Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Operating experience data”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Operation and performance of the MEG II detector

Abstract The MEG II experiment, located at the Paul Scherrer Institut (PSI) in Switzerland, is the successor to the MEG experiment, which completed data taking in 2013. MEG II started fully operational data taking in 2021, with the goal of improving the sensitivity of the$$\upmu ^+ \rightarrow {\textrm{e}}^+ \upgamma $$ μ + → e + γ decay down to$$\sim 6 \times 10^{-14}$$ ∼ 6 × 10 - 14 almost an order of magnitude better than the current limit. In this paper, we describe the operation and performance of the experiment and give a new estimate of its sensitivity versus data acquisition time.

Physics↗
DOE OSTI2023

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The Jefferson Lab Eta Factory Experiment and Applications of PbWO4 Calorimeters in Future Experimental Facilities

The goal of the new JLab Eta Factory (JEF) experiment, conducted with the GlueX detector in Hall D at Jefferson Lab, is to perform measurements of various ¿(') decays with a primary focus on rare neutral modes. The experiment’s physics program ranges from precision tests of low-energy QCD to searches for gauge bosons with masses below 1 GeV that could couple the Standard Model (SM) sector to the dark sector. The experiment will collect a high-statistics data sample of ¿(') mesons produced via a beam of tagged photons. The GlueX detector features a large, nearly uniform acceptance for both neutral and charged particles, enabling efficient identification of complex multi-particle final states. To meet the requirements of the JEF experiment, the inner section of the forward lead-glass calorimeter in the GlueX detector has been upgraded with lead tungstate (PbWO4) scintillating crystals. PbWO4 offers exceptional characteristics, such as a small radiation length and Molire radius, and large light yield, that make it ideal for constructing high- granularity, high-resolution, radiation-hard detectors. These properties enable excellent spatial separation and energy resolution of reconstructed electromagnetic showers, establishing PbWO4 as the material of choice for many high-precision experiments. The JEF experiment began data collection in April 2025 and will operate concurrently with the GlueX experiment, whose primary objective is the search for gluonic excitations in the meson spectrum. I will give an overview of the JEF experiment, the GlueX detector, and the feasibility of further upgrades to support future ¿ physics studies. Special attention will be given to the newly constructed PbWO4 scintillating calorimeter and recent advancements in calorimeter instrumentation.

Somov, Alexander↗
DOE OSTI2022

Progress Report on SFR Metallic Fuel Data Qualification

This report summarizes the progress of SFR metallic fuel qualification related activities, which are focused on providing quality assurance relevant information applicable to experiments irradiated during the Integral Fast Reactor (IFR) program. A background of metallic fuel performance data and the associated databases, including the EBR-II Fuels Irradiation & Physics Database (FIPD) and Out-of-Pile Transient Database (OPTD), is included. The legacy data in the databases, including as-built, post-irradiation examination (PIE), operating parameters, and out-of-pile experiment data are introduced. The SFR metallic fuel Quality Assurance Program Plan (QAPP) and its implementation to qualify these legacy data is described in detail. Important PIE data QA documents and the specifications of four types of PIE measurements (contact profilometry, laser profilometry, neutron radiography and gamma scan) are provided. Examples of the implementation of the QAPP to qualify each of those types of PIE data are provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Search for an Anomalous Production of Charged-Current 𝜈 𝑒 Interactions without Visible Pions across Multiple Kinematic Observables in MicroBooNE

This Letter presents an investigation of low-energy electron-neutrino interactions in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment, motivated by the excess of electron-neutrino-like events observed by the MiniBooNE experiment. This is the first measurement to use data from all five years of operation of the MicroBooNE experiment, corresponding to an exposure of 1.11 × 10 21 protons on target, a 70% increase on past results. Two samples of electron neutrino interactions without visible pions are used, one with visible protons and one without any visible protons. The MicroBooNE data show reasonable agreement with the nominal prediction, with 𝑝 values ≥26.7% when the two 𝜈 𝑒 samples are combined, though the prediction exceeds the data in limited regions of phase space. The data are further compared to two empirical models that modify the predicted rate of electron-neutrino interactions in different variables in the simulation to match the unfolded MiniBooNE low energy excess. In the first model, this unfolding is performed as a function of electron neutrino energy, while the second model aims to match the observed shower energy and angle distributions of the MiniBooNE excess. This measurement excludes an electronlike interpretation of the MiniBooNE excess based on these models at >99% CLs in all kinematic variables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Best Practices for Nuclear Experiment Data Preservation at Idaho National Laboratory: A Guide for Researchers and Reactor Operators

