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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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How numerical simulations helped to achieve breakeven on the NIF

The inertial confinement fusion program relies upon detailed simulations with inertial confinement fusion (ICF) codes to design targets and to interpret the experimental results. These simulations treat as much physics from essential principles as is practical, including laser deposition, cross beam energy transfer, x-ray production and transport, nonlocal thermal equilibrium kinetics, thermal transport, hydrodynamic instabilities, thermonuclear burn, and transport of reaction products. Improvements in radiation hydrodynamic code capabilities and vast increases in computing power have enabled more realistic, accurate 3D simulations that treat all known asymmetry sources. We describe how numerical simulations helped to guide the program, assess the impediments to breakeven, and optimize every aspect of target design. A preshot simulation of the first National Ignition Facility experiment that surpassed breakeven predicted an increased yield that matches the experimental result, within the preshot predicted uncertainty, with a target gain of 1.5. We will cover the key developments in Lawrence Livermore National Laboratory ICF codes that enabled these simulations and give specific examples of how they helped to guide the program.

Marinak, M. M. (ORCID:0009000331127696)↗

Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections

This article focuses on the speed planning problem for connected and automated vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic signal timing for signalized intersections on the road is considered. The eco-driving problem is formulated as a data-driven chance-constrained robust optimization problem. Effective red-light duration (ERD) is defined as a random variable, and describes the feasible passing time through the signalized intersections. Usually, the true probability distribution for ERD is unknown. Consequently, a data-driven approach is adopted to formulate chance constraints based on empirical sample data. This incorporates robustness into the eco-driving control problem with respect to uncertain signal timing. Dynamic programming (DP) is employed to solve the optimization problem. The simulation results demonstrate that the proposed method can generate optimal speed reference trajectories with 40% less vehicle fuel consumption, while maintaining the arrival time at a similar level compared to a modified intelligent driver model (IDM). The proposed control approach significantly improves the controller's robustness in the face of uncertain signal timing, without requiring to know the distribution of the random variable a priori.

33 ADVANCED PROPULSION SYSTEMS↗

RECOVERY ACT: DYNAMIC ENERGY CONSUMPTION MANAGEMENT OF ROUTING TELECOM AND DATA CENTERS THROUGH REAL-TIME OPTIMAL CONTROL (RTOC): Final Scientific/Technical Report

This final scientific report documents the Industrial Technology Program (ITP) Stage 2 Concept Development effort on Data Center Energy Reduction and Management Through Real-Time Optimal Control (RTOC). Society is becoming increasingly dependent on information technology systems, driving exponential growth in demand for data center processing and an insatiable appetite for energy. David Raths noted, 'A 50,000-square-foot data center uses approximately 4 megawatts of power, or the equivalent of 57 barrels of oil a day1.' The problem has become so severe that in some cases, users are giving up raw performance for a better balance between performance and energy efficiency. Historically, power systems for data centers were crudely sized to meet maximum demand. Since many servers operate at 60%-90% of maximum power while only utilizing an average of 5% to 15% of their capability, there are huge inefficiencies in the consumption and delivery of power in these data centers. The goal of the 'Recovery Act: Decreasing Data Center Energy Use through Network and Infrastructure Control' is to develop a state of the art approach for autonomously and intelligently reducing and managing data center power through real-time optimal control. Advances in microelectronics and software are enabling the opportunity to realize significant data center power savings through the implementation of autonomous power management control algorithms. The first step to realizing these savings was addressed in this study through the successful creation of a flexible and scalable mathematical model (equation) for data center behavior and the formulation of an acceptable low technical risk market introduction strategy leveraging commercial hardware and software familiar to the data center market. Follow-on Stage 3 Concept Development efforts include predictive modeling and simulation of algorithm performance, prototype demonstrations with representative data center equipment to verify requisite performance and continued commercial partnering agreement formation to ensure uninterrupted development, and deployment of the real-time optimal control algorithm. As a software implementable technique for reducing power consumption, the RTOC has two very desirable traits supporting rapid prototyping and ultimately widespread dissemination. First, very little capital is required for implementation. No major infrastructure modifications are required and there is no need to purchase expensive capital equipment. Second, the RTOC can be rolled out incrementally. Therefore, the effectiveness can be proven without a large scale initial roll out. Through the use of the Impact Projections Model provided by the DOE, monetary savings in excess of $100M in 2020 and billions by 2040 are predicted. In terms of energy savings, the model predicts a primary energy displacement of 260 trillion BTUs (33 trillion kWh), or a 50% reduction in server power consumption. The model also predicts a corresponding reduction of pollutants such as SO2 and NOx in excess of 100,000 metric tonnes assuming the RTOC is fully deployed. While additional development and prototyping is required to validate these predictions, the relative low cost and ease of implementation compared to large capital projects makes it an ideal candidate for further investigation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Trajectory Modulated Arc Therapy: A Fully Dynamic Delivery With Synchronized Couch and Gantry Motion Significantly Improves Dosimetric Indices Correlated With Poor Cosmesis in Accelerated Partial Breast Irradiation

