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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Inspecta Technical Report

Sandia National Laboratories (SNL) is in the process of creating Inspecta (International Nuclear Safeguards Personal Examination and Containment Tracking Assistant), an Artificial Intelligence (AI)-powered smart digital assistant (SDA) with robotic capabilities, aimed at enhancing the effectiveness, efficiency, and safety of international nuclear safeguards inspections. This innovative tool is designed to assist inspectors on-site by supporting or automating tasks that are typically mundane, hazardous, or susceptible to errors. In 2021, the development team established the specifications for Inspecta by analyzing International Atomic Energy Agency (IAEA) documents and consulting with former IAEA inspectors and subject matter experts. This process involved aligning in-field inspection tasks with existing commercial or open-source technologies to outline a roadmap for the initial prototype of Inspecta, while also identifying areas needing further research and development. From 2022 – 2024, the focus has shifted to integrating a critical inspection activity, the examination of seals, into an early version of Inspecta. This has involved developing both the software and hardware capabilities necessary for this task. This report outlines the ongoing advancements in Inspecta's functionalities, specifically those supporting the seal examination process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Inspecta Technical Report

Sandia National Laboratories (SNL) has developed Inspecta (International Nuclear Safeguards Personal Examination and Containment Tracking Assistant), an AI-powered smart digital assistant (SDA) equipped with robotic capabilities. This innovative tool aims to enhance the effectiveness, efficiency, and safety of international nuclear safeguards inspections. Inspecta is designed to assist inspectors on-site by supporting or automating tasks that are often mundane, hazardous, or prone to errors. In 2021, the development team established the specifications for Inspecta by thoroughly analyzing International Atomic Energy Agency (IAEA) documents and consulting with former IAEA inspectors and subject matter experts. This process involved aligning in-field inspection tasks with existing commercial and open-source technologies, thereby creating a roadmap for the initial prototype of Inspecta and identifying areas requiring further research and development. From 2022 to 2025, the focus shifted to integrating a critical inspection activity—the examination of seals—into an early version of Inspecta. This phase has involved the development of both software and hardware capabilities essential for this task. This report outlines the advancements in Inspecta’s functionalities, particularly those that support the seal examination process.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Improvements in Optical Surface Measurement Using Reflected Computer Vision Targets

Since 2021, NREL has been developing a system to measure heliostats by measuring the deflection of printed computer vision targets, called the Reflected Target Non-intrusive Assessment (ReTNA) [6], [7]. While this system will have lower resolution than a fringe deflectometry system, it has several important advantages that make it a complimentary technology: 2D surface slope measurement can be generated from a single image, it can operate in ambient lighting, target points can be directly located in 3D space with photogrammetry allowing for a non-flat target, and it's well suited to using a smaller target, and multiple images to measure larger optical surfaces. ReTNA has undergone several significant changes and improvements, described below. This talk will summarize new system layouts designed for commercial use, new computer vision algorithms used to automate the analysis process and validation campaigns for the ReTNA software.

computer vision↗
DOE OSTI2025

Employing MACS/ViBRANT as a Surrogate MARVEL Reactor for Startup Reactivity Tuning and Supervisory Control Processes