Preserving experimental data is essential for supporting advancements in nuclear science and ensuring the longevity of Idaho National Laboratory's contributions to reactor technology and safety. This report provides a comprehensive guide to best practices for experimental data management and preservation, focusing on standardized data formats, redundancy in storage, metadata documentation, and alignment with international standards. By following these recommendations, experimentalists and reactor operators can enhance the accessibility, reproducibility, and utility of critical datasets for regulatory review, validation computational methods, and future research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Status Update of Permanent Magnet Radiation Resiliency Studies at CEBAF

The proposed energy upgrade of the Continuous Electron Beam Accelerator Facility (CEBAF) incorporates Fixed-Field Alternating-gradient (FFA) arcs utilizing permanent magnet technology. Given the radiation environment within the CEBAF tunnel enclosure, validating the long-term magnetic stability of these materials is a critical step for the project's technical feasibility. This contribution presents an overview of the ongoing permanent magnet radiation resiliency program at Jefferson Lab. We briefly review the experimental methodology used to monitor demagnetization in situ and summarize the operational experience from the initial data-taking campaign. Furthermore, we discuss the upgrades implemented for the second exposure campaign, currently underway, which aims to refine dose correlation and reduce systematic uncertainties. We report on the general status of the program and the roadmap for certifying permanent magnet optics for the proposed upgrade energies.

Bodenstein, R. [Thomas Jefferson National Accelera↗
DOE OSTI2024

Real‐time XFEL data analysis at SLAC and NERSC: A trial run of nascent exascale experimental data analysis

X‐ray scattering experiments using free electron lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID‐19 viral proteins). XFEL experiments are a challenge to computing in two ways: (i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; (ii) data‐collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min—sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS‐NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Completing data analysis within 10 min is a first for XFEL experiments and an important milestone if we are to keep up with data‐collection trends.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Design and Commissioning of a Deuterium-Tritium Gas Delivery System for Muon Catalyzed Fusion in a Diamond Anvil Cell

We report the design, commissioning, and operation of deuterium-deuterium (DD) and deuterium-tritium (DT) gas delivery systems developed to load a diamond anvil cell (DAC) beam target for muon-catalyzed fusion (muCF). The DAC approach enables DT fuel to be compressed to GPa pressures at more than twice the liquid density and heated from cryogenic temperatures through 500 K, opening access to a substantially expanded parameter range for muCF kinetics and yield measurements. In this approach, DT is cryo-condensed to a liquid in a minichamber and then compressed in the DAC using a helium-driven pneumatic membrane, achieving high pressures in a millimeter-scale DT sample volume. A DD gas delivery system was designed and used to validate the experimental apparatus, measure the gas quantities needed for filling, develop operational experience, and collect kinetics and yield data with DD targets. The DT gas delivery system adds tritium-specific capabilities for inventory minimization, secondary containment, and activity monitoring. The DT system integrates depleted uranium storage beds and a liquid helium cryogenic condenser used for pressure building and cryopumping. High-purity delivery is provided by a rapid-response palladium permeator. The system is housed in a helium-atmosphere glovebox held at negative pressure with continuous cleanup. We present the process and instrumentation design, a failure modes and effects analysis (FMEA), and data from the experiment's in situ Raman spectrometer, which provides direct confirmation of target loading and composition through the optically clear diamond anvils. The 2024 and 2025 DT campaigns achieved repeatable target fills and operation with no measurable tritium releases to the stack, demonstrating safe, high-purity DT loading at novel density-temperature conditions for muCF studies.

Koukina, Elena [Acceleron Fusion]↗
DOE OSTI2026

DuraMAT Data Hub

The DuraMAT Data Hub has been supporting the consortium for the past six years. The Data Hub has had success in supporting the projects, providing a platform for sharing data within projects and to the public, and learning how to better leverage the existing software platform and the available Amazon Web Services environment. During this new generation of the Data Hub, we are looking at ways to help improve the data hub architecture, user experience, and improve operations by taking advantage of new technology platforms and software that will be more impactful on the consortium researchers and the broader scientific community. In this poster we will look at the current operational capabilities, data dissemination, and development that will improve the system in the near and far future.

