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DOE OSTI2020

OpenACC unified programming environment for GPU and FPGA multi-hybrid acceleration

Attached accelerators have been frequently used in recent High Per- formance Computing (HPC) systems because of their high performance/power ratio. In particular, the Graphics Processing Unit (GPU) is the most popu- lar accelerator owing to its high peak FLOPS performance and high memory bandwidth supported by HBM2, etc. However, the performance of GPU depends highly on a large degree of SIMD parallelism and has difficulty sustaining a high performance on programs with frequent branch operations or a partially low degree of parallelism.By contrast, a Field Programmable Gate Array (FPGA) has received attention as a different type of accelerator than GPU as a fully reconfigurable processor fitting the target applications. The high performance of FPGA is mainly provided by a pipelined operation and optimized circuit suitable for any operation even with frequent conditional branches. We have been focusing on the flexibility of FPGA to compensate for the weakness of GPU. We believe that the coupling of GPU with FPGA can result in one of the most powerful accelerating platforms available.However, the program coding of GPU and FPGA coupling can be quite difficult for application users. Traditionally, CUDA by NVIDIA has been the most popular programming language with the largest share of GPUs used in HPC, whereas a hardware description language such as Verilog HDL has been used in FPGA programming. OpenCL coding has recently become available even on high-end FPGAs. Moreover, several recent studies have also enabled the OpenACC coding for use in FPGA. In this study, we provide a unified programming system based on OpenACC for a platform equipped with both GPU and FPGA aiming at the next-generation accelerated supercomputer framework. Our programming environment is called Multi-Hybrid OpenACC Translator (MHOAT), and in this paper, we describe the basic concept and prototype system of MHOAT based on an evaluation on the amount of coding required and the performance of a hybrid multi-device accelerated system.

Tsunashima, Ryuta↗

DOE OSTI2013

A dynamic, dependent type system for nuclear fuel cycle code generation

The nuclear fuel cycle may be interpreted as a network or graph, thus allowing methods from formal graph theory to be used. Nodes are often idealized as nuclear fuel cycle facilities (reactors, enrichment cascades, deep geologic repositories). With the advent of modern object-oriented programming languages - and fuel cycle simulators implemented in these languages - it is natural to define a class hierarchy of facility types. Bright is a quasi-static simulator, meaning that the number of material passes through a facility is tracked rather than natural time. Bright is implemented as a C++ library that models many canonical components such as reactors, storage facilities, and more. Cyclus is a discrete time simulator, meaning that natural time is tracked through out the simulation. Therefore a robust, dependent type system was developed to enable inter-operability between Bright and Cyclus. This system is capable of representing any fuel cycle facility. Types declared in this system can then be used to automatically generate code which binds a facility implementation to a simulator front end. Facility model wrappers may be used either internally to a fuel cycle simulator or as a mechanism for inter-operating multiple simulators. While such a tool has many potential use cases it has two main purposes: enabling easy performance of code-to-code comparisons and the verification and the validation of user input.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Communicating with future generations - Major obstacles to overcome - 15009

The Department of Energy (DOE) Carlsbad Field Office (CBFO) is responsible for managing activities related to the disposal of transuranic (TRU) and TRU-mixed waste in the geologic repository at the Waste Isolation Pilot Plant (WIPP), 2150 feet (655 meters) below the land surface, at the WIPP, near Carlsbad, New Mexico. The Environmental Protection Agency (EPA) regulations Title 40 Code of Federal Regulations Part 191.14(c) [1] and Title 40 Code of Federal Regulations Part 194.43 [2] require a Passive Institutional Controls program be developed for the WIPP. The primary purpose of the Passive Institutional Controls program is to indicate the location of the repository and the risk it presents to an intruder, thus reducing the likelihood of inadvertent human intrusion into the repository. A plan was put into place during the 1990's with records management and storage, awareness triggers, permanent marker design concepts, including languages, graphics and engineered drawings, plus testing schedules for materials and structures. This work included the thoughts of expert panels and individuals. The plan held up under peer review and met the regulatory requirements. But will it really work? Will future generations a thousand years, two thousand years, or seven thousand years from now really be able to decipher and understand the message? This is the challenge international constructors, operators, and regulators of repositories for the disposal of any hazardous materials must face. It is important to explore the history of language, both written words and graphics; and what we can learn from the past to assist us in communicating with the future. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Can we use Pictograms in a Long Term Communication System Related to a Radioactive Waste Repository? - 18301

