Search NASA⌕ Search

SEARCH · Search NASA

Results for “Accelerator Driven Systems”

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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗
DOE OSTI2025

Airport Ground Support Equipment Infrastructure & Logistics Electrification Assessment Tool: 2025 Data Development, Modeling and Analysis for DFW

The aviation industry is increasingly turning to modernize freight facilities by integrating electric Ground Support Equipment (eGSE) to enhance operational efficiency of freight facility moving vehicles and equipment. Airports worldwide are adopting eGSE to streamline cargo movement, reduce fuel and maintenance costs, and improve logistics coordination.1 North America, with its advanced aviation infrastructure, leads this transition, leveraging Internet of things (IoT)-enabled automation and zero emission technologies to boost reliability and reduce human errors.2 Electrification of freight facility moving vehicles and equipment boosts turnaround times, improves equipment reliability, and optimizes logistics coordination, giving operators a competitive advantage. With rising fuel price volatility and the pressure to meet stringent performance benchmarks, airports are focusing on cost-effective, scalable solutions for long-term financial and operational gains. To further accelerate electrification, airports are integrating Zero Emission Vehicles (ZEVs) into rental car fleets and deploying electric baggage carts, requiring strategic investments in charging infrastructure. 3 The shift, however, presents challenges, such as limited technical expertise, high capital costs, and complex procurement processes. By forging strategic partnerships, leveraging advanced technologies, and optimizing infrastructure investments, airports can create a resilient, future-ready ecosystem that enhances the movement of people and goods through electrification-driven efficiency. Supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO), this electrification effort provides a scalable, cost-effective solution to improve airport freight operations. Through targeted investments and innovation, airports enhance efficiency, reduce costs, and meet performance benchmarks while advancing toward a resilient, electrified future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Fusion-Driven Subcritical System FDS-SFB and Its Blanket Materials Activation Analysis

Fusion-fission hybrid reactor is a bridge between fusion and fission nuclear energy and it is a solution to overcome difficulties of fission energy development with the features of energy production, fuel breeding and waste transmutation. Many researches on fusion-fission hybrid reactor have been done in last decades. Fusion Driven Subcritical Spent Fuel Burning System (FDS-SFB) was proposed as an intermediate step toward the final application of fusion energy and designed to transmute the long-lived nuclear wastes from fission power plants and to produce fissile nuclear fuels for feeding fission power plants. The design of FDS-SFB is based on relatively easy-achieved plasma parameters extrapolated form the successful operation of the experimental advanced superconducting Tokamak (EAST) in China and other tokamaks in the world, and the subcritical blanket is designed based on the well-developed technologies of fission reactor. Materials activation characteristic is one of the key issues for hybrid reactor research. The activation of materials would substantially influence not only the safety of the reactors but also the recycling of used reactor materials. The selection of materials is very important to control the neutron-induced activation on fusion reactor as well as to ensure the attractiveness of fusion-fission nuclear power regarding safety and environmental aspects. The reduced activation ferritic-martensitic (CLAM) steel is preliminarily considered as the candidate structural materials for FDS-SFB. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Update on developments at SNIF

The Small Negative Ion Facility (SNIF) at CCFE has been undergoing continuous development and enhancement to both improve operational reliability and increase diagnostic capability. SNIF uses a CW 13.56MHz, 5kW RF driven volume source with a 30kV triode accelerator. Improvement and characterisation work includes: Installation of a new “L” type RF matching unit, used to calculate the load on the RF generator. Use of the electron suppressing biased insert as a Langmuir probe under different beam extraction conditions. Measurement of the hydrogen Fulcher molecular spectrum, used to calculate gas temperature in the source. Beam optimisation through parameter scans, using copper target plate and visible cameras, with results compared with AXCEL-INP to provide beam current estimate. Modelling of the beam power density profile on the target plate using ANSYS to estimate beam power and provide another estimate of beam current. This work is described, and has allowed an estimation of the extracted beam current of approximately 6mA (4mA/cm2) at 3.5kW RF power and a source pressure of 0.6Pa.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

STREAM2016: Streaming Requirements, Experience, Applications and Middleware Workshop

