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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

End-to-end differentiable digital twin for the IOTA/FAST facility

As the design complexity of modern accelerators grows, there is more interest in using controllable-fidelity simulations that have fast execution time or can yield additional insights about accelerator state. One notable example of additional information are gradients of physical observables with respect to design parameters produced by differentiable simulations. The IOTA/FAST facility has recently begun a program to implement and experimentally validate an end-to-end digital twin to serve as a virtual accelerator test stand, allowing for rapid prototyping of new software and experiments with minimal beam time costs. In this contribution we will discuss our plans and progress. Specifically, we will cover the selection and benchmarking of both physics and ML codes for linac and ring simulation, the development of generic interfaces between surrogate and physics-based sections, and presenting the control interface as either a deterministic event loop or a fully asynchronous EPICS soft input/output controller. We will also discuss challenges in model calibration and uncertainty quantification, as well as future plans to implement larger proton accelerators like PIPII and Booster.

Kuklev, N. [Fermilab]↗
DOE OSTI2026

FOCAL Campaign II/III: Applying Active Hull Controls Using Tuned-mass Dampers/Hull Flexibility and Internal Loads

Campaign II and III of the Floating Offshore-wind Controls Advanced Laboratory Experimental Program (FOCAL) aimed to generate a dataset enabling the validation of the performance and loads of a scaled hull, with and without structural hull control. The floating platform was subjected to a variety of wave environments and controlled using tuned-mass dampers (TMDs) tuned to two of the systems natural frequencies (Platform Pitch and Tower-bending). The floating platform is fully instrumented to record a variety of parameters in real time such as platform dynamics, accelerations, and loads at different points in the structure. The test data considered was generated at the University of Maine's Harold Alfond Wind and Wave (W2) testing facility. This testing was focused only on validation of wave loading, and wind conditions were not considered. As such the platform does not support a working turbine, and instead supports a structure designed to have the same mass properties as the 1:70 IEA 15MW Reference turbine. The Load Cases (LC) considered in this testing campaign are as follows: LC 1.X - Platform Static Offset (TMD off); LC 2.X - Platform Free-decays (TMD off); LC 3.X - Wave Cases (Regular Wave, Irregular Wave, Pink Noise Wave) with and without TMDs active. Detailed properties on the model system are found in the following reference: Lenfest E., Floating Offshore-wind Controls Advanced Laboratory (FOCAL) Experimental Program - Campaigns 2 and 3: 1:70 Model-scale Testing of the IEA-Wind 15MW Reference Turbine and the VolturnUS-S Hull. UMaine ASCC Report Number 23-56-1183.

17 WIND ENERGY↗
DOE OSTI2025

Bare Module Testing for the CMS Vertex Detector Upgrade

The High-Luminosity LHC will continue exploring physics beyond the standard model with proton-proton collisions at ten times the luminosity of previous LHC runs. The physics program depends on precise measurements of charged particles near the beams. The CMS experiment will use layers of high-granularity hybrid silicon pixel detectors. Pixelation is achieved by bonding silicon to tiny electrodes, which are directly connected to the specially designed integrated circuit, CROCv2. Pixel detector components are in the pre-production phase, and evaluating prototypes of sensor-chip assemblies from different vendors provides quick feedback and improves reliability during production. This article presents the efforts to test the viability of bare detector assemblies using a manual probe station and electronic charge-injection methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Neutrino Program at Fermilab - Enhancing Proton Beam Power and Accelerator Infrastructure

The upcoming long baseline neutrino experiments aim to enhance proton beam power to multi-MW scale and utilize large-scale detectors to address the challenge of limited event statistics. The DUNE experiment at LBNF will test the three-neutrino flavor paradigm and directly search for CP violation by studying oscillation signatures in the high intensity (anti-) beam to (anti-) measured over a long baseline. Higher beam power and improved accelerator up-time will enhance neutrino flux for the neutrino program by increasing the number of protons on target. LBNF/DUNE, as well as PIP-II upgrade and Accelerator Complex Evolution (ACE) plan, play a vital role in this effort. The scientific potential of ACE plan extends beyond neutrino physics, encompassing endeavors such as the Muon Collider, Charged Lepton Flavor Violation (CLFV), Dark Sectors, and exploration of neutrinos beyond DUNE.\par In the era of higher-power accelerator operation, research in target materials and beam instrumentation is crucial for optimizing design modifications. This abstract discusses Fermilab ACE, the science opportunities it provides, and how Fermilab is pushing the limits of proton beam power and accelerator infrastructure. By tackling neutrino beam challenges and exploring research and development ideas, we are advancing our understanding of fundamental particles and their interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Advanced Sensors and Instrumentation (ASI) Program Plan. Nuclear Energy Enabling Technologies (NEET)

