelerate deployment of improved, certified distributed wind energy technology. NREL manages the projects through a defined period of performance subcontract, providing technical monitoring and coordinating technical assistance from DOE labs.

advanced manufacturing↗
DOE OSTI2022

Versatile Test Reactor Conceptual Core Design

The VTR is a 300-MW(thermal) sodium-cooled fast reactor (SFR) designed for the specific purpose of delivering unique testing capabilities to enable the advancement of all reactor technologies. With its flux level, irradiation volume, and operational flexibility, the VTR will enable accelerated testing of materials, fuels, and various components needing irradiation testing. Proven SFR technologies and design approaches have been leveraged in designing the VTR core, ensuring the highest possible readiness level. This resulted in the VTR using ternary metallic fuel and delivering fast flux levels in excess of 4 x 10 15 n/cm 2 ∙ s over large useful volumes, corresponding to about 60 dpa/year in steel. As part of the design efforts, the VTR core performance has been determined for a representative configuration, ensuring that the reactivity control systems offer sufficient shutdown margins, that the core can be safely cooled in all situations, and that reactivity feedback coefficients are conducive to a favorable safety behavior. Furthermore, the incorporation of features such as fuel assembly storage in the shield region supports the flexible and reliable operation of the VTR. Additional design work has been ongoing as well. This includes thorough shielding performance evaluations to ensure safe operation of the VTR, verification and validation of the design tools used to achieve compliance with Nuclear Quality Assurance (NQA-1) requirements, early assessment of the impact of irradiation experiments on the core performance envelope and associated margins, and in-depth uncertainty quantification efforts to quantify the anticipated range of performance characteristics. An experimental program supporting the VTR core design has been set up, with the current focus being on thermal-hydraulic experiments. The purpose of this experimental program is to obtain confirmatory measurements to serve directly as part of the core design basis or as part of the validation cases supporting the simulation tools used.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

A perspective on Bayesian methods applied to materials discovery and design

For more than two decades, there has been increasing interest in developing frameworks for the accelerated discovery and design of novel materials that could enable promising and transformative technologies. The Integrated Computational Materials Engineering (ICME) program called for integrating computational tools to establish linkages along process-structure-property-performance (PSPP) chains. The Materials Genome Initiative called for integrating experiments and computations within data science frameworks as a strategy to accelerate the materials development cycle. While these frameworks and paradigms have been quite influential, traditional ICME or data science-based approaches tend to have some limitations, mainly when querying the materials space is costly and very little information is available. Bayesian methods are more suitable in this context due to their efficiency gains. To this end, the materials discovery problem is framed as a Bayesian Optimization (BO). Different examples in which BO has been applied to solve materials discovery problems are presented. The methods/examples discussed include BO under model uncertainty, multi-information source BO, multi-objective and multi-constraint BO, and batch BO. Bayesian Materials Discovery is a promising area of research that is likely to become more influential as more attention is put on autonomous materials discovery platforms. Therefore, a discussion is provided on the potential development of such methods to increase the ability of existing platforms in materials discovery. Here, the ultimate goal is to pave the way to autonomous materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Overview of the LHeC and FCC-he accelerator concepts

The Large Hadron–Electron Collider is designed to move the field of deep inelastic scattering to the energy and intensity frontier of particle physics. Exploiting energy-recovery technology, it collides a novel, intense electron beam with a proton or ion beam from the High-Luminosity Large Hadron Collider. The accelerator and interaction regions are designed for concurrent electron–proton and proton–proton operations. This paper represents the concepts of an updated design study and discusses the design challenges of the project.