The Department of Energy (DOE) Office of Science (SC) facilities including accelerators, light sources and neutron sources and sensors that study, the environment, and the atmosphere, are producing streaming data that needs to be analyzed for next-generation scientific discoveries. There has been an explosion of new research and technologies for stream analytics arising from the academic and private sectors. However, there has been no corresponding effort in either documenting the critical research opportunities or building a community that can create and foster productive collaborations. The two-part workshop series, STREAM: Streaming Requirements, Experience, Applications and Middleware Workshop (STREAM2015 and STREAM2016), were conducted to bring the community together and identify gaps and future efforts needed by both NSF and DOE. This report describes the discussions, outcomes and conclusions from STREAM2016: Streaming Requirements, Experience, Applications and Middleware Workshop, the second of these workshops held on March 22-23, 2016 in Tysons, VA. STREAM2016 focused on the Department of Energy (DOE) applications, computational and experimental facilities, as well software systems. Thus, the role of “streaming and steering” as a critical mode of connecting the experimental and computing facilities was pervasive through the workshop. Given the overlap in interests and challenges with industry, the workshop had significant presence from several innovative companies and major contributors. The requirements that drive the proposed research directions, identified in this report, show an important opportunity for building competitive research and development program around streaming data. These findings and recommendations are consistent with vision outlined in NRC Frontiers of Data and National Strategic Computing Initiative (NCSI) [1, 2]. The discussions from the workshop are captured as topic areas covered in this report's sections. The report discusses four research directions driven by current and future application requirements reflecting the areas identified as important by STREAM2016. These include (i) Algorithms, (ii) Programming Models, Languages and Runtime Systems (iii) Human-in-the-loop and Steering in Scientific Workflow and (iv) Facilities.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2011

Dimuon radiation at relativistic energies available at the CERN Super Proton Synchrotron within a (3 + 1)D hydrodynamic + cascade model

We analyze dilepton emission from hot and dense matter using a hybrid approach based on the ultrarelativistic quantum molecular dynamics (UrQMD) transport model with an intermediate hydrodynamic stage for the description of heavy-ion collisions at relativistic energies. During the hydrodynamic stage, the production of lepton pairs is described by radiation rates for a strongly interacting medium in thermal equilibrium. In the low-mass region, hadronic thermal emission is evaluated by assuming vector meson dominance including in-medium modifications of the {rho} meson spectral function through scattering from nucleons and pions in the heat bath. In the intermediate-mass region, the hadronic rate is essentially determined by multipion annihilation processes. Emission from quark-antiquark annihilation in the quark gluon plasma (QGP) is taken into account as well. When the system is sufficiently dilute, the hydrodynamic description breaks down and a transition to a final cascade stage is performed. In this stage dimuon emission is evaluated as commonly done in transport models. By focusing on the enhancement with respect to the contribution from long-lived hadron decays after freezeout observed at the SPS in the low-mass region of the dilepton spectra, the relative importance of the different thermal contributions and of the two dynamical stages is investigated. We find that three separated regions can be identified in the invariant mass spectra. Whereas the very low and the intermediate-mass regions mostly receive contribution from the thermal dilepton emission, the region around the vector meson peak is dominated by the cascade emission. Above the {rho}-peak region the spectrum is driven by QGP radiation. Analysis of the dimuon transverse mass spectra reveals that the thermal hadronic emission shows an evident mass ordering not present in the emission from the QGP. A comparison of our calculation to recent acceptance-corrected NA60 data on invariant as well as transverse mass spectra is performed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene

The precise details of the interaction of intense X-ray pulses with matter are a topic of intense interest to researchers attempting to interpret the results of femtosecond X-ray free electron laser (XFEL) experiments. An increasing number of experimental observations have shown that although nuclear motion can be negligible, given a short enough incident pulse duration, electronic motion cannot be ignored. The current and widely accepted models assume that although electrons undergo dynamics driven by interaction with the pulse, their motion could largely be considered 'random'. This would then allow the supposedly incoherent contribution from the electronic motion to be treated as a continuous background signal and thus ignored. The original aim of our experiment was to precisely measure the change in intensity of individual Bragg peaks, due to X-ray induced electronic damage in a model system, crystalline C 60 . Contrary to this expectation, we observed that at the highest X-ray intensities, the electron dynamics in C 60 were in fact highly correlated, and over sufficiently long distances that the positions of the Bragg reflections are significantly altered. Our paper describes in detail the methods and protocols used for these experiments, which were conducted both at the Linac Coherent Light Source (LCLS) and the Australian Synchrotron (AS) as well as the crystallographic approaches used to analyse the data.