14 SOLAR ENERGY↗
DOE OSTI2022

U.S. Nuclear Operating Experience Program for Probabilistic Risk Assessment Parameter Estimations

This presentation describes the overall process of the U.S. Nuclear Regulatory Commission (NRC)operating experience (OpE) program to estimate probabilistic risk assessment parameters, the estimations of initiating event frequencies, the component unreliability, and the quality assurance and quality control activities for the OpE program. This presentation can be used for training, workshop, or knowledge transfer purpose.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Special Considerations for the Removal and Disposal of Micro-Reactor Experiments

Idaho National Laboratory (INL) is preparing to host several microreactor experiments through 2030 and beyond. Two new test beds currently under development will operate as microreactor or nuclear system experiment user facilities. Test bed experiments will be performed in series, with each nuclear experiment installed, operated, and removed before installation of the subsequent experiment. The necessarily brief transition period between experiments introduces unique equipment removal and radioactive waste disposal challenges. This paper evaluates equipment removal and radioactive waste management topics associated with tight sequencing of nuclear experiments and presents some of the solutions and approaches currently planned to meet these special considerations for one such experiment, the Molten Chloride Reactor Experiment (MCRE), slated for operation at INL’s Laboratory for Operation and Testing in the United States (LOTUS). The MCRE project is a collaboration between Southern Company Services, TerraPower, and INL, among others, to provide integral nuclear data that will advance molten salt fast reactor technology. MCRE will be the first critical fast-spectrum circulating fuel system ever operated and the first experiment operated in the LOTUS test bed. The experiment will nominally operate at zero power with planned low power excursions as part of operational planning for MCRE has been to minimize at-power operations to limit fission product formation while still achieving experimental objectives. After the experiment is complete, MCRE will be allowed to radioactively decay for a short period (nominally 90 days) prior to system defueling, flushing, removal, and disposal of all MCRE equipment, readying the test bed for the next nuclear experiment. Equipment removal and radioactive waste disposal have been integral to MCRE project planning since the Cooperative Research and Development Agreement was formalized in 2021. Due to requirements for future use of the test bed, the MCRE system, of necessity, must be removed in a much shorter time frame than typical for historic reactor decommissioning projects at INL. This results in minimal time for radioactive decay, resulting in not only elevated radiation dose rates but also the presence of short-to-medium-lived isotopes not typically encountered in the reactor decommissioning and radioactive waste management space. Additional unique constraints placed on the project include lack of intrinsic remote-operations capabilities in the test bed, space constraints in the test bed once MCRE has been installed, and contamination minimization requirements to return the test bed to as-found conditions to enable future use. This paper discusses planned solutions to these challenges. Approaches for implementing remote or semi-remote technologies in a non-hot cell environment with limited space availability are discussed. The paper also summarizes the systems engineering approach for concept development and design of equipment removal systems, which are being implemented concurrent with the MCRE design phase, providing feedback to system designers to incorporate features enabling efficient and safe equipment removal approaches. The timing of this paper at a relatively early phase of the project is meant to highlight the importance of early planning for nuclear system decommissioning while reactor design is ongoing to allow design feedback on componentry driven from decommissioning system needs. As new, innovative nuclear reactor technologies enter the nuclear market sector, reactor experiments are crucial for providing new integral nuclear data that establish safe operational margins for technology advancement. Microreactor experiments at INL will require safe, effective, and timely decommissioning approaches, which in turn require nuclear systems designed to expedite decommissioning. In addition to the advancement of nuclear reactor technologies, these nuclear experiments provide an opportunity to demonstrate, deploy, and test new removal and disposal capabilities to support the next generation of nuclear reactor technology.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Multifidelity deep operator networks for data-driven and physics-informed problems

Operator learning for complex nonlinear systems is increasingly common in modeling multi-physics and multi-scale systems. However, training such high-dimensional operators requires a large amount of expensive, high-fidelity data, either from experiments or simulations. In this work, we present a composite Deep Operator Network (DeepONet) for learning using two datasets with different levels of fidelity to accurately learn complex operators when sufficient high-fidelity data is not available. Additionally, we demonstrate that the presence of low-fidelity data can improve the predictions of physics-informed learning with DeepONets. We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland, using two different fidelity models and also using the same physical model at two different resolutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Nuclear data covariances are critical input to determine upper sub-critical limits and to design experiments to increase it [Slides]