Advanced nuclear reactors are a key part of the future of nuclear energy both in the United States and globally. They offer unique benefits for various energy-demanding applications, including use in remote locations, compact size, modular manufacturing, remote monitoring, low and/or variable power rating operation, and reliance on novel technologies to enhance operational safety. To achieve economic feasibility, advanced reactors must significantly reduce their workforces in comparison with the current fleet. Achieving this reduction will occur through reducing staff workloads using technology to achieve autonomous or semi-autonomous operations, demonstrated by comprehensive testing and validation activities. These operations will require both software and hardware platforms during the design and testing phases. While simulations are useful during the design phase, their performance can significantly deviate during actual deployment on hardware. This report presents the outcomes of a collaborative technical initiative between the U.S. Department of Energy (DOE) Microreactor Program (MRP) and Advanced Sensors and Instrumentation (ASI) Program. The collaboration utilized the Microreactor Automated Control System (MACS) hardware platform to bridge the gap between theoretical reactor design and actual startup and control operations. Two key use cases were investigated: facilitating the startup testing period and demonstrating supervisory control. The first use case details the key Microreactor Applications Research Validation and Evaluation (MARVEL) reactor startup physics testing activities conducted using the MACS platform. These activities included drum worth measurements, shutdown margin assessment, temperature feedback analysis, and scram time evaluation, as well as unique testing that would apply to the MARVEL reactor to demonstrate the testing methodologies in a low-risk environment. The MACS platform, serving as a surrogate representation of the MARVEL reactor, proved instrumental in performing these tests. The exercise revealed aspects that led to optimized processes, refined hardware design, and enhanced base software capabilities. By maturing methods and technologies in this manner, the initiative promises to reduce wasted time in the actual on-site reactor deployment effort, thereby saving significant time and resources. The second use case focuses on the development and implementation of supervisory control methods aimed at managing core tilt, which can result from asymmetrical operations or manufacturing imperfections in fuel rods or reactivity control devices. A key objective was to assess and compare the use of artificial intelligence (AI) for supervisory control. The effort aimed to define the role of supervisory control to enhance performance without risking control instability. This effort explored three distinct approaches: rules-based (RB) methods, optimization techniques, and reinforcement learning (RL) algorithms. Each approach was evaluated for its ease of implementation, its usability, and its effectiveness in responding to asymmetries in neutron flux. Comparative analysis of these approaches provided valuable insights into their applicability and effectiveness, offering a robust framework for advanced reactor operations. Together, these two use cases highlight the potential of hardware test beds to help streamline the design, operation, and control of advanced nuclear reactors. This collaborative effort underscores the importance of continued innovation and experimentation in achieving the next generation of safe, reliable, and economically viable nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Assessment of Flow-Enhanced Electrochemical Sensor Testing and Deployments for MSRs

This report serves as the deliverable for Milestone M3RS-23AN0401061 that is part of Work Package RS-23AN040106 (Flow Enhanced Sensors for MSRs – ANL). The goal of this milestone was to determine performance of the flow enhanced electrochemical sensor (FEES) and modular flow instrumentation testbed (MFIT) in safeguards relevant scenarios. Flow enhanced electrochemical sensors are a type of electroanalytical sensor that has been developed at Argonne National Laboratory to be installed directly into MSR flow conduits to make measurements of the salt composition. These sensors represent a significant improvement in capabilities compared to earlier electroanalytical sensors that instead can only be operated in quiescent conditions. Previous work has focused on testing of the FEES in flowing conditions provided by the MFIT to assess the accuracy and precision of the sensor measurements. To further improve this capability, in FY23 we undertook a campaign of safeguards relevant scenarios in molten salt containing a range of uranium chloride concentrations (0 to 3 wt%). All the testing carried out in FY23 was aided by a control system designed to automatically actuate flow conditions and collect data. This new automation system is estimated to have increased experimental throughput by a factor of four and enabled testing in a variety of complex conditions. The advancements in throughput and repeatability led to improved quantification of actinide concentrations using the in-flow sensors, with a reduction of the mean absolute relative error from 5.6% in FY22 to 3.1% in FY23. In addition to safeguards scenarios run in the MFIT, FY23 work included deployment of a FEES at a partner institution where it will be tested in a flowing salt loop. The FEES was successfully integrated into that loop and is being tested prior to loop startup. In FY23, work also continued on the smaller flow system that we have named the mini-MFIT. This smaller system is capable of rapid prototyping of new sensor designs prior to installation in the larger MFIT radiological flow system. Work was carried out to test this new system in non-radiological molten salts in a separate glovebox. This work is helping us to enhance the accuracy of our salt monitoring capabilities through the integration of multiple types of sensors. The high degree of accuracy required by 10 CFR 74 represents a significant challenge, and further design evolution and integration of the sensors into multimodal sensing frameworks will be needed to further push the measurement accuracy to the needed level.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids

Polymer–protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer–protein hybrid materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Assessment of Thin Plastic Scintillation Detectors for Beta-Particle Measurements at the Advanced Test Reactor Critical Facility

