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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 OSTI2018

On the role of last closed drift shell dynamics in driving fast losses and Van Allen radiation belt extinction

We present observations of very fast radiation belt loss as resolved using high time resolution electron flux data from the constellation of Global Positioning System (GPS) satellites. The time scale of these losses is revealed to be as short as ~0.5–2 hr during intense magnetic storms, with some storms demonstrating almost total loss on these time scales and which we characterize as radiation belt extinction. The intense March 2013 and March 2015 storms both show such fast extinction, with a rapid recovery, while the September 2014 storm shows fast extinction but no recovery for around 2 weeks. By contrast, the moderate September 2012 storm which generated a three radiation belt morphology shows more gradual loss. Here, we compute the last closed drift shell (LCDS) for each of these four storms and show a very strong correspondence between the LCDS and the loss patterns of trapped electrons in each storm. Most significantly, the location of the LCDS closely mirrors the high time resolution losses observed in GPS flux. The fast losses occur on a time scale shorter than the Van Allen Probes orbital period, are explained by proximity to the LCDS, and progress inward, consistent with outward transport to the LCDS by fast ultralow frequency wave radial diffusion. Expressing the location of the LCDS in L*, and not model magnetopause standoff distance in units of RE, clearly reveals magnetopause shadowing as the cause of the fast loss observed by the GPS satellites.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Beam loss modeling and mitigation due to intra-beam stripping

Intra-Beam Stripping (IBS) is a critical beam loss mechanism in high-intensity H- linacs and presents a significant limitation to increasing beam power. This work presents a computational framework to evaluate and mitigate IBS-induced beam loss along the Spallation Neutron Source (SNS) LINAC. Our calculation is based on an analytic theory and involves evaluation of a 9D integral using the Monte-Carlo technique. We first benchmarked our calculations against simplified, analytically solvable cases. We then applied our algorithm to Gaussian bunches with a known probability density function (PDF). We next expanded our algorithm to arbitrary bunch distributions using the Neural Spline Flow (NSF) models trained on PyORBIT tracking data. In the future, we plan to validate our algorithm experimentally and apply it to design IBS mitigation strategies.

Nln, Shivam [ORNL]↗
DOE OSTI2012

Laser Processing of Metals and Polymers

A laser offers a unique set of opportunities for precise delivery of high quality coherent energy. This energy can be tailored to alter the properties of material allowing a very flexible adjustment of the interaction that can lead to melting, vaporization, or just surface modification. Nowadays laser systems can be found in nearly all branches of research and industry for numerous applications. Sufficient evidence exists in the literature to suggest that further advancements in the field of laser material processing will rely significantly on the development of new process schemes. As a result they can be applied in various applications starting from fundamental research on systems, materials and processes performed on a scientific and technical basis for the industrial needs. The interaction of intense laser radiation with solid surfaces has extensively been studied for many years, in part, for development of possible applications. In this thesis, I present several applications of laser processing of metals and polymers including polishing niobium surface, producing a superconducting phase niobium nitride and depositing thin films of niobium nitride and organic material (cyclic olefin copolymer). The treated materials were examined by scanning electron microscopy (SEM), electron probe microanalysis (EPMA), atomic force microscopy (AFM), high resolution optical microscopy, surface profilometry, Fourier transform infrared spectroscopy (FTIR) and x-ray diffraction (XRD). Power spectral density (PSD) spectra computed from AFM data gives further insight into the effect of laser melting on the topography of the treated niobium.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Land Model Testbed: Accelerating Development, Benchmarking and Analysis of Land Surface Models