Pictograms are often mentioned as a possible tool for sending messages about radioactive waste repositories in a far future, when the understand ability of our languages will have been lost. But they have also limits, related to the use of conventions that may fade over time. The goals of a signaling system for the long term may be (1) to alert (raise attention), (2) to inform of an underlying danger due to the existence of radioactive wastes, (3) to induce a favorable behavior (provoke or deter actions) in order not to disturb the performance of the repository. The quest for trans-cultural and trans-generational performance requires that some universal semiotic features be identified, shared by human beings of various cultures nowadays, and from far in the past to the present. Considering the typology of colors in various languages, the use of colors in ancient rock art and in children's drawings, a dedicated role may be assigned to each of the three colors, white, black and red/ocher, with a high potential for sustainable meaning: 1) red background = pay attention to this object because of the simple presence of the red color, that is to say the first color felt as color; 2) white inscription = absence of the object / action represented; 3) black inscription = presence of the object / action represented. The representation of a human body in movement inside a context is also understandable by people from various cultures. The combination of these 'semiotic primitives', used as relevant semantic features of the object and/or the experience to be represented, and 'topo-sensitive' criteria, used for pictogram production processes, would thus become a way to conceive a trans-cultural and trans-generational sign system for the signaling of radioactive danger. The choice of the stone could guarantee the durability of the support; the color could guarantee its visibility; and both could guarantee the transmission of the message. Combining these criteria could open the way to a long term communication system about radioactive waste repositories. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

The Legislative and Regulatory Histories Related to Greater-Than-Class-C Low-Level Radioactive Waste - 18602

The history surrounding the legislative and regulatory developments regarding Greater-Than-Class C Low Level Radioactive Waste (GTCC LLW) is both interesting and complex. On October 25, 1978, the U.S. Nuclear Regulatory Commission (NRC) published an Advanced Notice of Proposed Rulemaking pertaining to the development of regulations for the land disposal of LLW. This regulation was one of the first undertaken by NRC since its inception pursuant to the Energy Reorganization Act of 1974. Given the problematic challenges that many States were facing with releases of radioactivity from waste disposal facilities at the time, the technical bases supporting this rulemaking were both forward-thinking and comprehensive. The rule included a waste classification system that was tiered in four waste classes: Class A, B, C and GTCC LLW. On December 27, 1982, the NRC finalized the requirements for disposing of these classes of radioactive waste in Title 10 of the Code of Federal Regulations, Part 61 (10 CFR 61). The NRC established the waste classification system to protect both members of the public and individuals that may someday inadvertently intrude in the waste placed in the disposal facility after expiration of the institutional control period. This waste classification was developed to ensure that radiation doses to members of the public and an inadvertent intruder would not exceed 0.25 mSv y{sup -1} (25 mrem y{sup -1}) or 5.0 mSv y{sup -1} (500 mrem y{sup -1}), respectively. At the time the regulation was promulgated, GTCC LLW was considered generally not suitable for near surface disposal. It was recognized that disposal of GTCC LLW would require disposal technologies having greater confinement capacity or protection than 'normal' near-surface disposal. In 1980 Congress enacted the Low-Level Waste Policy Act of 1980, as amended in 1985 (LLWPAA of 1985) that assigned responsibility of disposing of GTCC LLW generated in the commercial sector or owned or generated by the federal government to the U.S. Department of Energy (DOE). At the time States were having difficulties in developing disposal facilities within the time constraints mandated in the LLWPA of 1980. Therefore, Congress believed that if DOE was assigned responsibility of developing disposal facilities for GTCC LLW, then the States would be more likely to develop new LLW disposal facilities. Congress also assigned the NRC with the responsibility to license the disposal of GTCC LLW generated or owned by the federal government or by the commercial sector. Lastly, Congress changed the definition of LLW that had been defined in the LLWPA of 1980 removing the 'transuranic' exclusionary language. Four years later, on May 25, 1989, the NRC promulgated its regulation needed to implement the mandates specified in the LLWPAA of 1985. The NRC revised 10 CFR 61.55 specifying that the NRC was responsible for licensing the disposal of GTCC LLW. However, it inadvertently did not revise the definition of LLW needed to remove the 'transuranic' exclusionary language in this rulemaking consistent with the LLWPAA of 1985. This seemingly inconsequential omission was not discovered until a Petition for Rulemaking was filed in Texas in July 2014. Moreover, this omission has effectively orphaned waste with certain transuranic radionuclides with concentrations exceeding 100 nanocuries per gram (100 nCi/g). In December 2015, the NRC Commissioners directed its staff to move forward with a rulemaking to resolve this issue and to perform a regulatory analysis to determine whether or not an Agreement State can license the disposal of GTCC LLW pursuant to Section 274 of the Atomic Energy Act of 1954. The paper will discuss past and current legislation, rulemaking, and regulatory developments pertaining to resolving the transuranic waste discrepancy and providing a pathway to disposition GTCC LLW. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Implementation of new PRODIAG algorithm and simulation-based acceptance test - 219

