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DOE OSTI2024

IDAES-PSE 2.7.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.7.0 Release Highlights New features: AutoScaler and CustomScalerBase classes: Such tools are the core of the new scaling framework being implemented in IDAES. Wider adoption of scaling tools among users will result in quicker and more robust model solutions. Scaler for equilibrium reactor and saponification properties: These scaler models are examples to follow for how to use the new scaling tools. ONNX Surrogate support from Optimization & Machine Learning Toolkit (OMLT): ONNX is an open standard format to save and load ML/AI models that is widely supported by all major frameworks. This capability makes it easier for IDAES users to create surrogate models and use them without having to support each framework individually. 1D Membrane Model for CO2 Capture and Utilization: Supports ongoing efforts for modeling and optimizing polymer membrane processes for CO2 capture and conversion into formic acid. StreamScaler unit model: Unrelated to the CustomScalerBase, this unit model allows a stream’s extensive variables to be scaled by a fixed factor. This allows streams being processed by multiple units in parallel to be scaled down to unit scale and scaled back up to process scale. Bug fixes or improvements: Scaling, EoS, Diagnostics tool, Modular Properties, tests & documentation Deprecations: Old Cubic EoS

AS↗

DOE OSTI2020

U.S. Domestic Small Modular Reactor Security by Design

U.S. nuclear power facilities face increasing challenges in meeting evolving security requirements caused by evolving and expanding threats while keeping cost reasonable to make nuclear energy competitive. The addition of security features after a facility has been designed and without attention to optimization (the past approach) can lead to cost overruns. Incorporating security in the design process can provide robust, cost effective, and sufficient physical protection systems. The purpose of this work is to develop a framework for the integration of security into the design phase of Small Modular Reactors (SMRs) and the use of modeling and simulation tools to optimize the design of physical protection systems. This effort will intend to integrate security into the design phase of a model SMR that meets current NRC physical protection requirements and provide advanced solutions to improve physical protection and decrease costs. A suite of tools, including SCRIBE3D, PATHTRACE and Blender were used to model a hypothetical generic domestic SMR facility. Physical protection elements such as sensors, cameras, portal monitors, barriers, and guard forces were added to the model based on best practices for physical protection systems. One outsider sabotage scenario was examined with 4-8 adversaries to determine security metrics. This work will influence physical protection system designs and facility designs for U.S. domestic SMRs. The purpose of this project is to demonstrate how a series of experimental and modeling capabilities across the Department of Energy Complex can impact the design of U.S. domestic SMRs and the complete Safeguards and Security by Design (SSBD) for SMRs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

U.S. Domestic Microreactor Security-by-Design

U.S. nuclear power facilities face increasing challenges in meeting dynamic security requirements caused by evolving and expanding threats while keeping cost reasonable to make nuclear energy competitive. The past approach has often included implementing security features after a facility has been designed and without attention to optimization, which can lead to cost overruns. Incorporating security in the design process can provide robust, cost effective, and sufficient physical protection systems. The purpose of this work is both to develop a framework for the integration of security into the design phase of a microreactor and increase the use of modeling and simulation tools to optimize the design of physical protection systems. Specifically, this effort focuses on integrating security into the design phase of a model microreactor that meets current Nuclear Regulatory Commission (NRC) physical protection requirements and providing advanced solutions to improve physical protection and decrease costs. A suite of tools, including SCRIBE3D©, PATHTRACE© and Blender© were used to model a hypothetical, generic domestic microreactor facility. Physical protection elements such as sensors, cameras, barriers, and guard forces were added to the model based on best practices for physical protection systems. Multiple outsider sabotage scenarios were examined with four-to-eight adversaries to determine security metrics. The results of this work will influence physical protection system designs and facility designs for U.S. domestic microreactors. This work will also demonstrate how a series of experimental and modeling capabilities across the Department of Energy (DOE) Complex can impact the design of and complete Safeguards and Security by Design (SSBD) for microreactors. The conclusions and recommendations in this document may be applicable to all microreactor designs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Pressure Gain, Stability, and Operability of Methane/Syngas Based RDEs Under Steady and Transient Conditions (Final Project Report)