Purpose: To develop planning and delivery capabilities for linear accelerator–based nonisocentric trajectory modulated arc therapy (TMAT) and to evaluate the benefit of TMAT for accelerated partial breast irradiation (APBI) with the patient in prone position. Methods and Materials: An optimization algorithm for volumetrically modulated arc therapy (VMAT) was generalized to allow for user-defined nonisocentric TMAT trajectories combining couch rotations and translations. After optimization, XML scripts were automatically generated to program and subsequently deliver the TMAT plans. For 10 breast patients in the prone position, TMAT and 6-field noncoplanar intensity modulated radiation therapy (IMRT) plans were generated under equivalent objectives and constraints. These plans were compared with regard to whole breast tissue volume receiving more than 100%, 80%, 50%, and 20% of the prescription dose. Results: For TMAT APBI, nonisocentric collision-free horizontal arcs with large angular span (251.5 ± 7.9°) were optimized and delivered with delivery time of ∼4.5 minutes. Percentage changes of whole breast tissue volume receiving more than 100%, 80%, 50%, and 20% of the prescription dose for TMAT relative to IMRT were −10.81% ± 6.91%, −27.81% ± 7.39%, −14.82% ± 9.67%, and 39.40% ± 10.53% (P≤.01). Conclusions: This is a first demonstration of end-to-end planning and delivery implementation of a fully dynamic APBI TMAT. Compared with IMRT, TMAT resulted in marked reduction of the breast tissue volume irradiated at high doses.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

RAFT: Reconfigurable Array of High-Efficiency Ducted Turbines for Hydrokinetic Energy Harvesting

Diversifying the energy harvesting portfolio is crucial to achieving the ambitious goal of transitioning to clean energy by 2030. Marine hydrokinetic energy has garnered renewed interest due to its high harvesting potential in the U.S., and the resource's reliability and predictability—remaining relatively constant on a daily basis and available 24/7. However, there are currently few commercial devices capable of harnessing the energy from flowing water. This project aims to bridge that gap by designing and evaluating a novel hydrokinetic turbine concept that can efficiently harvest energy from both rivers and tidal streams. The RAFT (Reconfigurable Array of High-Efficiency Ducted Turbines) concept introduces a duct surrounding the turbine rotor and creates an array of small 5-kW units. The duct serves two primary purposes: (1) it enhances hydrodynamic efficiency by accelerating flow to the rotor, and (2) it functions as a structural component, facilitating the formation of modular arrays that lower costs. This project focuses on demonstrating this concept and validating these benefits through simulations and scaled prototype testing. The project team includes 8 faculty members and over 20 students from 3 universities, organized into three core areas: hydrodynamics, electrical systems, and structural analysis, with additional teams dedicated to system integration, environmental assessment and risk management, and tech-to-market strategy. The team successfully demonstrated the increased hydrodynamic efficiency of a ducted turbine compared to an unducted version using high-fidelity simulations and prototype tests. Moreover, design optimization efforts led to surpassing the SHARKS program's goal of 60% reduction of the levelized cost of energy with a significant margin.