This paper presents the improvement of an automated-reasoning computer program for nuclear power plant diagnosis, namely PRODIAG. PRODIAG, first developed at the Argonne National Laboratory, uses confluence-based reasoning to perform fault diagnosis in engineering systems that can be described by the conservation laws for mass, energy, and momentum. Therefore, unlike other plant configuration-dependent rule-based diagnosis algorithms, the code is generally applicable to a wide variety of plant configurations by simply replacing the plant instrumentation diagram (PID) database. The need for improvement was raised when the original PRODIAG failed to provide the requisite degree of extensibility and maintainability for new applications. These shortcomings have been addressed through a new code architecture that uses an objected-oriented logic-programming language in place of the original Prolog language and uses an open-source automated reasoning engine in place of the original hardwired rule-evaluation sequence. This paper presents the architecture and implementation details of the code transition. Verification and validation studies were performed for the new PRODIAG, which involved re-running the distinct fault cases defined at the time the code was originally developed. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

Using Python For 3D Visualizations of MCNP output

MCNP is ubiquitously used within industries, universities and national laboratories as a tool for accurately modeling radiation. With so much focus on the details of transport, attention is seldom given to the visualizations of these meticulously constructed outputs. Python is a programing language designed to promote code readability and has steadily grown in popularity since the 1990's. Python is also a heavily crowd-sourced programing language which generally means that the problems that you are trying to solve with computer codes are already solved; The user is often left with the now-simplified task of cobbling together a good and useful compilation of these tools and adapting them to solve the problem at hand. I have taken advantage of this situation and constructed some visualization tools in python that can be used to duplicate some of the 2D radiation fields that MCNP produces and also produce a meaningful 3D representation of a radiation field. (author)

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2017

Models and Simulation Environment for the Design and Optimization of Hybrid Energy Systems

The energy landscape in the U.S. is changing with proposed federal regulations limiting carbon dioxide emissions and with government credits for 'clean' energy technologies. This shift in the market conditions, especially in deregulated markets, has already resulted in the closing of coal power plants. Previously these coal plants served to balance variation in load. The resulting loss of electricity production is being made up in part with increased renewable generation. This set of governmental policy changes potentially presents expanded opportunities for both renewable energy and nuclear energy to become significant energy sources in the future. Potential synergies exist that can reduce greenhouse gas (GHG) emissions and provide affordable, sustainable energy. Combining multiple energy production, conversion, storage, and consumption facilities can leverage these potential synergies. This research focuses on integrating nuclear power plants within systems with increased renewable penetration, referred to as a nuclear renewable hybrid energy system (NRHES). Industrial-scale nuclear renewable hybrid energy systems have the potential to provide grid services such as dispatchable generation, storage, ancillary services, and power regulation. The problem of designing, analyzing, and optimizing the NHRES is being undertaken by a multi-laboratory team. The effort requires the collaboration of experts across engineering disciplines with methods and results to be shared by these experts. To facilitate collaborations a common system modeling and analysis language is adopted along with open-source libraries and interface standards that support data exchange among the models. Modelica was selected as the common language based on its prior use by team members at each of Idaho National Laboratory, Oak Ridge National Laboratory, and Argonne National Laboratory.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2014

Evaluation of Low Power ARM Processors for Monte Carlo Particle Transport: MCNP6 on the ARM Cortex-A8 - Paper 83

