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DOE OSTI2023

Understanding the Structure and Dynamics of Conjugated Polymers by Advancing Deuteration Chemistry and Neutron Scattering (Final Report)

The overarching goal of the proposed work was to set up a partnership between the University of Southern Mississippi (USM) and Oak Ridge National Laboratory (ORNL) to develop novel approaches to measure the backbone rigidity of conjugated polymers (CPs) and understand the critical role of sidechains on the backbone conformation and the materials macroscopic property. The backbone rigidity greatly influences the electronic properties of CPs, which ultimately determines the functionality and performance of these materials. Improvements in the electronic properties of CPs would allow for enhanced charge transport in semiconductor devices, improved photovoltaic performance, recycling of waste heat in thermoelectrics, and discovery of new phenomena that will enable the next generation of energy technologies. Although significant progress has been made to optimize the optical and electronic properties of CPs, largely through Edisonian methodologies, it remains a challenge to experimentally characterize conjugated backbone conformation (chain rigidity, torsion, planarity, and short-range order) and relate these to the fundamental optical and electronic properties (electronic coupling, charge transport, etc.). This has left fundamental gaps in our knowledge of the most basic structure/property relationships within these systems, precluded the study of fundamental physical phenomena, and constrained the design and realization of new electronic and device functionalities. Thus, the major goal of this work is to use novel deuteration methodologies via systematic synthetic approaches, and neutron scattering techniques to comprehensively characterize the structural and dynamic properties of CPs in contrast-matching solvents. Our work would, for the first time, elucidate the relationship between backbone rigidity and macroscopic properties. They will also allow a rational formulation of design principles for next-generation CPs that are resilient to disorder through precise control of the delocalized electrons along the polymer backbone. Overall, this project will advance our understanding of the structure, dynamics, and fundamental physics of these materials, which is crucial for enabling the prediction, design, control, and manipulation of current and emerging material electronic properties.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Attack Surface Analysis of the Digital Twins interface with Advanced Sensor and Instrumentation Interfaces: Cyber Threat Assessment and Attack Demonstration for Digital Twins in Advanced Reactor Architectures

A digital twin is a virtual representation of a physical system or object using real-time data that can predict and analyze how the system or object performs. This relatively new technology can be applied to the field of nuclear power generation, to aid in the design and development of new nuclear power plants and reduce operation costs using predictive maintenance and other data analytical methods. While there are already companies utilizing simulation software to train operators and technicians in the nuclear industry, some are now transitioning to utilizing their existing technology, software, and methods to develop digital twin solutions for the next generation of nuclear power plants, offering their services to utilities and government organizations around the world.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Need for advanced research reactors for the next-generation reactor physics, analysis tools, and technology

Full text of publication follows. There is an urgent need for design and deployment of advanced research and test reactors in support of design, licensing and operation of advanced power reactors and education of next generation nuclear workforce. Existing research reactors mostly were designed and constructed decades ago with the main objectives of training operators, performing reactor physics experiments, and educating nuclear engineers and scientists. There are already gaps and significant concern about future capabilities for the existing research reactor facilities to address modern instrumentation and/or flexible environments for: performing reactor physics studies for advanced designs which have significantly different core materials forms and compositions, reactor shapes and size; validation of advanced high-fidelity software; development of machine learning algorithms for enhancement of human-machine collaboration in support of reactor monitoring, operation and safeguards; and, effective education of the next-generation workforce. The authors will focus on the need for advanced research reactors to improve and validate fast and accurate simulation tools for high-fidelity modeling and analysis of nuclear reactors in support of their design, optimization, licensing, operation, and monitoring. In the past, simulation tools were limited to relatively coarse models using approximate methodologies that benefited from two main factors: i) allowance for large margins and tolerances; ii) ability to construct prototype (e.g., zero power) reactors for adjustment of approximate methodologies. The next generation reactors have to be designed mainly by using novel high-fidelity computational tools that are accurate and fast, and therefore can be used for parametric studies and uncertainty quantification. To sufficiently demonstrate the accuracy of these tools, advanced research reactors are needed. The authors argue the need for new computational paradigms such as the MRT (Multistage, Response- function Transport) methodology which has resulted in the development of the novel high-fidelity RAPID (Real-time Analysis for Particle-transport and In-situ Detection) code system. Such code systems have to be robust in modeling any complex system, and should be fast and accurate, henceforth their uncertainties can be quantified at reasonable costs. Again, advanced research reactors are needed for the validation of the fidelity and accuracy of new computational tools. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Optimization of a High Pressure Turbine Blade and Sector-Based Annular Rig Design for Supercritical CO2 Power Cycle Representative Testing

