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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Multi-Objective Optimization of Uranium Target Assembly–3: A Comparison of Genetic and Traditional Methods

Commonly produced as a byproduct of uranium fission, 99 Mo is a key medical isotope that is in high demand in the United States. An international goal is to switch from medical isotope production technologies that require highly enriched uranium to medical isotope production technologies that require only low-enriched uranium. Niowave Inc. is contributing to this goal by developing an accelerator-driven subcritical assembly called the Uranium Target Assembly (UTA). This work compares the performance of Dakota’s Multi-Objective Genetic Algorithm (MOGA) against traditional sensitivity analysis in the neutronic optimization of the UTA-3 system. The design objectives are k-eigenvalue (k eff ) and natural uranium fission power, which are directly correlated with the amount of 99 Mo produced. Dakota:MOGA did not perform as well as human engineering ingenuity in optimization studies with high numbers of input parameters, such as fuel rod type selection and fuel rod placement. However, Dakota:MOGA did outperform traditional sensitivity analysis in optimization studies with fewer than 20 parameters and revealed the degree to which each parameter influences the optimal design space for k eff and natural uranium fission power (to a lesser extent). As the design model became more complex in the final stage of design, the computational resources required to calculate the design objective values in the Monte Carlo N-Particle transport code from selected input parameter combinations limited Dakota:MOGA’s performance, and, unfortunately, human intervention was required to discern the optimal design space. In conclusion, future work will attempt to reduce computational resource constraints by incorporating areduced-order neutronics model into the optimization cycle.

Accelerator-driven systems↗
DOE OSTI2026

Automation of Laser Plasma Focused Ion Beam Microscopy for Next-Gen Energy Materials

Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics. To streamline cross-sectioning and lamella fabrication, we have implemented fully automated workflows that standardize laser ablation and PFIB milling sequences. These workflows incorporate pre-programmed protocols for material removal, alignment, and thinning, reducing user intervention and ensuring consistency across different sample types. Machine learning algorithms further enhance automation by predicting optimal milling strategies and adapting parameters based on material properties and sectioning requirements. This approach significantly improves throughput while maintaining the structural integrity of prepared samples for high-resolution imaging and analysis, including transmission electron microscopy. Beyond sample preparation, our automation platform enables the acquisition of large, high-resolution datasets through serial sectioning, image alignment, and 3D reconstruction. These automated routines facilitate multi-scale characterization, capturing structural and compositional details from the nanoscale to the device level. By reducing variability and increasing efficiency, our automated approach enhances defect analysis, failure diagnostics, and process optimization, accelerating advancements in materials research and device engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Seasonally Modulated Stratospheric Aerosol Geoengineering Alters the Climate Outcomes

Abstract By reflecting some incoming solar radiation, stratospheric aerosol intervention using SO 2 would reduce global mean temperature. Previous research has shown that multiple injection latitudes can be used to maintain not only global mean temperature, but also interhemispheric and equator‐to‐pole temperature gradients. However, the regional climate response depends not only on where the SO 2 is injected, but also on when. We show here that even with these same objectives and same choices of latitudes, injecting in only one season instead of continuously throughout the year results in significant differences in regional climate, for instance in the magnitude of precipitation changes over India. The differential outcomes highlight the potential for underlying trade‐offs, with different choices regarding deployment leading to a different distribution of benefits or harms. This aspect of climate engineering should be considered in developing governance and emphasizes the need for a deeper understanding of the mechanisms underlying the regional responses.

Visioni, Daniele↗
DOE OSTI2021

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. Furthermore, the model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Comparing the effect of virtual and in-person instruction on students’ performance in a design for additive manufacturing learning activity

The goal of this work is to compare the outcome of a design for additive manufacturing (DfAM) heuristics lesson conducted in a virtual learning environment to the same in an in-person learning environment. Prior work revealed that receiving DfAM heuristics at different points in the design process impacts the quality and novelty of designs produced afterward, but this work may have been limited by the solely virtual format. In this work, an identical experiment was performed in a face-to-face learning environment. Results indicate that neither learning format presents an advantage over the other when it comes to the quality of designs produced during the intervention. Participants across all experimental groups reported an increase in self-efficacy after the intervention, with improved performance on quiz-type questions. Furthermore, the novelty and variety of the designs produced by the in-person experimental groups were significantly lower than that of the virtual experimental groups. In addition to validating the effectiveness of virtual instruction as a teaching method, these results also support the authors’ hypothesis that the priming effect is stronger in an in-person classroom than in a virtual classroom.