The U.S. Department of Energy Office of Nuclear Energy (DOE-NE) sponsors a crosscutting technology development (CTD) program addressing common instrumentation & controls (I&C) needs in all DOE-NE sponsored programs. In 2012, DOE-NE initiated the Nuclear Energy Enabling Technology (NEET) Program to conduct research, development, and demonstration (RD&D) in crosscutting technologies that directly support and enable the development of existing, new and advanced reactor designs, and fuel cycle technologies. These technologies will advance the state of nuclear technology, improve its competitiveness, and promote continued contribution to meeting our Nation’s energy and environmental challenges. The NEET Program has spurred innovation in the field of sensors and instrumentation, advanced methods for manufacturing, and cyber security. The Advanced Sensors and Instrumentation (ASI) program is one element of the NEET CTD that has spurred innovation in the measurement science field by funding research on sensors, instrumentation and controls, communication, and advanced analytics that measure system parameters, gauge component control input, and provide power plant owners and operators critical awareness so they can plan and safely manage operational evolutions. The NEET ASI Program has carried out research and development (R&D) required to develop and deploy innovative and advanced sensors, and instrumentation and control capabilities for current and future nuclear energy systems, and to enable the advanced technologies essential to NE’s R&D efforts needed to realize mission goals. The concept of technology readiness assessment is helpful for the classification of instrumentation technology as part of the program strategy. A technology readiness assessment evaluates technology maturity using the Technology Readiness Level (TRL) scale and was pioneered by the National Aeronautics and Space Administration in the 1980s for space technology. It has recently been adopted to advance fuels and materials. The NEET ASI program objective is to mature instruments technology to TRL 6, which is “demonstration of the technology in relevant environment.” While there may be instances in which a different strategy is justified, the aim of the program is to respond to DOE program and stakeholder needs for nuclear instrumentation then hand off the technology after demonstration for integration in a more complex system, for example an irradiation test, experimental loop or a plant component such as the core, primary system or heat exchanger. A mechanism to continue some level of support from NEET ASI during and after the adoption (for example, to maintain fabrication and calibration capabilities) will be defined as the first example of technology hand off comes to fruition. In addition, several paths exist within DOE to complement NEET ASI activities to extend the maturity beyond TRL, for example through GAIN, technology commercialization funding, and small-business programs. Role of NEET ASI CINR projects in the TRL space, and integration with direct funded demonstration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

First results of AUP Nb3Sn quadrupole horizontal tests

The Large Hadron Collider will soon undergo an upgrade to increase its luminosity by a factor of ~10 [1]. A crucial part of this upgrade will be replacement of the NbTi focusing magnets with Nb3Sn magnets that achieve a ~50% increase in the field strength. This will be the first ever large-scale implementation of Nb3Sn magnets in a particle accelerator. The High-Luminosity LHC Upgrade, HL-LHC is a CERN project with a world-wide collaboration. It is under construction and utilizes Nb3Sn Magnets (named MQXF) as key ingredients to increase tenfold the integrated luminosity delivered to the CMS and ATLAS experiments in the next decade. The HL-LHC AUP is the US effort to contribute approximately 50% of the low-beta focusing magnets and crab cavities for the HL-LHC. This paper will present the program to fabricate the Nb3Sn superconducting magnets. We are reporting the status of the HL-LHC AUP project present the results from horizontal tests of the first fully assembled cryo-assembly.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Preliminary Feasibility of Printing, Microstructure Analysis and Mechanical Performance of a Down Selected Ni Alloy

