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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Joint Estimation of Topology and Injection Statistics in Distribution Grids with Missing Nodes

Optimal operation of distribution grid resources relies on accurate estimation of its state and topology. Practical estimation of such quantities is complicated by the limited presence of real-time meters. This article discusses a theoretical framework to jointly estimate the operational topology and statistics of injections in radial distribution grids under limited availability of nodal voltage measurements. In particular, we show that our proposed algorithms are able to provably learn the exact grid topology and injection statistics at all unobserved nodes as long as they are not adjacent. The algorithm design is based on novel ordered trends in voltage magnitude fluctuations at node groups, that are independently of interest for radial physical flow networks. The complexity of the designed algorithms is theoretically analyzed and their performance is validated using both linearized and nonlinear ac power flow samples in test distribution grids.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Better Than Worst-Case Decoding for Quantum Error Correction

The overheads of classical decoding for quantum error correction in cryogenic quantum systems grow rapidly with the number of logical qubits and their correction code distance. Decoding at room temperature is bottlenecked by refrigerator I/O bandwidth while cryogenic on-chip decoding is limited by area/power/thermal budget. To overcome these overheads, we are motivated by the observation that in the common case (over 90% of the time), error correction 'syndromes' are fairly trivial with high redundancy / sparsity, since the error correction codes are over-provisioned to be able to correct for uncommon worst-case complex scenarios (to ensure substantially low logical error rates). If suitably exploited, these trivial scenarios can be handled with insignificant overhead, thereby alleviating any bottlenecks towards handling the worst-case scenarios by state-of-the-art means. Here, we propose Better Than Worst-Case Decoding for Quantum Error Correction, targeting cryogenic quantum systems and Surface Code, consisting of: On-chip Clique Decoder: An extremely lightweight decoder for correcting trivial common-case errors, designed for the cryogenic domain. The decoder is implemented and evaluated for SFQ logic. Statistical Off-chip Bandwidth Allocation: A statistical confidence-based technique for allocation of off-chip decoding bandwidth, to efficiently handle the rare complex decodes that are not covered by the Clique Decoder. Decode-Overflow Execution Stalling: A method to stall circuit execution, for the worst-case scenarios in which the provisioned off-chip bandwidth is insufficient to complete all requested off-chip decodes. In all, BTWC decoding achieves 70-99+% off-chip bandwidth elimination across a range of logical and physical error rates, without significantly sacrificing the accuracy of a state-of-the-art off-chip decoder. Further, it achieves 10-1000x bandwidth reduction over prior bandwidth reduction techniques, as well as 15-37x resource overhead reduction compared to prior on-chip decoding.

cryogenic optimization↗
DOE OSTI2024

gcamfaostat: An R package to prepare, process, and synthesize FAOSTAT data for global agroeconomic and multisector dynamic modeling

The gcamfaostat R package is designed for the preparation, processing, and synthesis of the Food and Agriculture Organization (FAO) Statistics (FAOSTAT) agroeconomic data. The primary purpose is to facilitate FAOSTAT data use in global economic and multisector dynamic models while ensuring transparency, traceability, and reproducibility. Here, we provide an overview of the development of gcamfaostat (v1.0) and demonstrate its capabilities in generating and maintaining agroeconomic data required for the Global Change Analysis Model (GCAM). Our initiative seeks to enhance the quality and accessibility of data for the global agroeconomic modeling community, with the aim of fostering more robust and harmonized outcomes in a collaborative, efficient, and open-source framework. One of the important features of the package is the possibility to construct the FAO Food Balance Sheets at the disaggregated commodity level (with over 500 commodities), which provides a comprehensive and detailed data input for a variety of analytical and modeling applications. The processed data and visualizations offered by gcamfaostat can also be valuable to a broader audience interested in gaining insights into the intricacies of global agriculture.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Compressive Response and Energy Absorption of Additively Manufactured Elastomers with Varied Simple Cubic Architectures