13 HYDRO ENERGY↗

Switching Device-Cognizant Sequential Distribution System Restoration

This paper presents an optimization framework for sequential reconfiguration using an assortment of switching devices and repair process in distribution system restoration. Compared to existing studies, this paper considers types, capabilities and operational limits of different switching devices, making it applicable in practice. We develop a novel multi-phase method to find the optimal sequential operation of various switching devices and repair faulted areas. We consider circuit breakers, reclosers, sectionalizers, load breaker switches, and fuses. The switching operation problem is decomposed into two mixed-integer linear programming (MILP) subproblems. The first subproblem determines the optimal network topology and estimates the number of steps to reach that topology, while the second subproblem generates a sequence of switching operations to coordinate the switches. For repairing the faults, we design an MILP model that dispatches repair crews to clear faults and replace melted fuses. After clearing a fault, we update the topology of the network by generating a new sequence of switching operations, and the process continues until all faults are cleared. To improve the computational efficiency, a network reduction algorithm is developed to group line sections, such that only switchable sections are present in the reduced network. The proposed method is validated on the IEEE 123-bus and 8500-bus systems.

distribution system↗

Platform for Remote Deployment and Training for Enhanced Building Operation Practices (Building Re-Tuning and On-going Commissioning)

While a building’s energy usage is driven largely by its design and use, building operator behavior has a strong influence on its energy consumption. This project developed and piloted a specific, data-driven coaching methodology to help operators understand how they can adjust operations and/or affect no/low-cost repairs or upgrades to their specific building HVAC systems to reduce energy consumption. Named BuildingCoach, the operational optimization method used is based on the Building Re-tuning approach developed by the Pacific Northwest National Laboratory. A building operations analytics market has matured over the past decade, though its potential to affect energy-saving changes has not been fully realized. Training operators to understand the methods for operational optimization with the explicit approach of using building-system performance data is hypothesized to create a more effective, longer lasting result in building energy efficiency, and this strategy is the fundamental premise of this project. With the support of an Industry Advisory Board, the project succeeded in developing materials and recruiting for and delivering three pilot cohorts. Deliverables included twenty-two self-paced training modules (accessed via a Learning Management System) and a web-based platform that includes access to real-time building system data and a repository for building system documentation. The project set out to have 100 participants from 50 buildings in three pilot cohorts. In the end, there were 28 participants from 17 buildings, i.e., a significant shortfall. The first two pilot cohorts had only two buildings in each, and this was partially due to difficulties in deploying the Building Operator Coaching Solution (“the BOCS”), which is technology that extracts the data from the controls network and presents it as prescribed for coaching. In the third cohort, the project team deployed the BOCS successfully to 13 buildings, the methodology was piloted as intended, and numerous opportunities for optimization were identified. The BuildingCoach business plan charts a path to an economically sustainable effort. However, even with a licensing model captured in the final version of the business plan, the scalability is still limited to keeping less than 1,000 buildings affected by 2033. Even so, there are unexplored paths to greater scalability that are being considered. CUNY BPL is working to perpetuate and grow the use of BuildingCoach. As of this writing, about twenty buildings have either been connected or will be connected with operators coached / to be coached in the NYC municipal portfolio, twelve buildings across four campuses in NY State will use BuildingCoach, a NY upstate county wishes for six or seven buildings to participate with the support of funding from NYSERDA, and others have also expressed interest. In the decades to come, there will be an increasing percentage of large and mid-sized buildings that incorporate automated system optimization (ASO), and the building operators’ role will shift to spend more time on maintenance and monitoring. Meanwhile, programs such as BuildingCoach will play a critical role in optimizing operations. And, regardless of the emergence of ASO, operators will still need to understand how their systems operate so that they can monitor them properly. Within that context, BuildingCoach is an important step towards operators’ understanding of efficient building system operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

TU-FG-201-10: Quality Management of Accelerated Partial Breast Irradiation (APBI) Plans

