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DOE OSTI1968

Kryptonate Hydrogen Detection System (Final Report)

A prototype model of a multipurpose instrument which demonstrates the applicability of the Kryptonate® technique for detecting hydrogen gas was developed. This report describes the construction of the instrument and presents a laboratory evaluation of its performance. Performance was excellent. The instrument was designed to detect hydrogen in air at concentrations of 0-3% by volume, and to detect hydrogen in nitrogen or other inert gas at concentrations of 0-10% by volume. Other detection levels (both higher and lower) could be set if required. Ultimate response times can be made of the order of 0.5 seconds. The instrument is an all solid state device operating from 120V 60 Hz power source. The instrument has been designed to enable modifications to be made with a minimum amount of difficulty.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Milestone 49 Report: Batched Sparse LA Phase 5 Implementation

Batched sparse linear algebra operations in general, and solvers in particular, have become the major algorithmic development activity and foremost performance engineering effort in the numerical software libraries work on modern hardware with accelerators such as GPUs. Many applications, ECP and non-ECP alike, require simultaneous solutions of many small linear systems of equations that are structurally sparse in one form or another. In order to move towards high hardware utilization levels, it is important to provide these applications with appropriate interface designs to be both functionally efficient and performance portable and give full access to the appropriate batched sparse solvers running on modern hardware accelerators prevalent across DOE supercomputing sites since the inception of ECP. To this end, we present here a summary of recent advances on the interface designs in use by HPC software libraries supporting batched sparse linear algebra and the development of sparse batched kernel codes for solvers and preconditioners. We also address the potential interoperability opportunities to keep the corresponding software portable between the major hardware accelerators from AMD, Intel, and NVIDIA, while maintaining the appropriate disclosure levels conforming to the active NDA agreements. The presented interface specifications include a mix of batched band, sparse iterative, and sparse direct solvers with their accompanying functionality that is already required by the application codes or we anticipated to be needed in the near future. This report summarizes progress in Kokkos Kernels and the xSDK libraries MAGMA, Ginkgo, hypre, PETSc, and SuperLU.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Fully Distributed Acoustic and Magnetic Field Monitoring Via a Single Fiber Line for Optimized Production of Unconventional Resource Plays

This is the final technical report for the Virginia Tech Center for Photonics Technology (VT-CPT) research project entitled “Fully Distributed Acoustic and Magnetic Field Monitoring Via a Single Fiber Line for Optimized Production of Unconventional Resource Plays”. In the the four-year effort, VT-CPT collaborated with the Departments of Mathmatics at VT and Sentek Instrument, LLC (Sentek) to develop a fiber-optic sensing system capable of real-time simultaneous distributed measurement of multiple subsurface, drilling, and production parameters. The ultra-sensitive fiber optic distributed acoustic sensing technology, picoDAS, developed by Sentek was integrated with a novel multi-material optical sensing fiber fabricated by CPT-VT to obtain distributed acoustic and magnetic field measurements with ultrahigh sensitivity and high spatial resolution. The sensing technology successfully developed and demonstrated under this research program is truly unique has application beyond subsurface imaging to include carbon storage and electrical grid monitoring, as well as healthcare and nuclear fusion. The project, sponsored by the Advanced Technology Program, began in 2019 and had the original goal of developing the next generation of harsh environment sensing systems. Exhaustive theoretical modeling was performed to optimize the multi-material optical fiber design. VT developed the processing techniques employed to successfully fabricate single mode fibers with metal cladding wires. Sensing fibers with nickel and Metglas cladding wires were fully characterized, inscribed with sensors, and fully integrated with the picoDAS system. Laboratory scale testing was performed to demonstrate performance upon exposure to lateral and transverse magnetic fields. Preliminary field trials were performed for prototype magnetic and acoustic sensing systems upon near surface deployment at VT. The distributed acoustic and magnetic field sensing system was advanced from a (Technology Readiness Level) TRL=2 to a TRL=5 via laboratory scale system validation in relevant environments. The truly one-of-its kind distributed magnetic and acoustic sensing system is expected to find immediate applications that require subsurface monitoring, to include carbon storage site monitoring, and has the potential to be a distributive technology in healthcare and electric grid markets. The technologies developed by Virginia Tech’s Center for Photonics Technology will support the mission of the National Energy Technology Laboratory (NETL) to drive innovation and deliver solutions for an environmentally sustainable and prosperous energy future by ensuring affordable, abundant, and reliable energy that drives a robust economy and national security. Technical accomplishments during the program are summarized briefly here and described in detail in the remainder of the report.