Additive manufacturing, and particularly the vat photopolymerization process, enables the fabrication of complex geometries at high resolution and small length scales, making it well-suited for fabricating cellular structures (e.g., foams and lattices). Among these, elastomeric cellular structures are of growing interest due to their tunable compliance and energy dissipation. However, comprehensive data on the compressive behavior of these structures remains limited, especially for investigating the structure-property effects from changing the density and distribution of material within the cellular structure. This study explores how the mechanical response of polyurethane-based simple cubic structures changes when varying volume fraction, unit cell length, and unit cell patterning, which have not been systematically investigated previously in additively manufactured elastomers. Increasing volume fraction from 10% to 50% yielded significant changes in compressive stress–strain performance (decreasing strain at 0.5 MPa by 41.6% and increasing energy absorption density by 3962.5%). Although changing the unit cell length between 2.5 and 7 mm in ~30 mm parts did not result in statistically different stress–strain responses, modifying the configuration of struts of different thicknesses across designs with 30% volume fraction altered the stress–strain behavior (differences of 12.5% in strain at 0.5 MPa and 109.4% for energy absorption density). Power law relationships were developed to understand the interactions between volume fraction, unit cell length, and elastic modulus, and experimental data showed strong fits (R 2 > 0.91). These findings enhance the understanding of how multiple structural design aspects influence the performance of elastomeric cellular materials, providing a foundation for informing strategic design of tailorable materials for diverse mechanical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Applicability study of Bayesian optimization in core neutronic design using a toy model

At the Japan Atomic Energy Agency (JAEA), an innovative design approach named ARKADIA (Advanced Reactor Knowledge- and AI-aided Design Integration Approach through the whole plant life cycle) for advanced nuclear reactors is currently under development. One task in ARKADIA is to build a system that automatically optimizes core and fuel designs by conducting core neutronic and thermal-hydraulic calculations, fuel integrity evaluations, and plant dynamic analyses. This system will be implemented to automatically find an optimal design that minimizes (or maximizes) objective function defined by core performance while varying the core and fuel design parameters such as fuel pin diameter, core height and diameter. In this study, as the first step of system development, we focused only on core neutronic design and conducted a study of automatic optimization. As the optimization algorithm, Bayesian optimization (BO), an effective method for optimization problems with expensive computational cost of objective function, was utilized. The applicability of BO was studied based on single- and two-objective optimization examples of core neutronic design in a toy model. As a result, in the former, it was shown that BO can give the optimal solution, which matches the reference solution calculated by a brute force calculation well, with a small number of required calculations. Usability on core neutronic designs, where the computational cost per case is high, was confirmed. In the latter, it was found that BO can give a Pareto solutions-set that shows good agreement with the reference solution. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Multi-spatio-temporal scales PIV in a turbulent buoyant jet discharging in a linearly stratified environment

Time-resolved particle image velocimetry is implemented with a camera array and several laser sheets; this results in a multi-spatio-temporal scale system that is modular and flexible. The setup is optimized to capture global flow features, while locally resolving in space and time near the Kolmogorov scale. The apparatus relies extensively on machine vision CMOS cameras; they are inexpensive and stream to computer hard drive with virtually continuous time-resolved records (up to one hour for the current system). This leads to statistically converged data and also helps in minimizing down time between experimental runs. Details of the implementation and design of experiment are reported. The system instruments a vertical buoyant jet discharging in a linearly stratified environment. Refractive index matched aqueous solutions enable precise optical deployment. The density difference is 3% and the fluids have similar dynamic viscosity. The jet Reynolds number is 2.00 x 10 4 and is above the mixing transition. Such flows are typically challenging to instrument and few velocity field data are available to date. The velocity statistics are reported as well as first insights gained from the campaign.

42 ENGINEERING↗
DOE OSTI2026

Hierarchical semi-Markov models with duration-aware dynamics for activity sequences

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Tailored approach to study Legionella infection using a lattice light sheet microscope (LLSM)

Legionella is a genus of ubiquitous environmental pathogens found in freshwater systems, moist soil, and composted materials. More than four decades of Legionella research has provided important insights into Legionella pathogenesis. Although standard commercial microscopes have led to significant advances in understanding Legionella pathogenesis, great potential exists in the deployment of more advanced imaging techniques to provide additional insights. The lattice light sheet microscope (LLSM) is a recently developed microscope for 4D live cell imaging with high resolution and minimum photo-damage. We built a LLSM with an improved version for the optical layout with two path-stretching mirror sets and a novel reconfigurable galvanometer scanner ( RGS ) module to improve the reproducibility and reliability of the alignment and maintenance of the LLSM. We commissioned this LLSM to study Legionella pneumophila infection with a tailored workflow designed over instrumentation, experiments, and data processing methods. Our results indicate that Legionella pneumophila infection is correlated with a series of morphological signatures such as smoothness, migration pattern and polarity both statistically and dynamically. Our work demonstrates the benefits of using LLSM for studying long-term questions in bacterial infection. Our free-for-use modifications and workflow designs on the use of LLSM system contributes to the adoption and promotion of the state-of-the-art LLSM technology for both academic and commercial applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Issue Summary of INL Phase IV Transient Results for IAEA CRP on HTGR UAM Benchmark