43 PARTICLE ACCELERATORS↗
NASA NTRS2022

Application Table: A Bridge Connecting the Designing “With-The-Material” and “The-Material” Paradigms

Over the last few decades, advances in high-performance computing, new material characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. In 2016 NASA sponsored a 2040 Vision study (which appeared in 2018) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. The study envisions a cyber-physical-social ecosystem comprised of experimentally verified and validated (V & V) computational models, tools, and techniques, along with the associated digital tapestry, that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of “fit-for-purpose” materials, components, and systems. Consequently, the development of a robust information management system that incorporates (across the full life cycle) both experimental (real data) and virtual data resulting from the application of various simulation tools (at single or multiple length scales), therefore enabling the virtual design and optimization of materials throughout their processing – internal structure – property – performance envelope, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality, and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. This is particularly true when attempting to merge ICME practices with recent additive manufacturing technology which will enable production of the resulting 2040 Vision material and structural designs. At NASA Glenn Research Center we are exploring the future of material science through the use of novel characterization methodologies, high performance computing, and recently an emphasis on integrated computational materials engineering (ICME). Herein, recent efforts to incorporate an Application Table within NASA Glenn Research Center’s ICME Granta MI database is presented. The goal is to provide a place where material and structural application information/requirements can be linked so as to marry the “design the-material” and the “design-with-material” paradigms and thereby enable application-driven design and optimization of materials and structures by providing a central location that links material processing at various length scales to the application’s performance requirements. This paper discusses the specifics of this Application Table as well as best practices and key principles for the development of a robust materials information management system to enable the 2040 Vision integrated materials and structures ecosystem. Furthermore, it presents the intended role of the Application Table in the future of ICME design of “fit-for-purpose” materials, showing the need for a well-established framework that can successfully bridge the gap between the design “the material” and design “with-the-material” paradigms.

Materials↗
NASA NTRSDate not supplied

Application Table: A Bridge Connecting the Designing “With-the-Material” and “the-Material”

Over the last few decades, advances in high-performance computing, new material characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. In 2016 NASA sponsored a 2040 Vision study (which appeared in 2018) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. The study envisions a cyber-physical-social ecosystem comprised of experimentally verified and validated (V & V) computational models, tools, and techniques, along with the associated digital tapestry, that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of “fit-for-purpose” materials, components, and systems. Consequently, the development of a robust information management system that incorporates (across the full life cycle) both experimental (real data) and virtual data resulting from the application of various simulation tools (at single or multiple length scales), therefore enabling the virtual design and optimization of materials throughout their processing – internal structure – property – performance envelope, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality, and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. This is particularly true when attempting to merge ICME practices with recent additive manufacturing technology which will enable production of the resulting 2040 Vision material and structural designs. At NASA Glenn Research Center we are exploring the future of material science through the use of novel characterization methodologies, high performance computing, and recently an emphasis on integrated computational materials engineering (ICME). Herein, recent efforts to incorporate an Application Table within NASA Glenn Research Center’s ICME Granta MI database is presented. The goal is to provide a place where material and structural application information/requirements can be linked so as to marry the “design the-material” and the “design-with-material” paradigms and thereby enable application-driven design and optimization of materials and structures by providing a central location that links material processing at various length scales to the application’s performance requirements. This paper discusses the specifics of this Application Table as well as best practices and key principles for the development of a robust materials information management system to enable the 2040 Vision integrated materials and structures ecosystem. Furthermore, it presents the intended role of the Application Table in the future of ICME design of “fit-for-purpose” materials, showing the need for a well-established framework that can successfully bridge the gap between the design “the material” and design “with-the-material” paradigms.