The Fission Wire Measurement System is a custom measurement system designed in the 1960s to measure the beta-particle activity of irradiated uranium-aluminum fission wires. This measurement is conducted to determine the fission rate profile of the Advanced Reactor Test Critical facility. The Advanced Test Reactor Critical facility is an open-pool, low-power test reactor used to qualify experiment configurations and verify core models prior to full-power experiment irradiations in the Advanced Test Reactor. Power distribution measurements in ATR-C use uranium-aluminum wires that are distributed throughout the core to validate simulation and modeling results. These measurements require from 340 to 1500 wires to be irradiated and measured within a 12-hour window. The system consists of 4 measurement channels and one reference channel, each with a 2-pi proportional gas flow detector and the measurement channels each have an automated sample changer. The gas flow detectors are of a custom design for this detector system that use methane gas with a large anode wire compared to modern proportional counters. These detectors, which are nearly 60 years old are irreplaceable. The measurements from these gas detectors are affected by the gas flow rate, atmospheric and line pressure, and are very sensitive to the applied high voltage. Recent improvements have been made to the control and data acquisition system, but the detectors have remained the same. The nature of the measurement of the fission product decay activity is such that the energy spectrum of the signal is changing with time. Thin, 250-um thick, plastic scintillators were commercially obtained as a potential replacement for the gas flow detectors. The original calibration of the uranium-aluminum fission wires was conducted in 1965 using a series of irradiations of gold foils and the wires in a well-characterized thermal neutron field. These measurements provided a time-dependent fission rate conversion factor from the gold foil data to calibrate the fission wires based on the response from the 2-pi proportional gas detectors. Transitioning to the new detectors requires qualification and testing. The sensitivity of the scintillators to changes in the energy spectrum of the fission wires and translation of the calibration factor have been completed. These measurements indicated that the sensitivity of the scintillators over time changes at a different rate than the sensitivity of the gas flow detectors. However, the inverse activity of measurements of both detector types is linear with time. Initial results indicate that the scintillator detectors will be a sufficient replacement for the gas detectors with minor adjustments to the fission rate conversion factor. Replacement of the detectors will improve the fission wire measurements and provide a more stable and reliable measurement system.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2022

Invertible Neural Networks for Aerodynamic Design of Wind Turbine Blades

The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. Our design technique is a significant improvement over the state-of-the-practice linearized blade element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades.

aerodynamic design↗
DOE OSTI2024

Tunable Solid‐State Properties and Anisotropic Charge Mobility in Hydrogen‐Bonded Diketopyrrolopyrrole Polymers via Automated Device Fabrication and Characterization

The optoelectronic properties of semiconducting polymers and device performance rely on a delicate interplay of design and processing conditions. However, screening and optimizing the relationships between these parameters for reliably fabricating organic electronics can be an arduous task requiring significant time and resources. To overcome this challenge, Polybot is developed—a robotic platform within a self‐driving lab that can efficiently produce organic field‐effect transistors (OFETs) from various semiconducting polymers via high‐throughput blade coating deposition. Polybot not only handles the fabrication process but also can conduct characterization tests on the devices and autonomously analyze the data gathered, thus facilitating the rapid acquisition of data on a large scale. This work leverages the capabilities of this platform to investigate the fabrication of OFETs using hydrogen bonding‐containing semiconducting polymers. Through high‐throughput fabrication and characterization, various data trends are analyzed, and large extents of anisotropic charge mobility are observed in devices. The materials are thoroughly characterized to understand the role of processing conditions in solid state and electronic properties of these organic semiconductors. The findings demonstrate the effectiveness of automated fabrication and characterization platforms in uncovering novel structure–property relationships, facilitating refinement of rational chemical design, and processing conditions, ultimately leading to new semiconducting materials.

automated fabrication and characterization↗
DOE OSTI2021

Towards Automatic and Agile AI/ML Accelerator Design with End-to-End Synthesis

Domain-specific designs offer greater energy efficiency and performance gain than general-purpose processors. For this reason, modern system-on-chips have a significant portion of their silicon area with custom accelerators. However, designing hardware by hand is laborious and time-consuming, given the large design space and the performance, power, and area constraints that are not realized in the software. Moreover, domain-specific algorithms (e.g., machine learning models) are evolving quickly, challenging the accelerator design further. To address these issues, this paper presents SODA Synthesizer, an automated open-source high-level ML framework to Verilog modular compiler targeting AI/ML Application-Specific Integrated Circuits (ASICs) accelerators. SODA tightly couples the Multi- Level Intermediate Representation (MLIR) compiler infrastructure [24] and open-source HLS approaches. Thus, SODA can support various ML frameworks and algorithms and can perform optimizations that combine specialized architecture templates and conventional HLS to generate the hardware modules. In addition, SODA’s closed-loop design space exploration (DSE) engine allows developers to perform end-to-end design space explorations on different metrics and technology nodes.