The scope of this work addresses key issues associated with losses associated with the detonation wave and other processes internal to the RDE operation, as well as it develops modeling tools for the evaluation of these losses and exhaust emissions in RDEs. The main challenge in studying RDEs is that RDE performance is highly reliant on the specifics of the design so much so that simple/canonical systems alone cannot provide useful engineering information, but practical RDE designs are sufficiently complex and involve extreme operational environments that detailed access either experimentally (laser diagnostics, for instance) or computationally (direct numerical simulations) are as yet to become practical. To overcome this challenge, we have conducted a combined experimental/simulation/analytical study investigating key phenomena that control the characteristics of operation of RDEs. As a result, the study has developed tools and methods that can be used to evaluate performance and design approaches using reduced-physics models, with the assumptions validated using detailed simulations, and the model prediction tested using experimental observations. The specific objectives of the research were: (1) Develop and demonstrate a low-loss fully axial injection concept, taking advantage of stratification effects to alter the detonation structure and position the wave favorably within the combustor; (2) Obtain stability and operability characteristics of an RDE across operating conditions to aid in the development of operability and performance rules for the operations of other systems; and (3) Develop quantitative metrics for performance gain as well as quantitative description of the loss mechanisms through a combination of diagnostics development, reduced-order modeling, and detailed simulations. The work conducted here has made contribution on design of low-loss inlets that has broad application within the power generation industry for use with pressure gain combustion. The operability and stability of different designs, while focusing on axial air inlet designs, has been analyzed. The effect of nozzle and injection conditions was studied. Models and simulations of exhaust emissions, focusing on NOx emission has been developed and used to investigate how operation of the RDE affect NOx production using Lagrangian analysis of RDE simulations. This work has built on previous programs, with the goal of further understanding operation of RDEs and elevate the readiness of design consideration. In addition, a suite of diagnostic and modeling tools have been developed to obtain quantitative metrics on performance based on measurements, which can be readily transferred to other experimental configurations.

08 HYDROGEN↗

DOE OSTI2026

Report on the Update and Codification of Expert Knowledge into ASTM Standard Practice E521

The overall purpose of the Grand Challenge Integrated Research Project (IRP), Accelerating the Qualification of Materials to Enable Rapid Deployment of Advanced Reactors, is to fulfill several objectives. The first objective is to complete and demonstrate/establish the process for predicting the microstructure and properties of structural materials in reactor and at high doses using ion irradiation as an accurate predictive tool for assessing behavior under reactor irradiation. The second objective is for this process to be adopted as part of an ASTM standard, and to work with the US Nuclear Regulatory Commission to utilize this standard in licensing decisions on advanced reactor designs for which such data are nonexistent or impractical to achieve. The need for standardized procedures is apparent based on the outcomes of a recent round-robin experiment from laboratories using ion beams for radiation damage studies and the roadmap for ion beam technologies to address challenges for the advancement of nuclear energy technologies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Report on the Update and Codification of Expert Knowledge into ASTM Standard Practice E521

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Hybrid Energy-Powered Electrochemical Direct Ocean Capture Model

Offshore synthetic fuel production and marine carbon dioxide removal can be enabled by direct ocean capture, which extracts carbon dioxide from the ocean that then can be used as a feedstock for fuel production or sequestered underground. To maximize carbon capture, plants require a variety of low-carbon energy sources to operate, such as variable renewable energy. However, the impacts of variable power on direct ocean capture have not yet been thoroughly investigated. To facilitate future deployments, a generalizable model for electrodialysis-based direct ocean capture plants is created to evaluate plant performance and electricity costs under intermittent power availability. This open-source Python-based model captures key aspects of the electrochemistry, ocean chemistry, post-processing, and operation scenarios under various conditions. To incorporate realistic energy supply dynamics and cost estimates, the model is coupled with the National Renewable Energy Laboratory’s H2Integrate tool, which simulates hybrid energy system performance profiles and costs. This integrated framework is designed to provide system-level insights while maintaining computational efficiency and flexibility for scenario exploration. Initial evaluations show similar results to those predicted by the industry, and demonstrate how a given plant could function with variable power in different deployment locations, such as with wind energy off the coast of Texas and with wind and wave energy off the coast of Oregon. The results suggest that electrochemical systems with greater tolerances for power variability and low minimum power requirements may offer operational advantages in variable-energy contexts. However, further research is needed to quantify these benefits and evaluate their implications across different deployment scenarios.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