Abstract As part of the ongoing research into the design of hardware for zero emission cycles, a first-stage high-pressure turbine (HPT) blade is optimized for a 300 MWe supercritical CO2 (sCO2) power cycle using the surrogate-assisted genetic algorithm optimizer in Numeca FINE/Design three-dimensional with objectives of increasing efficiency and decreasing heat load to the blade. Supercritical CO2 property tables are constructed from NIST REFPROP data for the condensable gas simulation in FINE/Turbo. A detailed mesh sensitivity study is performed for a baseline design to identify the proper-grid refinement and efficiently allocate resources for the optimization. Seventy design variables are selected for the initial population generation. Self-organizing maps are then used to focus the design variables on the most important ones affecting the objective functions. The optimization results in approximately 3000 three-dimensional Reynolds Averaged Navier Stokes simulations of different blade shapes with increases in efficiency of up to 0.85% and decreases in heat load of 14%. Families of blade shapes are identified for experimental testing in an annular rig at the Purdue Experimental Turbine Aerothermal Laboratory. A design to adapt the annular cascade for testing optimized geometries is introduced, which features eccentric radius sectors allowing for scaled-up geometries of sCO2 optimized blade profiles to be tested at design cycle representative conditions at high Reynolds numbers in dry air. Analysis into the effects of Reynolds number, working fluid, and geometric relations are presented to prove the efficacy of the test method.

Engineering↗

DOE OSTI2023

Towards 5G-Enabled Operational Technology for Process Monitoring and Network Slicing

Cyber-Physical Systems (CPS) are deployed to monitor physical processes in critical cyber-enabled services like power generation. However, CPS ecosystems are typically designed without robust security. While it is important to ensure optimal performance of the Operational Technology (OT) environments, security cannot be overlooked. To modernize traditional OT services, 5G technology is being integrated. 5G technology offers low latency and high availability, making it a suitable infrastructure for managing and monitoring physical processes. How-ever, integrating 5G mechanisms into large-scale OT networks introduces new implementation and performance challenges. Therefore, this paper presents a 5G-enabled CPS architecture (5G-CPS) that describes the necessary components, services, and communication protocols and conducts feasibility study to integrate 5G technology in industrial control system networks to understand the performance merits. The 5G-CPS architecture aims to minimize implementation and operational challenges associated with integrating 5G technology into constrained OT.

Aguayo, Jared M.↗

DOE OSTI2020

High-Throughput Functional Genomics for Energy Production

Functional genomics remains a foundational field for establishing genotype-phenotype relationships that enable strain engineering. High-throughput (HTP) methods accelerate the Design-Build-Test-Learn cycle that currently drives synthetic biology towards a forward engineering future. Trackable mutagenesis techniques including transposon insertion sequencing and CRISPR-Cas-mediated genome editing allow for rapid fitness profiling of a collection, or library, of mutants to discover beneficial mutations. Due to the relative speed of these experiments compared to adaptive evolution experiments, iterative rounds of mutagenesis can be implemented for next-generation metabolic engineering efforts to design complex production and tolerance phenotypes. Further, the expansion of these mutagenesis techniques to novel bacteria are opening up industrial microbes that show promise for establishing a bio-based economy.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Metal oxide candidates for thermochemical water splitting obtained with a generative diffusion model

Generative diffusion models (DMs) for inorganic crystalline materials are being actively investigated for their potential to expand the chemical and structural design spaces for known functional materials. Generative candidates are particularly useful for applications where few functional, let alone commercially viable, materials currently exist, such as metal oxides for thermochemical water-splitting, which have strict requirements for defect thermodynamics and host stability. Here, we critically examine generated metal oxides from the M ATTER G EN DM conditioned on select chemical systems for thermochemical water splitting applications. Perhaps most notably, we find that M ATTER G EN predicts a novel, thermodynamically stable, quinary metal oxide, Ba 2 SrInFeO 6 , although this compound represents an ordered and layered substitution within the same A 3 B 2 O 6 structural prototype as its two ternary end members. Detailed density functional theory calculations and spin configuration sampling for this material and its possible decomposition products—beyond what existed in M ATTER G EN training data—are required to quantitatively validate hull energy predictions and conclusions of stability. Furthermore, the material exhibits oxygen defect formation energies appropriate for thermochemical water splitting, warranting targeted investigation in an experimental validation campaign, along with other future M ATTER G EN candidates in this application space.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Cigeo Design Options, Risks and Opportunities Management: A Continuous Project Management Goal - 20015