Holzer, B. J.↗
DOE OSTI2023

DuraMAT Technology Scouting Report: Assessing Module Reliability Risks Associated with Projected Technological Changes

Maintaining the reliability of photovoltaic (PV) modules in the face of rapidly changing technology is critical to maximizing solar energy's contribution to global decarbonization. Our presentation describes expected changes in PV technology and their impacts on performance and reliability. We leverage PV market reports, interviews with PV researchers and other industry stakeholders, and peer-reviewed literature to narrow the multitude of possible changes into a manageable set of 11 impactful trends likely to be incorporated in near-term crystalline-silicon module designs. We group the trends into four categories (module architecture, interconnect technologies, bifacial modules, and cell technology) and explore the drivers behind the changes, their interactions, and associated reliability risks and benefits. Our analysis identifies specific areas that would benefit from accelerating the PV reliability learning cycle, to assess emerging module products and designs more accurately. We recommend that researchers continue tracking module technologies and their reliability implications so efforts can be focused on the most impactful trends. As the rapid technological turnover continues, it is also critical to incorporate fundamental knowledge into models that can predict module reliability. Predictive capabilities complete the PV reliability learning cycle-reducing the time required to assess new designs and mitigating the risks associated with large-scale deployment of new products.

bifacial↗
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 OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High-Fidelity Accelerated Design of High-performance Electrochemical Systems

Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, meso-scale and continuum simulations. To accelerate this process, our team has developed the AutoMat platform. AutoMat can accelerate development of new electrochemical materials along two avenues: first, automated input generation and management of simulations at multiple lengthscales as well as “handoff” of outputs from one lengthscale as inputs to the next; and second, replacement of the most computationally intensive simulation processes with machine-learned surrogate models. The crux of our team’s effort was not “reinventing the wheel” by developing entirely new techniques, but rather building a “superhighway” that allows existing state-of-the-art techniques to run faster and more smoothly than before. AutoMat can utilize tools spanning from first-principles quantum chemistry computations to automated robotic experimentation, and is driven by design space search techniques to reduce the number of iterations through the full simulation loop by rapidly targeting promising regions of design spaces such as single-atom alloy catalysts or blends of liquid electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2024

Ensemble Manufacturing Techniques for Steam Turbine Components Across Length Scales

Faster design to manufacturing requirements were sought for steam turbine components to meet the changing demands of today’s power grid of improved efficiency through operating temperature increases and enhanced operational flexibility from baseload to cyclic operations. Advances in multiple advanced manufacturing (AM) process enabled redesign of turbine components for extreme environments with the potential to reduce cost. AM is of particular interest to improve component functionality, higher temperature capability, and superior durability in large scale steam turbine applications. AM methods have an immense potential to open-up the design space by working directly with the 3D model to produce near-net shapes, enable fast design-manufacturing iterations, thereby significantly reducing product cost and lead-time up to 25 % from current baseline. However, these benefits cannot be fully realized due to the potential for unknown AM processing defects and their resultant effect upon component performance in service. Siemens is partnering with Oak Ridge National Laboratory (ORNL), Electric Power Research Institute (EPRI), and Connecticut Center for Advanced Technology (CCAT) to advance the knowledge of complex process-material interactions for desired microstructures and properties that are closely interlinked to component geometries across different length scales. The proposed program utilized an ensemble of multidisciplinary technologies to accelerate the development of materials, high-throughput experiments for their qualification, and design flexibility/topology optimization for repair/redesign of components to address critical failure mechanisms for improved performance and increased reliability of existing power plant components. The proposed activities, if successfully demonstrated for identified components, will enable paradigm shift in customized manufacturing and accelerated qualification/certification towards increased steam turbine component durability, increased turbine efficiency, and reduced CO 2 emissions in load-following environments compared to today’s technology. Technology maturation is built into the project as successful research will include customized process-component down-selection enabling AM methodologies to be incorporated directly into the existing supply chain. The specific activities of the proposed effort are: 1. Topology optimization of down-selected steam turbine parts that are amenable to additive and hybrid manufacturing for cost/performance improvement. 2. Process-structure-property relationships for five AM processes for steam turbine materials of interest to compare with conventional materials. 3. Perform part/assembly build process using advanced additive/hybrid machine tools followed by quality inspection of the built components for insight into qualification for production scale-up. Steam turbine rig testing of printed components under targeted, well monitored and characterized environmental conditions of for performance comparison of baseline and redesigned components.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Conceptual Design of a HTS Dipole Insert Based on Bi2212 Rutherford Cable

