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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Validation of a synthetic fast ion loss detector model for Wendelstein 7-X

Abstract We present the first validated synthetic diagnostic for fast ion loss detectors (FILDs) in the Wendelstein 7-X (W7-X) stellarator. This model has been developed on, and validated against experimental data from, a FILD provided by the National Institute for Fusion Science (NIFS-FILD), with potential future applicability to the existing Faraday Cup FILD (FC-FILD) on W7-X as well as the scintillating FILD (S-FILD) currently under development. A workflow combining Monte Carlo codes BEAMS3D and ASCOT5 is used to track fast ions produced by neutral beam injection from the moment of ionization until they are thermalized or lost from the last closed flux surface, and from there to a virtual plane which serves as a projection of the entrance aperture to the FILD. Simulations in ASCOT5 are analyzed via a geometric method to determine the probability of transmission through the FILD aperture and onto the detector as a function of normalized momentum, pitch angle, gyrophase, and position at the virtual plane. This probability is then applied to the simulated ions arriving from the plasma, producing a simulated signal from a computationally tractable number of simulated fast ions. Simulated signals are presented for two W7-X experiments with neutral beam injection and quantitatively compared with experimental measurements from the NIFS-FILD diagnostic. An estimate of the frequency of charge-exchange with neutral particles in the edge is performed, and it is found that this process may have a significant impact on the measured signals.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Redox Biochemistry of Energy Conservation in Methanogens and their Syntrophic Partners

The primary objective of this project was to uncover previously unknown modes by which living organisms harvested energy. Specifically, it queried microbial systems, which were once thought to not be able to utilize oxygen. Through the use of cutting-edge approaches, we were able to identify novel mechanistic processes that allow organisms to thrive under varying environmental conditions. This constitutes a paradigm shift in the field. Our work has opened new vistas for expanding the horizons of energy conservation in biological systems, as well as shed light on green energy innovations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Characterization of Digital Silicon Photomultipliers

Digital Silicon photomultipliers (Digital SiPMs) have become an increasingly viable option for single-photon detection due to their compact size and precise timing and resolution. The following work characterizes the noise performance of a diSiPM designed and tested in-house at Fermilab. The SiPM consists of a 26x32 array of single-photon avalanche diode (SPAD) cells. Through the implementation of a shift register masking circuit, individual SPAD cells can be enabled and disabled at will, allowing for precise detection measurements of single columns (accumulator channels) and cells. The provided circuit design operates on voltage clock pulses and collects data on clock edges allowing for precise tuning of the data collection process by varying the clock frequency, pulse width, and integration period.

Shoemaker, Oliver [U. Chicago (main)]↗
DOE OSTI2023

Defect Characterization of Monocrystalline Silicon Solar Cells with Polysilicon Passivated Contact Using Electrically-Detected Magnetic Resonance (EDMR) Spectroscopy

As the c-Si based solar cell efficiencies are approaching over 26%, it is becoming critical to characterize the low concentrations of the defects – as low as 10^10-10^11 cm-3 (for e.g., iron contamination in high-lifetime Ga-doped wafers3 and n-type wafers), and further reduce them. Also, atomistic level understanding of the mechanisms of the low concentration process-induced-defects and reliability limiting defects (such as light and elevated temperature induced degradation, surface passivation degradation) is needed to design the mitigation strategies. The conventional characterization techniques are limited due to their detection limitations. Some of the techniques based on lifetime spectroscopies can still be used for low concentration characterization however, they are based on estimations and theoretical models and hence, indirect and cannot fully reveal information about the microscopic mechanism of the defects. Thus, we present the application of an ultrasensitive magnetic resonance-based technique for the direct spectroscopic detection of the defects in Si PV - electrically detected magnetic resonance (EDMR). In this work, we aim to focus on establishing a process flow for fabrication of minicells with (miniature replica of the larger-area cells) and setting up the routine for EDMR measurements on them with the EDMR instrumentation capability at NREL. For the EDMR measurements, sample size is limited by the dimensions of sample holder tube (width less than 3.2 mm, active area - 20 mm). Thus, we have designed c-Si based minicells with polysilicon (poly-Si) passivated contacts same as the larger-area cells that we fabricate in our group at NREL. We also modified our minicell process flow for fabricating the textured minicells for preserving the texture during processing and taking care of the laser-ablation edge damage which can significantly affect the performance of such small devices. We have achieved comparable performance on these newly fabricated minicells as that of our 4 cm2 devices with same structure (comparable VOC, JSC, FF). We also conducted EDMR measurements on the minicells and observed a distinct EDMR signal at g-value ~2.005 at temperatures 30K and above, as shown in Fig. 2. We associate this signal to the presence of silicon dangling bonds based on the g-value. We also observed an EDMR signal at g-value ~1.998 at temperature ~5K. The origin of this signal is still being investigated. Thus, we show the proof of concept of minicells and EDMR measurements with which we now aim to study some of the unknown defects in silicon solar cell devices.