02 PETROLEUM↗

DOE OSTI2024

Integrating Human Factors in Dynamic Rideshare Assignment: Willingness-To-Pay for Delay

Dynamic ride-sharing (DRS) has been projected to be a key solution to lowering system-wide congestion. Despite recent developmental progress, demand studies for DRS suggest low levels of willingness for travelers to use such services. The disconnect between DRS system designs and user preferences limits the application impacts of DRS in the real world. Therefore, this paper aims to design a new DRS trip/vehicle assignment strategy by considering the user preferences of choices under different levels of service. In this study, an agent-based simulation approach is used to model a fleet of shared vehicles that allows DRS. An optimization model is developed to match riders to vehicles while accounting for traveler delay and delay acceptance. Travelers are also dynamically issued predictive discounts, catered to their expected willingness to pay, to incentivize them to accept longer trip delays. Results show that the proposed approach can improve system efficiency by increasing average vehicle occupancy by up to 1.0 persons/trip and DRS acceptance by up to 38.9% depending on fleet size. Additionally, congestion is eased through the decrease of empty vehicle miles traveled by up to 7.1%.

Paul, Joseph↗

DOE OSTI2023

Technical Challenges and Gaps in Integration of Advanced Sensors, Instrumentation, and Communication Technologies with Digital Twins for Nuclear Application

This paper explores integrating advanced sensor, instrumentation, and communication technologies with digital twin technologies for nuclear energy application. Digital twins and digital-twin-enabling technologies are expected to integrate with future nuclear reactor designs and have the potential to impact currently operating nuclear power plants. Greater digital integration, advanced instrumentation and control systems, and advanced operations and maintenance practices are all associated with digital-twin-enabling technologies. Advanced sensors, instrumentation, and communication technology are expected to comprise important elements of the infrastructure required to develop and operate a nuclear digital twin system. This paper identifies and discusses challenges and gaps in developing and implementing advanced sensors and instrumentation and communication technology to be integrated with a digital twin in current and advanced reactor applications. It is important to address some challenge and gap to enable a successful near-term deploying advanced sensors, instrumentation, and communication technologies integrated with digital twins.

Yadav, Vaibhav↗

DOE OSTI2025

Fermilab's controls development with virtual accelerator

Control Systems development is often the last thing considered when designing and building new equipment, e.g. a new detector or superconducting RF LINAC; however when the new equipment is installed, it is the first thing desired to be operational for testing. Due to frequent delays in building new equipment and project deadlines, control system development and testing is often curtailed. A way to alleviate this problem is to simulate the control system, though this will be challenging for complex systems.The Fermilab PIP-II (proton improvement plan - II) project is being constructed at Fermilab to deliver $800\,MeV$ protons of $>1\,MW$ beam power to replace the present LINAC for the remainder of the existing accelerator complex. The new LINAC consists of a warm front end (WFE), 23 superconducting RF cryomodules (of 5 types), and a beam transfer line (BTL) to the existing complex.The accelerator physics group has a parallel project to create a digital twin (DT) of the PIP-II accelerator. We have coupled the EPICS controls to this DT and are developing both the DT and EPICS software in parallel. This will allow us to develop the EPICS software framework, the HMIs, sequences, high level physics applications, and other services for use in a fully functional control system.This presentation will detail the work that we have performed to date and show demonstrations of controlling and monitoring the status of the accelerator, as well as future plans for this work.