A Land Model Testbed (LMT), designed to provide a computational framework for systematically assessing model fidelity and supporting rapid development of complex multiscale models, offers a general-purpose workflow for conducting large ensemble simulations of multiple land surface models, post-processing large volumes of model output, and evaluating model results. It leverages existing tools for launching model simulations and the International Land Model Benchmarking (ILAMB) package for assessing model fidelity through comparison with best-available observational datasets. Increased complexity and proliferation of uncertain parameters in process representations in land surface models has driven the need for frequent and intensive testing and evaluating of models to quantify uncertainties and optimize parameters such that results are consistent with observations. The LMT described here meets these needs by providing tools to run thousands of ensemble simulations simultaneously and post-process their output files, by automating execution of an enhanced version of ILAMB with site-specific benchmarks and multivariate functional relationships, and by offering ensemble diagnostics and a customizable dashboard for displaying model performance metrics and associated graphics. We envision the LMT capabilities will serve as a foundational computational resource for a proposed user facility focused on terrestrial multiscale model--data integration.

Sreepathi, Sarat↗
DOE OSTI2018

Automation of Data Collection Methods for Online Monitoring of Nuclear Power Plants

Maintenance activities in the United States nuclear power industry are mainly centered on preventive and corrective maintenance. Predictive maintenance, which is a variable time basis maintenance strategy that is dependent on the condition of the equipment, is less utilized but potentially more effective. In the current maintenance practices, the nuclear power plants rely on manual processes to supply information to a human decision making process. The plants have several processes that generate information that is not typically used beyond the intended target for collecting that information. This information is a source of data that is manually collected by labor intensive tasks. The data is therefore expensive to collect, yet limited in use. The industry has realized the opportunity present in reducing the labor intensive tasks by automating them and increasing the uses of the data collected by either developing advanced predictive maintenance methods using data-driven decision making that could span from trending to machine learning and artificial intelligence. This shift in maintenance strategy may result in high cost saving impact. In addition to reducing labor requirements and frequency of maintenance activities, this shift in strategy will reduce the materials portion of the operations and maintenance cost as well. The aim of this specific effort is to focus on the automation of data collection process. It is part of a series of planned efforts to target multiple elements of migrating the current preventive maintenance approach in nuclear power plants to a data-driven approach. These elements include data collection, management, analysis, visualization, in addition to value analysis and change enabling. The other five elements of migration were not considered because they are planned to be explicitly studied in multiple parallel efforts. Challenges including regulations requirements or limitations are planned in a future effort that focuses on change enabling. The risk and benefit/cost of the approach shift is planned in another effort targeted towards value. The storage, communication, computational power, data architecture, and protection are part of the information infrastructure and are planned in dedicated data management effort. The best means to present the data for assisted human decision making is planned in a data visualization effort. The data interpretation and patterns recognition for machine decision making is planned through multiple efforts in data analytics over the following years. This work identified seventeen data sources collection methods in nuclear power plants that could be automated for an integrated data platform that enables comprehensive and informed decision making. For each of the identified seventeen data sources, the base, modern, and state of the art states were described. The current state of data collection used by most utilities was identified as the "base state". The current achievable state was identified as the "modern state", a state that has either been achieved by at least some US utilities or in other industries. The future state that requires research and development to reach was identified as the "state of the art" state. The described states used an out-of-the-box thinking approach. This effort found that there is a hug potential for task automation and data fidelity improvements by evolving the current industry data collection methods. The majority of the processes analyzed were found to be manual and labor intensive and fell in the base state. As a result, the modern state often targeted reducing the manual logging efforts by using electronic and semi-intelligent means or complementing the work with sensors and mobile or fixed technologies that can reduce the demand of labor activities. The state of the art state involved applications advanced methods of data correlation, data mining, and machine learning along with increasing the spatial distribution and fidelity of data, coupling multiple data sources, and enabling the use of sophisticated mobile devices and machine comprehension methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication

We report accurate estimation of cropping intensity (CI), an indicator of food production, is well aligned with the ongoing efforts to achieve sustainable development goals (SDGs) under diminishing natural resources. The advancement in satellite remote sensing provides unprecedented opportunities for capturing CI information in a spatially continuous manner. However, challenges remain due to the lack of generalizable algorithms for accurately and efficiently mapping global CI with a fine spatial resolution. In this study, we developed a 30-m planetary-scale CI mapping framework with the reconstructed time series of Normalized Difference Vegetation Index (NDVI) from multiple satellite images. Using a binary crop phenophase profile indicating growing and non-growing periods, we estimated pixel-by-pixel CI by enumerating the total number of valid cropping cycles during the study years. Based on the Google Earth Engine cloud computing platform, we implemented the framework to estimate CI during 2016–2018 in eight geographic regions across continents that are representative of global cropping system diversity. Comparison with PhenoCam network data in four cropland sites suggests that the proposed framework is capable of capturing the seasonal dynamics of cropping practices. Spatially, overall accuracies based on validation samples range from 80.0% to 98.9% across different regions worldwide. Regarding the CI classes, single cropping systems are associated with more robust and less biased estimations than multiple cropping systems. Finally, our CI estimates reveal high agreement with two widely used land surface phenology products, including Vegetation Index and Phenology V004 (VIP4) and Moderate Resolution Imaging Spectroradiometer Land Cover Dynamics (MCD12Q2), meanwhile providing much more spatial details. Due to its robustness, the developed CI framework can be potentially generalized to produce global fine resolution CI products for food security and other applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

MemHC: An Optimized GPU Memory Management Framework for Accelerating Many-body Correlation

The many-body correlation function is a fundamental computation kernel in modern physics computing applications, e.g., Hadron Contractions in Lattice quantum chromodynamics (QCD). This kernel is both computation and memory intensive, involving a series of tensor contractions, and thus usually runs on accelerators like GPUs. Existing optimizations on many-body correlation mainly focus on individual tensor contractions (e.g., cuBLAS libraries and others). In contrast, this work discovers a new optimization dimension for many-body correlation by exploring the optimization opportunities among tensor contractions. More specifically, it targets general GPU architectures (both NVIDIA and AMD) and optimizes many-body correlation’s memory management by exploiting a set of memory allocation and communication redundancy elimination opportunities: first, GPU memory allocation redundancy: the intermediate output frequently occurs as input in the subsequent calculations; second, CPU-GPU communication redundancy: although all tensors are allocated on both CPU and GPU, many of them are used (and reused) on the GPU side only, and thus, many CPU/GPU communications (like that in existing Unified Memory designs) are unnecessary; third, GPU oversubscription: limited GPU memory size causes oversubscription issues, and existing memory management usually results in near-reuse data eviction, thus incurring extra CPU/GPU memory communications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Influence of intrinsic decoherence on tripartite entanglement and bipartite fidelity of polar molecules in pendular states

An array of ultracold polar molecules trapped in an external electric field is regarded as a promising carrier of quantum information. Under the action of this field, molecules are compelled to undergo pendular oscillations by the Stark effect. Particular attention has been paid to the influence of intrinsic decoherence on the model of linear polar molecular pendular states, thereby we evaluate the tripartite entanglement with negativity, as well as fidelity of bipartite quantum systems for input and output signals using electric dipole moments of polar molecules as qubits. According to this study, we consider three typical initial states for both systems, respectively, and investigate the temporal evolution with variable values of the external field intensity, the intrinsic decoherence factor, and the dipole-dipole interaction. Thus, we demonstrate the sound selection of these three main parameters to obtain the best entanglement degree and fidelity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Research software engineering: Professionalization, roles, and identity

This report summarizes the results of a study of research software professionals based on 17 interviews conducted at U.S. national laboratories and academic institutions. The study focuses on understanding the emerging professional role of research software engineer (RSE) and examining how the history of software engineering might be relevant to the emergence of research software engineering as a professional movement. The report first uses interview data to identify several models of professional identity for research software professionals. These include dominance models in which a person strongly identifies with a single professional identity (such as RSE), and multiplicity models in which an individual sees an identity like RSE as just one facet of their overall professional role. Next, it identifies three types of work situations for software professionals at national laboratories, covering those who work in dedicated RSE organizations, those who play an integral role in large multiphysics code projects, and those who work in a more isolated capacity to provide software development support for less computationally intensive research projects. It then reviews some key ideas from the sociology of professions in the context of the software engineering profession, suggesting parallels between the current RSE professional movement and aspects of the history of software engineering. It concludes by suggesting some potential practical implications of these findings for the RSE movement and professional organizations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