Purpose: Since 2008, over 700 patients received high dose rate (HDR) APBI treatment at Virginia Hospital Center. The complexity involved in the planning process demonstrated a broad variation between patient geometry across all applicators, in relation to anatomical regions of interest. A quality management program instituting various metrics was implemented in March 2013 with the goal of ensuring an optimal plan is achieved for each patient. Methods: For each plan, an in-house complexity index, geometric conformity index, and plan quality index were defined. These indices were obtained for all patients treated. For patients treated after the implementation, the conformity index and quality index were maximized while other dosimetric limits, such as maximum skin and rib doses, were strictly kept. Subsequently, all evaluation parameters and applicator information were placed in a database for cross-evaluation with similar complexity. Results: Both the conformity and quality indices show good correlation with the complexity index. They decrease as complexity increases for all applicators. Multi lumen type balloon applicators demonstrate a minimal advantage over single lumen applicators in increasingly complex patient geometries, as compared to SAVI applicators which showed considerably greater advantage in these circumstances. After the implementation of the in-house planning protocol, there is a direct improvement of quality for SAVI plans. Conclusion: Due to their interstitial nature, SAVI devices show a better conformity in comparison to balloon-based devices regardless of the number of lumens, especially in complex cases. The quality management program focuses on optimizing indices by utilizing prior planning knowledge based on complexity levels. The database of indices assists in decision making and has subsequently aided in balancing the experience level among planners. This approach has made APBI planning more robust for patient care, with a measurable improvement in the plan quality.

60 APPLIED LIFE SCIENCES↗

Copper Evaluation and SCALE S/U [Slides]

This presentation discusses progress that has been made so far on the RRR evaluation for n+ 63,65 Cu and on SCALE S/U work, especially optimizations for TSUNAMI-IP.

07 ISOTOPE AND RADIATION SOURCES↗

Superstructure Optimization for Brine Valorization from Brackish Water Desalination

This poster presents preliminary results from a superstructure optimization framework developed to identify cost-optimal brine valorization configurations for brackish water desalination plants across diverse U.S. regional feed chemistries. The study uses brackish groundwater compositions from Arizona, California, Florida, New Mexico, and Texas. Using Pyomo Generalized Disjunctive Programming (GDP) within the WaterTAP modeling environment, the optimization framework simultaneously evaluates thousands of candidate treatment configurations, spanning nanofiltration, reverse osmosis, and chemical precipitation, to minimize the levelized cost of water (LCOW) while meeting water recovery targets and product recovery constraints. Results across eight representative feed clusters demonstrate water recovery rates of 57–87% and net LCOW values ranging from -$0.032/m³ (net revenue-positive) to $0.80/m. Notably, no single process configuration was optimal across all feed types, underscoring the necessity of feed-specific optimization. Products targeted include calcium carbonate (CaCO₃) at $0.01/kg and sodium chloride (NaCl) at $0.10/kg, both at 95% purity, with product revenues offsetting treatment costs in several scenarios. The work advances NAWI's process systems engineering capabilities for multi-configuration screening.

58 GEOSCIENCES↗

Model Predictive Control of Discrete-Continuous Energy Systems via Generalized Disjunctive Programming

Generalized Disjunctive Programming (GDP) provides an alternative framework to model optimization problems with both discrete and continuous variables. The key idea behind GDP involves the use of logical disjunctions to represent discrete decisions in the continuous space, and logical propositions to denote algebraic constraints in the discrete space. Compared to traditional mixed-integer programming (MIP), the inherent logic structure in GDP yields tighter relaxations that are exploited by global branch and bound algorithms to improve solution quality. In this paper, we present a general GDP model for optimal control of hybrid systems that exhibit both discrete and continuous dynamics. Specifically, we use GDP to formulate a model predictive control (MPC) model for piecewise-affine systems with implicit switching logic. As an example, the GDP-based MPC approach is used as a supervisory control to improve energy efficiency in residential buildings with binary on/off, relay-based thermostats. A simulation study is used to demonstrate the validity of the proposed approach, and the improved solution quality compared to existing MIPbased control approaches.