Design for additive manufacturing↗
DOE OSTI2022

Radiation Effects on Line Heat Detection Cable AP Hot Cells Fire Detection at Brookhaven National Laboratory

Hot cells are used to safely shield workers from high radiation environments while allowing a process to take place within the cell's shielded enclosure, often with the assistance of remote manipulators. The interior of a cell may not be accessible by personnel for years at a time, either due to the lingering radiation environment or due to the constant use of these specialized pieces of equipment. High radiation levels are known to cause undesirable effects and changes to different materials in varying degrees. Selecting equipment for a radiation environment requires an evaluation of materials used (preferably completed assembles), to ensure all interactions of materials are known and ensure that the equipment will have a high degree of reliability and functionality over the intended life of the facility. Hot cells contain systems to support the primary purpose of the enclosure. Fire detection is one such support system. Safety of hot cell operations often establish limits on the amounts of combustibles with the cells, but the presence of some combustible materials is unavoidable. Fire detection is provided to quickly detect a fire and summon manual intervention before damage is too great. Detection may also be used to initiate fire suppression systems. As a safety system, fire detection needs to remain functional to protect the facility throughout the facility's life. Equally important is that the false alarms from the fire detection are minimized to retain confidence in the alarm system and avoid unnecessary interruptions in the hot cell operations. There are many types of fire detection available with various pros and cons. Not all types of fire detection are suited for hot cell application.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Improving the Academic Climate of an R1 STEM Department: Quantified Positive Shifts in Perception

Ongoing efforts to improve diversity in science, technology, engineering, and mathematics (STEM) primarily manifest as attempts to recruit more women and individuals from historically marginalized groups. Yet, these efforts fail to repair the specific, systemic issues within academic communities that hinder diverse individuals from persisting and thriving in STEM. Here, we present the results of a quantitative, multiyear effort to make the academic climate of an R1 STEM department more inclusive. We use a student-led, department-specific, faculty-supported initiative to assess and improve the climate of the Department of Chemistry at the University of California, Berkeley, as a case study. Our results provide quantitative evidence that community discussions grounded in our own data, alongside cooperative community efforts to address the issues present in those data, are effective methods for driving positive change. Longitudinal assessment of our academic climate from 2018 to 2020 via annual department-wide surveys indicates that these interventions have succeeded in shifting the perception of our academic climate. This study confirms the positive outcomes of having a practical, sustainable, and data-driven framework for affecting change within a graduate community.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Implementation of a feature selection algorithm in FARM to identify important state variables and time-invariant matrices

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM aids the HERON software module in the evaluation of the optimal dispatch for the different IES components. Set-point trajectories are required to meet limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To evaluate the feasibility of HERON generated set-points and to do so in an acceptable time, FARM employs reduced order models to represent the dynamic behavior of the systems to be dispatched. These surrogate models take the form of a linear dynamic system with sets of Linear Parameter Varying (LPV) matrices that are mapped to the system operating space. These matrices are derived from the trajectories of system state variables and system output variables during transients. The accuracy of LPV matrices depends on the selection of state variables. In previous reports, state variables were selected by adopting a complicated workflow requiring multiple software licenses and an advanced level of user expertise. In this report, a new workflow that automates the state variable selection process is presented. It significantly reduces the frequency of user interventions and does not require multiple software licenses. Each module in the new workflow is described in detail, and the input / output examples in each step of the workflow are provided. It was demonstrated that this workflow can greatly reduce the complexity of the state variable selection process, and that the updated FARM-Gamma and FARM-Delta validators can benefit from this workflow when solving the power dispatch problem of a representative IES test case. Finally, some code improvements that can further enhance the efficiency are suggested.

