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DOE OSTI2024

Commercial Building Prototypes Based on ANSI/ASHRAE/IES Standard 90.1-2019 Appendix G PRM: Technical Support Document

The two paths for documenting compliance with ANSI/ASHRAE/IES Standard 90.1 are the prescriptive path and the performance path. Beyond code programs and rating systems (for example, USGBC-LEED ) are primarily known to use a third path – the Appendix G Performance Rating Method. An update in the 2016 edition of Standard 90.1 approved the Appendix G Performance Rating System for code compliance, extending its application and allowing for greater consistency of modeling rules for code and beyond code building energy modeling. The Appendix G PRM provides rules for the development of whole building energy models of baseline and proposed models for calculating the “performance cost index target” value using the simulated energy results of the baseline and proposed models and the building performance factors published in Table 4.2.1.1 of the Standard. This report documents (1) the methodology used for development of the baseline and proposed energy models of the Pacific Northwest National Laboratory and U.S. Department of Energy commercial building prototypes using the Appendix G Performance Rating Method; and (2) the building performance factors that were calculated using those models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Towards Using Eshelby Calculations to Enhance Kinetic Model for Zirconium Hydride Precipitation

A C++ library (called Eshelby ) was implemented in fiscal year 2015 based upon the formulas documented in this report. The library implements a generalized version of Eshelby's inclusion problem. The library was written as a set of functions which can be called from another program; the principle intended use cases are kinetic models of precipitate formation in zirconium claddings where use of the Eshelby library provides needed elastic energy density calculations, as well as calculations of stress and strain in and around precipitates; it is intended that the library will be made open source. For isotropic inclusions in the form of oblate and prolate ellipsoids, the Eshelby library can be used for nearly any relevant/appropriate shape parameters to calculate strains, stresses and energy density at interior and exterior points. The Eshelby library uses a combination of analytical formulas and numerical routines making it very extensible. For example, the library can can easily be extended to include inclusions such as spheres since analytical expressions exist for the required elliptic integrals; similarly, general ellipsoids do not have analytical results for the required elliptic integrals but those integrals can be numerically evaluated and thus fit naturally into the Eshelby library. This report documents all formulas implemented in the Eshelby library and presents some demonstration calculations relevant to the intended application.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Gaussian Process Optimization of Sensitivity-Based Similarity Metrics between New Nuclear Applications and New/Existing Benchmarks

Confidently designing safe, new nuclear criticality experiments requires expert judgement, which could take years of experience. Sensitivity/uncertainty (S/U) analysis can be utilized by less experienced individuals to conservatively estimate uncertainties in important parameters, such as k eff , in newly proposed nuclear applications. This type of analysis relies on matching new nuclear applications with existing benchmark experiments. The Whisper-1.1 software package included in MCNP6.2 ®1 contains more than 1,100 International Criticality Safety Benchmark Evaluation Project (ICSBEP) benchmarks. These benchmarks however rarely match new nuclear applications. The number of benchmarks available to match a given set of materials or geometric configurations varies significantly. Furthermore, recently performed benchmark experiments may not have had enough time to be properly documented and published. Benchmarks are vital for determining the accuracy of nuclear data and can assist nuclear physics and evaluators in improving nuclear data libraries. Exhaustively exploring the parameter space using simulations with software such as MCNP is too computationally expensive. In this work, Gaussian process optimization was implemented to reduce the number of simulations needed for optimization over multiple parameters. This optimization scheme was designed to selectively generate new benchmarks with high sensitivity-based similarity metrics to user-defined nuclear applications. Two benchmark models of spherically nested shells containing plutonium, uranium, tantalum, and water were used in the optimization to match an application containing plutonium plates, stacked in a tantalum reflector, surrounded by water.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2015

Trinity Benchmarks on the Intel Xeon Phi (Knights Corner)