In the push towards exascale computing, the limiting factors are power requirements and heat dissipation. Using current technology, the power required to drive an exascale supercomputer would near 400-MW. Clearly, future computing hardware will have to be more energy efficient in order to achieve exascale performance. Most of the current research in advanced computing architectures is in the use of standard CPUs with accelerators. However, these accelerators are not generally supported by the traditional scientific programming languages (C, C++, and Fortran) and the scientific software must be tailored specifically to take advantage of the accelerators. Large scale scientific software (hundreds-of-thousand to millions of lines of code) can not be easily ported to the computing platform du jour. The widespread use of low power ARM based processors in the mobile computing market offer a low power alternative to accelerators that are also supported by the traditional scientific programming languages and programming paradigms such as MPI and OpenMP. Computing architectures for scientific computing are often assessed using standard benchmarks such as LINPACK benchmark or using suites of mini-applications that are greatly simplified stand-ins for scientific software. Often the real applications that will deployed on the scientific supercomputers are not ported or tested until delivery of the machines. In order to evaluate the use of ARM processors for scientific computing the Monte Carlo particle transport code, MCNP version 6 was compiled on the ARM Cortex-A8 and a set of benchmark problems were used to assess performance in comparison to an Intel Xeon E5-2670 and the low power Intel Atom N270. MCNP is a continuous energy Monte Carlo particle transport code in development at Los Alamos National Laboratory since 1977. MCNP is used to transport neutrons, photon, electrons, and charged particles using continuous energy interaction data. It is commonly used in fields of nuclear power, nuclear medicine, radiation protection, and high-energy physics through-out the world. MCNP version 6 is approximately 450 thousand lines of code, written primary in Fortran90, with some C and C++. MCNP supports distributed multiprocessing using MPI and threading with OpenMP. The ARM Cortex-A8 executes MCNP6 using about twice as much power as the Intel Xeon E5-2670 and about the same amount of power as the Intel Atom N270. However, these results are encouraging for the use of low-power ARM chips in future supercomputers. The ARM Cortex-A8 is a 32-bit CPU and it's NEON general-purpose SIMD (single-instruction multiple data) engine does not support double-precision floating-point arithmetic. More recent ARM processors (ARM Cortex-A53 and Cortex-A57) support 64-bit addressing and have NEON co-processors that support double-precision floating-point arithmetic. ARM claims that the A50 series delivers a performance increase of 15 times the Cortex-A8 performance for similar power usage. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

AMIA KDDM Working Group Collaborative Workshop: Enriching Electronic Health Records with Social Determinants of Health to Improve Outcomes and Health Equity

Prior research has demonstrated that social determinants of health (SDoH) are major drivers of health outcomes and contributors to widespread health inequities. It was estimated that, in the United States, SDoH could be responsible for up to 40% of all preventable deaths, significantly higher than the 10-15% for which better medical care is responsible. Public health interventions that target SDoH are instrumental for improving health outcomes and reducing long-standing health inequities. Currently, most mainstream EHR vendors have implemented SDoH screeners in their EHR systems. However, the utility of the screeners is low, rendering patient-level SDoH still widely unavailable in the structured fields. SDoH are sometimes mentioned in free-text clinical notes (e.g., social context section) where natural language processing (NLP) can be applied to extract relevant information. Contextual-level SDoH can be identified from multiple data sources, many of which are publicly available and spatiotemporally linked to EHR data. As such, there is an opportunity for the KDDM research community to create innovative solutions to draw meaningful insights by creating and using rich data with SDoH to improve health outcomes while reducing disparities. In this workshop organized by AMIA Knowledge Discovery and Data Mining Working Group (AMIA KDDM WG), we will invite world-leading experts from academia, national laboratories, and life science industry with varied backgrounds in biomedical informatics, epidemiology, data science, machine learning, natural language processing, and pediatric cardiology to discuss the best practice of capturing, standardizing, and using SDoH information in various applications aiming at improving outcomes and health equity.