Hanlet, Pierrick [Fermilab]↗

DOE OSTI2026

Spaced out: An economic framework to explore the impacts of PV panel spacing on large-scale farming in Colorado

CONTEXT Agrivoltaic systems co-locate solar technologies with agricultural operations on an integrated plot of land and potentially provide benefits to both energy and agricultural systems. To date, large-scale (>5-MW) agrivoltaic projects in the United States have been limited to grazing and ecovoltaic applications, raising questions about the impact and scalability of agrivoltaic crop systems. Many agrivoltaic designs raise the height of the solar panels to accommodate agricultural practices while keeping energy density high. However, raising the panels results in increased photovoltaic (PV) development costs, which often are higher than the economic returns of crop production underneath the panels. This leads to unfavorable project economics and the need for other agrivoltaic solutions than raising panels. OBJECTIVE To explore other solutions, we perform an initial feasibility analysis for an agrivoltaic solution that can integrate with large-scale farming practices by increasing the row spacing in between panels. Increased PV row spacing is a low-cost approach for scaling agrivoltaics to accommodate crop production and this spacing can be tailored to required crop equipment for different regions. Increasing row spacing will reduce the power density (PV installed per acre), but in areas that are not land limited, these agrivoltaic designs could be economically feasible. Our analysis establishes a framework for a feasibility analysis for where and with what crops spaced out panel agrivoltaic solutions might be economical. METHODS Using a case study for large-scale agriculture crops in Colorado, we establish a framework for wide-row agrivoltaic economic feasibility analysis. We utilized the System Advisor Model to calculate technoeconomic metrics to compare different row spacing solutions and capture tradeoffs of these system designs. RESULTS AND CONCLUSIONS We find that, in some circumstances, wider row agrivoltaic solutions that allow for continued mechanized crop production can provide economic benefits over a traditional utility-scale PV system. For most crops examined in this analysis, roughly $\$$200/acre in agricultural profit justified spacing out the panels to at least 31.7 ft. to accommodate agrivoltaic configurations versus PV only configurations. Additionally, opportunities for increased agricultural revenue with agrivoltaic systems allow PV project economics to tolerate a larger range of CAPEX variability while remaining economically viable relative to the PV only configurations. SIGNIFICANCE This framework can be adapted for a wide variety of crops and regions and allows for examination of economically favorable sites for future agrivoltaic systems that utilize different configuration and expand opportunities for agrivoltaics.

14 SOLAR ENERGY↗

DOE OSTI2024

Reduced-dimension Bayesian optimization for model calibration of transient vapor compression cycles

Development and calibration of first-principles dynamic models of vapor compression cycles (VCCs) is of critical importance for applications that include control design and fault detection and diagnostics. Nevertheless, the inherent complexity of models that are represented by large systems of differential–algebraic equations leads to significant challenges for model calibration processes that utilize classical gradient-based methods. Bayesian optimization (BO) is a sample-efficient and gradient-free approach using a probabilistic surrogate model and optimal search over a feasible parameter space. Despite the benefits of BO in reducing computational costs, challenges remain in dealing with a high-dimensional calibration task resulting from a large set of parameters that have significant impacts on system behavior and need to be calibrated simultaneously. This paper presents a reduced-dimension BO framework for calibrating transient VCCs models where the calibration space is projected to a low-dimensional subspace for accelerating convergence of the solution algorithm and consequently reducing the number of transient simulations. The proposed approach was demonstrated via two case studies associated with different VCC applications where 10 parameters were calibrated in each case using laboratory measurements. The reduced-dimension BO framework only required 1 / 8 th of the iterations associated with a standard BO method that deals with high-dimensional calibration parameters for converged solutions and yielded comparable accuracy. Furthermore, both calibrated models revealed significant accuracy improvements compared to uncalibrated models.