This report is submitted as completion of a Milestone 3 deliverable under work package ORNL CT-23OR1304051 in support of the Advanced Materials and Manufacturing Technologies (AMMT) program. The AMMT program is aiming at the faster incorporation of new materials and manufacturing technologies into complex nuclear-related systems. An integrated approach, combining advanced characterization, high-throughput and accelerated testing, modeling and simulation including machine learning and artificial intelligence will be employed. While 316H (Fe-16-18Cr-10-14Ni-2-3Mo-0.04-0.1C) has been identified as a key alloy to be integrated into the AMMT accelerated alloy qualification approach due its relevance for many current and future nuclear energy reactors, many other alloys could be considered for the advanced fabrication of innovate high-performance nuclear components. ANL, INL, ORNL and PNNL are collaborating on identifying the most promising alloy candidates relevant for the AMMT program. A selection criteria matrix was established to evaluate the alloys considering their relative importance and technological readiness levels for nuclear energy applications, with a focus on laser powder bed fusion (LPBF). Due to the broad range of potential candidate alloys, ORNL and INL focused on Ni-based alloys, while ANL and PNNL mainly evaluated Fe-based alloys. PNNL previously published materials scorecards reports on several key alloys and this report is providing a broader overview of Ni-based candidate alloys, expending beyond alloys well-known to the nuclear community. Of particular interest are alloys that are currently commercially available in powder form due to the growing demand from other industries that have invested heavily in additive manufacturing. Among these alloys, Haynes 282 (Fe-20Cr-20Co-8Mo) was selected due to its superior strength at high temperature compared to the code-qualified alloy 617 (Ni-20-24Cr-10-15Co-8-10Mo). To assess the integration of this new alloy into the AMMT digital manufacturing framework, we performed the rapid optimization of alloy 282 printing parameters on a Renishaw 250 machine, the fabrication of sufficient materials for extensive characterization and mechanical testing both at ORNL and INL, and added the printing data into the digital platform via the Peregrine software. A detailed analysis of the LPBF 718 alloy was also conducted, with creep specimens being tested at 600-650°C and characterized by advanced electron microscopy. The alloy superior mechanical strength and great printability associated with the extensive database that has already been generated highlight the promising potential of LPBF 718 as a candidate alloy for the AMMT program. INL, ANL and PNNL have generated similar reports and all the information will be compiled into a final M2 milestone report to provide the AMMT leadership team with clear recommendations on the down selection of reactor materials, as well as establish a roadmap for the qualification of these selected alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Stochastic DC optimal power flow with reserve saturation

We propose an optimization framework for stochastic optimal power flow with uncertain loads and renewable generator capacity. Our model follows previous work in assuming that generator outputs respond to load imbalances according to an affine control policy, but introduces a model of saturation of generator reserves by assuming that when a generator’s target level hits its limit, it abandons the affine policy and produces at that limit. This is a particularly interesting feature in models where wind power plants, which have uncertain upper generation limits, are scheduled to provide reserves to balance load fluctuations. Here, the resulting model is a nonsmooth nonconvex two-stage stochastic program, and we use a stochastic approximation method to find stationary solutions to a smooth approximation. Computational results on 6-bus and 118-bus test instances demonstrate that by considering the effects of saturation, our model can yield solutions with lower expected generation costs (at the same target line violation probability level) than those obtained from a model that enforces the affine policy to stay within generator limits with high probability.

17 WIND ENERGY↗
DOE OSTI2023

M-Star ® Software Test and Verification

Savannah River Mission Completion (SRMC) currently manages the risk for retained hydrogen in the Defense Waste Processing Facility (DWPF) vessels by implementing a Retained Hydrogen Program. The current program relies on Sludge Batch (SB) 8 Gas Chromatograph data and on conservative assumptions concerning gas release and retention to determine allowable vessel Quiescent time (Q-time). The authors identified M-Star ® CFD as a software that could simulate processes such as fluid flow, heat transfer, species transport, chemical reactions, particle transport, and retained hydrogen gas release. Preliminary simulation results suggest that more realistic assumptions on gas retention and release may be feasible for the DWPF retained hydrogen program. Because of the desire to use the M-Star ® software to perform analyses that support nuclear safety, SRMC has requested Savannah River National Laboratory (SRNL) to upgrade the software classification level of M-Star ® CFD from level D to level A to perform analyses that support nuclear safety.