EDMR↗
DOE OSTI2022

Trace Impurities Identified as Forensic Signatures in CMX-5 Fuel Pellets Using X-ray Spectroscopic Techniques

We report that small-particle analysis is a highly promising emerging forensic tool for analysis of interdicted special nuclear materials. Integration of microstructural, morphological, compositional, and molecular impurity signatures could provide significant advancements in forensic capabilities. We have applied rapid, high-sensitivity, hard X-ray synchrotron chemical imaging to analyze impurity signatures in two differently fabricated fuel pellets from the 5th Collaborative Materials Exercise (CMX5) of the IAEA Nuclear Forensics International Working Group. The spatial distributions, chemical compositions, and morphological and molecular characteristics of impurities were evaluated using X-ray absorption near-edge structure (XANES) and X-ray fluorescence chemical imaging to discover principal impurities, their granularity, particle sizes, modes of occurrence (distinct grains vs incorporation in the UO 2 lattice), and sources and mechanisms of incorporation. Differences in UO 2+x stoichiometry were detected at the microscale in nominally identical UO 2 ceramics (CMX5-A and CMX5-B), implying the presence of multiple UO 2 host phases with characteristic microstructures and feedstock compositions. Al, Fe, Ni, W, and Zr impurities and integrated impurity signature analysis identified distinctly different pellet synthesis and processing methods. For example, two different Al, W, and Zr populations in the CMX5-B sample indicated a more complex processing history than the CMX5-A sample. K-edge XANES measurements reveal both metallic and oxide forms of Fe and Ni but with different proportions between each sample. Altogether, these observations suggest multiple sources of impurities, including fabrication (e.g., force-sieving) and feedstock (mineral oxides). This study demonstrates the potential of synchrotron techniques to integrate different signatures across length scales (angstrom to micrometer) to detect and differentiate between contrasting UO 2 fuel fabrication techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era

Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Edge-Confined Excitons in Monolayer Black Phosphorus

Quantum confinement of two-dimensional excitons in van der Waals materials via electrostatic trapping, lithographic patterning, Moiré potentials, and chemical implantation has enabled significant advances in tailoring light emission from nanostructures. While such approaches rely on complex preparation of materials, natural edges are a ubiquitous feature in layered materials and provide a different approach for investigating quantum-confined excitons. Here, we observe that certain edge sites of monolayer black phosphorus (BP) strongly localize the intrinsic quasi-one-dimensional excitons, yielding sharp spectral lines in photoluminescence, with nearly an order of magnitude line width reduction. Through structural characterization of BP edges using transmission electron microscopy and first-principles GW plus Bethe–Salpeter equation (GW-BSE) calculations of exemplary BP nanoribbons, we find that certain atomic reconstructions can strongly quantum-confine excitons resulting in distinct emission features, mediated by local strain and screening. Here we observe linearly polarized luminescence emission from edge reconstructions that preserve the mirror symmetry of the parent BP lattice, in agreement with calculations. Furthermore, we demonstrate efficient electrical switching of localized edge excitonic luminescence, whose sites act as excitonic transistors for emission. Localized emission from BP edges motivates exploration of nanoribbons and quantum dots as hosts for tunable narrowband light generation, with future potential to create atomic-like structures for quantum information processing applications as well as exploration of exotic phases that may reside in atomic edge structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Design and Challenges of Edge Computing ASICs on Front-End Electronics