CFD Benchmark of Pressure Drop in a 61-Pin Wire-Wrapped Assembly with Blocked Channels Using NekRS

Thermal hydraulic behaviors of wire-wrapped rod bundles continue to be a subject of intense research. One of the leading next-generation designs, the sodium fast reactor, relies on a hexagonal assembly of wire-wrapped fuel pins. The issue of potential channel blockage has been raised as a safety concern due to the tightly packed arrangement of the fuel pins. This has led to several recent experimental and computational studies working to quantify the potential impact on the fluid flow and heat transfer behaviors of such blockages. The objective of the present study is to benchmark the high-fidelity NekRS CFD solver in predicting pressure drop for large blockages against available experimental data. A 61-pin wire-wrapped fuel assembly with two flow blockage configurations has been simulated and investigated at various low to moderate Reynolds numbers. The NekRS solver has been shown to yield exponentially decreasing spatial discretization errors with increasing polynomial order. All simulated results agreed well with measured data, which indicates that the overall methodology is adequate and consistent. The results of this benchmark study demonstrate the accuracy of NekRS for sodium fast reactor hydrodynamic simulations, increasing the confidence in its use for design, licensing, and analysis activities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Challenges and opportunities in understanding microbial communities with metagenome assembly (accompanied by IPython Notebook tutorial)

Metagenomic investigations hold great promise for informing the genetics, physiology, and ecology of environmental microorganisms. Current challenges for metagenomic analysis are related to our ability to connect the dots between sequencing reads, their population of origin, and their encoding functions. Assembly-based methods reduce dataset size by extending overlapping reads into larger contiguous sequences (contigs), providing contextual information for genetic sequences that does not rely on existing references. These methods, however, tend to be computationally intensive and are again challenged by sequencing errors as well as by genomic repeats. While numerous tools have been developed based on these methodological concepts, they present confounding choices and training requirements to metagenomic investigators. To help with accessibility to assembly tools, this review also includes an IPython Notebook metagenomic assembly tutorial. This tutorial has instructions for execution any operating system using Amazon Elastic Cloud Compute and guides users through downloading, assembly, and mapping reads to contigs of a mock microbiome metagenome. Despite its challenges, metagenomic analysis has already revealed novel insights into many environments on Earth. As software, training, and data continue to emerge, metagenomic data access and its discoveries will to grow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Nucleosynthesis for Science and Security: Preparing for a Nuclear and Chemical Sciences Program at FRIB

Nuclear reactions are vital to nuclear science and its many applications: they are the mechanism for forging elements in the cosmos, the source of energy for both stars and nuclear weapons, the principal probes used to study nuclear properties, and the primary diagnostic signatures for stockpile stewardship and nuclear forensics. Yet, most reaction experiments have been confined to the small fraction of all nuclei that are stable, and a theoretical foundation for reactions with a true predictive capability is lacking. The Facility for Rare Isotope Beams (FRIB), the flagship U.S. facility in low-energy nuclear science, will offer the opportunity to rectify the long-standing gap in understanding reactions on radioactive nuclei. The work performed in this LDRD project brought together the experimental nuclear physics, radiochemistry, nuclear theory, and high-performance computing expertise of the Nuclear and Chemical Sciences Division to develop new techniques that will be needed to exploit FRIB beams to better understand nuclear reactions. Novel isotope-harvesting techniques were developed to demonstrate that samples could be collected at the National Superconducting Cyclotron Laboratory (NSCL) at Michigan State University (the facility which FRIB builds upon) for direct measurements of long-lived radioisotopes. For isotopes too short lived for direct measurements, indirect approaches were developed that would ultimately be used with high-intensity FRIB radioactive beams. Finally, to better interpret experimental results and reliably extend predictions beyond the available data, a foundation was established for a fully microscopic and unified description of the structure, interactions, and dynamics of two reacting nuclei that moves beyond empirical models. This work has positioned the team for a leadership role at FRIB, enabling LLNL to maintain a world-class nuclear science program and to compete for future talent and funding, both of which are vital for continuing support of the laboratory’s stockpile stewardship mission.