Ma, Jiacheng↗

DOE OSTI2022

van der Waals Semiconductor Empowered Vertical Color Sensor

We report biomimetic artificial vision is receiving significant attention nowadays, particularly for the development of neuromorphic electronic devices, artificial intelligence, and microrobotics. Nevertheless, color recognition, the most critical vision function, is missed in the current research due to the difficulty of downscaling of the prevailing color sensing devices. Conventional color sensors typically adopt a lateral color sensing channel layout and consume a large amount of physical space, whereas compact designs suffer from an unsatisfactory color detection accuracy. In this work, we report a van der Waals semiconductor-empowered vertical color sensing structure with the emphasis on compact device profile and precise color recognition capability. More attractive, we endow color sensor hardware with the function of chromatic aberration correction, which can simplify the design of an optical lens system and, in turn, further downscales the artificial vision systems. Also, the dimension of a multiple pixel prototype device in our study confirms the scalability and practical potentials of our developed device architecture toward the above applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Design, fabrication, and testing of diamond axicons for X-ray microscopy applications

This work presents the design, fabrication, and experimental validation of a refractive diamond axicon for X-ray beam shaping. The diamond axicon was developed to overcome the limitations of polymer-based axicons particularly for application in Transmission X-ray Microscopy (TXM) systems, offering superior mechanical strength, thermal stability, and radiation resistance, making it ideal for synchrotron applications. The axicon was fabricated using femtosecond laser ablation and tested at 11 keV under various coherence conditions. Results demonstrated that the axicon efficiently transformed the X-ray beam into a ring-shaped profile with over 80% transmission. Simulations confirmed the experimental findings and highlighted the potential for further improvements. This work paves the way for the use of diamond axicons in next-generation synchrotron facilities, with future efforts focusing on optimizing fabrication and testing the axicon in full TXM systems.

Samadi, Nazanin↗

DOE OSTI2024

TChem-atm v1.0

SAND2024-11300O TChem-atm is a software library that was developed to solve complex kinetic models for atmospheric chemistry applications. TChem-atm interface employs a hierarchical parallelism design to exploit the massive parallelism available from modern computing platforms. It also supports gas atmospheric chemistry applications, e.g., the energy exascale earth system model. TChem can be used as a box model or coupled with a climate model to compute the time evolution of gas tracer species. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Safta, Cosmin↗

DOE OSTI2020

Sensors with Intelligent Measurement Platform and Low-cost Equipment (SIMPLE) – Performance Characterization

Design and performance of a digital signal processing platform for enhancing off-the-shelf voltage and current sensors are presented. The platform was developed for enabling advanced distribution grid applications with existing low-cost sensors. This digital sensor system leverages a variety of off-the-shelf voltage and current sensing technologies, analog-to-digital conversion, and sensor correction algorithms to yield more accurate and reliable digital measurements that support a multitude of applications and use cases. Sample performance and accuracy results are provided, including measurements at power frequency and higher harmonics. The system extends the native performance of the medium voltage sensors used in the system, for example, broadening measurement bandwidth beyond a few kHz, appropriate for measuring switching surges and harmonics on medium voltage distribution systems. The digital sensor system is installed and characterized in the field with a high-bandwidth optical calibration system to determine site-dependent calibration and performance issues.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

New systems in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) serves as a common library of classes between applications developed for advanced reactor analysis, fusion device engineering, spent fuel cask analysis, geochemistry studies, among other fields. These applications drive the development of the framework to meet their needs. Systems in MOOSE group capabilities that share a common purpose and generally common code. They can be leveraged by all downstream applications, providing extensive code re-use and shared maintenance. They facilitate the discovery by new users of the classes meeting at least partially their needs, and offer the same opportunities for customization as other systems. The addition of a new system to MOOSE opens new ways of solving or discretizing nonlinear problems, of performing distributed postprocessing, and a plethora of other needs. While new systems can be introduced in downstream applications rather than at the framework level, the framework team monitors common needs across the community and often triggers their addition. Documentation, training material, development needs can be centralized, limiting duplicated work across the community. The last three years have seen a large expansion in the capabilities of MOOSE. The supporting role of the framework in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has created numerous feature requests to support neutronics, thermal hydraulics, computational fluid dynamics and thermo-mechanics simulations in the Griffin, SAM, Pronghorn and Bison applications respectively. Similarly, laboratory-directed research and development (LDRD) projects in additive manufacturing, high-Reynolds flow simulations, structure optimization also necessitate an expansion of the framework capabilities. This summary reports on the new systems created in MOOSE, their design, their capabilities and some of the relevant interfaces.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Simulation of Isotopic Concentrations and Gamma Spectra from Dynamic Fission Sources

