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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Ischemic heart disease in Latin American women current perspective and call to action

Cardiovascular diseases (CVDs) constitute the main cause of death in Latin America (LA), with ischemic heart disease (IHD) as the principal cause in most countries. Women are particularly at risk of premature death by CVDs. Nevertheless, in LA, as in other parts of the world, there is a misconception at public level of the importance of CVDs in women; thus, CVDs do not get as much coverage as breast cancer campaigns. Women frequently have atypical symptoms and nonobstructive coronary artery disease as well as inequity in medical and interventional treatments; therefore, care should be built upon these differences. We show the epidemiological situation of IHD in LA women and present a strategy to face the problem, considering the control of risk factors, the optimized utilization of available resources, and the role of research. The role of the International Atomic Energy Agency is highlighted. To adequately face the problem of CVD in LA women, a coordinated approach is necessary, involving medical professionals, governments, scientific societies, and international organizations, as well as the population concerned. In low- and middle-income countries, where the best use of available financial and technological resources is mandatory, a clinical management combining prevention, guidelines, and clinical judgment is required.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Leveraging inter-firm influence in the diffusion of energy efficiency technologies: An agent-based model

Energy efficiency technologies (EETs) are crucial for saving energy and reducing carbon dioxide emissions. However, the diffusion of EETs in small and medium-sized enterprises is rather slow. Literature shows the interactions between innovation adopters and potential adopters have significant impacts on innovation diffusion. Enterprises lack the motivation to share information, and EETs usually lack observability, which suppress the inter-firm influence. Thus, an information platform, together with proper policies encouraging or forcing enterprises to disclose EET-related information, should help harness inter-firm influence to accelerate EETs' diffusion. To explore whether and how such an information platform affects EETs' diffusion in small and medium-sized enterprises, this report builds an agent-based model to mimic EET diffusion processes. Based on a series of controlled numerical experiments, some counter-intuitive phenomena are discovered and explained. The results show that the information platform is a double-edged sword that notably accelerates EETs' diffusion by approximately 47% but may also boost negative information to diffuse even faster and delay massive adoption of EETs. Increasing network density and the intensity of inter-firm influence are effective to speed EET diffusion, but their impacts diminish drastically after reaching some critical values (0.05 and 0.15 respectively) and eventually harm the stability of the system. Ultimately, the findings implicate that EET suppliers should carefully launch their promising but immature products; policies that can reduce the perceived risk by enterprises and the effort to maintain an informative rather than judgmental information platform can prominently mitigate the negative side effects brought by high fluidity of information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Virtual refrigerant charge sensor for variable-speed heat pumps based on feature selection

The refrigerant charge level in heat pump systems significantly impacts their energy efficiency. Virtual refrigerant charge (VRC) sensing technology has been comprehensively investigated and well-established due to its lower cost compared to physical sensors. However, the previous VRC research often relied on expert judgment and physical reasoning for their variable selection, which can potentially select redundant (or highly correlated) or insignificant features, and it is also primarily focused on single-speed systems. To address these challenges, this study proposes a VRC algorithm for variable-speed heat pumps that selects features through a rigorous feature selection method in combination with physical insights. We also propose a piecewise linear model structure segmented by subcooling temperature to accurately predict charge levels, particularly when subcooling temperatures are substantially low. The proposed algorithm was evaluated using experimental data of a residential R410A heat pump, and the performance was compared with two baseline VRC algorithms. The results are: (1) The proposed algorithm outperforms for the case with subcooling temperature less than 1 °C. (2) The proposed algorithm achieves a tested mean absolute percentage error (MAPE) of 4.23%, and improves the overall accuracy for cooling conditions by approximately 60%, compared with the two baseline algorithms. (3) The proposed algorithm uses two fewer features and improves the accuracy for undercharge cooling conditions by 68.0%, compared with baseline algorithm 2. These improvements enhance prediction accuracy and prevent overfitting, providing a more reliable refrigerant charge level prediction and helping improve the heat pump energy efficiency.