42 ENGINEERING↗
DOE OSTI2026

Desmearing Bonse–Hart USANS data using Bayesian Gaussian process regression

Ultra-small-angle neutron scattering (USANS) enables access to micrometer-scale structures but is intrinsically affected by strong, anisotropic resolution smearing arising from slit-geometry optics. As a result, recovery of the intrinsic scattering intensity constitutes an ill-posed inverse problem, and commonly used iterative desmearing methods lack rigorous uncertainty quantification. We present a Bayesian desmearing framework for slit-geometry USANS based on Gaussian process regression. In this approach, the scattering intensity is modeled as a smooth random function, and the instrumental point spread function is incorporated explicitly as a forward operator. The resulting formulation yields a closed-form maximum a posteriori solution with well-defined credibility intervals. Computational benchmarks and experimental validation using combined USANS and small-angle neutron scattering (SANS) measurements demonstrate that the framework enables stable desmearing, suppresses experimental noise, and preserves physically meaningful structural features under realistic conditions.

Tung, Chi-Huan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2018

Mechanism-based constitutive modeling of ZEK100 magnesium alloy with crystal plasticity and in-situ HEXRD experiment

The constitutive behavior of a hexagonal close-packed (HCP) polycrystalline ZEK100 magnesium alloy was investigated here using combined high energy X-ray diffraction (HEXRD) from a synchrotron source and crystal plasticity modeling approach. The in-situ tensile test data coupled with the HEXRD enabled the tracking of the lattice strain evolution during deformation. The microscopic behavior represented by lattice strain and the macroscopic behavior represented by stress-strain curves were then used together as objective function to estimate the critical resolved shear stress (CRSS) and hardening parameters of available slip and deformation twin systems in the ZEK100 alloy. An enhanced predominant twinning reorientation (ePTR) scheme was proposed in the current work, and the ePTR parameters were determined for the first time by the use of basal plane peak intensity along loading direction measured from HEXRD. Two crystal plasticity models, the computationally efficient elastic-plastic self-consistent (EPSC) and crystal plasticity finite element (CPFE) models, were developed incorporating the deformation twinning for the HCP-structured metals. The determined constitutive parameters were further validated by comparing the predicted deformation texture with the measured one. The work provides a useful and computationally-efficient modeling scheme to understand the slip/twin induced deformation behaviors of the ZEK100 alloy in micro- and macro-scales.

36 MATERIALS SCIENCE↗
DOE OSTI2023

BEYONDPLANCK XIII. Intensity foreground sampling, degeneracies, and priors

We present the intensity foreground algorithms and model employed within the BEYONDPLANCK analysis framework. The BEYONDPLANCK analysis is aimed at integrating component separation and instrumental parameter sampling within a global framework, leading to complete end-to-end error propagation in the Planck Low Frequency Instrument (LFI) data analysis. Given the scope of the BEYONDPLANCK analysis, a limited set of data is included in the component separation process, leading to foreground parameter degeneracies. In order to properly constrain the Galactic foreground parameters, we improve upon the previous Commander component separation implementation by adding a suite of algorithmic techniques. These algorithms are designed to improve the stability and computational efficiency for weakly constrained posterior distributions. These are: (1) joint foreground spectral parameter and amplitude sampling, building on ideas from MIRAMARE; (2) component-based monopole determination; (3) joint spectral parameter and monopole sampling; and (4) application of informative spatial priors for component amplitude maps. We find that the only spectral parameter with a significant signal-to-noise ratio using the current BEYONDPLANCK data set is the peak frequency of the anomalous microwave emission component, for which we find $ν$ p = 25.3 ± 0.5 GHz; all others must be constrained through external priors. Future works will be aimed at integrating many more data sets into this analysis, both map and time-ordered based, thereby gradually eliminating the currently observed degeneracies in a controlled manner with respect to both instrumental systematic effects and astrophysical degeneracies. When this happens, the simple LFI-oriented data model employed in the current work will need to be generalized to account for both a richer astrophysical model and additional instrumental effects. This work will be organized within the Open Science-based COSMOGLOBE community effort.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Rapid 3D nanoscale coherent imaging via physics-aware deep learning

Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-CDI-NN, a deep convolutional neural network and differential programing framework trained to predict 3D structure and strain, solely from input 3D X-ray coherent scattering data. Our networks are designed to be “physics-aware” in multiple aspects; in that the physics of the X-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods. Our integrated machine learning and differential programing solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Effect of alloying on intrinsic ductility in WTaCrV high entropy alloys

Tungsten (W) exhibits desirable properties for extreme applications, such as the divertor in magnetic fusion reactors, but its practicality remains limited due to poor formability and insufficient irradiation resistance. In this work, we study the intrinsic ductility of body-centered cubic WTaCrV based high entropy alloys (HEAs), which are known to exhibit excellent irradiation resistance. The ductility evaluations are carried out using a criterion based on the competition between the critical stress intensity factors for emission (K Ie ) and cleavage (K Ic ) in the {110} slip planes and {110} crack planes, which are evaluated within the linear elastic fracture mechanics framework and computed using density functional theory. The results suggest that increasing the alloying concentrations of V and reducing the concentrations of W can significantly improve the ductility in these HEAs. The elastic anisotropy for these HEAs is analyzed using the Zener anisotropy ratio and its correlation with the concentration of W in the alloys is studied. Results indicate that these alloys tend to be fairly isotropic independently from the concentration of W in them. The computed data for the elastic constants of these HEAs is also compared against available experimental data. The results are in good agreement, validating the robustness and accuracy of the computational methods. Multiple phenomenological ductility metrics were also computed and analyzed against the analytical model. Some metrics, mainly the surrogate D parameter, show a good correlation with the Rice model. The potential of these empirical metrics to serve as surrogate screening models for optimizing the compositional space is also discussed.

Anisotropy↗
DOE OSTI2011

Data Processing for the SNS EQ-SANS Diffractometer

The scattering data on the Extended Q-Range Small Angle Scattering Diffractometer at the Spallation Neutron Source are stored as neutron events. Each event has two parts: the time of fight of the detected neutron, and an identifier for the neutron's location on the detector. In addition, each event is associated with the source pulse from which the neutron is originated. Due to the high neutron beam intensity at the SNS, the size of the stored data files can easily exceed several Gigabytes. Processing and reducing these data can be very time consuming. The standard way of handling the data at the SNS has been to first transform the data into histograms. On the EQ-SANS, these histograms are further processed into intensity I vs. neutron momentum transfer Q files, I(Q). For a large data, the whole process can take many minutes to hours to complete, which severely limits the turnaround time of an experiment. Experimenters often want to reduce the data quickly and depending on the result, make decisions on the setup of next measurements. They may also need to reduce the data using different inputs and conditions. In this work, we present the implementation of a fast data processing scheme for the EQ-SANS diffractometer. The scattering data are processed directly from neutron event files into I(Q) or other desired formats, greatly reducing the needed computation time. Further speed gains are obtained by the implementation of parallel computing under the Message Passing Interface framework. Most data on the EQ-SANS can now be handled in mere seconds. The process is highly automated. All relevant experimental information that is stored in instrument configuration files are read and processed. In addition, the new implementation is highly flexible. All the data corrections and handlings can be controlled transparently through user inputs. Finally, software implementation enables the slicing of neutron event, which can be used for the study of time dependent phenomena.

Data Processing↗