Zhang, Jeff↗
DOE OSTI2023

An unstructured mesh based neutronics optimization workflow

We have developed a fully automated workflow to optimize the neutronics performance of the Second Target Station (STS) at the Oak Ridge National Laboratory’s Spallation Neutron Source. The optimization workflow starts with the parametrized solid CAD engineering models and converts them into the unstructured mesh (UM) models for the neutronics calculations with MCNP6.2. Calculations are executed and their results are loaded into the Dakota optimization toolkit. Dakota analyzes the results and proposes new geometry parameters for the next design iteration. The cycle repeats until the optimal parameters are found. The automated CAD to MCNP conversion, the use of high-fidelity UM models, and the use of modern optimizer are the key elements that advance the entire optimization workflow in comparison with the original workflow. The original workflow was based on a simplified constructive solid geometry (CSG) modeling with MCNPX, mcnp_pstudy tool, and an in-house optimizer. Herein to demonstrate the new workflow, we present a case of neutronics optimization of the moderator–reflector assembly (MRA). Apart from the MRA, the workflow can optimize other major STS components, such as the spallation target, neutron beamlines, radiation shielding, and various accelerator components. Importantly, the new workflow opens the door to the advanced multi-physics multi-parameter optimization and has the potential for use in other nuclear physics and accelerator applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Dynamic Facade Dashboard v0.1.0

The dashboard is a useful tool for early-stage building design decision-making and communication, as it can help users quickly compare the energy and non-energy related performance of various automated, integrated facade systems using a library of pre-computed data. Users can explore the impacts of various design choices by selecting different facade glazing and shading systems, facade control strategies, and lighting control strategies across multiple climate zones. The dashboard instantly visualizes key metrics, including energy usage in HVAC and lighting, peak cooling and heating load, and daylight availability, allowing immediate trade-off analysis to optimize building efficiency and comfort.

Yu, Tammie [Lawrence Berkeley National Laboratory ↗
DOE OSTI2020

Invited: Software defined accelerators from learning tools environment

Next generation systems, such as edge devices, will need to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. In this paper we present the Software Defined Accelerators From Learning Tools Environment (SODALITE), an automated open source high-level ML framework-to-verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets. The SODALITE approach will implement optimal designs by seamlessly combining custom components generated through high-level synthesis (HLS) with templated and fully tunable Intellectural Properties (IPs) and macros, integrated in an extendable resource library. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimensions.

High-Level Synthesis, Accelerators, Hardware-Softw↗
DOE OSTI2024

Machine Learning-Enabled Image Classification for Automated Electron Microscopy

Abstract Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of “big data” and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Materials Science↗
DOE OSTI2024

Using scalable computer vision to automate high-throughput semiconductor characterization

Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Science & Technology - Other Topics↗
DOE OSTI2025

Automating Sensor Characterization with Bayesian Optimization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Cuevas-Zepeda, Julian [Chicago U., KICP; Chicago U↗
DOE OSTI2021

PlasmidMaker: a Versatile, Automated, and High Throughput End-to-End Platform for Plasmid Construction

Plasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high-throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute ( Pf Ago)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate ~100 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments within 3 days. PlasmidMaker should greatly expand the potential of synthetic biology.

Enghiad, Behnam↗
DOE OSTI2025

If We Build Them, They Will Run: Automated HPC Apps Deployment and Profiling with eBPF in Cloud

The high performance computing (HPC) community is in a period of transition. The rise of AI/ML coupled with a changing landscape of resources deems portability a new metric of performance, and methods to move between on-premises and cloud environments and assess compatibility are paramount. Here we design and test a strategy for bridging the gap between traditional HPC and Kubernetes environments – first containerizing applications, providing automated orchestration to run studies, and packaging the setup with automated means to assess performance using low overhead eXtended Berkeley Packet Filter (eBPF) programs. We first assess different designs for eBPF collection, demonstrating a tradeoff between number of programs deployed on a node and overhead added. We develop 5 low overhead eBPF programs that combine with streaming ML models to assess CPU, futex, TCP, shared memory, and file access across four different builds of an HPC application for CPU and GPU. We use eBPF data to generate insights into the possible underlying etiology of scaling issues. We then assess compatibility of a well-known benchmark, HPCG, across matrices of micro-architectures and optimization levels (217 containers across 24 instance types and over 7500 runs). We provide to the community 30 applications to deploy in our automated setup and perform a scaling study from 4 to a maximum of 256 nodes for both CPU and GPU applications. Finally, we use our gained knowledge about performance to generate compatibility artifacts that are used by a newly developed Kubernetes controller to intelligently select instance type based on optimizing a figure of merit. Along with insights to scaling in this environment with a collection of applications and templates to work from, we provide an overall strategy for approaching HPC application deployment and image selection based on compatibility in cloud.

Computer science↗