08 HYDROGEN↗
DOE OSTI2026

New opportunities for rare charm from $Z\rightarrow c\bar{c}$ decays

We analyze the potential of rare charm decays as probes of new physics at a high-luminosity flavor facility operating at the Z pole, such as the FCC-ee or CEPC. In particular, we identify clean null-test observables in $D^0 → π^+ π^- v\bar{v}$ and in polarized $Λ^+_c → p ℓ^+ ℓ^-$ decays with $ℓ = e, μ$. Complementarity with the LHC and HL-LHC flavor programs arises from the characteristic features of a Tera-Z environment: the capability to study missing-energy modes and charm production with significant polarization. We improve the theoretical description of $D^0 → π^+ π^- v\bar{v}$ decays and work out the phenomenology of polarization-induced null-test observables in $Λ^+_c → p ℓ^+ ℓ^-$ decays. In regions of dilepton mass near the φ resonance, polarization asymmetries can reach $O$(5%) for muons and $O$(14%) for electrons times the $Λ^+_c$ polarization. We also point out synergies between the dineutrino and the dilepton modes using the SMEFT framework of heavy new physics. Using the IDEA detector concept at FCC-ee, we find in simulation studies that dineutrino branching fractions as low as ~ 2 x 10 -7 can be probed, which reaches well into the parameter space of new physics, and also allows for discrimination of lepton flavor structures. Furthermore, the measurement of asymmetries in $Λ^+_c → p ℓ^+ ℓ^-$ at $O$(1%) will be possible. Similar sensitivities are expected for dielectron final states, although robust predictions will require further dedicated studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Deriving Simulation Parameters for Storage-Type Water Heaters Using Ratings Data Produced from the Uniform Energy Factor Test Procedure: Preprint

Building energy modeling (BEM) is commonly used to estimate the energy usage of residential buildings. Uses for BEM include calculating home energy ratings, demonstrating compliance with performance-based energy codes, and establishing whether designs meet voluntary program requirements, such as ENERGY STAR® qualified homes. BEM requires simulating subsystems within the building, including storage-type water heaters. To properly simulate the in-situ performance of residential storage water heaters, it is necessary to determine key water heater parameters, including the overall heat loss coefficient (UA) and conversion efficiency (?c) of the water heater, based on the rated efficiency of the water heater. The testing procedure and rating standard for residential water heaters have recently changed: the new rating standard provides a Uniform Energy Factor (UEF) rather than the Energy Factor (EF) used previously. This paper discusses how to derive the necessary model parameters from the ratings data produced from the latest test procedure.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE

Summary Protein property prediction via machine learning with and without labeled data is becoming increasingly powerful, yet methods are disparate and capabilities vary widely over applications. The software presented here, “Artificial Intelligence Driven protein Estimation (AIDE)”, enables instantiating, optimizing, and testing many zero-shot and supervised property prediction methods for variants and variable length homologs in a single, reproducible notebook or script by defining a modular, standardized application programming interface (API), i.e. drop-in compatible with scikit-learn transformers and pipelines. Availability and implementation AIDE is an installable, importable python package inheriting from scikit-learn classes and API and is installable on Windows, Mac, and Linux. Many of the wrapped models internal to AIDE will be effectively inaccessible without a GPU, and some assume CUDA. The newest stable, tested version can be found at https://github.com/beckham-lab/aide_predict and a full user guide and API reference can be found at https://beckham-lab.github.io/aide_predict/. Static versions of both at the time of writing can be found on Zenodo.