Liang, Chenjiyu↗
DOE OSTI2017

Mapping environmental sensitivity: A systematic online approach to support environmental assessment and planning

Environmental sensitivity analysis provides a framework for systematically and objectively determining the potential for significant environmental impacts. The higher the natural or acquired sensitivity of the receiving environment, the less capable it is to cope with human-induced change. Given that sensitivity is context- and spatially-specific, Geographic Information Systems have been applied to develop an operational Webtool to analyse it. The Webtool enables a rapid and replicable spatial examination of environmental sensitivities and potential for land-use conflicts that supports Strategic Environmental Assessment and, ultimately, informed planning and decision-making. The novelty is on the provision of an online geoprocessing Widget that enables creation of context-specific maps. Pilot testing the Webtool in land-use and renewable energy planning through stakeholder engagement has validated its applicability. Stakeholders confirmed that it enables replicating and, in some cases, improving in-house SEA mapping processes while saving time and effort. However, its full reliance on publicly available spatial datasets renders completeness and resolution issues. The Webtool provides a critical starting-point for sectoral planning discussions and for developing plan/programme alternatives that avoid or minimise potentially incompatible or unsustainable zonings, while promoting consistency and transparency in impact assessment. - Highlights: •Sensitivity mapping can enhance baseline information for environmental assessments. •A novel online geoprocessing tool supports systematic sensitivity assessment. •It enables combining context-specific environmental criteria and value judgments. •Pilot testing it through stakeholder engagement has validated its applicability. •The tool promotes assessment consistency and transparency but data issues remain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The discretionary power of the environmental assessment practitioner

Discretion is an essential and unavoidable element of most decision-making and is thus often closely related to the judgment exercised by politicians and practitioners alike. It is evident that discretionary power can be executed in different ways, leading to different results. Therefore, it also has a significant influence on the effectiveness of the environmental assessment (EA) as examined in recent environment impact assessment (EIA) and strategic environment assessment (SEA) literature. However, limited attention has been given to the practitioners' role and how they exercise their discretion, while the effectiveness and implementation of decisions has been a recurrent theme in EA literature. This article explores the connections between discretion and some of the fundamental ideas behind how EIA and SEA function in our societies. Firstly, the article develops and presents a theoretical model of discretion, allowing us to explore the phenomena of discretion from four dimensions: source, form, value and dynamics of discretion. Secondly, a review of EA literature is performed with the purpose of mapping how discretion is studied and what kind of discretion is found in the context of EA – focusing on one of the dimensions – ‘source of discretion’. The results imply that it is prevalent for practitioners to exercise rule, value and task discretion in every choice they make at each stage of the EA implementation process, which influences EA effectiveness, either positively or negatively, depending on how discretionary power is exercised and reflected in EA practitioners' practice. It draws both the management's attention to how to regulate EA policies and the practitioners' attention to how to make a difference.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

On choosing the resolution of normative models

Long time horizon normative models are frequently used for policy analysis, strategic planning, and system analysis. Choosing the granularity of the temporal or spatial resolution of such models is an important modeling decision, often having a first order impact on model results. This type of decision is frequently made by modeler judgment, specifically when the predictive power of alternative choices cannot be tested. In this paper, we show how the implicit tradeoffs modelers make in these formulation decisions, in particular in the tradeoff between the accuracy of representation enabled by the available data and model parsimony, may be addressed with established information theoretic ideas. The paper provides guidance for modelers making these tradeoffs or, in certain cases, enables explicit tests for assessing appropriate levels of resolution. Here, we focus on optimization based normative models in the discussion, and draw our examples from the energy and climate domain.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Statistical change detection of building energy consumption: Applications to savings estimation

