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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Several Small or Single Large? Quantifying the Catchment-Wide Performance of On-Site Wastewater Treatment Plants with Inaccurate Sensors

On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. Here, we develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

2023 FORCE Development Status Update

Technical and economic analysis of integrated energy systems (IES) using software models is a complex process requiring multiple commodity market decision analysis, optimal control, process modeling, and stochastic analysis. Many assumptions used in traditional energy analysis tools do not hold in future energy markets with significant storage and variable renewable energy sources (VRE), let alone with multiple commodity markets. Capturing these intricate elements for accurate techno-economic analysis of IES led to the development of the Framework for Optimization of ResourCes and Economics (FORCE) tool suite under the U.S. Department of Energy’s Integrated Energy Systems crosscutting technology program. With the aim of a full framework release in 2025, many improvements to the FORCE tool suite were developed in fiscal year 2023 (FY23). These improvements broadly fit into three focuses for development of FORCE: capability, accessibility, and reliability. Capability refers to the ability of FORCE to accurately model the technical and economic viability of various IES. Accessibility refers to ease-of-use for new and existing IES analysts to efficiently set up, analyze, and produce results using FORCE. Reliability refers to the consistency of the software, allowing consistency to analysis regardless of erstwhile changes to the software. In addition to many smaller changes, there are three major capability improvements in FORCE in FY23. In summary, FORCE developments in FY23 have moved us close to all the capability requirements for FORCE 1.0 to be delivered in FY25. Inclusion of Bayesian optimization, resilience metrics, and levelized cost analysis expand the capability, accessibility, and reliability of FORCE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Developing a Nuclear Quality Assurance Compliant Design Methodology for Neutronic Analysis of Xe-100 Design

The primary objective of this work is to develop a design methodology compliant with nuclear quality assurance standards for the Xe-100 neutronic design verification studies. To achieve this, a Monte Carlo model of the Xe-100 reactor was constructed using the exclusion principle, transformation technique, and universe-based level specification following Idaho National Laboratory (INL) NQA level-1 compliant standards and an NQA-1 compliant version of MCNP6. The model encompasses the entire reactor core structures, including the upper plenum, core region, and lower plenum sections, along with all sub-components. The active core section was represented using the spectral regions, each comprising a particular fuel composition and temperature averaged over the considered zone, calculated by X-energy using Very Superior Old Programs (VSOP). Additionally, a component-wise temperature map was implemented into the model, not only for the core region but also for the structural components. Temperature-dependent cross-section libraries, along with thermal scattering law libraries, generated using INL NQA-1 compliant version of NJOY21, were utilized for each isotope in the burnt fuel and the structural materials. Furthermore, the volume of each modeled component was estimated using a stochastic approach with the ray tracing method in MCNP and criticality calculations were performed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Probabilistic Nanomagnetic Memories for Uncertain and Robust Machine Learning

This project evaluated the use of emerging spintronic memory devices for robust and efficient variational inference schemes. Variational inference (VI) schemes, which constrain the distribution for each weight to be a Gaussian distribution with a mean and standard deviation, are a tractable method for calculating posterior distributions of weights in a Bayesian neural network such that this neural network can also be trained using the powerful backpropagation algorithm. Our project focuses on domain-wall magnetic tunnel junctions (DW-MTJs), a powerful multi-functional spintronic synapse design that can achieve low power switching while also opening the pathway towards repeatable, analog operation using fabricated notches. Our initial efforts to employ DW-MTJs as an all-in-one stochastic synapse with both a mean and standard deviation didn’t end up meeting the quality metrics for hardware-friendly VI. In the future, new device stacks and methods for expressive anisotropy modification may make this idea still possible. However, as a fall back that immediately satisfies our requirements, we invented and detailed how the combination of a DW-MTJ synapse encoding the mean and a probabilistic Bayes-MTJ device, programmed via a ferroelectric or ionically modifiable layer, can robustly and expressively implement VI. This design includes a physics-informed small circuit model, that was scaled up to perform and demonstrate rigorous uncertainty quantification applications, up to and including small convolutional networks on a grayscale image classification task, and larger (Residual) networks implementing multi-channel image classification. Lastly, as these results and ideas all depend upon the idea of an inference application where weights (spintronic memory states) remain non-volatile, the retention of these synapses for the notched case was further interrogated. These investigations revealed and emphasized the importance of both notch geometry and anisotropy modification in order to further enhance the endurance of written spintronic states. In the near future, these results will be mapped to effective predictions for room temperature and elevated operation DW-MTJ memory retention, and experimentally verified when devices become available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Per-Phase and 3-Phase Optimal Coordination of Directional Overcurrent Relays Using Genetic Algorithm