42 ENGINEERING↗
DOE OSTI2022

Techno-Economic Evaluation of Strategies to Approach Net-Zero Carbon Sustainable Aviation Fuel via Woody Biomass Gasification and Fischer-Tropsch Synthesis

Reducing the carbon dioxide (CO2) emissions from the transportation industry is a key target for achieving global net-zero carbon goals. Wide-spread electrification, efficient engine design, and alternative fuel implementations have been introduced for light-duty vehicles and are projected to significantly reduce light-duty emissions in the near future. Conversely, the aviation sector contributes considerably towards transportation-based carbon emissions, but current projections do not show substantial reductions in carbon emissions over time. Challenges for the aviation sector include low compatibility with electrification, relative inflexibility to variations in fuel properties, and requirement of high energy density fuels. Thus, identifying pathways to decarbonize the aviation sector via liquid low- or net-zero carbon biofuels that are compatible with current aviation infrastructure is crucial. This work investigates the economic feasibility of approaching net-zero carbon sustainable aviation fuel (SAF) from woody biomass via gasification and Fischer-Tropsch synthesis. In doing so, this assessment identifies the economic opportunities and trade-offs of several carbon mitigation strategies coupled with renewable resource interventions required to approach net-zero carbon fuels via biomass. Renewable natural gas and green hydrogen utilization strategies are assessed to examine the impact of fuel yield improvements versus renewable resource cost. Additionally, both carbon capture and sequestration (CCS) and carbon capture and utilization (CCU) are considered in this analysis to help mitigate carbon loss to the atmosphere. Each scenario was assessed on cost, carbon efficiency, energy efficiency, and overall technology-readiness level (TRL). The results of this analysis show that renewable natural gas, green hydrogen, and CCS can be viewed as a low-cost, near-term carbon abatement strategies. However, in the long-term, deployment of more expensive and less technologically mature CCU technologies can make use of point sources of CO2 to boost overall fuel production.

aviation fuel↗
DOE OSTI2025

Optimizing Facility Operations by Applying Machine Learning to the Army Reserve Enterprise Building Control System (Final Report)

Thousands of U.S. Department of Defense (DoD) buildings have building automation systems (BASs) and/or advanced meters. Although these systems have a wealth of data, performance optimization requires time and expertise to review and act on that information. Machine learning (ML) can provide automated and actionable insights to controls operators. This demonstration implemented proven ML methods on the Army Reserve Enterprise Building Control System. ML refers to algorithms that “learn” from data and improve their performance on a given task over time. In the buildings domain these tasks range from predicting future energy consumption, to identifying operational issues before faults occur, to optimizing control decisions. To learn, ML requires input data, which – for buildings – typically consists of instrument data such as energy consumption data and subsystem controls information such as set-point temperatures, and context data consisting of information such as the physical location of the building, the area of the building, and the weather. ML models use the relationships learned from the input data to make predictions with new, previously unseen, data. The team was able to investigate and successfully implement the following ML use cases: labeling consumption data as anomalous or non-anomalous; baseline whole-building load prediction (unknown fault status); fault detection (validation not possible); and site prioritization for energy-related projects. Due to the constraints of the project, interventions were not able to be implemented during the demonstration; therefore, assessments of operational cost savings and maintenance avoided could not be performed. The project has been presented at two leading national building conferences and two additional publications to peer-reviewed journals are currently in preparation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks

The evaluation of constitutive models, especially for high-risk and high-regret engineering applications, requires efficient and rigorous third-party calibration, validation and falsification. While there are numerous efforts to develop paradigms and standard procedures to validate models, difficulties may arise due to the sequential, manual, and often biased nature of the commonly adopted calibration and validation processes, thus slowing down data collections, hampering the progress towards discovering new physics, increasing expenses and possibly leading to misinterpretations of the credibility and application ranges of proposed models. This work attempts to introduce concepts from game theory and machine learning techniques to overcome many of these existing difficulties. Here, we introduce an automated meta-modeling game where two competing AI agents systematically generate experimental data to calibrate a given constitutive model and to explore its weakness such that the experiment design and model robustness can be improved through competitions. The two agents automatically search for the Nash equilibrium of the meta-modeling game in an adversarial reinforcement learning framework without human intervention. In particular, a protagonist agent seeks to find the more effective ways to generate data for model calibrations, while an adversary agent tries to find the most devastating test scenarios that expose the weaknesses of the constitutive model calibrated by the protagonist. By capturing all possible design options of the laboratory experiments into a single decision tree, we recast the design of experiments as a game of combinatorial moves that can be resolved through deep reinforcement learning by the two competing players. Our adversarial framework emulates idealized scientific collaborations and competitions among researchers to achieve a better understanding of the application range of the learned material laws and prevent misinterpretations caused by conventional AI-based third-party validation. Numerical examples are given to demonstrate the wide applicability of the proposed meta-modeling game with adversarial attacks on both human-crafted constitutive models and machine learning models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗
DOE OSTI2021

Methane Mitigation Thermoelectric Generator (MMTEG) (Final Scientific/ Technical Report)

Gas Technology Institute (GTI) has completed a $1.815M (plus $500K cost share), 54-month Co-operative agreement with the Department of Energy’s National Energy Technology Laboratory to develop a Methane Mitigation Thermoelectric Generator (MMTEG) system for gas field applications. This novel system uses fugitive gas to produce electrical power and consists of Thermoelectric Generators (TEG) driven by a linear burner, an air compressor, an accumulator, valves, batteries, and power electronics. The electrical power generated by the system is used to compress air and, in turn, the air is used to operate the pneumatic valves at the well site instead of using natural gas (NG) which would then be vented. The team completed the project objectives which included (1) Design, fabricate and test an integrated 6We nominal MMTEG prototype system, (2) Design a “retrofit kit” MMTEG system capable of being field tested at a gas well production site, and (3) build and test the field MMTEG system in a laboratory environment. Multiple system options were developed prior to selecting a “Passive” system which meets cost, NG savings, and greenhouse gas (GHG) reduction targets although not as efficient as initially planned. The MMTEG system is built primarily from commercial off-the-shelf parts (in some cases re-purposed) including the heat exchanger, heat rejection, electronic components, and also the TEGs. The team developed and implemented a novel system including the control system developed by Morrison Applied Sciences (MAS). The team completed incremental demonstrations of the hardware prior to the MMTEG system demonstration. A one-year simulation of the air delivery and battery charging subsystems was completed prior to the integration into the MMTEG system. In addition, the team simulated two-years of thermal cycles for integrated Burner/TEG/heat rejection subsystem. Finally, the entire MMTEG system was assembled, and troubleshooting was completed over a two-week period. Next, the MMTEG system was tested to simulate over 15 weeks of entire system operation over approximately four weeks in an accelerated test fashion with minimal intervention (such as changing fuel tanks). The MMTEG system met the key goals of a unit cost of under $1500 while saving 97% of NG expended today (including leakage) on average by pneumatic systems venting to the atmosphere. The MMTEG system reduced GHG emissions by 1000X (using the methane intensification factor of 28 relative to CO2). GTI is currently pursuing a field test of the MMTEG system. Interfacing with producers has provided additional insight into system improvements. Planned improvements include additional weather protection and control system improvements including the implementation of a long- range radio capability to notify operators if there is a fault.

03 NATURAL GAS↗
DOE OSTI2026

Design and Integration of a Compact Mobile Outdoor Soil Sampling Agricultural Robot

The lack of feedback on soil management for crop systems necessitates the collection of soil samples to derive insights into prevailing soil conditions. Achieving an accurate model of these plantations requires a substantial number of soil samples, which is a labor-intensive process that can suffer from inconsistencies in both the methodology of collection and the locations from which samples are gathered. In addition, the close spacing of crop rows constrains the maneuverability of mobile agricultural equipment during sample collection. This article proposes the use of agricultural robotics to facilitate the acquisition of composite soil samples with minimal human intervention, allowing for precise data collection for a Populus plant system. More specifically, this article presents a case study on the design and construction of a compact agricultural robot for soil sample collection. The performance of the robot, including its soil collection efficacy, maneuverability, and positional accuracy, is evaluated.