Bhattacharya, Arnab↗

METHODOLOGY AND APPLICATION OF PHYSICAL SECURITY EFFECTIVENESS BASED ON DYNAMIC FORCE-ON-FORCE MODELING

This paper describes ongoing work within the Light Water Reactor Sustainability (LWRS) Program at Idaho National Laboratory (INL) to optimize security and cost of nuclear power plants (NPPs). It reviews the conservatisms in conventional physical security posture and regulations. It introduces the dynamic risk assessment tool developed at INL, Event Modeling Risk Assessment using Linked Diagrams (EMRALD). The dynamic assessment methodology leverages EMRALD to process results of force-on-force (FOF) simulations and crediting safety mitigation actions from probabilistic risk assessment (PRA) models as well as diverse and flexible coping strategies (FLEX) mitigation strategies. Timing information from these simulations are compared against the available time to perform mitigations obtained from Reactor Excursion and Leak Analysis Program (RELAP5) simulations. To illustrate the methodology, a station blackout (SBO) attack scenario was modeled in commercially available FOF simulation tools. The simulation results provide valuable insights into possible attack outcomes and as the probabilistic risk of a core damage event given these outcomes. Safety mitigation procedures were modeled in EMRALD, and were dependent on the attack outcomes by considering human operator uncertainties. RELAP5 simulations incorporating human and hardware uncertainties were performed to estimate the distribution of time-to-core damage. The results demonstrate that, even in the extreme case of a successful adversarial attack, plant mitigation strategies provide significantly high-likelihood of preventing radiological release. The proposed modeling and simulation framework of integrating FLEX equipment with FOF models enables the NPPs to credit FLEX portable equipment in the plant security posture, resulting in an efficient and optimized physical security.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Coordinative Voltage Control Strategy with Multiple Resources for Distribution Systems of High PV Penetration

This paper presents an optimal voltage control methodology with coordination among different voltage-regulating resources, including controllable loads, distributed energy resources such as energy storage and photovoltaics (PV), and utility voltage-regulating devices such as voltage regulators and capacitors. The proposed methodology could effectively tackle the overvoltage and voltage regulation device distortion problems brought by high penetrations of PV to improve grid operation reliability. A voltage-load sensitivity matrix and voltage-regulator sensitivity matrix are used to deploy the resources along the feeder to achieve the control objectives. Mixed-integer nonlinear programming is used to solve the formulated optimization control problem. The methodology has been tested on the IEEE 123-feeder test system, and the results demonstrate that the proposed approach could improve the voltage profile of distribution system operation.

14 SOLAR ENERGY↗

Coordinative Voltage Control Strategy with Multiple Resources for Distribution Systems of High PV Penetration: Preprint

This paper presents an optimal voltage control methodology with coordination among different voltage-regulating resources, including controllable loads, distributed energy resources such as energy storage and photovoltaics (PV), and utility voltage-regulating devices such as voltage regulators and capacitors. The proposed methodology could effectively tackle the overvoltage and voltage regulation device distortion problems brought by high penetrations of PV to improve grid operation reliability. A voltage-load sensitivity matrix and voltage-regulator sensitivity matrix are used to deploy the resources along the feeder to achieve the control objectives. Mixed-integer nonlinear programming is used to solve the formulated optimization control problem. The methodology has been tested on the IEEE 123-feeder test system, and the results demonstrate that the proposed approach could actively tackle the voltage problem brought about by high penetrations of PV and improve the reliability of distribution system operation.

14 SOLAR ENERGY↗

Analysis of the Effect of Vehicle Platooning on the Optimal Control of a Heavy Duty Engine Thermal System

One promising method for reducing fuel consumption and emissions, particularly in heavy duty trucks, is platooning. As the distance between vehicles decreases, the following vehicles will experience less aerodynamic drag on the front of the vehicle. However, reducing the velocity of the air contacting the front of the vehicle could have adverse effects on the temperature of the engine. To compensate for this effect, the energy consumption of the engine cooling system might increase, ultimately limiting the overall improvements obtained with platooning. Understanding the coupling between drag reduction and engine cooling load requirement is key for successfully implementing platooning strategies. Additionally, in a Connected and Automated Vehicle (CAV) environment, where information of the future engine load becomes available, the operation of the cooling system can be optimized in order to achieve the maximum fuel consumption reduction. In this paper, a control-oriented physics-based model for the engine cooling loop of a Volvo engine is developed and validated against road data. Starting from the validated model, an optimal control problem for the coolant system is formulated considering the tradeoff between the tracking of the engine temperature setpoint and the corresponding fuel consumption under different trailing distances. To compare the coolant system performance, Dynamic Programming (DP) is used to determine the global optimal solution for the coolant system actuator. The coupling between optimal cooling system operation and reduction in ram air are evaluated by comparing the results obtained from the DP under different platooning conditions against the unrestricted scenario. In addition, the paper analyzes the changes in the tradeoff between fuel consumption and setpoint tracking for different vehicle distances. This analysis will provide useful insight on the sensitivity of the coolant system controller calibration to the platoon distance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