Andra is currently completing the detailed engineering phase of the stepwise design development of Cigeo, the French HLW and IL-LLW Deep Geological Repository. If licensed in the coming years, Cigeo's progressive construction, industrial operation and closure will last over more than a century. Such a duration, associated with the requirements in terms of reversibility, lead to the choice of an incremental design that allows, for next operator generations, to integrate improvements derived from scientific and technical progress as well as from experience feedback from its operation. This incremental design approach, associated with a permanent risks and opportunities management process, will continue after licensing of Cigeo as a tool for the governance of the reversibility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Machine learning application to single channel design of molten salt reactor

This study proposes a robust approach to quickly design a nuclear reactor core and explores the best performing machine learning (ML) technique for predicting feature parameters of the core. Here we implemented the approach into a hypothetical channel of molten salt reactors to demonstrate the applicability of the method. We prepared a Python tool, named Plankton, which couples to a reactor physics code and an optimization tool, and imports ML methods. The tool performs three consecutive phases: reactor database generation, machine learning application, and design optimization. We identified the extra trees method as the best performing estimator. With the estimator, we found nine optimum designs in total, one for each fuel-salt pair, and estimated all the performance metrics of the designs with a <5% prediction error compared to their actual values. U-Pu-NaCl fuel-salt gave promising results with the highest conversion ratio, the most negative feedback coefficient, and the lowest fast flux.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Charge Concentration Limits the Hydrogen Evolution Rate in Organic Nanoparticle Photocatalysts

Colloidal organic nanoparticles have proven to be a promising class of photocatalyst for performing the Hydrogen Evolution Reaction (HER) due to their dispersibility in aqueous environments, their strong absorption within the visible region, and the tunabilty of their component materials' redox potentials. Currently, there is little understanding of how charge generation and accumulation in organic semiconductors change when these materials are formed into nanoparticles that share a high interfacial area with water, nor is it known what mechanism limits the hydrogen evolution efficiency in recent reports on organic nanoparticle photocatalysts. Herein, we use Time-Resolved Microwave Conductivity to study aqueous-soluble organic nanoparticles and bulk thin films composed of various blend ratios of the non-fullerene acceptor EH-IDTBR and conjugated polymer PTB7-Th and examine the relationship between composition, interfacial surface area, charge carrier dynamics, and photocatalytic activity. We quantitatively measure the rate of Hydrogen Evolution Reaction by nanoparticles composed of various donor:acceptor blend ratio compositions and find that the most active blend ratio displays a Hydrogen Quantum Yield of 0.83% per photon. Moreover, we find that nanoparticle photocatalytic activity corresponds directly to charge generation, and that nanoparticles have 3x more long-lived accumulated charges relative to bulk samples of the same material composition. Here these results suggest that, under our current reaction conditions, with approximately 3x solar flux, catalytic activity by these nanoparticles is limited by the concentration of electrons and holes in operando and not a finite number of active surface cites or the catalytic rate at the interface. This provides a clear design goal for the next generation of efficient photocatalytic nanoparticles.

08 HYDROGEN↗

DOE OSTI2026

Covalent Organic Framework Photocatalysts for Water Oxidation and Overall Artificial Photosynthesis