36 MATERIALS SCIENCE↗
DOE OSTI2023

TEACHING AN OLD ACCELERATOR NEW TRICKS

The Argonne Tandem Linac Accelerator System (ATLAS) has been a National User Facility since 1985. In that time, many of the systems that help operators retrieve, modify, and store beamline parameters have not kept pace with the advancement of technology. Development of a new method of storing and retrieving beamline parameters resulted in the testing and installation of a time-series database as a potential replacement for the traditional relational database. InfluxDB was selected due to its self-hosted Open-Source version availability as well as the simplicity of installation and setup. A program was written to periodically gather all accelerator parameters in the control system and store them in the time-series database. This resulted in over 13,000 distinct data points, captured at 5-minute intervals. A second test captured 35 channels on a 1-minute cadence. Graphing of the captured data is being done on Grafana, an Open-Source version is available that co-exists well with InfluxDB as the back-end. Grafana made visualizing the data simple and flexible. The testing has allowed for the use of modern graphing tools to generate new insights into operating the accelerator, as well as opened the door to building large data sets suitable for Artificial Intelligence and Machine Learning applications.

Novak, D.↗
DOE OSTI2025

Method of Test for Evaluating Building Performance Simulation Software

ANSI/ASHRAE Standard 140 Method of Test for Evaluating Building Performance Simulation Software specifies the method to test the core competency of building performance simulation (BPS) software, a broad class of software which includes building energy modeling (BEM) software. Standard 140 has been widely used by BEM software vendors to help diagnose and compare the results from the program with other modeling programs. The Standard is also referenced by codes, standards, government programs, and other incentive programs as a part of minimum requirements for qualifying BEM software. The explanatory information, summary tables and figures in this 140 User’s Manual (referred to as “this Manual”) are provided to help users in implementing various suites of tests specified in Standard 140-2023 (referred to in this manual as “Standard 140” or “the Standard”).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Creation of the VADER Code in SCALE [Abstract]

The VADER (Validation Analysis Data Evaluation Resource) is a new module in SCALE 6.3 that has been derived from the legacy USLSTATS program. VADER is a tool that allows the determination of bias and bias uncertainty for criticality safety computational methods. The older USLSTATS program, written in Java, existed outside of SCALE and provided tools to calculate only the confidence band with administrative margin (sometimes called USL-1) and the single-sided uniform width closed interval (USL-2). For normality testing it only offered a crude chi- squared normality test that had no user-configurable options and presented a simple pass/no-pass functionality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Network Optimization of the Electrosynthesis of Chemicals from CO2

Carbon dioxide electroreduction (ECO2R) is gaining attention due to its capacity to mitigate CO2 emissions while using electricity that would otherwise be curtailed. Its foreseeable industrial implementation requires of holistic methods to assess the technological and economic performance of ECO2R processes and integrate them in current chemical supply chains and power systems. Here, we combine techno-economic assessment and mathematical programming to find the optimal paths to electroreduce CO2 into valuable chemicals under variable electricity prices. The proposed approach is tested with a case study addressing the CO2 capture from flue gas or direct air and its electricity-powered reduction into carbon monoxide, formic acid or multi-carbon compounds. The results obtained demonstrate the ability of the framework to build ECO2R networks and provide operation profiles that respond to fluctuating electricity prices.

carbon dioxide↗
DOE OSTI2025

Enhancing ChatPORT with CUDA-to-SYCL Kernel Translation Capability

Large Language Models (LLMs) have shown strong capabilities in general code translation. However, code translation involving parallel programming models remains largely unexplored. This work enhances the capabilities of code LLMs in CUDA-to-SYCL kernel translation with parameter-efficient fine-tuning. The resultant fine-tuned LLM, called ChatPORT, is an effort to provide high-fidelity translations from one programming model to another. We describe the preparation of datasets from heterogeneous computing benchmarks for model fine-tuning and testing, the parameter-efficient fine-tuning of 19 open-source code models ranging in size from 0.5 to 34 billion parameters and evaluate the correctness rates of the SYCL kernels by the fine-tuned models. The experimental results show that most code models fail to translate CUDA codes to SYCL correctly. However, fine-tuning these models using a small set of CUDA and SYCL kernels can enhance the capabilities of these models in kernel translation. Depending on the sizes of the models, the correctness rate ranges from 19.9% to 81.7% for a test dataset of 62 CUDA kernels.

Jin, Zheming [ORNL] (ORCID:000000027197780X)↗