Penetration of the power grid by renewable energy sources, distributed storage, and distributed generators is becoming increasingly common. Increased utilization of these distributed energy resources (DERs) has given rise to additional protection coordination concerns, particularly where they are utilized in an unbalanced manner or where loading among phases is unbalanced. Digital relays such as the SEL-751 (produced by Schweitzer Engineering Laboratories, Pullman, WA, USA) series have the capability of being set on a per-phase basis. This capability is underutilized in common practice. Additionally, in optimization algorithms for determining relay settings, the time-overcurrent characteristics (TOCs) of relays are generally not treated as variables and are assigned before running the optimization algorithm. In this paper, TOC options themselves are treated as discrete variables to be considered in the optimization algorithm. A mixed integer nonlinear programming problem (MINLP) is set up where the goal is to minimize relay operating times. A genetic algorithm (GA) approach is implemented in MATLAB where two cases are considered. In the first case, the TOC and Time dial setting (TDS) of each relay is set on a three-phase basis. In the second case, per-phase settings are considered. Relay TDSs and TOCs are both considered as simultaneous discrete control variables. Despite the stochastic nature of using per-phase settings for unbalanced systems is found to generally allow for shorter operating times. However, for relatively balanced systems, it is best to use three-phase settings if computation time is of importance.

Matthews, Ronald C.↗
DOE OSTI2023

Chapter 6: Abuse Response of Batteries Subjected to Mechanical Impact

Electrochemical and thermal models to simulate nominal performance and abuse response of lithium-ion cells and batteries have been reported widely in the literature. Studies on mechanical failure of cell components and how such events interact with the electrochemical and thermal response are relatively less common. This chapter outlines a framework developed under the Computer Aided Engineering for Batteries program to couple failure modes resulting from external mechanical loading to the onset and propagation of electrochemical and thermal events that follow. Starting with a scalable approach to implement failure criteria based on thermal, mechanical, and electrochemical thresholds, we highlight the practical importance of these models using case studies at the cell and module level. The chapter also highlights a few gaps in our understanding of the comprehensive response of batteries subjected to mechanical crash events, the stochastic nature of some of these failure events, and our approach to build safety maps that help improve robustness of battery design by capturing the sensitivity of some key design parameters to heat generation rates under different mitigation strategies.

abuse simulations↗
DOE OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Generalized Bayesian Framework for Evaluation of Integral Benchmark Experiments

A recently published generalized Bayesian optimization framework has provided a way to retract any or all of the three common assumptions underlying the conventional Generalized Linear Least Squares (GLLS) optimization method based on the concepts introduced in Ref. [2]. These assumptions are: 1. Perfection: The model used for data evaluation and the prior probability distribution function (PDF) of generalized data are perfect. 2. Normality: The prior and posterior PDF are normal. 3. Linearity: The model is linear. In this work we outline how the framework in [1] could be directly adopted for improved evaluation of nuclear criticality integral benchmark experiments (IBEs) by: 1. Removing the first assumption alone by utilizing the concept of imperfections introduced in [1] to enable evaluation in the presence of discrepancies between the data and model or of missing covariance information by a GLLS method that will be seen as a generalization of the conventional GLLS method employed by the TSURFER code, and by 2. Removing the remaining two assumptions by implementing a Markov Chain Monte Carlo method for computation of the posterior PDF in the SAMPLER code, where TSURFER and SAMPLER are the uncertainty quantification (UQ) codes for IBEs in the SCALE code system based on the GLLS and the stochastic method, respectively. The graphic in Figure 1 categorizes the methods discussed in terms of the assumptions that they employ to determine posterior PDFs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Bayesian Monte Carlo Evaluation Framework for Imperfect Nuclear Data

Bayesian evaluation of resolved resonance region (RRR) nuclear data has historically been carried out using the generalized least squares (GLS) formalism, as implemented in, e.g., SAMMY. We have recently developed a prototype of Bayesian Monte Carlo (BMC) evaluation framework, implemented using a Markov Chain Monte Carlo (MCMC) method with a Metropolis-Hastings (MH) acceptance criterion. This was done in order to remove the approximations underlying the conventional GLS evaluations, namely, the linear approximation, and the approximation that all probability density functions (PDFs) are of the normal kind. Recent works by others have used similar stochastic approaches to quantify cross section uncertainties from ENDF evaluated co-variances, and/or, from integral benchmark data, but those have not been conceived as an evaluation framework like the one presented here.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Computational Math Problems for a Clean Energy Future