Artificial photosynthesis through proton reduction or CO 2 reduction to generate chemical fuels has gained increasing attention as an attractive strategy for solar-to-fuel conversion. In these systems, the oxygen evolution reaction (OER) provides the necessary electrons and protons for driving the overall reaction but represents the rate-limiting step due to its inherently sluggish kinetics. Therefore, efficient overall artificial photosynthesis requires photocatalysts that can drive both the oxidation and reduction half-reactions, which impose stringent demands on catalyst design. Covalent organic frameworks (COFs) offer a versatile platform for designing such photocatalysts, owing to their strong light-harvesting capabilities , periodic architectures, and highly tunable frameworks that allow programmable catalytic sites and adjustable electronic band structures. While notable progress has been made in developing COF photocatalysts for the OER and overall artificial photosynthesis, these advances remain scattered across the literature and existing reviews, and a dedicated, systematic overview of the OER and its central role in integrated artificial photosynthetic processes is still lacking. This Review systematically summarizes recent advances in COF-based photocatalysts for the OER half-reaction and overall artificial photosynthesis. Our aim is to offer a comprehensive roadmap that establishes fundamental design principles for next-generation COF-based photocatalysts toward efficient and sustainable artificial photosynthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Multiple-objective Reinforcement Learning for Inverse Design and Identification

The aim of inverse chemical design is to develop new molecules with given optimized molecular properties or objectives. Recently, generative deep learning (DL) networks are considered as the state-of-the-art in inverse chemical design and have achieved early success in generating molecular structures with desired properties in the pharmaceutical and material chemistry fields. However, satisfying a large number (> 10 objectives) of molecular objectives is a limitation of current generative models. To improve the model’s ability to handle a large number of molecule design objectives, we developed a Reinforcement Learning (RL) based generative framework to optimize chemical molecule generation. Our use of Curriculum Learning (CL) to fine-tune the pre-trained generative network allowed the model to satisfy up to 21 objectives and increase the generative network’s robustness. The experiments show that the proposed multiple-objective RL-based generative model can correctly identify unknown molecules with an 83% to 100% success rate, compared to the baseline approach of 0%. Additionally, this proposed generative model is not limited to just chemistry research challenges; we anticipate that problems that utilize RL with multiple objectives will benefit from this framework.

reinforcement learning, generative model, inverse ↗

DOE OSTI2026

Development of a Chlorine Cathode and Anode Basket Assembly for Production of Uranium Chloride

A chlorine cathode has been developed for in situ chlorination of metals, oxides, and oxychlorides in molten chloride electrolytes that could be used to support synthesis of chloride fuel salts for molten salt reactors. The chlorine cathode is designed to electrochemically reduce chlorine gas to generate chloride ions that, when paired with a metal anode, chlorinate that metal as it is oxidized into the salt. The designed porous carbon electrode effectively distributes Cl 2 to the electrode surface and efficiently generates Clions in the molten chloride electrolyte. This report highlights recent improvements made to the electrode assembly, with a focus on operational control of the anode basket stability. Synthesis tests demonstrated the successful chlorination of uranium metal, resulting in 3.6 wt% uranium generated in the LiCl-KCl base salt in 45 minutes, performed in a bench-scale chlorination apparatus. Higher concentrations could be achieved by applying longer chlorination times or increasing the amount of uranium loaded in the anode basket. Overall, the chlorine cathode can be used with an appropriately designed anode to chlorinate uranium in situ for synthesis of molten salt reactor fuel salts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Direct stellarator coil optimization for nested magnetic surfaces with precise quasi-symmetry

We present a robust optimization algorithm for the design of electromagnetic coils that generate vacuum magnetic fields with nested flux surfaces and precise quasi-symmetry. The method is based on a bilevel optimization problem, where the outer coil optimization is constrained by a set of inner least squares optimization problems whose solutions describe magnetic surfaces. The outer optimization objective targets coils that generate a field with nested magnetic surfaces and good quasi-symmetry. The inner optimization problems identify magnetic surfaces when they exist, and approximate surfaces in the presence of magnetic islands or chaos. We show that this formulation can be used to heal islands and chaos, thus producing coils that result in magnetic fields with precise quasi-symmetry. We show that the method can be initialized with coils from the traditional two-stage coil design process, as well as coils from a near-axis expansion optimization. We present a numerical example where island chains are healed and quasi-symmetry is optimized up to surfaces with aspect ratio 6. Another numerical example illustrates that the aspect ratio of nested flux surfaces with optimized quasi-symmetry can be decreased from 6 to approximately 4. In conclusion, the last example shows that our approach is robust and a cold-start using coils from a near-axis expansion optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

A Scalable Compact Additively Manufactured Molten Salt to Supercritical Carbon Dioxide Heat Exchanger for Solar Thermal Application