The surge in interval meter data availability and associated activity in energy data analytics has inspired new interest in advanced methods for building efficiency savings estimation. Statistical and machine learning approaches are being explored to improve the energy baseline models used to measure and verify savings. One outstanding challenge is the ability to identify and account for operational changes that may confound savings estimates. In the measurement and verification (M&V) context, ‘non-routine events’ (NREs) cause changes in building energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model’s independent variables. In the M&V process NREs must be accounted for as ‘adjustments’ to appropriately attribute the estimated energy savings to the specific efficiency interventions that were implemented. Currently this is a manual and custom process, conducted using professional judgment and engineering expertise. As such it remains a barrier in scaling and standardizing meter-based savings estimation. Here in this work, a data driven methodology was developed to (partially) automate, and therefore streamline the process of detecting NREs in the post-retrofit period and making associated savings adjustments. The proposed NRE detection algorithm is based on a statistical change point detection method and a dissimilarity metric. The dissimilarity metric measures the proximity between the actual time series of the post-retrofit energy consumption and the projected baseline, which is generated using a statistical baseline model. The suggested approach for NRE adjustment involves the NRE detection algorithm, the M&V practitioner, and a regression modeling algorithm. The performance of the detection and adjustment algorithm was evaluated using a simulation-generated test data set, and two benchmark algorithms. Results show a high true positive detection rate (75%-100% across the test cases), higher than ideal false positive detection rates (20%-70%), and low errors in energy adjustment (<0.7%). These results indicate that the algorithm holds for helping M&V practitioners to streamline the process of handling NREs. Moreover, the change point algorithm and underlying statistical principles could prove valuable for other building analytics applications such as anomaly detection and fault diagnostics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

No adaptive response is induced by chronic low-dose radiation from Ra-226 in the CHSE/F fish embryonic cell line and the HaCaT human epithelial cell line

Purpose: To determine whether chronic low-dose α-particle radiation from Ra-226 over multiple cell generations can lead to an adaptive response in CHSE/F fish embryonic cells or HaCaT human epithelial cells receiving subsequent acute high-dose γ-ray radiation. Methods: CHSE/F and HaCaT cells were exposed to very low doses of Ra-226 in medium for multiple generations prior to being challenged by a higher dose γ-ray radiation. The clonogenic assay was used to test the clonogenic survival of cells with or without being pretreated by radiation from Ra-226. Results: In general, pretreatment with chronic radiation has no significant influence on the reaction of cells to the subsequent challenge radiation. Compared to unprimed cells, the change in clonogenic survival of primed cells after receiving challenge radiation is mainly due to the influence of the chronic exposure, and there's little adaptive response induced. However at several dose points, pretreatment of CHSE/F fish cells with chronic radiation resulted in a radiosensitive response to a challenge dose of γ-ray radiation, and pretreatment of HaCaT cells resulted in no effect except for a slightly radioresistant response to the challenge radiation which was not significant. Conclusion: The results suggest that chronic low-dose radiation is not effective enough to induce adaptive response. There was a difference between human and fish cells and it may be important to consider results from multiple species before making conclusions about effects of chronic or low doses of radiation in the environment. The term “radiosensitive” or “adaptive” make no judgment about whether such responses are ultimately beneficial or harmful. - Highlights: • No obvious adaptive response is induced by chronic low-dose radiation from Ra-226. • Priming radiation from Ra-226 sensitized CHSE/F cells to the challenge radiation. • Linear model is inconsistent with current work using chronic low-dose radiation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Governing in Time: Temporal Capacity and the Feasibility of Energy Transitions