Cutting edge computational mathematics are ubiquitous in renewable energy research. Problems in resilient and reliable electric grid operations, infrastructure planning, wind farm yaw control, and more demand sophisticated and scalable computational tools that enable the transition of renewable energy technologies from proof of concept to deployment into our energy system. The mission of the Computational Science Center at NREL is to lead the lab's efforts to solve energy challenges using high-performance computing (HPC), computational science, applied mathematics, scientific data management, visualization, and informatics. In this poster, we provide a short overview of three areas of computational mathematics research at NREL: wind power scenario generation for stochastic grid operations and infrastructure planning, improved rational function approximations for electromagnetic transients codes, and wind farm yaw control using a combination of the Alternating Direction Method of Multipliers (ADMM) and reinforcement learning (RL). Increasing penetrations of renewable energy into power grids motivate the investigation of new approaches to characterizing uncertainty for five-minute economic dispatch problems. Similarly, as the penetration of distributed energy resources on power grids increases, it becomes important to revisit our methods of modelling transient phenomena, i.e. electromagnetic transients programs. Finally, the combination of ADMM and RL for wind farm yaw control presented here can potentially increase the efficiency of the deployed distributed controllers by orders of magnitude.

ADMM↗
DOE OSTI2026

Refined Sensitivity Estimates for Single-Molecule Magnet Dark Matter Detectors

We revisit the sensitivity of Single Molecule Magnet (SMM) crystals as detectors for low-mass dark matter. In previous work, we established the concept of the ``magnetic bubble chamber'', where energy deposited by dark matter triggers a magnetic avalanche in a metastable crystal. The original sensitivity estimates relied on a conservative criterion requiring the spin relaxation time to be strictly shorter than the thermal diffusion time. Here, we demonstrate that this criterion effectively ignores the stochastic nature of spin relaxation. We derive a refined analytic estimate which accounts for the fraction of spins that relax even when diffusion is fast. We show that the Zeeman energy released by this fraction contributes to local heating, significantly lowering the energy threshold for avalanche formation. We present simulation results confirming this effect and report on experimental verification of the assumed low-temperature thermal properties of two representative SMM crystals, Mn$_{12}$-acetate and Mn$_{32}$. Together, these efforts extend this pathfinder program toward the realization of SMM-based detectors with controlled material properties and enhanced dark matter sensitivity.

Eberhardt, Andrew [Tokyo U., IPMU]↗
DOE OSTI2022

Rolling Optimization of Transmission Network Recovery and Load Restoration Considering Hybrid Wind-Storage System and Cold Load Pickup

A common solution to deal with the stochasticity introduced by fast-ramping wind power integration is to equip wind farms (WFs) with energy storage systems (ESSs) to formulate hybrid WF-ESSs. In addition to leveling off wind power fluctuations during normal operations, a hybrid WF-ESS can be a flexible power source to accumulate the power system restoration. In this paper, we propose a rolling optimization model for transmission network recovery and load restoration considering the contributions of WF-ESSs. The proposed model is formulated as a mixed integer linear programming problem that simultaneously optimizes the amount and location of restorable load blocks as well as the restoration lines. The cold load pickup features of interrupted loads considering the outage duration are modeled in detail. A chance-constrained method is employed to deal with the uncertainty of wind power, and a rolling horizon-based framework is adopted to reduce the influence of forecast error. Case studies are conducted on both New England 39-bus system and part of a provincial power system in China. The results show that the load restoration process can be significantly accelerated by employing the proposed method and contributions of hybrid WF-ESSs to power system restoration are validated.

chance-constrained optimization↗
DOE OSTI2021

Characterization and Simulation of Optical Delay System for the Proof-of-Principle Experiment of Optical Stochastic Cooling at IOTA