BETO 2021 Peer Review - Overview of the Chemical Catalysis for Bioenergy Consortium

Catalysis plays a central role in converting biomass and carbon-rich waste feedstocks into fuels and chemicals; however, critical catalysis challenges exist that are limiting commercialization of emerging bioenergy technologies. By leveraging unique U.S. Department of Energy National Laboratory capabilities and expertise, the Chemical Catalysis for Bioenergy consortium seeks to overcome these catalysis challenges and accelerate the catalyst and process development cycle. The foundation of the consortium consists of an integrated and collaborative portfolio of catalytic technologies and enabling capabilities, which positions ChemCatBio to address both technology-specific and overarching catalysis challenges across the development cycle from discovery to scale-up. The core catalysis projects target technological advancements for specific conversion processes, such as catalytic upgrading of biochemical process intermediates, catalytic fast pyrolysis, C1 and C2 upgrading, and electrochemical CO2 reduction, while the enabling technologies provide access to world-class capabilities and expertise in computational modeling, materials synthesis, advanced in situ and in operando catalyst characterization, and catalyst design tools.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Transformer Neural Networks with Spatiotemporal Attention for Predictive Control and Optimization of Industrial Processes

In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.

Gallup, Ethan R.↗

DOE OSTI2020

Small animal PET: a review of what we have done and where we are going

Abstract Small animal research is an essential tool in studying both pharmaceutical biodistribution and disease progression over time. Furthermore, through the rapid development of in vivo imaging technology over the last few decades, small animal imaging (also referred to as preclinical imaging) has become a mainstay for all fields of biologic research and a center point for most preclinical cancer research. Preclinical imaging modalities include optical, MRI and MRS, microCT, small animal PET, ultrasound, and photoacoustic, each with their individual strengths. The strong points of small animal PET are its translatability to the clinic; its quantitative imaging capabilities; its whole-body imaging ability to dynamically trace functional/biochemical processes; its ability to provide useful images with only nano- to pico- molar concentrations of administered compounds; and its ability to study animals serially over time. This review paper gives an overview of the development and evolution of small animal PET imaging. It provides an overview of detector designs; system configurations; multimodality PET imaging systems; image reconstruction and analysis tools; and an overview of research and commercially available small animal PET systems. It concludes with a look toward developing technologies/methodologies that will further enhance the impact of small animal PET imaging on medical research in the future.

Engineering↗

DOE OSTI2022

Cross-Cutting Metrology Tools for In Operando Characterization of Carrier Dynamics in Photovoltaic Devices (Final Technical Report)

Understanding the nature of recombination and its dependence on defects and interfaces is essential for engineering materials and contacts for higher V oc and photovoltaic (PV) efficiency. Time-resolved photoluminescence (TRPL) has conventionally been used to evaluate recombination, but not all materials are strongly emissive or otherwise suitable. Time-resolved terahertz spectroscopy is a pump – probe method that presents valuable complementary information, wherein an optical pump pulse photoexcites carriers within the absorber and the transient photoconductivity is probed with a terahertz pulse. Until now, experimental constraints have prohibited the use of terahertz probes to interrogate full device stacks, and measurements were instead made on exfoliated films or films grown on unconventional substrates. However, interfaces are critical to the behavior of photoexcited carriers in solar cells, and the substrates themselves often influence the film growth and bulk properties. Therefore, it is important to probe the behavior of PV absorbers as close as possible to their normal operating conditions. Here we have developed cross-cutting metrology that enable in operando characterization of carrier dynamics and recombination mechanisms in working PV devices. Through a combination of complementary non-contact TRTS and TRPL experiments and modeling, we have obtained key parameters including photoexcited carrier bulk lifetime and interface and back surface recombination velocities with greater precision and accuracy than can be achieved with conventional TRPL alone. By varying pulsed photoexcitation conditions, the contributions of interface and bulk recombination mechanisms can be determined. We have developed and refined this characterization approach by investigating CdTe PVs, which is a well-established technology but with significant margin for further improvements in efficiency. Results were published in Journal of Applied Physics (DOI: 10.1063/5.0064730) and Proceedings of the IEEE PVSC (DOI: 10.1109/PVSC43889.2021.9518559). After validating the approach, we have applied it to correlate recombination rates to processing conditions and material properties, including composition and defects as well as interfaces and surface treatments. Such feedback can inform processing and design choices to enable higher PV efficiency, not only for CdTe, but also CdSe x Te 1-x , CIGS, perovskites, kesterites, and future technologies. A perspective on predicting solar cell performance from terahertz spectroscopy was published in Advanced Energy Materials (DOI: 10.1002/aenm.202102776). The cross-cutting metrology tools developed here will enable determination of the locus and mechanism of performance-limiting recombination in thin film PV devices. This feedback can guide the engineering of PV devices with higher V oc and efficiency, thereby leading to reductions in levelized cost of electricity to meet the SunShot 2030 target of $0.03/kWh for utility-scale PV. Lower costs will enable rapid expansion of renewable electricity generation that is nearly free of carbon emissions. Reducing carbon emissions of the electricity sector is one of the most important solutions to climate change. The project’s specific focus on CdTe PV can benefit First Solar, the US company that is the global leader in CdTe technology, through both technology and workforce development.