This report details the Parallel and Highly Innovative Simulation for Idaho National Laboratory (INL) Code System (PHISICS)/Reactor Excursions and Leak Analysis Program (RELAP5)-3D results obtained for the transient core exercises defined for Phase IV of the International Atomic Energy Agency (IAEA) Coordinated Research Project (CRP) on high-temperature gas cooled reactor (HTGR) uncertainty analysis in modeling (UAM). The Phase III models and results are linked to the earlier Standardized Computer Analyses for Licensing Evaluation (SCALE)/Sampler/New ESC-based Weighting Transport (NEWT) data generated for the lattice physics (lattice) stage Phase I of the CRP. The focus of this report is the Uncertainty/Sensitivity Assessment (U/SA) of the prismatic modular high-temperature gas cooled reactor (MHTGR)-350 design, and specifically for Exercises IV-1 and IV-2 of the benchmark: the Control Rod Withdrawal (CRW) and Pressurised Loss of Cooling (PLOFC) events. The statistical U/SA methodology is implemented and demonstrated using the RAVEN code, based on perturbed cross-section libraries obtained from the SCALE/Sampler sequence. Uncertainties in nuclear data (cross-sections and the average number of neutrons produced per fission, 235U[¯v ]) lead to standard deviations (uncertainties of one s) of approximately 0.5% in the core eigenvalues of the MHTGR-350 and core models. For the coupled neutronics/thermal fluid model, local power density uncertainties up to 3.6% were observed in the colder regions of the core, while the local maximum fuel temperature uncertainties reached 1.5% for the models that included thermal fluid uncertainties. The addition of thermal fluid uncertainties dominated the impacts of nuclear data uncertainties in all cases. The main contributors to uncertainties in the power density and fuel temperatures during the transients were uncertainties in the reactor operating conditions (total power, inlet mass flow rate and inlet gas temperature). Variations in the bypass flows did not have significant impact on any of the output variables. For the nuclear data uncertainties it was found that the 235U(¯v ) / 235U(¯v ) covariance produced the largest sensitivities in terms of its impact on the eigenvalue and peak reactor power. It was also observed that the impact of any nuclear data uncertainties on the maximum fuel temperature was much less significant that the impact on eigenvalue and power. Another important finding was that although the use of eight or more energy groups is recommended for best-estimate HTGR simulation, two-group models produced acceptable uncertainty and sensitivity results for most FOMs. Since the statistical U/SA methodology is computationally expensive, and most transient solver requirements will scale directly with the number of energy groups, two energy groups could be used by HTGR developers during the early stages of design when larger uncertainty margins can be tolerated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable, when interacting with its surroundings. The combined law of thermodynamics derived by Gibbs about 150 years ago laid the foundation of thermodynamics. In Gibbs combined law, the entropy production due to internal processes was not included, and the 2nd law was thus practically removed from the Gibbs combined law, so it is only applicable to systems under equilibrium, thus commonly termed as equilibrium or Gibbs thermodynamics. Gibbs further derived the classical statistical thermodynamics in terms of the probability of configurations in a system in the later 1800's and early 1900's. With the quantum mechanics (QM) developed in 1920's, the QM-based statistical thermodynamics was established and connected to classical statistical thermodynamics at the classical limit as shown by Landau in the 1940's. In 1960's the development of density functional theory (DFT) by Kohn and co-workers enabled the QM prediction of properties of the ground state of a system. On the other hand, the entropy production due to internal processes in non-equilibrium systems was studied separately by Onsager in 1930's and Prigogine and co-workers in the 1950's. In 1960's to 1970's the digitization of thermodynamics was developed by Kaufman in the framework of the CALculation of PHAse Diagrams (CALPHAD) modeling of individual phases with internal degrees of freedom. CALPHAD modeling of thermodynamics and atomic transport properties has enabled computational design of complex materials in the last 50 years. Our recently termed zentropy theory integrates DFT and statistical mechanics through the replacement of the internal energy of each individual configuration by its DFT-predicted free energy. The zentropy theory is capable of accurately predicting the free energy of individual phases, transition temperatures and properties of magnetic and ferroelectric materials with free energies of individual configurations solely from DFT-based calculations and without fitting parameters, and is being tested for other phenomena including superconductivity, quantum criticality, and black holes. Those predictions include the singularity at critical points with divergence of physical properties, negative thermal expansion, and the strongly correlated physics. Furthermore, those individual configurations may thus be considered as the genomic building blocks of individual phases in the spirit of the materials genome®. This has the potential to shift the paradigm of CALPHAD modeling from being heavily dependent on experimental inputs to becoming fully predictive with inputs solely from DFT-based calculations and machine learning models built on those calculations and existing experimental data through newly developed and future open-source tools. Furthermore, through the combined law of thermodynamics including the internal entropy production, it is shown that the kinetic coefficient matrix of independent internal processes is diagonal with respect to the conjugate potentials in the combined law, and the cross phenomena that the phenomenological Onsager flux and reciprocal relationships are due to the dependence of the conjugate potential of a molar quantity on nonconjugate molar quantities and other potentials, which can be predicted by the zentropy theory and CALPHAD modeling.