The Optical Stochastic Cooling (OSC) experiment at Fermilab’s IOTA storage ring uses two undulators to cool the beam over many turns. The radiation emitted by electrons in the first undulator is delayed and imaged in the second undulator where it applies a corrective energy kick on the electrons. Imperfections in the manufacturing of the delay plates can lead to a source of error. This paper presents the experimental characterization of the absolute thickness of these delay plates using an interferometric technique. The measured "thickness maps" are implemented in the Synchrotron Radiation Workshop (SRW) program to assess their impact on the delayed radiation pulse.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Experimental demonstration of OSC at IOTA: IOTA Run #3 (Report)

Optical Stochastic Cooling (OSC) is an optical-bandwidth extension of Stochastic Cooling that could advance the state-of-the-art cooling rate in beam cooling by three to four orders of magnitude. The concept of OSC was first suggested in the early 1990s by Zolotorev, Zholents and Mikhailichenko, and replaced the microwave hardware of SC with optical analogs, such as wigglers and optical amplifiers. A number of variations on the original OSC concept have been proposed, and while a variety of proof-of-principle demonstrations and operational uses have been considered, the concept was not experimentally demonstrated up to now. An OSC R&D program has been underway at IOTA for the past several years. Run #3 of the IOTA ring, which began in Nov. 2020 and concluded in Aug. 2021, was focused on the world’s first experimental demonstration of OSC. The experimental program was successful in demonstrating and characterizing the OSC physics with the major outcomes including strong cooling in one, two and three dimensions, validation of the theoretical models of OSC and the demonstration of OSC with a single electron. This report briefly describes the activities and major milestones of the OSC program during Run #3.

42 ENGINEERING↗
DOE OSTI2024

Go with the Radflow: Thermal radiation experiments in the high energy-density regime [Slides]

Los Alamos National Laboratory is a nuclear weapons laboratory supporting our nation's defense. In support of the mission is a high energy-density physics program in which we design and execute experiments to study radiation-hydrodynamics phenomena and improve the predictive capability of our large-scale multi-physics software codes on our supercomputers. The Radflow Project is maturing a unique spectroscopic measurement that yields a spatially dependent supersonic radiation wavefront profile. The spectroscopic measurement is much more constraining than the typically used radiography, which measures the density changes due to the shock as the radiation wave goes subsonic. Recently, the spectroscopic measurement has been applied to advanced targets with a detailed heterogeneity representing a single realization of a stochastic medium. The Radflow Project also conducts opacity experiments to explain the current conundrum of the experimental iron opacity differing from theory, which is important in modeling the sun. More than that, though, we need to be able to answer these science questions and be able to validate our theory and codes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Core Design of the Holos-Quad Microreactor

The Holos-Quad micro-reactor concept, developed by HolosGen LLC, is equipped with a 22 MWt (Mega-Watt thermal) core and an integral power conversion system converting the core thermal energy into approximately 10 MWe (Mega-Watt electric). This design can be configured to support a wide range of applications. It is a very innovative high-temperature gas-cooled reactor concept using TRI-structural ISOtropic particle fuel (TRISO) distributed in graphite hexagonal blocks, cooled with helium in a direct Brayton cycle independently executed by four Subcritical Power Modules (SPMs) fitted into a hardened 40-foot container whose dimensions are in compliance with ISO shipping containers requirements. In FY2019 HolosGen LLC was awarded by the Department of Energy Advanced Research Project Agency-Energy (DOE ARPA-E) under the MEITNER funding program. As part of the MEITNER award, the Argonne National Laboratory (ANL) contributed expertise through two specialized teams: The “Design Team” and the “Resource Team”. The Design Team was dedicated to validate feasibility of the Holos-Quad core and to optimize its core design through neutronics analyses. The Resource Team was dedicated to feasibility verification via high-fidelity codes of Holos-Quad thermal-hydraulic, heat transfer, shielding, and structural aspects. This report summarizes the activities conducted by ANL Design Team. A rigorous design approach based on multi-criteria optimization and code-to-code comparison involving stochastic and high-fidelity deterministic solutions was developed and employed at several evolutionary stages of the Holos-Quad design. Several generations of the Holos-Quad core were designed within this project before converging to the current full-scale Gen 2+ design that is detailed in this report. Figure EA-1 illustrates a cross-sectional view of Gen 2+ Holos-Quad core configuration, and Figure EA-2 provides a simplified perspective view of 1-of-4 SPMs. The Holos-Quad uses four thermal-hydraulically independent SPMs locked into stationary positions during power operation, surrounded by BeO reflector and structural component fully comprised within the dimensional constraints represented by traditional ISO containers. One of the benefits of this approach is to enable transportation of each SPM promptly after irradiation in shielded containers. The core is designed to operate for approximately 8 full-power years while the reactivity controls and power conversion system enable load-following operations. The reactivity controls are represented by independent, diversified, and redundant reactivity control systems based on control drums and redundant sets of shutdown rods. The high-fidelity simulation tools were used to assess detailed power and flux distributions of the three-dimensional full-core or quarter-core of the Gen 2+ configuration. Single-physics and multi-physics simulations of the neutronics code PROTEUS and the thermal-hydraulic code System Analysis Module (SAM) were performed to analyze the Holos design configurations with detailed high-fidelity solutions. The design work performed confirmed feasibility of the Holos-Quad concept, provided realistic design description for detailed design of the operational system, and identified several core design improvements to be further considered for future reactor development activities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Multimode Metastructures: Novel Hybrid 3D Lattice Topologies