This report documents the early experiences with porting and performance analysis of the Tri-Lab Trinity benchmark applications on Intel Xeon Phi (Knights Corner) (KNC) processor. KNC, the second generation of the Intel Many Integrated Core (MIC) architectures, uses a large number of small P54C-x86 cores with wide vector units and is deployed as PCI bus attached process accelerators. Sandia has experimental test beds of small InifiniBand clusters and workstations to investigate the performance of the MIC architecture. On these experimental test beds the programming models that may be investigated are "offload", "symmetric" and "native". Among these program usage models our primary interest is in the so called "native" mode, because the planned Trinity system to be deployed in 2016 using the next generation MIC processor architecture called Knights Landing would be self-hosted. Trinity / NERSC-8 benchmark programs cover a variety of scientific disciplines and they were used to guide the procurement of these systems. Architectures such as the Intel MIC are well suited to study evolving processor architectures and a usage model commonly referred to as MPI + X that facilitates migration of our applications to use both coarse grain and fine grain parallelism. Our focus with the applications selected is on the efficacy of algorithms in these applications to take advantage of features like: large number of cores, wide vector units, higher-bandwidth and deeper memory sub-system. This is a first step towards understanding applications, algorithms and programming environments for Trinity and future exascale computing systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

6th Annual Midwest Conference for Undergraduate Women in Physics, January 18-20, 2013, Urbana, Illinois

This document is the program for the 6th Annual Midwest Conference for Undergraduate Women in Physics, which was held at the University of Illinois at Urbana-Champaign on January 18-20, 2013. The goals of the conference were to foster a culture in which undergraduate women are encouraged and supported to pursue, and also to succeed in, higher education in physics; to provide career information to students in physics and related fields; to give women the resources, motivation, and confidence to apply to graduate school and successfully complete a Ph.D. program in Physics; to provide information and dispel misconceptions about the application process for graduate school and the diverse employment opportunities in physics and related fields, enabling women to make more informed decisions about their goals and attain them; and to connect female physics students with successful female physicists to whom they can relate and who can act as inspirational role models and mentors.

36 MATERIALS SCIENCE↗

DOE OSTI2015

A Multi-Year Plan for Enhancing Turbulence Modeling in Hydra-TH Revised and Updated Version 2.0

The purpose of this report is to document a multi-year plan for enhancing turbulence modeling in Hydra-TH for the Consortium for Advanced Simulation of Light Water Reactors (CASL) program. Hydra-TH is being developed to the meet the high- fidelity, high-Reynolds number CFD based thermal hydraulic simulation needs of the program. This work is being conducted within the thermal hydraulics methods (THM) focus area. This report is an extension of THM CASL milestone L3:THM.CFD.P10.02 [33] (March, 2015) and picks up where it left off. It will also serve to meet the requirements of CASL THM level three milestone, L3:THM.CFD.P11.04, scheduled for completion September 30, 2015. The objectives of this plan will be met by: maturation of recently added turbulence models, strategic design/development of new models and systematic and rigorous testing of existing and new models and model extensions. While multi-phase turbulent flow simulations are important to the program, only single-phase modeling will be considered in this report. Large Eddy Simulation (LES) is also an important modeling methodology. However, at least in the first year, the focus is on steady-state Reynolds Averaged Navier-Stokes (RANS) turbulence modeling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

RAVEN User Manual, Revision 4

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Development of a utility computer-based training program on setpoint methodology for nuclear power plants - 129

The purpose of this paper is to provide an overview of the development of the Computer- Based Training (CBT) Program for utility engineers on Setpoint Methodology for nuclear power plants. This computer based training module was prepared by the Advanced Engineering Training initiative. The Advanced Engineering Training initiative is a nuclear industry effort to develop technical computer-based training modules for targeted areas of engineering expertise. The target audience is engineers at commercial nuclear power facilities in the U. S. These modules are being developed using the systematic approach to training and are being designed to maintain industry engineering expertise and to facilitate engineering knowledge transfer. The objective of this training effort is to support the development of the next generation of site technical experts for specific topic areas. The training is one part of three key components that would facilitate knowledge transfer to the future site Subject Matter Expert (SME). The following are the major focus areas covered in the Setpoint Methodology CBT: Chapter 1 provides an overview of the role instrument setpoints perform in the defense-in-depth safety analysis for the plant as well introducing key definitions related to setpoints. Chapter 2 provides the history and current regulatory requirements related to setpoint control including the laws and guidance provided by the U.S. NRC. Chapter 3 provides describes the setpoint methodology including terms, formulae, methods of combination, graded approach and methods for drift analysis. This is a major part of the training module in covering the incremental uncertainty terms that need to be considered and how they are combined. Chapter 4 provides and overview of the role of setpoints in Technical Specifications and associated documents. Chapter 5 describes how the regulatory process, related to setpoint control, has been implemented in NRC inspections and how the major elements of the 10 CFR 50.59 reviews apply to the performance of a new or revised setpoint calculation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Additive Manufacturing of Fuel Injectors