In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Solid state reduction of nickelate thin films

The square-planar nickelates are a class of superconductors analogous to the cuprates and promise to provide insight into the pairing mechanism in high-temperature superconducting oxides. The parent phase of doped superconducting films is NdNiO 2 , which is prepared by reducing 3 + Ni in NdNiO 3 films. In this paper, we develop an ultrahigh vacuum reduction method using aluminum deposited on top of the 3 + nickelates and monitor the reduction process in real time through in situ crystal truncation rod measurements and diffraction x-ray absorption near edge spectroscopy measurements across the Ni K edge. Further, we establish a relation between Ni valence and the lattice constant of NdNiO 3-x and show that the process can precisely control the oxygen content in the films. Finally, we extend the reduction process to quintuple square-planar nickelates.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Synthetically generated, large graph networks serve as useful proxies to real-world networks for many graph-based applications. The ability to generate such networks helps overcome several limitations of real-world networks regarding their number, availability, and access. Here, we present the design, implementation, and performance study of a novel network generator that can produce very large graph networks conforming to any desired degree distribution. The generator is designed and implemented for efficient execution on modern graphics processing units (GPUs). Given an array of desired vertex degrees and number of vertices for each desired degree, our algorithm generates the edges of a random graph that satisfies the input degree distribution. Multiple runtime variants are implemented and tested: 1) a uniform static work assignment using a fixed thread launch scheme, 2) a load-balanced static work assignment also with fixed thread launch but with cost-aware task-to-thread mapping, and 3) a dynamic scheme with multiple GPU kernels asynchronously launched from the CPU. The generation is tested on a range of popular networks such as Twitter and Facebook, representing different scales and skews in degree distributions. Results show that, using our algorithm on a single modern GPU (NVIDIA Volta V100), it is possible to generate large-scale graph networks at rates exceeding 50 billion edges per second for a 69 billion-edge network. GPU profiling confirms high utilization and low branching divergence of our implementation from small to large network sizes. For networks with scattered distributions, we provide a coarsening method that further increases the GPU-based generation speed by up to a factor of 4 on tested input networks with over 45 billion edges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Machine-Protection System for the PIP-II Linear Accelerator: Architecture and Data Processing Schemes

PIP-II at Fermilab features a brand-new, 800 MeV leading-edge Linear Accelerator (Linac) that will enable the Fermilab complex to deliver more than a megawatt of beam power to the Long Base-line Neutrino Facility (LBNF), requiring a robust Machine Protection System (MPS) to prevent beam-induced damage to delicate cryomodules and vacuum components. Signals in various accelerator components, in both analog and digital format, are collected and processed to assest the operating conditions of the accelerator. In the situation a potential damage is foreseen, the beam permit signal will be drop. A critical element of this system is the fast Analog Machine Protection System, a high-bandwidth platform designed for real-time beam loss monitoring. The system utilizes a modular, FPGA-based architecture to digitize signals from beam-sensing devices, such as AC Current Transformers (ACCTs), non-invasive Ring Pickups (RPUs) and beam scrapers. To eliminate both high frequency as well as 60 Hz noise that might be picked up in the industrial environment, data processing schemes beyond regular digital filtering are planned and implemented.

Wu, Jinyuan [Fermilab] (ORCID:0000000344329521)↗
DOE OSTI2022

Sensing Electrical Networks Securely & Economically (SENSE)