With the rapid proliferation of additive manufacturing and 3D printing technologies, architected cellular solids including truss-like 3D lattice topologies offer the opportunity to program the effective material response through topological design at the mesoscale. The present report summarizes several of the key findings from a 3-year Laboratory Directed Research and Development Program. The program set out to explore novel lattice topologies that can be designed to control, redirect, or dissipate energy from one or multiple insult environments relevant to Sandia missions, including crush, shock/impact, vibration, thermal, etc. In the first 4 sections, we document four novel lattice topologies stemming from this study: coulombic lattices, multi-morphology lattices, interpenetrating lattices, and pore-modified gyroid cellular solids, each with unique properties that had not been achieved by existing cellular/lattice metamaterials. The fifth section explores how unintentional lattice imperfections stemming from the manufacturing process, primarily sur face roughness in the case of laser powder bed fusion, serve to cause stochastic response but that in some cases such as elastic response the stochastic behavior is homogenized through the adoption of lattices. In the sixth section we explore a novel neural network screening process that allows such stocastic variability to be predicted. In the last three sections, we explore considerations of computational design of lattices. Specifically, in section 7 using a novel generative optimization scheme to design novel pareto-optimal lattices for multi-objective environments. In section 8, we use computational design to optimize a metallic lattice structure to absorb impact energy for a 1000 ft/s impact. And in section 9, we develop a modified micromorphic continuum model to solve wave propagation problems in lattices efficiently.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Fuel Fabrication Specification Impact Analysis for NBSR LEU Conversion

As part of a national initiative to enhance nuclear security and reduce proliferation risks, significant efforts have been undertaken by the National Nuclear Security Administration Material Management and Minimization Office of Reactor Conversion Program to convert U.S. high performance research reactors (USHPRRs) from the use of highly enriched uranium (HEU) to low-enriched uranium (LEU), including the National Bureau of Standards Reactor (NBSR). The current plan is to procure LEU fuel assemblies from commercial fabricators according to fuel specifications tailored for each USHPRR. The analysis conducted at Brookhaven National Laboratory was part of an effort to identify the sources of uncertainty in the fuel specifications that may impact the performance of the NBSR core after its conversion and, in particular, to assess the range of acceptable tolerance limits from the perspective of core safety and reactor performance. Using the stochastic neutronics code MCNP 6.2, the variations in important NBSR neutronics characteristics were analyzed as a function of the specification parameters independently and in combination. The important NBSR specification parameters analyzed were the fuel isotopic composition, the amount of impurity content in cladding, the fuel plate thickness, and the fuel element 235U mass loading. The range of variation of each specification parameter was based on the technical specification limit or available as-fabricated assay data and uncertainties. The NBSR neutronics characteristics selected for analysis were the reactor reactivity characteristics at equilibrium and the equilibrium fuel cycle length. Results show that with variations in the fabrication parameters of the as-fabricated U-10Mo fuel within the specification limitations, the excess reactivity of the NBSR LEU core remains well below the 15% Δk/k technical specification limit, and the shutdown margin is always significantly greater than the required 0.68% Δk/k. This ensures that the NBSR can be operated safely and reliably shut down for all analyzed cases within the specified fabrication limits after the LEU conversion. In the prototypic case, the fuel cycle length was 1.5 days longer than the targeted 38.5 days. In a credible worst-case scenario, where all low-reactivity parameters were combined, the fuel cycle length was reduced to 35.5 days, which is still considered manageable for reactor operations. Variations in cycle length are primarily driven by changes in 235U loading, with other parameters having secondary effects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