14 SOLAR ENERGY↗

DOE OSTI2023

Infrared compatible rapid mixer to probe millisecond chemical kinetics

Fast microfluidic mixers are a valuable tool for studying solution-phase chemical reaction kinetics and molecular processes with spectroscopy. However, microfluidic mixers that are compatible with infrared vibrational spectroscopy have seen only limited development due to the poor infrared transparency of the current microfabrication material. We describe the design, fabrication, and characterization of CaF2-based continuous flow turbulent mixers, which are capable of measuring kinetics in the millisecond time window with infrared spectroscopy, when integrated into an infrared microscope. Kinetics measurements demonstrate the ability to resolve relaxation processes with 1 millisecond time resolution, and straightforward improvements are described that should result in sub-100 µs time-resolution.

Instruments & Instrumentation↗

DOE OSTI2023

Statistical methods for design and testing of 3D-printed polymers

Different statistical methods are used in various fields to qualify processes and products, especially in emerging technologies like Additive Manufacturing (AM) or 3D printing. Since several statistical methods are being employed to ensure quality production of the 3D-printed parts, an overview of these methods used in 3D printing for different purposes is presented in this paper. The advantages and challenges, to understanding the importance it brings for design and testing optimization of 3D-printed parts are also discussed. The application of different metrology methods is also summarized to guide future researchers in producing dimensionally-accurate and good-quality 3D-printed parts. Here, this review paper shows that the Taguchi Methodology is the commonly-used statistical tool in optimizing mechanical properties of the 3D-printed parts, followed by Weibull Analysis and Factorial Design. In addition, key areas such as Artificial Intelligence (AI), Machine Learning (ML), Finite Element Analysis (FEA), and Simulation require more research for improved 3D-printed part qualities for specific purposes. Future perspectives are also discussed, including other methods that can help further improve the overall quality of the 3D printing process from designing to manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Beyond Like-for-Like: A User-centered Approach to Modernizing Legacy Applications

When modernizing a legacy application, it is easy to fall back on a like-for-like replica with new tools and updated design stylings, but this is an opportunity to explore making a more intuitive application that supports user tasks and efficiency. Rather than having a blank canvas-unburdened by legacy tech debt-to create a new application, you are working with an existing application that is integral to accelerator operations and one that expert users are already familiar with. Due to this, you might assume people will prefer the like-for-like, but you could be carrying forward the pain points, processes that are inefficient, and ultimately wind up with an application that no one wants to use because it doesn't solve existing problems. Getting users involved can make all the difference in your approach to modernizing a legacy application that caters to both newer and expert users. It also can bridge the gap between like-for-like and introducing new GUI design. Having a legacy application doesn't have to make the modernized one difficult to develop, as the existing application is a tool in how you move forward with the new application. It provides insight into areas that an application with a clean slate doesn't give you.