The U.S. Magnet Development Program (US-MDP) is aimed at developing high-field accelerator magnets with magnetic fields beyond the limits of Nb 3 Sn technology. Recent progress with composite wires and Rutherford cables based on the first generation high-temperature superconductor Bi 2 Sr 2 CaCu 2 O 8-x (Bi2212) allows considering them for this purpose. However, Bi2212 wires and cables are sensitive to transverse stresses and strains, which are large in high-field accelerator magnets. This requires magnet designs with stress management concepts to control azimuthal and radial strains in the coil windings and prevent the degradation of the current carrying capability of Bi2212 conductor or even its permanent damage. This paper describes a novel stress management approach, which was developed at Fermilab for high-field large-aperture Nb 3 Sn accelerator magnets, and is now being applied to high-field dipole inserts based on Bi2212 Rutherford cables. The insert conceptual design and main parameters, including the superconducting wire and cable, as well as the coil stress management structure, key technological steps and approaches, test configurations and their target parameters, are presented and discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Topical Group on Application and Industry Community Engagement Frontier Snowmass 2021 (Summary Report)

HEP community leads and operates cutting-edge experiments for the DOE Office of Science which have challenging sensing, data processing, and computing requirements that far surpass typical industrial applications. To make necessary progress in the energy, material, and fundamental sciences, development of novel technologies is often required to enable these advanced detector and accelerator programs. Our capabilities include efficient co-design, which is a prerequisite to enable the deployment of advanced techniques in a scientific setting where development spans from rapid prototyping to robust and reliable production scale. This applies across the design spectrum from the low level fabrication techniques to the high level software development. It underpins the requirement for a holistic approach of innovation that accelerates the cycle of technology development and deployment. The challenges set by the next generation of experiments requires a collaborative approach between academia, industry and national labs. Just a single stakeholder will be unable to deliver the technologies required for the success of the scientific goals. Tools and techniques developed for High Energy Physics (HEP) research can accelerate scientific discovery more broadly across DOE Office of Science and other federal initiatives and also benefit industry applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Activity gradient mass transport accelerated corrosion of stainless steel in molten salt

Advanced energy systems based on molten salt technologies have gained increasing interest, and their design and construction are likely going to involve the use of materials of dissimilar chemistries. With dissimilar materials being used, galvanic coupling induced corrosion acceleration is one of the mechanisms believed to be an issue for the deployment of molten salt systems. However, non-galvanic interaction mechanisms such as activity gradient mass transport have also been theorized to represent a potential challenge. Unfortunately, the two effects have often been compounded in the literature, such that it is still unclear which degradation mechanisms are at play in these systems. In the following study, electrochemical measurements are used to predict the effect that galvanic coupling would have on dissimilar materials submerged in the same molten salt medium. Dissimilar material salt exposure tests are then performed to investigate whether galvanic coupling or activity gradient driven processes account for the levels of materials degradation observed. Finally, these separate effect electrochemical and corrosion experiments uniquely elucidate the degradation mechanisms in dissimilar materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Atomic- and Molecular-Scale Interphase Engineering for High-Performance Solid-State Batteries