Energy systems function as both technological systems and temporal institutions that shape how societies coordinate, justify, and support collective choices over time. This paper introduces the concept of governance horizons to explain why energy transitions can remain morally supported yet become institutionally weak under increasing pressure. We argue that governability depends on institutions' capacity to synchronize across multiple timeframes - aligning short-term decisions with intermediate coordination and long-term commitments. When this synchronization fails, transitions struggle not because their goals are dismissed, but because governance lacks sufficient time to justify, coordinate, and uphold decisions. Comparative analysis of San Antonio, Texas, and Interior Alaska reveals how energy system pressures generate distinct temporal configurations: San Antonio exhibits governance horizon stretching, where institutions must simultaneously meet near-term reliability demands and long-term transformation goals, while Interior Alaska exhibits horizon compression, where extreme environmental constraints force decision-making into short stabilization cycles. In both contexts, public support for sustainability goals coexists with institutional strain because evaluative judgments are unevenly distributed over time. A temporal configuration analysis is introduced as a diagnostic analytic stance for identifying these patterns. By treating temporal alignment as an explanatory variable rather than a background condition, this approach clarifies how feasibility, sequencing, and legitimacy are shaped by constraints on institutional time. The analysis demonstrates that successful energy transitions depend not only on technological innovation or institutional support, but on governance systems’ ability to sustain credible coordination across multiple time horizons.

Comparative case study↗
DOE OSTI2021

A Statistics-Based Threshold for the RMS-Energy Oscillation Detector

System operators and reliability coordinators currently rely on extensive baselining studies to set thresholds for their oscillation detectors. The resulting thresholds are based largely on engineering judgment and may vary significantly between organizations. In this paper, statistical distribution theory is used to derive a detection threshold for the widely deployed root-mean-square (RMS)-energy detector. This expression provides a theoretical basis for the detector's configuration, simplifies the process of selecting the threshold, and enables improved consistency among organizations that need to coordinate during system-wide events. Three methods for calculating the threshold using synchrophasor measurements are also proposed. These methods ensure that the threshold can be calculated reliably for various applications. Tests with simulated and field-measured data demonstrate that the statistics-based threshold provides consistent detection of grid disturbances while maintaining a low probability of false alarm.

Follum, James D.↗
DOE OSTI2013

Agreement Among RTOG Sarcoma Radiation Oncologists in Contouring Suspicious Peritumoral Edema for Preoperative Radiation Therapy of Soft Tissue Sarcoma of the Extremity

Purpose: Peritumoral edema may harbor sarcoma cells. The extent of suspicious edema (SE) included in the treatment volume is subject to clinical judgment, balancing the risk of missing tumor cells with excess toxicity. Our goal was to determine variability in SE delineation by sarcoma radiation oncologists (RO). Methods and Materials: Twelve expert ROs were provided with T1 gadolinium and T2-weighted MR images of 10 patients with high-grade extremity soft-tissue sarcoma. Gross tumor volume, clinical target volume (CTV)3cm (3 cm longitudinal and 1.5 cm radial margin), and CTV2cm (2 cm longitudinal and 1 cm radial margin) were contoured by a single observer. Suspicious peritumoral edema, defined as abnormal signal on T2 images, was independently delineated by all 12 ROs. Contouring agreement was analyzed using the simultaneous truth and performance level estimation (STAPLE) algorithm and kappa statistics. Results: The mean volumes of GTV, CTV2cm, and CTV3cm were, respectively, 130 cm{sup 3} (7-413 cm{sup 3}), 280 cm{sup 3} and 360 cm{sup 3}. The mean consensus volume computed using the STAPLE algorithm at 95% confidence interval was 188 cm{sup 3} (24-565 cm{sup 3}) with a substantial overall agreement corrected for chance (mean kappa = 0.71; range: 0.32-0.87). The minimum, maximum, and mean volume of SE (excluding the GTV) were 4, 182, and 58 cm{sup 3} (representing a median of 29% of the GTV volume). The median volume of SE not included in the CTV2cm and in the CTV3cm was 5 and 0.3 cm{sup 3}, respectively. There were 3 large tumors with >30 cm{sup 3} of SE not included in the CTV3cm volume. Conclusion: Despite the fact that SE would empirically seem to be a more subjective volume, a substantial or near-perfect interobserver agreement was observed in SE delineation in most cases with high-grade soft-tissue sarcomas of the extremity. A median of 97% of the consensus SE is within the CTV2cm (99.8% within the CTV3cm). In a minority of cases, however, significant expansion of the CTVs is required to cover SE.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Determinants of Patient Satisfaction During Receipt of Radiation Therapy