42 ENGINEERING↗
DOE OSTI2025

Pivotal trial characteristics and types of endpoints used to support Food and Drug Administration rare disease drug approvals between 2013 and 2022

Background/aims Rare disease drug development faces unique challenges, such as genotypic and phenotypic heterogeneity within small patient populations and a lack of established outcome measures for conditions without previously successful drug development programs. These challenges complicate the process of selecting the appropriate trial endpoints and conducting clinical trials in rare diseases. In this descriptive study, we examined novel drug approvals for non-oncologic rare diseases by the U.S. Food and Drug Administration’s Center for Drug Evaluation and Research over the past decade and characterized key regulatory and trial design elements with a focus on the primary efficacy endpoint utilized as the basis of approval. Methods Using the Food and Drug Administration’s Data Analysis Search Host database, we identified novel new drug applications and biologics license applications with orphan drug designation that were approved between 2013 and 2022 for non-oncologic indications. From Food and Drug Administration review documents and other external databases, we examined characteristics of pivotal trials for the included drugs, such as therapeutic area, trial design, and type of primary efficacy endpoints. Differences in trial design elements associated with primary efficacy endpoint type were assessed such as randomization and blinding. Then, we summarized the primary efficacy endpoint types utilized in pivotal trials by therapeutic area, approval pathway, and whether the disease etiology is well defined. Results One hundred and seven drugs that met our inclusion criteria were approved between 2013 and 2022. Assessment of the 107 drug development programs identified 150 pivotal trials that were subsequently analyzed. The pivotal trials were mostly randomized (80%) and blinded (69.3%). Biomarkers (41.1%) and clinical outcomes (42.1%) were commonly utilized as primary efficacy endpoints. Analysis of the use of clinical trial design elements across trials that utilized biomarkers, clinical outcomes, or composite endpoints did not reveal statistically significant differences. The choice of primary efficacy endpoint varied by the drug’s therapeutic area, approval pathway, and whether the indicated disease etiology was well defined. For example, biomarkers were commonly selected as primary efficacy endpoints in hematology drug approvals (70.6%), whereas clinical outcomes were commonly selected in neurology drug approvals (69.6%). Further, if the disease etiology was well defined, biomarkers were more commonly used as primary efficacy endpoints in pivotal trials (44.7%) than if the disease etiology was not well defined (27.3%). Discussion In the past 10 years, numerous novel drugs have been approved to treat non-oncologic rare diseases in various therapeutic areas. To demonstrate their efficacy for regulatory approval, biomarkers and clinical outcomes were commonly utilized as primary efficacy endpoints. Biomarkers were not only frequently used as surrogate efficacy endpoints in accelerated approvals, but also in traditionally approved rare disease drugs. The choice of primary efficacy endpoints varied by therapeutic area, approval pathway, and understanding of disease etiology.

Hong, Kyungwan [Rare Diseases Team, Office of New ↗
DOE OSTI2024

Parametric, Frequency-Domain Approach for Clutter Analysis & Rejection in Remote Sensing

A novel approach is presented for parametric analysis of remotely-sensed ground and cloud clutter. A spatial-frequency-domain clutter model is generated from an extensive, one-year database of weather imagery and statistics are given for each spatial frequency. This approach is useful for the analysis and design of spatial and temporal clutter-rejection filters, which can also be analyzed in this domain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Beam Commissioning Result of Polarized Target at SpinQuest

SpinQuest is a fixed-target experiment at Fermilab to measure the Drell-Yan process using transversely polarized NH$_3$ and ND$_3$ targets and unpolarized 120-GeV proton beam. In the Drell-Yan process, a quark in one scattering hadron and an anti-quark in the other hadron annihilate into a virtual photon and then decay into a muon (lepton) pair. The angular distribution of final-state muon pairs with respect to the target polarization is sensitive to the Sivers function of light antiquarks in the nucleon, which is one of the eight Transverse Momentum Dependent (TMD) parton distribution functions. The intensity of the proton beam is as high as $10^{12}$ protons/second, in order to accumulate the required statistics of Drell-Yan events. The polarized target system at SpinQuest has been carefully designed to accommodate such a high beam intensity. It is equipped with an evaporation refrigerator with a cooling power of around 5 W at 1 K. The target temperature is maintained at 1 K even under heat load caused by the proton beam and the DNP microwave. The target system and the spectrometer were commissioned with the proton beam from May through July 2024. The experimental setup of SpinQuest and the performance of the target system during the beam commissioning is reported.