42 ENGINEERING↗
DOE OSTI2020

Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale

Abstract High titer, rate, yield (TRY), and scalability are challenging metrics to achieve due to trade-offs between carbon use for growth and production. To achieve these metrics, we take the minimal cut set (MCS) approach that predicts metabolic reactions for elimination to couple metabolite production strongly with growth. We compute MCS solution-sets for a non-native product indigoidine, a sustainable pigment, in Pseudomonas putida KT2440, an emerging industrial microbe. From the 63 solution-sets, our omics guided process identifies one experimentally feasible solution requiring 14 simultaneous reaction interventions. We implement a total of 14 genes knockdowns using multiplex-CRISPRi. MCS-based solution shifts production from stationary to exponential phase. We achieve 25.6 g/L, 0.22 g/l/h, and ~50% maximum theoretical yield (0.33 g indigoidine/g glucose). These phenotypes are maintained from batch to fed-batch mode, and across scales (100-ml shake flasks, 250-ml ambr®, and 2-L bioreactors).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Approaches for effective negative pressure isolation space control to minimize airborne transmission of contaminants in residential homes

Controlling airborne transmission of contaminants including respired viruses such as SARS-CoV-2 is necessary to protect occupants living in the same house with a contagious person. The effectiveness of interventions requiring minor efforts that create a negative pressure isolation zone (IZ) for a contagious person has yet to be systematically tested for residential homes. In this study, ASHRAE Standard 170, which offers guidance for negative pressure isolation space control in healthcare facilities, was used in developing practical and attainable recommendations for residential single-family homes. The relative effectiveness of several control strategies was evaluated through experimentally conducting 17 different test cases in a manufactured single-family house laboratory. These cases were designed based on various heating, ventilation and air-conditioning (HVAC) operating scenarios, intervention measures including closing the IZ door and/or sealing over supply and return air grilles in the IZ, and utilization of bathroom exhaust or portable window fans for pressure control. Four out of 17 test cases were identified as having the potential for strong containment with adequate depressurization in the IZ. The most effective IZ depressurization was achieved through continuously operating the exhaust fan in the bathroom attached to the IZ, by installing a portable window fan that extracted air out of the IZ, and a portable room air conditioner with the AC unit exhaust duct installed in one of the IZ windows.

Construction & Building Technology↗
DOE OSTI2026

An end-to-end workflow for executing a classically bootstrapped variational quantum algorithm on an academic quantum computer

Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.

Clifford↗
DOE OSTI2023

Accident event progression, gaps, and key performance indicators for steam generator tube rupture events in water-cooled SMRs: A review

According to historical records of reactor-related incidents, a steam generator tube rupture (SGTR) is one of the most common occurrences at operating pressurized water reactors (PWRs). Such design-basis accidents (DBAs) could lead to a direct path for radionuclides to be released to the atmosphere via the safety and relief valves, making assessments of radionuclide discharge from operating nuclear power plants (NPPs) into the environment crucial for ensuring safety. In such analyses, the primary focus has been on the extent of radioactive release, not the potential damage to the core. Moreover, significant and timely intervention by operators is needed during the initial stages of SGTRs in order to avert overfilling of the SGs, as well as to restrict the dissemination of radioactive materials. Determining the event sequence and phases that occur during an SGTR incident in an advanced passive (e.g., AP1000) water-cooled nuclear reactor is crucial for implementing effective passive safety systems (PSSs) for a specific water-cooled small modular reactor (SMR) design. Here this study provides a comprehensive review of the accident event progression, associated physical phenomena, knowledge gaps, and key performance indicators that must be evaluated and assessed in thermal-hydraulics models, based on relevant test data, respectively.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