graphenv: a Python library for reinforcement learning on graph search spaces

Many important and challenging problems in combinatorial optimization (CO) can be expressed as graph search problems, in which graph vertices represent full or partial solutions and edges represent decisions that connect them. Graph structure not only introduces strong relational inductive biases for learning (Battaglia et al., 2018) - in this context, by providing a way to explicitly model the value of transitioning (along edges) between one search state (vertex) and the next - but lends itself to problems both with and without clearly defined algebraic structure. For example, classic CO problems on graphs such as the Traveling Salesman Problem (TSP) can be expressed as either pure graph search or integer programs. Other problems, however, such as molecular optimization, do no have concise algebraic formulations and yet are readily implemented as a graph search (V. et al., 2022; Zhou et al., 2019). Such "model-free" problems constitute a large fraction of modern reinforcement learning (RL) research owing to the fact that it is often much easier to write a forward simulation that expresses all of the state transitions and rewards, than to write down the precise mathematical expression of the full optimization problem. In the case of molecular optimization, for example, one can use domain knowledge alongside existing software libraries to model the effect of adding a single bond or atom to an existing but incomplete molecule, and let the RL algorithm build a model of how good a given decision is by "experiencing" the simulated environment many times through. In contrast, a model-based mathematical formulation that fully expresses all the chemical and physical constraints is intractable. In recent years, RL has emerged as an effective paradigm for optimizing searches over graphs and led to state-of-the-art heuristics for games like Go and chess, as well as for classical CO problems such as the TSP. This combination of graph search and RL, while powerful, requires non-trivial software to execute, especially when combining advanced state representations such as Graph Neural Networks (GNN) with scalable RL algorithms.

97 MATHEMATICS AND COMPUTING↗

Peak Power Reduction for HVAC Operations in Multi-unit Commercial Buildings

The load profiles of most commercial consumers are characterized by brief periods of very high power consumption followed by intervals of relatively lower demand. In order to flatten commercial load profiles, several power utilities in addition to billing energy consumption, levy a demand charge (DC) on the monthly peak demand. In this work, we consider the problem of joint optimization of energy costs (EC) and DC incurred by a multi-unit building which follows a demand response (DR) program. Despite the non-linear structure of the problem, we show how the optimal solutions can be obtained efficiently using linear programming. We evaluate the performance of the proposed power control scheme for various climate zones in the US. We show that depending on the ambient conditions and the prescribed tariff structure, our strategy can result in savings of up to nearly 19% compared to the baseline.

Raza Naqvi, Syed A.↗

Estimation of LOCA break size using cascaded support vector regression - 334

A failure of emergency core cooling system (ECCS) during a certain small break loss of coolant accident (SBLOCA) can cause a severe core uncovery and fuel failure. The safety injection systems (SISs) might not function properly in case of a SBLOCA, due to the slight change of pressure in the pipes. Early trend of the major parameters has to be observed and the accurate information has to be given to the nuclear power plant (NPP) operators by precisely identifying the break size to effectively manage accidents. In this study, the objective is to provide to operators the information on the break size in a short time by considering the accident situations such as hot-leg LOCA, cold-leg LOCA, steam generator tube rupture (SGTR). A cascaded support vector regression (CSVR) is used in order to estimate the break size. The simulation data set was obtained from the optimized power plant reactor 1000 (OPR1000) using modular accident analysis program (MAAP) code. And genetic algorithm (GA) was used to select the input variables of the CSVR model and optimize its parameters. As a result of this study, the CSVR model estimate very well the break size of LOCAs. If the operators can predict the break size in the LOCA, they can response quickly and properly to LOCA circumstances to prevent the core uncovery and fuel failure. Also, it will be possible to more efficiently manage beyond design basis accidents. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