Materials↗
DOE OSTI2012

Dynamic modeling and transient studies of a solid-sorbent adsorber for CO{sub 2} capture

The U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI) is dedicated to accelerating the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. In this multi-lab initiative in partnership with academic and industrial institutions, the National Energy Technology Laboratory (NETL) leads the development of a multi-scale modeling and simulation toolset for rapid evaluation and deployment of carbon capture systems. One element of the CCSI is focused on optimizing the operation and control of carbon capture systems since this can have a significant impact on the extent and the rate at which commercial-scale capture processes will be scaled-up, deployed, and used in the years to come. Capture processes must be capable of operating over a wide range of transient events, malfunctions, and disturbances, as well as under uncertainties. As part of this work, dynamic simulation and control models, methods, and tools are being developed for CO{sub 2} capture and compression processes and their integration with a baseline commercial-scale supercritical pulverized coal (SCPC) power plant. Solid-sorbent-based post-combustion capture technology was chosen as the first industry challenge problem for CCSI because significant work remains to define and optimize the reactors and processes needed for successful sorbent capture systems. Sorbents offer an advantage because they can reduce the regeneration energy associated with CO{sub 2} capture, thus reducing the parasitic load. In view of this, the current paper focuses on development of a dynamic model of a solid-sorbent CO{sub 2} adsorber-reactor and an analysis of its transient performance with respect to several typical process disturbances. A one-dimensional, non-isothermal, pressure-driven dynamic model of a two-stage bubbling fluidized bed (BFB) adsorber-reactor is developed in Aspen Custom Modeler (ACM). The BFB stages are of overflow-type configuration where the solids leave the stage by flowing over the overflow-weir. Each bed is divided into three regions, namely emulsion, bubble, and cloud-wake regions. In all three regions, the model considers mass and energy balances. Along with the models of the BFB stages, models of other associated hardware are developed and integrated in a single flowsheet. A valid pressure-flow network is developed and a lower-level control system is designed so that the overall CO{sub 2} capture can be maintained at a desired level in face of the typical disturbances. The dynamic model is used for studying the transient responses of a number of important process variables as a result of the disturbances that are typical of post-combustion CO{sub 2} capture processes.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2012

SOLAR ENERGETIC PARTICLE EVENTS AND THE KIPLINGER EFFECT

The Kiplinger effect is an observed association of solar energetic (E > 10 MeV) particle (SEP) events with a 'soft-hard-harder' (SHH) spectral evolution during the extended phases of the associated solar hard (E > 30 keV) X-ray (HXR) flares. Besides its possible use as a space weather predictor of SEP events, the Kiplinger effect has been interpreted as evidence of SEP production in the flare site itself, contradicting the widely accepted view that particles of large SEP events are predominately or entirely accelerated in shocks driven by coronal mass ejections (CMEs). We review earlier work to develop flare soft X-ray (SXR) and HXR spectra as SEP event forecast tools and then examine recent Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI) evidence supporting the association of SHH HXR flares with large SEP events. We point out that ad hoc prediction criteria using the CME widths and SXR flare durations of associated RHESSI hard X-ray bursts (HXBs) can yield results comparable to those of the SHH prediction criteria. An examination of the RHESSI dynamic plots reveals several ambiguities in the determination of whether and when the SHH criteria are fulfilled, which must be quantified and applied consistently before an SHH-based predictive tool can be made. A comparative HXR spectral study beginning with the large population of relatively smaller SEP events has yet to be done, and we argue that those events will not be so well predicted by the SHH criteria. SHH HXR flares and CMEs are both components of large eruptive flare events, which accounts for the good connection of the SHH HXR flares with SEP events.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Enabling CCS via Low-temperature Geothermal Energy Integration for Fossil-fired Power Generation

Here, among the key barriers to commercial scale deployment is the cost associated with CO 2 capture. This is particularly true for existing large, fossil-fired assets that account for a large fraction of the electricity generation fleet in developed nations, including the U.S. Fitting conventional combustion technologies with CO 2 capture systems can carry an energy penalty of thirty percent or more, resulting in an increased price of power to the grid, as well as an overall decrease in net plant output. Taken together with the positive growth in demand for electricity, this implies a need for accelerated capital build-out in the power generation markets to accommodate both demand growth and decreased output at retrofitted plants. In this paper, the authors present the results of a study to assess the potential to use geothermal energy to provide boiler feedwater preheating, capturing efficiency improvements designed to offset the losses associated with CO 2 capture. Based on NETL benchmark cases and subsequent analysis of the application using site-specific data from the North Valmy power plant, several cases for CO 2 capture were evaluated. These included geothermally assisted MEA capture, CO2BOLs capture, and stand-alone hybrid power generation, compared with a baseline, no-geothermal case. Based on Case 10, and assuming 2.7 MMlb/h of geothermally sourced 150 ºC water, the parasitic power load associated with MEA capture could be offset by roughly seven percent, resulting in a small (~1 percent) overall loss to net power generation, but at levelized costs of electricity similar to the no-geothermal CCS case. For the CO 2 BOLs case, the availability of 150°C geothermal fluid could allow the facility to not only offset the net power decrease associated with CO 2 BOLs capture alone, but could increase nameplate capacity by two percent. The geothermally coupled CO 2 BOLs case also decreases LCOE by 0.75 ¢/kWh relative to the non-hybrid CO 2 BOLs case, with the improved performance over the MEA case driven by the lower regeneration temperature and associated duty for CO 2 BOLs relative to MEA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Technology to Establish a Factory for High QE Alkali Antimonide Photocathodes