Additive manufacturing (AM), also known as 3D-printing, has been shifting from a novelty prototyping paradigm to a legitimate manufacturing tool capable of creating components for highly complex engineered products. An emerging AM technology for producing metal parts is the laser powder bed fusion (L-PBF) process; however, industry manufacturing specifications and component design practices for L-PBF have not yet been established. Solar Turbines Incorporated (Solar), an industrial gas turbine manufacturer, has been evaluating AM technology for development and production applications with the desire to enable accelerated product development cycle times, overall turbine efficiency improvements, and supply chain flexibility relative to conventional manufacturing processes (casting, brazing, welding). Accordingly, Solar teamed with EWI on a joint two-and-a-half-year project with the goal of developing a production L-PBF AM process capable of consistently producing high-nickel alloy material suitable for high temperature gas turbine engine fuel injector components. The project plan tasks were designed to understand the interaction of the process variables and their combined impact on the resultant AM material quality. The composition of the high-nickel alloy powders selected for this program met the conventional cast Hastelloy X compositional limits and were commercially available in different particle size distributions (PSD) from two suppliers. Solar produced all the test articles and both EWI and Solar shared responsibility for analyzing them. The effects of powder metal input stock, laser parameters, heat treatments, and post-finishing methods were evaluated. This process knowledge was then used to generate tensile, fatigue, and creep material properties data curves suitable for component design activities. The key process controls for ensuring consistent material properties were documented in AM powder and process specifications. The basic components of the project were: • Powder metal input stock: Powder characterization, dimensional accuracy, metallurgical characterization, and mechanical properties evaluation. • Process parameters: Laser parameter effects, post-printing heat-treatment development, mechanical properties evaluation, and post-finishing technique. • Material design curves: Room and elevated temperature tensiles, low cycle fatigue, and creep rupture properties curves generated. • AM specifications: Key metal powder characteristics, laser parameters, and heat-treatment controls identified.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Review of Graph Databases for Big Data Dynamic Entity Scoring

Modeling data as a graph enables users to quickly analyze networked phenomenon, such as social network-based marketing data (e.g., linking entities in social media based on their friends and their "likes", their friend-of-a-friend's "likes", etc.) and scientific data, such as in biology to assess a gene's proximity in a graph to particular disease phenotype. Numerous purpose-built graph databases (DBs) called “NoSQL” or “Not Only SQL” DBs are emerging to support this type of graph analysis. These DBs differ from traditional relational DBs in that they are optimized to improve storage and query performance across large, complex graphs. This document reviews the state of the art in graph DBs and identifies potential DBs that show promise for dynamic entity-scoring algorithms. Dynamic entity scoring is a new area of research that allows analysis of data within graphs using undirected methods to weigh the edges of the graph based on the proximity or ambient influence of near-by nodes. The EntityScore (eScore) methodology developed at Lawrence Livermore National Laboratory (LLNL) is one application of these emerging graph-analysis algorithms to help analysts identify and prioritize entities, based on complex, analyst-defined weighting criteria. Our review examines a wide range of different, well-known graph DBs in use since at least 2010 and identifies unique advantages and disadvantages that may impact eScore-specific requirements. Specifically, we review data structures employed, query features employed, load times, query calculation times, memory usage and several other important attributes reported in the literature. We limited our considerations for eScore to Blueprints-compliant graph DBs (https://github.com/tinkerpop/blueprints/) because they provide a standard, java application programming interface (API) based on the property graph model.