A tool was developed to rapidly generate synthetic gamma-ray spectra to evaluate safeguards material control and accounting methods for liquid-fueled molten salt reactors. Molten salt reactor operations pose unique challenges to nuclear safeguards methods and protocols compared to deployed reactors designs (e.g., light water reactors). This research evaluates the use of gamma-ray spectroscopy to monitor fission product isotopic flow through a reactor model to understand expected operations and investigate changes to the spectra with material diversion scenarios. The large design space of molten salt reactors (e.g., liquid-fueled, liquid-cooled, online separations) could potentially lead to many measurement points within the reactor system. The developed analytical tool generates and evaluates synthetic gamma-ray spectra from dynamic reactor simulations by extracting isotopic inventory to generate source terms. An implementation in the Gamma Detector Response and Analysis Software (GADRAS) Application Program Interface (API) uses the source terms, a model of the reactor component, a detector response function, and measurement plan to quickly generate and analyze spectra. Prospective measurements are then evaluated in the more accurate but slower Geant4 simulations. Utilization of the developed modeling tool and analysis of the subsequent spectra enables optimization of collimation, shielding requirements, and expected count rates that are used to determine key measurement points in the modeled reactor design.

O'Brien, Sean↗

DOE OSTI2023

Sizing and Selection of Pressure Relief Valves for High-Pressure Thermal–Hydraulic Systems

This study covers the critical concerns related to the sizing, selection, installation, maintenance, and testing of pressure safety valves (PSVs). The aim is to ensure the safety of pressurized systems, hydrostatic transmission systems, and hydraulic plants, including process plants, thermal power plants, and nuclear reactor systems. PSVs are devices that ensure the safety and reliability of pressurized vessels, lines, and systems during overpressure events. The task of selecting which PSV features are of greatest value for a specific purpose is complex—especially in the design of a high-pressure experimental thermal–hydraulic facility for hydrostatic and transient testing of the reactor system—when the systems are in the design and development phases and require qualification and demonstration to prove that they have reached a given level of technological readiness. The present study highlights the required steps for users to follow the associated rules, guidelines, and recommendations. As a part of this research, case studies are presented to help readers better understand the applicable strategy and standards. A discussion and a review of PSV performance degradation and failure are summarized to provide a better understanding of varied process applications and conditions, including fluid flow dynamics, boundary-layer formation and pressure drops, gas bubble formation and collapse, geometric configurations, inlet/outlet piping, abrupt pressure fluctuations, and acoustic resonance. Moreover, this study discusses the servicing and testing of PSVs in a multiphase pressurized system. Overall, it provides a basic overview of how PSVs ensure the safety of pressurized systems, supported by case studies and industrial practices.

02 PETROLEUM↗

DOE OSTI2022

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗

DOE OSTI2021

The Kokkos EcoSystem: Comprehensive Performance Portability For High Performance Computing

State of the art Engineering and Science codes have grown in complexity dramatically over the last two decades. As a consequence application teams have adopted more sophisticated development strategies, leveraging third party libraries, deploying comprehensive testing and using advanced debugging and profiling tools. In todays environment of diverse hardware platforms, these applications also desire performance portability - avoiding the need to duplicate work for various platforms - which makes it necessary that these tools and libraries also work across the various systems. The Kokkos EcoSystem provides that portable software stack. Based on the Kokkos Core Programming Model, the EcoSystem provides math libraries, interoperability capabilities with Python and Fortran, and Tools for analysing, debugging, and optimizing applications. In this paper we will provide an overview of the components, discuss some specific use cases, and highlight how co-designing these components enables a more developer friendly experience.

42 ENGINEERING↗

DOE OSTI2022

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

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