The growing adoption of distributed energy resources (DERs) like battery energy storage systems and roof top solar/PV and the rapid penetration of electric vehicles (EVs), the electric grid is undergoing a major transformation with elevated stress on legacy grid assets. Despite a lot of expenditure to address these challenges, both in dollars and manpower, utilities have not been able to receive the value that was promised. The gains have been most visible at the transmission and substation level, especially where the main objective was improving operational and economic efficiency for the utility. Improving visibility and control at a few select points enhances the existing and established paradigm of centralized command and control. With changing load patterns, load types and the overall transition to an “active grid”, the centralized control and coordination paradigm gets challenged. To address the challenges, a new architecture and mechanism is needed, one that supports decentralized control and decision making, extracting value streams at the grid edge, particularly as the changes are fueled by transitions occurring in the distribution system. To address this, a communications and data processing platform, “GAMMA” was developed and demonstrated through the project. At the heart of the platform, are distributed, intelligent edge nodes with sensing and compute capabilities, that can record and analyze information locally. They are embedded in sensors and actuators specific to different distribution system applications. Phase 1 of the project focused on developing novel sensor technology that can be used for monitoring utility pole top distribution transformers. The sensors were designed with the objective of being low-cost, communicating with the GAMMA cloud using novel “delay-tolerant” networking using Bluetooth and a secure mobile application. They were non-intrusive in nature so that they can be installed quickly in the field, resulting in overall low cost of deployment and operations. Following the successful completion of Phase 1, the team manufactured 100 units for a field demonstration in Phase 2. The field demonstration was carried out on two real feeder systems with the local utility partner. In total, 100 sensors were installed and operated over a period of 6 months in the state of Georgia. The platform is operational end to end, with the cloud infrastructure deployed on a distributed, serverless environment that can serve multiple data streams, an analytics engine and a portal to securely view the data from multiple assets. The data collected through the GAMMA Mobile Phone app showcased the viability of the novel delay tolerant networking architecture, and the data processing algorithms developed through the course of the project, were successful in extracting important information about the overall network, improving the utility’s visibility and situational awareness in the distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, and robustness. Inspired by the concept of Edge Artificial Intelligence (Edge AI), we propose a robust approach that enables fast FLI with no degradation of accuracy. This approach couples a recurrent neural network (RNN), which is trained to estimate the fluorescence lifetime directly from raw timestamps without building histograms, to SPAD TCSPC systems, thereby drastically reducing transfer data volumes and hardware resource utilization, and enabling real-time FLI acquisition. We train two variants of the RNN on a synthetic dataset and compare the results to those obtained using center-of-mass method (CMM) and least squares fitting (LS fitting). Results demonstrate that two RNN variants, gated recurrent unit (GRU) and long short-term memory (LSTM), are comparable to CMM and LS fitting in terms of accuracy, while outperforming them in the presence of background noise by a large margin. To explore the ultimate limits of the approach, we derive the Cramer-Rao lower bound of the measurement, showing that RNN yields lifetime estimations with near-optimal precision. To demonstrate real-time operation, we build a FLI microscope based on an existing SPAD TCSPC system comprising a 32 x 32 SPAD sensor named Piccolo. Four quantized GRU cores, capable of processing up to 4 million photons per second, are deployed on the Xilinx Kintex-7 FPGA that controls the Piccolo. Powered by the GRU, the FLI setup can retrieve real-time fluorescence lifetime images at up to 10 frames per second. The proposed FLI system is promising and ideally suited for biomedical applications, including biological imaging, biomedical diagnostics, and fluorescence-assisted surgery, etc.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗
DOE OSTI2021

University Research to Support the MPACT 2020 Milestone

University research is a strong focus of the Office of Nuclear Energy within the Department of Energy. This research complements existing work in the various program areas and provides support and training for students entering the field. Four university projects have provided support to the Material Protection Accounting and Controls Technologies (MPACT) 2020 milestone focused on safeguards for electrochemical processing facilities. The University of Tennessee Knoxville has examined data fusion of NDA measurements such as Hybrid K-Edge Densitometry and Cyclic Voltammetry. Oregon State University and Virginia Polytechnic Institute have examined the integration of accountancy data with process monitoring data for safeguards. The Ohio State University and the University of Utah have developed a Ni-Pt SiC Schottky diode capable of high temperature alpha spectroscopy for actinide detection of molten salts. Finally, the University of Colorado has developed a key enabling technology for the use of Microcalorimetry.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Damage evolution and ductile fracture of commercially-pure titanium sheets subjected to simple tension and cyclic bending under tension