42 ENGINEERING↗

DOE OSTI2024

Artificial Intelligence/Machine Learning Technology in Power System Applications

The primary purpose of this report is to provide an overview of the advancement in artificial intelligence and machine learning (AI/ML) technologies and their applications in power systems. It offers a foundation for understanding the transformative role of AI/ML in power systems and aims to stimulate further research and development in this area. This report begins with a historical perspective of AI/ML technologies, then explores their advancement to today’s prominence. The document highlights key contributors to the success of AI/ML technologies, including increased computational power, greater data availability, innovative algorithms, and advanced tools. It further introduces various AI/ML techniques, including supervised, unsupervised and reinforcement learning, graph neural networks, and generative AI. It also emphasizes the critical importance of ensuring the safety, security, and trustworthiness of these AI/ML techniques within this sector. The report reviews the recent representative advancements in various power system applications enhanced by AI/ML techniques, underscoring key developments and their transformative impact as evidenced by numerous studies. It also explores both the opportunities and challenges associated with the application of AI/ML technologies to improve power system applications. While the report extensively covers AI/ML applications in power systems, focusing primarily on the technical and operational aspects, it may not thoroughly explore the sociopolitical, economic, and broader regulatory implications of AI/ML integration in power systems. AI/ML techniques hold significant potential for enhancing power system applications; however, they are not omnipotent. It is crucial to acknowledge their limitations and understand that they may not be able to address all challenges in the power system domain. Various factors must be considered that influence the implementation, adoption, and effectiveness of AI/ML solutions, including but not limited to safety, security, transparency, and trustworthiness. Additionally, the incorporation of advanced human–machine interfaces is essential, as it enables humans to validate the effectiveness of AI/ML solutions while remaining actively engaged, fostering trust in AI/ML deployment. Finally, the report summarizes AI/ML research activities supported by the Department of Energy (DOE) Office of Electricity (OE) through the Advanced Grid Modeling (AGM) program. The work aligns with the interests and mission of DOE-OE AGM, with the report serving as a resource for identifying existing progress and for pinpointing future applications within AI/ML that need further exploration and support.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activaties are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Compare $\overline{ν}$ parameterizations for 235 U(n,f) with fitted fission barriers

In this document, we compare observables computed with CGMF to some available data for 235 U(n,f), to investigate the multi-chance fission probabilities (fission barriers from CoH were refit) and two TKE parametrization. The four parametrizations include i) default CGMF multi-chance fission probabilities with bends in TKE for all fission chances, ii) default CGMF multi-chance fission probabilities with a TKE bend in only first-chance fission iii) refitted multi-chance fission probabilities with bends in TKE for all fission chances, and iv) refitted multi-chance fission probabilities with a bend in TKE for only first-chance fission. In all figures where the four parametrizations are on the same plot, the ones with bends in TKE for all chance fission are in black, with only first-chance bend in red, the default multi-chance fission probabilities are dashed and the new multi-chance fission probabilities are the solid lines.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

Experiments and Computational Theory for Electrical Breakdown in Critical Components: THz Imaging of Electronic Plasmas.

This report describes the development of ultra-short pulse laser (USPL) induced terahertz (THz) radiation to image electronic plasmas during electrical breakdown. The technique uses three pulses from two USPLs to (1) trigger the breakdown, (2) create a 2 picosecond (ps, 10 -12 s), THz pulse to illuminate the breakdown, and (3) record the THz image of the breakdown. During this three year internal research program, sub-picosecond jitter timing for the lasers, THz generation, high bandwidth (BW) diagnostics, and THz image acquisition was demonstrated. High intensity THz radiation was optically-induced in a pulse-charged gallium arsenide photoconductive switch. The radiation was collected, transported, concentrated, and co-propagated through an electro-optic crystal with an 800 nm USPL pulse whose polarization was rotated due to the spatially varying electric field of the THz image. The polarization modulated USPL pulse was then passed through a polarizer and the resulting spatially varying intensity was detected in a high resolution digital camera. Single shot images had a signal to noise of %7E3:1. Signal to noise was improved to %7E30:1 with several experimental techniques and by averaging the THz images from %7E4000 laser pulses internally and externally with the camera and the acquisition system (40 pulses per readout). THz shadows of metallic films and objects were also recorded with this system to demonstrate free-carrier absorption of the THz radiation and improve image contrast and resolution. These 2 ps THz pulses were created and resolved with 100 femtosecond (fs, 10 -15 s) long USPL pulses. Thus this technology has the capability to time-resolve extremely fast repetitive or single shot phenomena, such as those that occur during the initiation of electrical breakdown. The goal of imaging electrical breakdown was not reached during this three year project. However, plans to achieve this goal as part of a follow-on project are described in this document. Further modifications to improve the THz image contrast and resolution are proposed, and after they are made, images of photo-induced carriers in gallium arsenide and silicon will be acquired to evaluate image sensitivity versus carrier density. Finally electrical breakdown will be induced with the first USPL pulse, illuminated with THz radiation produced with the second USPL pulse and recorded with the third USPL pulse.