Design of an additively manufactured molten salt (MS) to supercritical carbon dioxide (sCO 2 ) primary heat exchanger (PHE) for solar thermal power generation is presented. The PHE is designed to handle temperatures up to 720 °C on the MS side and an internal pressure of 200 bar on the sCO 2 side. In the core, MS flows through a three-dimensional periodic lattice network, while sCO 2 flows within pin arrays. The design includes integrated sCO 2 headers located within the MS flow, allowing for a counterflow design of the PHE. The sCO 2 headers are configured to enable uniform flow distribution into each sCO 2 plate while withstanding an internal pressure of 200 bar and minimizing obstruction to the flow of MS around it. The structural integrity of the design is verified on additively manufactured (AM) 316 stainless steel sub-scale specimens. An experimentally validated, correlation-based sectional PHE core thermofluidic model is developed to study the impact of flow and geometrical parameters on the PHE performance, with varied parameters including the mass flowrate, surface roughness, and PHE dimensions. A process-based cost model is used to determine the impact of parameter variation on build cost. The model results show that a heat exchanger with a power density of 18.6 MW/m 3 (including sCO 2 header volume) and effectiveness of 0.88 can be achieved at a heat capacity rate ratio of 0.8. As a result, the impact of design and AM machine parameters on the cost of the PHE are assessed.

14 SOLAR ENERGY↗

DOE OSTI2023

Towards Auto-Generated Data Systems

After decades of progress, database management systems (DBMSs) are now the backbones of many data applications that we interact with on a daily basis. Yet, with the emergence of new data types and hardware, building and optimizing new data systems remain as difficult as the heyday of relational databases. In this paper, we summarize our work towards automating the building and optimization of data systems. Drawing from our own experience, we further argue that any automation technique must address three aspects: user specification, code generation, and result validation. We conclude by discussing a case study using videos data processing, along with opportunities for future research towards designing data systems that are automatically generated.

Computer Science↗

DOE OSTI2024

Retrofittable Advanced Combined Cycle Integration for Flexible Decarbonized Generation

This is the final scientific / technical report for Front End Engineering Design (FEED) study “Retrofittable Advanced Combined Cycle Integration for Flexible Decarbonized Generation” funded by the US Department of Energy (DOE), Office of Fossil Energy and Carbon Management (FECM), Carbon Capture R&D Program under award DE-FE0032131. The prime recipient is General Electric Company working through its Gas Power division (GEGP) with subrecipients Southern Company and Linde Engineering plus subcontractor Kiewit. Southern Company’s Plant Barry, a 2x1 7F.04 CCGT, is the host site. GEGP led the overall integration of the carbon capture system (CCS) with the existing natural gas combined cycle (NGCC) facility. Linde provided the detailed process engineering and equipment costing for the carbon capture island. Kiewit focused on plant layout, constructability, and installation. With the FEED study finished, the engineering is approximately 50% complete towards the detailed design package needed to proceed to procure, install, build, commission, and operate the integrated NGCC+CCS facility. This FEED study focuses on retrofitting a 95% CO 2 capture system with attention on plant integration. The study was particularly challenging due to unique and substantial cost impacts related to global sourcing challenges associated with COVID. Additional incentives, regulatory support, and dispatch certainty are needed to move forward with implementing CCS at this or other sites in the US. This FEED study highlights the value of integrating NGCC+CCS and is a template for future CCS retrofit studies.

42 ENGINEERING↗

DOE OSTI2021

Particle-in-cell techniques for the study of space charge effects in the Advanced Cryogenic Gas Stopper

The Facility for Rare Isotope Beams, FRIB, will significantly increase the rates and varieties of rare isotopes compared to those presently available at existing rare-isotope facilities. The production of stopped beams via the current generation of linear gas stopping devices available is not well matched to the beam intensities that will be available at FRIB. Many experiments that make use of reaccelerated beams, from the ReA facility, greatly benefit from having the highest-intensity, rare-isotope beams possible. Some experiments would also benefit greatly from stopped beams with improved purity. In this paper, the intensity bottlenecks of FRIB’s newest linear gas stopper are studied via experiment and simulation in support of a future detailed conceptual design of a next-generation linear gas stopper that can provide rare-isotope beams with rates of 10^8 pps, a factor of 50 higher than the current state-of-the-art system, with improved beam purity.

43 PARTICLE ACCELERATORS↗