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At least 325 records · Page 18

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Analysis and mitigation of parasitic resistance effects for analog in-memory neural network acceleration

To support the increasing demands for efficient deep neural network processing, accelerators based on analog in-memory computation of matrix multiplication have recently gained significant attention for reducing the energy of neural network inference. However, analog processing within memory arrays must contend with the issue of parasitic voltage drops across the metal interconnects, which distort the results of the computation and limit the array size. This work analyzes how parasitic resistance affects the end-to-end inference accuracy of state-of-the-art convolutional neural networks, and comprehensively studies how various design decisions at the device, circuit, architecture, and algorithm levels affect the system's sensitivity to parasitic resistance effects. Here, a set of guidelines are provided for how to design analog accelerator hardware that is intrinsically robust to parasitic resistance, without any explicit compensation or re-training of the network parameters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

LEBT Buncher / Feed Forward / Frequency Generation: System Design Document (SDD)

The LAMP Low Energy Beam Transport (LEBT) transfers a continuous beam at 100 keV from the ion source to RFQ in LEBT. A LEBT buncher imposes an energy tilt to initiate velocy bunching in the chopped beam pulse about 25 ns long to form a short MPEG bunch. One possible option for the LEBT buncher based on a two-gap LC-circuit driven structure was considered, which is similar to the existing LANSCE low-frequncy buncher (LFB). Another possible option is a non-resonant element driven by a pulse-forming-network that provides a single pulse at the repetition frequency of MPEG beam, with a period of 1.8 µs. The LEBT operation is synchronized with the accelerator timing system.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Design of Controller Hardware-In-the-Loop Model of Microgrid with Modular Building Blocks and Automated Design Script

The scalability of controller hardware-in-the-loop (CHIL) simulation is critical for validating control coordination and energy management in microgrids with distributed energy resources, especially as these modern systems become more complex and decentralized. This paper presents a CHIL modeling methodology that combines modular building blocks with an automated design script to streamline the development of high-fidelity microgrid models. Standardized subsystem templates for resources, converters, and buses are integrated with a Python-based script that compiles structured JSON configuration files into simulation-ready initialization code. The proposed approach reduces development time, improves model consistency, and enhances simulation fidelity. The methodology is validated on a Typhoon HIL604 platform and is broadly applicable to real-time simulation of complex, networked microgrid systems. This framework establishes a foundation for automated, scalable CHIL validation and accelerates the design of next-generation distributed energy systems.

Kim, Namwon [ORNL] (ORCID:0000000200438489)↗

DOE OSTI2024

In-Silico Analysis of High Refractive Index Materials Through Principles of Materials Design

The intent of the paper is to use specific principles of Materials Design that were developed and applied in the electronics industry for enabling understanding and design of improved high refractive index materials. Further, by combining first-principle based ab-initio, semiempirical interatomic potential methods, and machine learning approaches in conjunction with experimental data, we identified specific determinants of high refractive index materials, which can be critically applied for informing materials design and accelerating discovery. Specifically, it was demonstrated that chalcogenides and perovskites as bulk materials can exhibit higher refractive indices with appropriate engineering of specific aspects of the materials.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Experimental Demonstration of a Two-Dimensional Nonlinear Integrable System in a Particle Accelerator

A two-dimensional nonlinear integrable system was experimentally demonstrated at the Fermilab Integrable Optics Test Accelerator. The system was implemented by inserting a special nonlinear magnet in a conventional accelerator lattice. We characterized the system by measuring lifetimes, transverse profiles and transverse oscillation frequencies of the 150-MeV electron beam as a function of the strength of the nonlinear insert. The measured shift of the working point and the amplitude-dependent detuning were consistent with theoretical predictions. We also observed the predicted bifurcation of the stable closed orbit. A striking consequence of the system's implementation was the possibility to operate the storage ring with integer tunes without lifetime degradation. This research opens up novel ways to design particle accelerators and to stabilize particle beams.