Nakano, Kenichi [Virginia U.]↗
DOE OSTI2023

Multi-objective Bayesian alloy design using multi-task Gaussian processes

In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. Here, we anticipate this refined methodology will better leverage material correlations, improving design optimization results.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An Analysis of FPGA LUT Bias and Entropy for Physical Unclonable Functions

Process variations within Field Programmable Gate Arrays (FPGAs) provide a rich source of entropy and are therefore well-suited for the implementation of Physical Unclonable Functions (PUFs). However, careful considerations must be given to the design of the PUF architecture as a means of avoiding undesirable localized bias effects that adversely impact randomness, an important statistical quality characteristic of a PUF. Here in this paper, we investigate a ring-oscillator (RO) PUF that leverages localized entropy from individual look-up table (LUT) primitives. A novel RO construction is presented that enables the individual paths through the LUT primitive to be measured and isolated at high precision, and an analysis is presented that demonstrates significant levels of localized design bias. The analysis demonstrates that delay-based PUFs that utilize LUTs as a source of entropy should avoid using FPGA primitives that are localized to specific regions of the FPGA, and instead, a more robust PUF architecture can be constructed by distributing path delay components over a wider region of the FPGA fabric. Compact RO PUF architectures that utilize multiple configurations within a small group of LUTs are particularly susceptible to these types of design-level bias effects. The analysis is carried out on data collected from a set of identically designed, hard macro instantiations of the RO implemented on 30 copies of a Zynq 7010 SoC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Computational thermodynamics and its applications

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable or un- stable, when interacting with the surroundings. In this overview, fundamentals of thermodynamics are briefly reviewed through the combination of first and second laws of thermodynamics for open and nonequilibrium systems to demonstrate that the reversible equilibrium and irreversible nonequilibrium thermodynamics can be integrated to enhance the power and utilities of thermodynamics. The recent progresses in computational thermodynamics, the remaining challenges, and potential impacts in broad scientific fields are discussed here. It is shown that computational thermodynamics enables the modeling of thermodynamics of a state as a function of both external and internal variables and quantitative calculations of a broad range of properties of a multicomponent system in terms of first and second derivatives of energy, including not only equilibrium states when there are no driving forces for any internal processes and but also non-equilibrium states with driving forces for internal processes. Consequently, external constraints such as fixed strain and internal degree of freedoms such as ordering and defects can be described in a coherent framework and applied to materials design. Furthermore, two important but largely overlooked aspects in thermodynamics will be discussed, i.e. the rigorous application of statistical thermodynamics with the probability of configurations and their contributions to system properties, and the applications of second derivatives of energy with respect to either two extensive variables or two potentials or a mixture of them in terms of understanding and predicting emergent behaviors, critical phenomena, kinetic coefficients, and mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics

In this paper, we describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and associated uncertainties. We overview the statistical principles and nuclear-physics contexts underlying the BAND toolset, with an emphasis on Bayesian methodology's ability to leverage insight from multiple models. In order to facilitate understanding of these tools we provide a simple and accessible example of the BAND framework's application. Four case studies are presented to highlight how elements of the framework will enable progress on complex, far-ranging problems in nuclear physics. By collecting notation and terminology, providing illustrative examples, and giving an overview of the associated techniques, this paper aims to open paths through which the nuclear physics and statistics communities can contribute to and build upon the BAND framework.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Early-stage Testing of Thermal Power Dispatch Simulator for Subjective Mental Workload and Evolution Time

The excess thermal energy produced by nuclear power plants (NPPs) during low electricity demands can be utilized in industrial processes, such as hydrogen production, through a thermal power dispatch (TPD) system. Initial testing of the first iteration of a single-train TPD design with a manual control mode at the Idaho National Lab (INL) revealed a high operator workload and degraded control capability. The current study evaluated the impact of an enhanced dual-train TPD design on operators’ subjective mental workload and evolution task-time while completing two operating scenarios in manual and automatic control modes. The results showed no statistically significant difference between participants’ mental workload using both control modes. Evolution time in automatic control mode took a shorter time than in manual control, with participants completing all evolutions in less than the 10-min set as the design specification limit. The shorter evolution time is discussed within the context of plant safety and operational efficiency.

Gideon, Olugbenga↗