Intense electron beams are key to a large number of scientific endeavors, including electron cooling of hadron beams, electron-positron colliders, secondary-particle beams such as photons and positrons, sub-picosecond ultrafast electron diffraction (UED), and new high gradient accelerators that use electron-driven plasmas. The last decade has seen a considerable interest in pursuit and realization of novel light sources such as Free Electron Lasers [1] and Energy Recovery Linacs [2] that promise to deliver unprecedented quality x-ray beams. Many applications for high-intensity electron beams have arisen in recent years in high-energy physics, nuclear physics and energy sciences, such as recent designs for an electron-hadron collider at CERN (LHeC) [3], and beam coolers for hadron beams at LHC and eRHIC [4,5]. Photoinjectors are used at the majority of high-brightness electron linacs today, due to their efficiency, timing structure flexibility and ability to produce high power, high brightness beams. The performance of light source machines is strongly related to the brightness of the electron beam used for generating the x-rays. The brightness of the electron beam itself is mainly limited by the physical processes by which electrons are generated. For laser based photoemission sources this limit is ultimately related to the properties of photocathodes [6]. Most facilities are required to expend significant manpower and money to achieve a workable, albeit often non-ideal, compromise photocathode solution. If entirely fabricated in-house, the photocathode growth process itself is laborious and not always reproducible: it involves the human element while requiring close adherence to recipes and extremely strict control of deposition parameters. Lack of growth reliability and as a consequence, slow adoption of viable photoemitter types, can be partly attributed to the absence of any centralized facility or commercial entity to routinely provide high peak current capable, low emittance, visible-light sensitive photocathodes to the myriad of source systems in use and under development. Successful adoption of photocathodes requires strict adherence to proper fabrication, operation, and maintenance methodologies, necessitating specialized knowledge and skills. Key issues include the choice of photoemitter material, development of a more streamlined growth process to minimize human operator uncertainties, accommodation of varying photoemitter substrate materials and geometries, efficient transport and insertion mechanisms preserving the photo-yield, and properly conveyed photoemitter operational and maintenance methodologies. AES, in collaboration with Cornell University in a Phase I STTR, developed an on-demand industrialized growth and centralized delivery system for high-brightness photocathodes focused upon the alkali antimonide photoemitters. To the end user, future photoemitter sourcing will become as simple as other readily available consumables, rather than a research project requiring large investments in time and personnel.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Livewire: A Model Platform for Data Quality Assessment and AI Readiness Across DOE Missions

High-quality, well-governed data is essential for accelerating discovery and achieving operational excellence across DOE and national laboratory missions. The Livewire Data Platform is a DOE-supported platform that offers automated assessments of data quality, standardization, provenance, and Artificial Intelligence (AI) readiness. It allows researchers and data practitioners to systematically and easily evaluate datasets against established governance criteria and prepare them for advanced analytics. Livewire addresses critical challenges in DOE's data ecosystem with integrated capabilities for metadata validation, provenance tracking, and schema alignment. This platform's automated workflows assist users in identifying data quality gaps, enhancing interoperability between datasets collected from various stakeholders, and ensuring compliance with DOE data standards, all while reducing manual curation efforts. Additionally, we will discuss its AI readiness framework, which is being developed to prepare datasets for training models, developing advanced analytic tools, and machine learning applications. Using some of the more than one hundred tabular datasets on Livewire, processed with this open-source methodology, we will demonstrate how Livewire can serve as a model for scalable, standards-driven data management. This approach provides a pathway to leverage existing and future datasets within the DOE, boosting innovation and efficiency across national laboratories.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Controlling Ion Uptake in Carboxylated Mixed Conductors