Wieland, John [Fermilab] (ORCID:0000000289718523)↗

DOE OSTI2020

Advancement of LANSCE accelerator facility as a 1-MW Fusion Prototypic Neutron Source

The Fusion Prototypic Neutron Source (FPNS) is considered to be a testbed for scientific understanding of material degradation in future nuclear fusion reactors (Zinkle and Moeslang, 2013; Summary Report on the FPNS Workshop, 2018; Pitcher et al., 2019). The primary mission of FPNS is to provide a damage rate in iron samples of 8-11 dpa/calendar year with He/dpa ratio of 10 appm in irradiation volume of 50 cm 3 or larger with irradiation temperature 300–1000 °C and flux gradient less than 20%/cm in the plane of the sample. The Los Alamos Neutron Science Center (LANSCE) is an attractive candidate for the FPNS project. The Accelerator Facility was designed and operated for an extended period as a 0.8-MW Meson Factory. The existing setup of the LANSCE accelerator complex can nearly fulfill requirements of the fusion neutron source station. The primary function of the upgraded accelerator systems is the safe and reliable delivery of a 1.25-mA continuous proton beam current at 800-MeV beam energy from the switchyard to the target assembly to create 1 MW power of proton beam interacting with a solid tungsten target. The present study describes existing accelerator setup and further development required to meet the needs of FPNS project.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Start-to-end simulation for LAMP front-end scoping studies

We present a design of a new front end in support of the LANSCE Modernization Project (LAMP). This is an updated front-end design that can largely meet LAMP threshold needs. The first half of this report details each section of the front end, including a summary of the physics design and function. The second half simulates several beam formats from the initial source all the way through the final DTL section (i.e. start-to-end simulations). Details and assumptions are described. The following table summarizes the key results for the different beam formats from this study.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Northstar Mo99 Window Tests

Tests were completed for window to “first disk” (actually a steel plate for these expariments) gaps of 20 mil as per design gap as well as 10 mil and 30 mil gaps to span the range of possible variations. With possible disk distortion in beam, another test with 0 gap was performed. Based on deflections measured during the pressure test, the 0 gap was most likely very near 3 mil.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Data-driven methods for diffusivity prediction in nuclear fuels

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels—uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. In conclusion, these results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Supercharge NY

The Energy Program for Innovation Clusters (EPIC) Supercharge NY enabled The Clean Fight to execute an inaugural accelerator program focused on enabling world-class, growth-stage energy storage companies to successfully scale deployments and establish domestic manufacturing pathways in New York State. The Clean Fight has developed and tested a successful accelerator model designed to reduce the barriers growth-stage climate tech companies face by collapsing the sales cycle to scaling solutions at speed. The Clean Fight’s Supercharge NY project allowed for the exploration into the understanding of New York-focused energy storage opportunities, challenges, and methods for enabling successful projects. The Clean Fight’s Supercharge NY project enabled high-touch matchmaking with industry leading customer and capital partners, technical assistance to facilitate scaling supply chain and manufacturing capacity, and access to non-dilutive funding for first-of-a-kind or demonstration projects in New York State. The results of the program provide a public benefit by accelerating deployments of energy storage to provide value-added energy services and sustainability benefits to communities while boosting economic opportunity and job creation for all.

25 ENERGY STORAGE↗

DOE OSTI2020

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. Here, we finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

LAMP-PSR Scoping Studies Report

This report summarizes work performed from July – September 2023, performing an initial evaluation for the LANSCE Proton Storage Ring upgrade option “B.” The studies identified potential pathsforward, performing initial beam dynamics studies, and surveying various technology options different aspects of the upgrade. Taken together, the studies provide an “early look” at the feasibility, challenges and alternative methods for pursuing Option “B,” and give guidance towards more detailed design concept development needed as work progresses. Generally speaking, no major obstacles were identified in pursuing the Option “B” PSR upgrade path in terms of the machine physics or overall system architecture. Several areas of concern have been identified; we note that these primarily center on technology choice and must be addressed regardless of the upgrade path selected, and none of the concerns are considered insurmountable.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Virtual Engineering: Python framework for engineering process design

Virtual Engineering (VE) is a Python software framework designed to accelerate the research and development of engineering processes that are fundamentally defined by multiple unit operations executed in series. VE supports a wide variety of different multi-physics models and integrates them to simulate a complete end-to-end process. To automate the execution of this model sequence, VE provides (i) a robust method to communicate between models, (ii) a high-level, user-friendly interface to set model parameters and enable optimization, and (iii) an overall model-agnostic approach that allows new computational units to be swapped in and out of workflows. Although the VE framework was developed to support the biochemical conversion of biomass to fuel, we have designed each component to easily accommodate new domains and unit models.