This presentation discusses how Upper Subcritical Limits (USL) are key parameters to determine operational limits in nuclear criticality safety evaluations. It also discusses an example of plutonium casting operation using tantalum at LANL PF-4. The Whisper tool at Los Alamos relies on many inputs, including covariance data, leading the presentation to ask if an existing benchmark data be used in Whisper to adjust nuclear data and covariances to justify a higher USL. If not, Whisper can be used to help design an optimal new benchmark experiment. The presentation also seeks to determine what the possible impacts are on USL and operational limits for plutonium casting. In conclusion, nuclear data covariances are used for by Whisper for: GSSL adjustment of nuclear data and covariances, identification of most similar existing benchmark experiments to application, simulation of Upper Subcritical Limit of application, and input to optimization techniques for designing most appropriate new benchmark experiment(s) to meet requirements. This requires a complete set of nuclear data covariances, benchmarks and k-effective sensitivity profiles (for both benchmarks and applications). The presentation concludes by asking if end users should trust results that depend on current covariance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Subspace-Driven Learning for Anomaly Detection in Process Transients

Nuclear power plant (NPP) monitoring and diagnostic centers are actively investigating and implementing automated anomaly detection algorithms to help plants catch anomalies sooner, thereby preventing or reducing the duration of unexpected shutdowns. Current machine learning-based anomaly detection methods are expected to be highly effective during stable, full-power operations because NPPs typically operate as baseload power generators, meaning there are extensive operating data available from plant equipment. However, it is expected that anomaly detection methods will face significant challenges during transient conditions (i.e., when power output falls below full power) because plants only occasionally operate at these lower power levels, generating sparse transient operational data, and resulting in false alarms or missed detections. Here, to address this issue, transfer learning is used, which for this problem leverages knowledge (in the form of learned features) from stable, full-power operations to improve detection accuracy during transient conditions, even with limited data. In this effort, a novel subspace approach is developed to transfer a subset of the data features from full power operation to transients. This approach is validated through experiments using synthetic data and was found to outperform two baseline transfer learning approaches in anomaly detection performance across a range of amounts of transient data used in the training process.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2022

Electroweak constraints from the COHERENT experiment

We compute bounds on coefficients of effective operators in the Standard Model that can be inferred from observations of neutrino scattering by the COHERENT experiment. While many operators are bound extremely well by past experiments the full future data set from COHERENT will provide modest improvements for some operators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Integration of Condition-Based, Diagnostic, Prognostic, And Anomaly Detection Data into Reliability Models to Support a Predictive Maintenance Context

Reliability data employed in plant reliability models are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating actual health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). This paper proposes a reliability modeling approach that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. We show how state-of-the art condition-based, diagnostic, prognostic, and anomaly detection models can be linked to system reliability models not in probability terms, but in terms of margin where margin is defined as the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Then, we show how the propagation of margin data from the asset to the system level is performed through classical reliability models such as fault trees or reliability block diagrams. The described method is in fact able to propagate heterogenous health data from the asset to the system level in order to analytically assess system health.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Progress Report on SFR Metallic Fuel Data Qualification

This report summarizes the progress of SFR metallic fuel qualification related activities, which are focused on providing quality assurance relevant information applicable to experiments irradiated during the Integral Fast Reactor (IFR) program. An overview of the metallic fuel performance data and the associated databases, including the EBR-II Fuels Irradiation & Physics Database (FIPD), Out-of-Pile Transient Database (OPTD), and TREAT Experimental Relational Database (TREXR) is included. The legacy data in the databases, including as-built, post-irradiation examination (PIE), operating parameters, and out-of-pile experiment post-test data are introduced. The SFR metallic fuel Quality Assurance Program Plan (QAPP) and its implementation to qualify these legacy data is described in detail. Important PIE data QA documents and the specifications of seven types of PIE measurements (contact profilometry, laser profilometry, neutron radiography, gamma scan, fission gas release fission gas chemistry, and metallography) are provided. Examples of the implementation of the QAPP to qualify each of those types of PIE data are provided.

Mo, Kun [Argonne National Laboratory (ANL), Argonn↗