He, Zhe↗

DOE OSTI2023

A Real-Time ANPC Inverter Digital Twin with Integrated Design-For-Trust

The demand for renewable energy has increased over the last few years, and so has the demand for greater expectations within the energy market. This increasing trend has been accompanied by more significant usage of internet-connected devices (IoT), leading to critical electrical infrastructure being connected to the internet. Implementing internet connectivity with such devices and systems provides benefits such as improving the system's performance, facilitating irregularity and anomaly mitigation, and providing additional situational awareness for enhanced decision-making. However, enhancing the connected system with IoT introduces a drawback – a greater vulnerability to cyber-attacks. Cyber-attacks targeting critical infrastructure in the electrical sector have occurred in the United States and Ukraine. These cyber-attacks highlight and expose vulnerabilities that a system inherits when connecting to the internet. These attacks left thousands of customers without electricity for hours until operators could regain control of the electric utility grid. Therefore, to address the vulnerabilities of an internet-connected power electronic device, this work focused on the hardware layer of the system. Implementing a cyber-control system inside the hardware layer can significantly reduce the possibility of an attacker patching malicious controller firmware into a photovoltaic grid-connected inverter, thus mitigating the likelihood that the inverter becomes inactive a cyber-attack scenario. With this mitigation technique, if a cyberattack is successful and an attacker gains control of the network, a cyber-defense technique is in place to mitigate the impact of the cyber-attack. This additional protection layer was developed based on an innovative concept known as Digital Twin (DT). A DT, in this case, replicates an Active-Neutral Point Clamped (ANPC) inverter and was designed using a hardware language known as VHDL (Very High-Speed Integrated Circuit Hardware Description Language) and applied to Field-Programmable-GateArray (FPGA). The DT is embedded within the FPGA and contained in a controller board, the UCB (Unified Controller Board), developed by the University of Arkansas electrical engineering team. This UCB also contains two Digital Signal Processors (DSPs) responsible for generating associated signals to control an authentic physical inverter. These DSP signals are received and processed by the FPGA that implements the DT of an ANPC; in other words, it simulates in realtime the expected output of an actual ANPC inverter using the signals from the DSP. When a new firmware is ready to be patched, the DT provides output signals simulating behavior that a real ANPC inverter would generate with the new firmware. The new firmware is tested to check if it meets all the operational requirements established using a Design-For-Trust technique (DFTr). If the new firmware fails in at least one of the DFT tests, it is considered malicious and must be rejected. This work is divided into sections, such as Background, which explains the pieces that were used and the strategy behind this work; Process and Procedure, which explains the methodology that was adopted to prove the reliability and effectiveness of this work; Results and Discussion, where the simulations and results are described and explained; followed by Conclusion and Future work section, which concludes this work and adds possible future projects to continue this work further.

do Amaral Custodio, Paulo Vitor↗

DOE OSTI2024

Mu2e experiment: Simulation of the Production Target

The Mu2e experiment will search for coherent, neutrinoless conversion of muons to elections in an aluminum stopping target. A simulation program is used to experiment and design the production target of the Mu2e experiment. The module uses programing languages and libraries to work. The program called the Offline uses C++ as the programing language in the module. To construct the simulation, the Geant4 library constructs shapes and uses them in the simulation program, ROOT. The offline program simplifies the creation of the tube by organizing them into parameter objects and nesting them with other variables required to construct the tube-like center point, material, or if it is allowed to conflict with other solids. The parameter varies for every solid tube. The geometry file uploads the parameters by declaring and organizing the parameters in C++ using the configuration tools. The author describes the development and the future of this project.

Szewczyk, Albert↗

DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗

DOE OSTI2026

Digitizing Today’s Buildings in the Real World: Lessons from Field Demonstrations

Digital twins, created by generating a virtual replica of a building, enable safe evaluation of operational scenarios and applications like fault detection and diagnosis and advanced controls. However, a prerequisite is the creation of a machine-readable digital representation of a building, currently hindered by fragmented information scattered across mechanical drawings, point lists, and natural language sequences. As a result, digital twin development remains labor-intensive, error-prone, and difficult to validate. To address these challenges, two efforts from ASHRAE aim to support the digitalization of buildings. ASHRAE s223 establishes a semantic model of buildings, representing system components, configuration, and data sources. ASHRAE s231 defines a vendor-neutral programming language for expressing their control logic. As the industry evaluates implementing them in their products, understanding the challenges that vendors and implementers may face is crucial. In this paper, we present findings and lessons learned from field demonstrations in five buildings that implemented control applications using ASHRAE s223 and s231. The demonstrations highlight how semantic modeling and formalized control descriptions can significantly reduce software development time, manual point mapping, and hard-coding. Beyond time efficiency, they enable reliable automation by minimizing human interpretation and providing a means for consistency across projects. We describe the processes and best practices for model creation and model usage, from translating heterogeneous building documentation into semantic representations to implementing control logic in real-world systems. Finally, we discuss the challenges that persist, including integration with legacy software environments, gaps in interoperability, and the level of expertise still required to effectively leverage semantic models.