Solid-state batteries (SSBs) promise a decisive advance beyond conventional Li-ion systems, yet their development remains constrained by persistent solid–solid interfacial instabilities that degrade performance and durability. Interfaces between solid electrolytes and both cathodes and Li metal often exhibit poor wettability, limited physical contact, and high charge–transfer resistance, leading to chemical decomposition, mechanical failure, and impedance growth. Overcoming these limitations requires interphase engineering with atomic-scale precision—capabilities that conventional coating methods cannot reliably deliver. Atomic layer deposition (ALD) and molecular layer deposition (MLD) uniquely meet this need by enabling ultrathin, conformal, and composition-tunable films that stabilize reactive surfaces, suppress parasitic reactions, and regulate Li-metal morphology. Importantly, this Perspective highlights ALD/MLD systems that have already demonstrated effectiveness in liquid-electrolyte cells and discusses how these validated strategies can be deliberately translated to solid-state architectures. By grounding future directions in experimentally proven concepts rather than speculative hypotheses, we outline how atomic- and molecular-scale design principles can accelerate the development of robust, high-performance SSB technologies.

atomic and molecular layer deposition↗
DOE OSTI2025

Next-generation electronics by co-design with chalcogenide materials

As Moore’s law approaches its limits, chalcogenides offer a promising route to next-generation computing and sensing, thanks to their topological, magnetoelectric, excitonic, and spintronic properties. Yet the same traits that make them appealing, e.g., exotic properties at monolayer thickness, clean van der Waals interfaces, and strong many-body effects, also heighten sensitivity to fabrication, hindering translation into scalable devices. Progress is further constrained by fragmented knowledge across synthesis, processing, and integration, and by the lack of systematic links between fabrication parameters and performance metrics. This Perspective examines key obstacles in controlling chalcogenide heterostructures and stresses the need for an integrated co-design framework, uniting materials growth, processing, and device architecture to accelerate practical technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ku-band electron linac for battery-powered hand-portable 2-MeV X-ray generator

X-ray generators, producing radiation in MeV range, are a critical tool for radiography, non-destructive testing and security applications. Field operation of such source requires them to be hand-portable, autonomous and allow parameter adjustability. RF linear accelerators can serve as a flexible, reliable, and robust radiation generator alternative to dangerous radioisotopes and bulky betatrons that are currently used for field radiography if their size, weight, cost, and imaging performance are matched to these sources. Here, in this paper, we present the design and test results of a 2 MeV Ku-band electron linac for a hand-portable X-ray generator system for field radiography being developed by RadiaBeam. The dramatic scale of miniaturization and cost-reduction is achieved thanks to the implementation of innovative technologies such as air-cooled Ku-band air-traffic control magnetrons, a split accelerating structure fabrication technique, and solid-state Marx modulators. This paper presents the design of the first prototype of the accelerator, its operation from Li-Ion batteries, as well as high-power and beam measurements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Brochure for the DOE Office of Science Workshop on Envisioning Frontiers in AI and Computing for Biological Research

In February of 2025 a joint ASCR/BER workshop was held to identify key transformational research directions for understanding biology using artificial intelligence (AI), digital twins and high-performance (HPC) computational methods to facilitate scientific discovery and innovation in support of the Department of Energy mission. AI technologies offer exciting new groundbreaking methods to analyze large volumes of complex biological data, thereby greatly accelerating the ability to understand, predict, and design biological processes for beneficial purposes. In the laboratory, the bridging of AI-enabled automated experimental technologies, HPC and digital twins will provide potent tools for researchers to explore the fundamental nature of biology and harness its inherent metabolic potential for a variety of beneficial purposes. The focus of this workshop was on how high-performance computational methods can impact this objective by exploring digital twins, foundational models, and data-driven approaches with applications to advance automated laboratory experiments, modeling of complex living systems and engineering new functions into plants and microbial systems relevant to DOE mission. Workshop attendees with expertise in plant science, microbiology, mathematics, computer science, and AI assessed the current state of the science, trends, and AI challenges at the interface of plant and microbial systems biology and computational science to identify opportunities for high-impact research. This collaborative effort capitalized on ASCR's advancements in applied mathematics, computer science, and Exascale systems, and BER's expertise in basic genomics-enabled research on DOE relevant plant and microbial systems. The workshop culminated in four key priority research directions to guide future research and development within DOE Office of Science programs.

59 BASIC BIOLOGICAL SCIENCES↗