Purpose: To evaluate the correlations and relative contributions of components of a radiation oncology-specific patient satisfaction survey to their overall satisfaction scores. Methods and Materials: From September 2006 through August 2012, we prospectively collected data from 8069 patients receiving radiation treatments with a 26-question survey. Each question was rated on a 10-point Likert scale. We analyzed the correlation between scores for each question and the overall satisfaction question. We also dichotomized the scores to reflect satisfaction versus dissatisfaction and used logistic regression to assess the relationship between items in 4 domains (the patient–provider relationship, access and environmental issues, wait times, and educational information) and overall satisfaction. Results: Scores on all questions correlated with overall patient satisfaction scores (P<.0001). Satisfaction with patient–provider relationships had the greatest influence on overall satisfaction (R{sup 2}=0.4219), followed by wait times (R{sup 2}=0.4000), access/environment (R{sup 2}=0.3837), and patient education (R{sup 2}=0.3700). The specific variables with the greatest effect on patient satisfaction were the care provided by radiation therapists (odds ratio 1.91) and pain management (odds ratio 1.29). Conclusions: We found that patients' judgment of provider relationships in an outpatient radiation oncology setting were the greatest contributors to their overall satisfaction ratings. Other measures typically associated with patient satisfaction (phone access, scheduling, and ease of the check-in process) correlated less strongly with overall satisfaction. These findings may be useful for other practices preparing to assess patient ratings of quality of care.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Interactive Decision-Support Tool for Risk-Based Radiation Therapy Plan Comparison for Hodgkin Lymphoma

Purpose: To present a novel tool that allows quantitative estimation and visualization of the risk of various relevant normal tissue endpoints to aid in treatment plan comparison and clinical decision making in radiation therapy (RT) planning for Hodgkin lymphoma (HL). Methods and Materials: A decision-support tool for risk-based, individualized treatment plan comparison is presented. The tool displays dose–response relationships, derived from published clinical data, for a number of relevant side effects and thereby provides direct visualization of the trade-off between these endpoints. The Quantitative Analyses of Normal Tissue Effects in the Clinic reports were applied, complemented with newer data where available. A “relevance score” was assigned to each data source, reflecting how relevant the input data are to current RT for HL. Results: The tool is applied to visualize the local steepness of dose–response curves to drive the reoptimization of a volumetric modulated arc therapy treatment plan for an HL patient with head-and-neck involvement. We also use this decision-support tool to visualize and quantitatively evaluate the trade-off between a 3-dimensional conformal RT plan and a volumetric modulated arc therapy plan for a patient with mediastinal HL. Conclusion: This multiple-endpoint decision-support tool provides quantitative risk estimates to supplement the clinical judgment of the radiation oncologist when comparing different RT options.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

To what extent can decommissioning options for marine artificial structures move us toward environmental targets?

Switching from fossil fuels to renewable energy is key to international energy transition efforts and the move toward net zero. For many nations, this requires decommissioning of hundreds of oil and gas infrastructure in the marine environment. Current international, regional and national legislation largely dictates that structures must be completely removed at end-of-life although, increasingly, alternative decommissioning options are being promoted and implemented. Yet, a paucity of real-world case studies describing the impacts of decommissioning on the environment make decision-making with respect to which option(s) might be optimal for meeting international and regional strategic environmental targets challenging. To address this gap, we draw together international expertise and judgment from marine environmental scientists on marine artificial structures as an alternative source of evidence that explores how different decommissioning options might ameliorate pressures that drive environmental status toward (or away) from environmental objectives. Synthesis reveals that for 37 United Nations and Oslo-Paris Commissions (OSPAR) global and regional environmental targets, experts consider repurposing or abandoning individual structures, or abandoning multiple structures across a region, as the options that would most strongly contribute toward targets. This collective view suggests complete removal may not be best for the environment or society. However, different decommissioning options act in different ways and make variable contributions toward environmental targets, such that policy makers and managers would likely need to prioritise some targets over others considering political, social, economic, and ecological contexts. Current policy may not result in optimal outcomes for the environment or society.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Enhancing nuclear data validation analysis by using machine learning