Organic mixed ionic‐electronic conductors (OMIECs) have garnered significant attention due to their capacity to transport both ions and electrons, making them ideal for applications in energy storage, neuromorphics, and bioelectronics. However, charge compensation mechanisms during the polymer redox process remain poorly understood, and are often oversimplified as single‐ion injection with little attention to counterion effects. To advance understanding and design strategies toward next‐generation OMIEC systems, a series of p‐channel carboxylated mixed conductors is investigated. Varying side‐chain functionality, distinctive swelling character is uncovered during electrochemical doping/dedoping with model chao‐/kosmotropic electrolytes. Carboxylic acid functionalized polymers demonstrate strong deswelling and mass reduction during doping, indicating cation expulsion, while ethoxycarbonyl counterparts exhibit prominent mass increase, pointing to an anion‐driven doping mechanism. By employing operando grazing incidence X‐ray fluorescence (GIXRF), it is revealed that the carboxyl functionalized polymer engages in robust cation interaction, whereas ester functionalization shifts the mechanism towards no cation involvement. It is demonstrated that cations are pivotal in mitigating swelling by counterbalancing anions, enabling efficient anion uptake without compromising performance. These findings underscore the transformative influence of functionality‐driven factors and side‐chain chemistry in governing ion dynamics and conduction, providing new frameworks for designing OMIECs with enhanced performance and reduced swelling.

carboxylated polythiophenes↗
DOE OSTI2011

Operation Status of the J-PARC Negative Hydrogen Ion Source

A cesium-free negative hydrogen ion source driven with a lanthanum hexaboride (LaB{sub 6}) filament is being operated without any serious trouble for approximately four years in J-PARC. Although the ion source is capable of producing an H{sup -} ion current of more than 30 mA, the current is routinely restricted to approximately 16 mA at present for the stable operation of the RFQ linac which has serious discharge problem from September 2008. The beam run is performed during 1 month cycle, which consisted of a 4-5 weeks beam operation and a few days down-period interval. At the recent beam run, approximately 700 h continuous operation was achieved. At every runs, the beam interruption time due to the ion source failure is a few hours, which correspond to the ion source availability of more than 99%. The R and D work is being performed in parallel with the operation in order to enhance the further beam current. As a result, the H{sup -} ion current of 61 mA with normalized rms emittance of 0.26 {pi}mm.mrad was obtained by adding a cesium seeding system to a J-PARC test ion source which has the almost same structure with the present J-PARC ion source.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Workshop to Identify Opportunities in Biological and Environmental Research Uniquely Enabled by the APS Upgrade

The development of synchrotron-based x-ray science has enabled tremendous scientific advances, driven both by discoveries of new fundamental ways to derive insight from the interaction of x-rays with matter and by the evolution of x-ray light sources themselves. Over the last 20-plus years, the U.S. Department of Energy’s (DOE’s) Advanced Photon Source (APS) at Argonne has yielded a multitude of transformational insights into science questions across numerous areas relevant to the biological, geological, geochemical, biogeochemical, and environmental sciences. Recent developments in accelerator technology, particularly the multi-bend achromat lattice, have led to plans to dramatically upgrade the APS, allowing an upgraded APS to offer orders-of-magnitude improvements in key parameters such as coherence and brightness, while retaining high-energy capabilities. These enhanced parameters are directly relevant to almost all hard x-ray imaging, scattering, and spectroscopy experiments (Eriksson et al., 2014). The APS Upgrade Project exceeds the capabilities of today’s synchrotrons by 2 to 3 orders of magnitude in brightness and coherent flux in the hard x-ray range, enabling a transformational range of new probes for the structure, properties, and functionality of matter. With vastly improved coherent flux, nearly all techniques can become microscopies. For example, frontiers inaccessible today will be opened by x-ray instruments that provide three-dimensional (3-D) real-space resolution down to nanometers across, up to millimeters of field of view, providing the ability to follow in real time relevant processes and trace element sensitivity down to a few atoms. In addition to the unprecedented spatial resolution across a large field of view, and with appropriate time resolution, the ability for high-throughput, four-dimension/five-dimension data collection enables unprecedented statistical analysis of complex biological and environmental systems in changing environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