09 BIOMASS FUELS↗

DOE OSTI2025

MEBT Bunchers: System Design Document (SDD)

The LAMP Medium Energy Beam Transport (MEBT) transfers bunched beam at the energy 3 MeV from RFQ to the Drift-Tube Linac (DTL) entrance. The beam particles (protons or H- ) in the LAMP MEBT have velocity β = v/c = 0.08, where v is the beam velocity, c is the speed of light. The MEBT bunchers keep beam bunches from spreading longitudinally as they propagate through the MEBT, where some unwanted bunches are removed by a chopper to create a required beam pattern. The MEBT bunchers are RF cavities operating at the frequency 201.25 MHz; possible design options were considered in.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

NorthStar Helium Cooling System. Final Report on LANL Contribution

From the beginning of NNSA support to the NorthStar development of a production facility for the medical isotope Molybdenum-99 (Mo99), LANL has been responsible for the design and testing of a target and its cooling. The target is comprised of a stack of Mo100 disks separated by coolant flow gaps between the disks and between the first disk and a housing window, which separates the target and its coolant from the vacuum of the beam line. The target, and therefore its cooling requirements, evolved steadily over the years of system development

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2025

Literature Review Investigating Historical Plutonium Solubility in SRS Tank Waste

The Savannah River Site (SRS) has designed the Accelerated Basin De-inventory (ABD) program to accelerate the de-inventory of L-Basin and accelerate the Spent Nuclear Fuel (SNF) disposition mission. Similarly, the H-Canyon facility at SRS is reestablishing the 6.3D electrolytic dissolver for dissolving unirradiated stainless-steel (SS) clad Fast Critical Assembly (FCA) fuel. In both discard types (ABD and FCA), plutonium is present and its complex solubility when composited to Concentration, Storage, and Transfer Facility (CSTF) sludge is being investigated as it may have downstream impacts to the liquid waste (LW) organization. This literature review aims to highlight and compile the existing literature on plutonium solubility in waste streams relevant to ABD and FCA discards, as well as discuss some considerations in analyzing solubility data of plutonium. This review serves to help define the analysis methods for future experiments involving plutonium (and other actinides) and in designing appropriate testing conditions surrounding these studies. This review is broken up into five parts and will discuss: (i) The possible effects of testing hold time and temperature on plutonium solubility, (ii) the influence of neutralization rate and particle size of freshly precipitated discards, (iii) the coprecipitation of plutonium with iron and uranium, (iv) predictive solubility modeling and the influence of supernate anions on solubility, and (v) the speciation of plutonium in solutionas a result of supernate anions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Smart culture medium optimization for recombinant protein production: Experimental, modeling, and AI/ML-driven strategies

Recombinant protein production (RPP) is central to biotechnology, where recombinant proteins are used as either end products or catalysts in the synthesis of chemicals, fuels, and materials. Among the major cost drivers, culture medium plays a pivotal role in determining protein yield and quality. This review presents a comprehensive perspective on the critical stages of “smart” culture medium optimization: planning, screening, modeling, optimization, and validation. In the planning stage, we examine the nutritional and energetic roles of medium components, including carbon, nitrogen, amino acids, salts, and trace metals, and their impacts on culture parameters such as pH, oxidative state, and osmolality. We highlight the variability in trace metal content due to water sources, culture vessels, and raw materials, which can substantially influence RPP. The screening stage covers Design of Experiments (DoE) approaches, assessing their theoretical basis, implementation, and limitations. For modeling, we describe methods that integrate experimental data to develop predictive models for smart medium formulation. Model-based optimization strategies can then be employed to select optimal media compositions for a given application. The validation stage aims to evaluate model predictions and provide feedback for model training and refinement. Finally, we survey mechanistic and artificial intelligence/machine learning (AI/ML)-driven models as integrated, transformational tools for predictive modeling of bioprocess conditions, nutrient availability, cellular metabolism, and protein quality, with the goal of optimizing culture media to enhance protein yields while reducing costs and environmental impact. We conclude by addressing the challenges of translating laboratory-scale medium optimization to industrial-scale settings and exploring future AI/ML-driven approaches that may overcome current bottlenecks and accelerate medium design for RPP. Overall, this review provides a unified framework for advancing smart medium design in RPP.

Artificial Intelligence/Machine Learning (AI/ML)↗