In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Impact of representative ground motion level on seismic PSA with the boundary between overestimation and underestimation

One commonly used approach in seismic probabilistic safety assessment (PSA) is the discrete method. This method follows the standard PSA framework and can be applied to various models, such as multi-unit models, while reducing computational costs using standard software. However, due to the inability to subdivide intervals infinitely, the discrete method approximates with a finite number of subintervals. In practice, different numbers of subintervals are applied, and the representative ground motion level is selected based on expert judgment. When employing a smaller number of subintervals, it is important to take caution to prevent underestimation. This study analyzes the impact of the representative ground motion level on seismic risk. It confirms that underestimation can occur with a small number of subintervals depending on the representative ground motion level. This study also proposes a method for determining the boundary of underestimation and overestimation. The method is demonstrated through examples, providing a mathematical foundation for selecting appropriate representative ground motion levels. By avoiding underestimation, this research helps prevent the oversight of significant risk contributors and enhances the understanding of seismic risk.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

A computational risk assessment approach to the integration of seismic and flooding hazards with internal hazards

Probabilistic risk assessment (PRA) of nuclear power plants historically focuses on internal events at the plant, rather than external hazards. Although the importance of external hazards risk analysis is now well recognized, the methods for assessing the risk associated with low probability external hazards rely heavily on subjective judgment of specialists, often using conservative elements in the analysis, as is the case with the Conservative Deterministic Failure Margin approach commonly used for seismic PRA. The U.S. Department of Energy Light Water Reactor Sustainability program has been investigating the use of first principles analyses to provide a more realistic assessment of the risk of external hazards, in a process called computational risk assessment (CRA). Efforts undertaken are described in the development of CRA methods to integrate the risk associated with seismic and flooding events into traditional PRA. Further, the results of four case studies are presented.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Sensitivity analysis, surrogate modeling, and optimization of pebble-bed reactors considering normal and accident conditions

This research provides a valuable tool that streamlines the optimization process while significantly increasing its accuracy. This study creates a robust framework for reactor design optimization by incorporating comprehensive modeling using the Comprehensive Reactor Analysis Bundle, or BlueCRAB, within the Multiphysics Object-Oriented Simulation Environment (MOOSE). BlueCRAB is the United States Nuclear Regulatory Commission's code suite for non-light water reactor analysis and includes the Griffin, Pronghorn, and Bison applications. This not only improves the efficiency of the optimization process but also enhances the reliability of the results. Such a tool is essential for advancing the state-of-the-art in pebble-bed reactor technology and is critical for achieving the goals of Generation IV reactors, which aim for safe, sustainable, and economically viable nuclear energy solutions. This work presents and applies this workflow on pebble-bed reactors while considering both normal and off-normal conditions. A representative gas-cooled pebble-bed reactor at equilibrium core conditions serves as the nominal design specification for normal operation and is based on previous research. The depressurized loss-of-forced-cooling accident is deployed for off-normal conditions in this work. After defining design-related parameters and quantities of interest regarding reactor safety and performance, this multiphysics model is sampled using the MOOSE stochastic tools module. The result is a comprehensive dataset of configurations, enabling sensitivity analysis and the generation of surrogate models. Subsequently, the dataset and surrogate models are employed in two optimization studies aimed at maximizing fuel utilization and economic profit while adhering to safety and operational constraints. Performing the optimization process with fuel utilization as the metric leads to an improvement of approximately 10%, compared to engineering-judgment-based nominal conditions. The optimization on economic profit leads to an estimated increase of ~300 million USD over the lifetime of the reactor.

97 MATHEMATICS AND COMPUTING↗