Polzin, M. [Fermilab]↗

DOE OSTI2022

AbBERT: Learning Antibody Humanness via Masked Language Modeling

Understanding the degree of humanness of antibody sequences is critical to the therapeutic antibody development process to reduce the risk of failure modes like immunogenicity or poor manufacturability. We introduce AbBERT, a transformer-based language model trained on up to 20 million unpaired heavy/light chain sequences from the Observed Antibody Space database. We first validate AbBERT using a novel “multi-mask” scoring procedure to demonstrate high accuracy in predicting complementary determining regions—including the challenging hypervariable H3 region. We then demonstrate several uses of AbBERT at various points along the antibody design process. AbBERT enhances in silico antibody optimization via deep reinforcement learning by utilizing its learned embeddings as additional observations during optimization. Within a larger computational antibody design platform, AbBERT has been successfully applied as an additional design objective, where it displays strong correlations with computational tools predicting antibody structural stability. Finally, mutant antibody sequences that have been scored as unfavorable by AbBERT have shown corresponding low yields when expressed in cells. These use cases demonstrate the power of language modeling within computational antibody design.

Bioinformatics↗

DOE OSTI2024

"Designing, simulating, and performing the 100-AV field test for the CIRCLES consortium: Methodology and Implementation of the Largest mobile traffic control experiment to date"

Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.

Ameli, Mostafa↗

DOE OSTI2023

Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML)

Although the interest in the use of artificial intelligence (AI) and machine learning (ML) in nuclear energy is increasing rapidly, at present their implementation is limited. This rapid increase in interest is not surprising considering that implementing AI and ML technology would allow for continuous monitoring, facilitate the implementation of predictive maintenance with optimized staffing plans, enable automation and autonomy opportunities that could drastically reduce fixed operation and maintenance costs, and provide training for operations and maintenance. Other industries are using AI for construction, and in the nuclear arena AI could provide great benefit in decommissioning activities. The ability of AI and ML to operate in real time vastly increases their potential impact. Before AI can be used in design, operations, or as a regulatory tool, the specifics on the regulations applicable to the use of AI for nuclear power applications need to be established. The difficulty is that the specific use cases will dictate the applicability of regulations. For example, even within the application domain associated with operations, the regulations might vary if the AI is used to create a virtual reference for plant operations or is used for training, optimization of maintenance intervals, prioritization of maintenance activities, etc. Different still is if the AI is to be used for design or setting technical specifications, which will introduce additional requirements. US Nuclear Regulatory Commission (NRC) licensing reviews are based on an applicant’s design meeting its performance assessment based on (1) safety goals and objectives, (2) deterministic and/or probabilistic analysis of accident scenarios, and (3) quantitative assessment of design alternatives against the safety goals and objectives using accepted engineering tools, methodologies, and performance criteria. The current regulatory framework does not explicitly address AI or autonomous control. However, as implementing AI technology will require the use of a digital platform, it must meet the requirements of an instrumentation and control (I&C) system. The regulatory requirements for AI, which will be incorporated into the I&C system, will be very dependent on how it is used (i.e., its functionality, safety classification, etc.). The licensing process is primarily risk-based with the identification of components and systems as nonsafety, important to safety, or safety related. A risk-informed approach allows further gradation of components and systems based on risk metrics such as core damage frequency or large early release fractions. Thus, the use cases and the risk categorization of impacted systems and components will determine the regulatory requirements. Regardless of how AI is used it presents new opportunities for risk-informing operating, maintenance, and regulatory decisions. Trustworthiness, transparency, and the ability to validate and verify the results will be paramount in showing that the systems and plant still meet their performance requirements. This report describes the results of research to identify regulatory implications of AI technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of AI for design (including design changes or new designs including advanced reactors), construction, operations, training, maintenance, research, testing, and as a regulatory tool. AI can be automated at different levels from purely informative purposes to autonomous controls. The focus of this review included determination of constraints on the application of AI technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. Currently, any use of AI at nuclear power plants is focused on nonsafety-related applications. The NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than create new regulations to address the use of AI and ML. This approach seems to be the best to encourage AI development without adding regulatory uncertainty.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction

Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile.

47 OTHER INSTRUMENTATION↗