Prakash, Anand Krishnan↗

DOE OSTI2026

An Autonomous MCP Bridge to Rucio: Enhancing Data Management Accessibility for High Energy Physics

The Rucio Data Management System [1] is an important tool used by High Energy Physics experiments, including those at Fermi National Accelerator Laboratory, to store and manage exabyte-scale scientific datasets. Despite its central role in coordinating data across globally distributed storage sites, Rucio's command line interface (CLI) presents a steep learning curve, and makes it difficult for scientists to navigate through. To solve this issue, a containerized Model Context Protocol (MCP) [2] server was built that connects Large Language Models directly to Rucio, allowing AI agents to handle data tasks by using simple, natural language rather than memorized terminal commands. The core engineering focus of this project was moving the server away from slow terminal commands that require text parsing and replacing them with a native Python Client API toolset and a planned REST API framework. Moving to the Python API handles data operations directly in memory, which helps clear up formatting errors, provides the AI with clean, structured JSON data and speeds up tool execution. To prove that the system actually works, a benchmarking pipeline was also built with various questions to test the AI across four different model configurations. The questions included finding data scopes, tracking down specific datasets, and checking replication rules. Through benchmarking, early runs showed that with raw terminal text, the model would get confused and stuck, whereas switching to the Python API to feed the AI clean, structured data yielded massive improvement. By creating an intelligent and autonomous bridge to a storage network, this project shows how AI can be implemented in scientific data management, which ultimately helps scientists at Fermilab spend less time sorting through data and more time focusing on their experiments and analysis.

Akella, Kashyap [William Rainey Harper Coll.]↗

DOE OSTI2010

Reformulation RELAP5-3D in FORTRAN 95 and Results

RELAP5-3D is a nuclear power plant code used worldwide for safety analysis, design, and operator training. In keeping with ongoing developments in the computing industry, we have re-architected the code in the FORTRAN 95 language, the current, fully-available, FORTRAN language. These changes include a complete reworking of the database and conversion of the source code to take advantage of new constructs. The improvements and impacts to the code are manifold. It is a completely machine-independent code that produces machine independent fluid property and plot files and expands to the exact size needed to accommodate the user’s input. Runtime is generally better for larger input models. Other impacts of code conversion are improved code readability, reduced maintenance and development time, increased adaptability to new computing platforms, and increased code longevity. The conversion methodology, code improvements and testing upgrades are presented in a manner that will be useful to future conversion projects for other such large codes. Comparison between the pre- and post-conversion code are made on the basis of code metrics and code performance.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

CiteSoft_Py

There is a need to provide a way for dev-users (Scientists, Engineers, and other Software Developers) to get credit (citations) when they contribute to an important software package - particularly a large package that is already established. Various solutions (including cross package) solutions do exist, but a simple standard format that can work between multiple packages and multiple languages in a modular way is not widely available. CiteSoft_py is a python implementation of CiteSoft. CiteSoft is a plain text standard consisting of a format and a protocol that exports the citations for the end-users for whichever softwares they have used. CiteSoft has been designed so that software dev-users can rely upon it regardless of coding language or platform, and even for cases where multiple codes are working in a coupled manner. The CiteSoft_py implementation includes python decorators to make wrappers for facile use. The purpose is that when dev-users (scientists, engineers and professional programmers) contribute to a collaborative software project, that the appropriate citations (e.g., journal article, conference proceeding) are provided to the end-user. The end-user does not need to know how to use a command line interface, API, etc.

Savara, Aditya↗

DOE OSTI2025

NEPATEC2.0: NEPA Text Corpus v2.0

The National Environmental Policy Act of 1969, as amended (NEPA), is a major environmental law in the United States, requiring Federal agencies to consider and document potential environmental impacts before deciding on a proposed action. Modernization of NEPA and permitting processes faces significant challenges due to the lack of standardized formats and interoperable systems for organizing and sharing NEPA-related information across agencies. Much of the information gathered during NEPA reviews is written into documents such as categorical exclusions, environmental assessments, and environmental impact statements, then filed in predominately independent agency file stores that may or may not be publicly accessible. The application of metadata and data standards, such as those recommended by the Council on Environmental Quality (CEQ), to NEPA documents offers a shared vocabulary and structure for key entities like projects, processes, and documents that can streamline information exchange and enhance collaboration across systems. In this work, we publicly release NEPATEC2.0, an expanded corpus of NEPA documents with associated metadata. NEPATEC2.0 encompasses approximately 120,000 documents from 60,000 projects prepared by more than 60 different agencies. Modeled to align with CEQ metadata standards, NEPATEC2.0 promotes consistency in environmental reviews and supports the ongoing effort to modernize permitting technologies by facilitating more transparent, efficient, and data-driven decision-making. Importantly, NEPATEC2.0 demonstrates the possibilities and limitations of large language model-based prompting to extract information from NEPA documents at scale.

environmental review↗