This paper describes a study into the evolution of damage in commercial-purity titanium (CP–Ti) sheets subjected to cyclic bending under tension (CBT) and uniaxial tension (simple tension, ST). Sections were taken from sheets that were strained to various levels under both methods and were imaged using X-ray computed tomography (XCT) to reveal insights into the size, density, and distribution of microvoids in the sheets. Digital image correlation (DIC) was also used to observe the surface strain of CBT and ST sheets during testing. These results showed that elongation to failure (ETF) for CBT deformation is about 2.5× higher than for ST, local longitudinal strains in the bulk of the CBT sample are around 1.3× higher than the peak strain in the ST necked region, and 1.7× higher in the localized region of CBT failure. It was found that the volume-density of voids in both sheets followed a similar exponential increase with strain, reaching nearly 45× higher in the CBT than the ST sheets before the onset of failure, mainly due to the higher strain levels achieved. A higher volume density of voids developed in the center of sheets at high levels of strain, for both processes, with the density in the sheet center becoming approximately double that found near the edges. Scanning electron microscopy (SEM) was used to examine the fracture surface of CBT and ST sheets after failure. The observations are presented and discussed highlighting the CBT process as effective to delay ductile fracture of the CP-Ti sheets.

Commercially pure titanium↗
DOE OSTI2022

Facile alignment estimation in carbon nanotube films using image processing

Whether a macroscopic assembly of carbon nanotubes can exhibit the one-dimensional properties expected from individual nanotubes critically depends on how well the nanotubes are aligned inside the assembly. Therefore, a simple and accurate method for assessing the degree of alignment is desired for the rapid characterization of carbon nanotube films and fibers. Here, we present an end-to-end solution for determining the global and local spatial orientation of carbon nanotubes in films within a short amount of time using a fast, precise, and economical approach based on an image processing method applied to scanning electron microscopy images. Further, we first use Laplacian edge enhancement filtering for improving the appearance of edge regions, which is followed by image partitioning into multiple blocks to capture the nanoscale orientation characteristics and total variation-based image decomposition of these image blocks. We then perform a 2D-fast Fourier transform on the image decomposed textural components of these edge-enhanced image blocks to determine the orientation distribution, which is utilized to estimate the 2D nematic order parameter. To show the effectiveness of our method, we corroborated our results against results obtained with other state-of-the-art image processing and experimental techniques.

2D-FFT↗
DOE OSTI2021

Flattening the Radial Temperature Profile across the Transformational Challenge Reactor Core

The Transformational Challenge Reactor (TCR) program is demonstrating an agile development approach to advanced nuclear reactor design, which has traditionally utilized a linear design process. In leveraging artificial intelligence, additive manufacturing, advanced materials, and cutting-edge modeling and simulation, the TCR program aims to minimize the high cost and lengthy deployment timelines now standard in the nuclear industry. Within a relatively short period of time, a robust and mature advanced gas-cooled reactor was iteratively designed under the TCR program, using these cutting-edge technologies. The TCR is a 3 MWt gas-cooled microreactor fueled with uranium nitride (UN) tristructural isotropic (TRISO) fuel particles. Though manufactured via traditional means, these UN TRISO particles are loaded into additively manufactured silicon carbide (SiC) cans [4]. Once loaded with TRISO particles, the SiC cans are densified using a chemical vapor infiltration process. The additively manufactured SiC enables significantly more freedom in the design of the fuel form than could ever be achieved using traditionally manufactured SiC. The helium coolant, pressurized to 5 MPa, enters the core at 300°C and nominally exits it at 500°C. Typically, the most thermally limiting components in any reactor design are the fuel assemblies in the core center. To provide a wide thermal margin in these central fuel assemblies, the flow may be biased toward the center of the core to more effectively cool these fuel assemblies with more power deposition and flatten the core’s radial temperature distribution. An analytical fluid model of the TCR core was developed to explore methods for biasing the flow away from the cooler outer fuel assemblies and towards the hotter inner ones. Higher-fidelity models developed in STAR-CCM+ 2020.3.1, a computational fluid dynamics code, were then utilized to verify the analytical model’s findings.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