36 MATERIALS SCIENCE↗

DOE OSTI2017

The MARS Code System User’s Guide Version 15(2016)

This paper is a user’s guide to the current version of the MARS15 Monte Carlo code. MARS allows inclusive and, in many cases, exclusive simulations of three-dimensional hadronic and electromagnetic cascades and modeling of heavy ion, muon and low energy neutron-photon transport in accelerator, detector, spacecraft and shielding components. Particles can have energies from a fraction of an electronvolt up to about 100 TeV. The code has undergone substantial improvements since the last documented version MARS13(95) and since previous releases of MARS15; all these as well as other specific features of the MARS code system are explained in detail. Descriptions of general input and output with commentary and recommendations are given. Examples are given for running the program with distributed sources, complex compounds, arbitrary geometries, and magnetic fields. A built-in graphical-user interface allows the visualization and detailed analysis of the geometry description. Enhanced options are described for the MARS code being coupled with: the DPMJET hadron event generator, the MCNP code for low-energy neutron transport, the ANSYS system for thermal and stress analysis, physics analysis and graphics packages, the MAD accelerator lattice description via a universal MAD-MARS beam line builder, the STRUCT program for tracking particles in accelerator lattices with beam loss recording, the DETRA code for nuclide decay and transmutation calculations, the EGS5 code for precise modeling of low-energy electromagnetic showers, and the ROOT system for description and visualization of arbitrary geometries. Use of the code in a multistage mode, coupled with event generators (DPMJET), with the STRUCT program for tracking particles in accelerator lattices with beam loss recording, and with physics analysis and graphics packages is demonstrated with typical input and output examples.

43 PARTICLE ACCELERATORS↗

DOE OSTI2017

RAVEN User Manual

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Nanosecond Gated CMOS Camera (NSGCC) ICD (Rev. 2.1)

The Ultra-Fast X-ray Imager (UXI) program is an ongoing effort at Sandia National Laboratories to create high speed, multi-frame, time-gated Read Out Integrated Circuits (ROICs), and a corresponding suite of photodetectors to image a wide variety of High Energy Density (HED) physics experiments on both Sandia’s Z-Machine and LLNL’s National Ignition Facility (NIF). Several cameras have been designed over the length of the program; one of the most recent is the Icarus, which is an improvement on past imagers (Furi and Hippogriff). A second sensor that can be connected is the Daedalus sensor. The Icarus is a 1024 × 512-pixel array with either 25 μm or 8 µm spatial resolution containing four frames of storage per pixel and has improved timing generation and distribution components while achieved 2 ns time gating. The Daedalus sensor is also a 1024 x 512-pixel array with 25 µm special resolution containing three frames of storage per pixel and has an increased set of features for a wider variety of applications from interlacing of rows in each frame to configurability of all shutters. See Section 14 for details regarding the Icarus implementation of the firmware and Section 15 for details regarding the Daedalus implementation. Due to the unique test environments UXI sensors are targeted for, full custom hardware was required to physically mount an Icarus or Daedalus sensor, manage its various functions, and read out pixel data for transfer to a host computer. Beyond experimental functionality, the hardware also needed to accommodate sensor characterization requirements. Lawrence Livermore National Laboratory’s ‘Version 4.0 Board’ was the result of these efforts. It mounts all the components required to fully utilize the Icarus and Daedalus sensors including analog to digital converters to convert pixel data and various system voltages to digital form for readout and analysis, DAC channels for remote configuration of critical bias voltages, static random-access memories to buffer pixel data, RS422 and Gigabit Ethernet communications for remote access, and an FPGA to tie these components together. This document describes the FPGA electrical interfaces in detail to allow the reader a greater understanding of the device, and to facilitate implementation of custom software to control and manage it. The Version 4.0 Board is a continuation of the Nano-second Gated CMOS hardware design that retains much of the functionality of the Version 1.0 Board while adding features including a DAC instead of digital potentiometers, as well as sensors for pressure and radiation. The Version 4.0 board is intended for applications requiring tight form-factor enclosures. It is composed of two stacking boards; one holds the FPGA and regulators to power the various components of the board while the other contains the mating connector to the sensor, image-readoff ADCs, the DAC, and other components.

42 ENGINEERING↗