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DOE OSTI2024

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

25 ENERGY STORAGE↗

DOE OSTI2024

SQMS Quantum R&D in Machine Learning, Optimization and Sensing beyond Fundamental Physics Applications

This newly formed team at SQMS under the Ecosystem Thrust is looking to develop capabilities impacting societal advances outside the core domain of HEP and condensed matter physics. We explicitly leverage the experimental and algorithmic innovations developed across all groups as well as connect to broad-scope external projects of the diverse team of PIs. As the inaugural set of projects, we are studying numerically quantum machine learning models inspired by efficiently trainable echo-state and orthogonal neural networks and developing designs for related experiments to be performed on quantum processors based on SQMS SRF cQED technology and Rigetti s transmon arrays. Investigated models exploit ideas and lessons learned from multiple prior work by SQMS team members in a variety of internal and external activities [R1]. Target initial applications include noisy signal processing, potentially captured by quantum sensors or noisy QPUs, as well as simulation and classification of healthcare data. For instance, image reconstruction of the brain s electrical properties by solving the inverse Maxwell equation problem with uncertainty [R2] through a hybrid quantum-classical physics-informed architecture for time-dependent processes [R3]. The group is also investigating the application and development of novel quantum sensors based on magnetic levitation of a superconducting sphere coupled to a superconducting qubit. This coupling enables high-precision measurements of the position of the sphere, which can be used for sensitive detection of forces, enabling practical applications such as gravimetry for geophysics analysis, or accelerometry for GPS-denied navigation [R4] [R1] Rieffel, Eleanor G., Ata Akbari Asanjan, M. Sohaib Alam, Namit Anand, David E. Bernal Neira, Sophie Block, Lucas T. Brady et al. "Assessing and advancing the potential of quantum computing: A NASA case study." Future Generation Computer Systems (2024). [R2] Yu, X., Serrall s, J.E., Giannakopoulos, I.I., Liu, Z., Daniel, L., Lattanzi, R. and Zhang, Z., 2023. Pifon-ept: Mr-based electrical property tomography using physics-informed fourier networks. IEEE Journal on Multiscale and Multiphysics Computational Techniques. [R3] Wudarski, Filip, Daniel OConnor, Shaun Geaney, Ata Akbari Asanjan, Max Wilson, Elena Strbac, P. Aaron Lott, and Davide Venturelli. "Hybrid quantum-classical reservoir computing for simulating chaotic systems." arXiv preprint arXiv:2311.14105 (2023). [R4] Higgins, Gerard, Saarik Kalia, and Zhen Liu. "Maglev for dark matter: Dark-photon and axion dark matter sensing with levitated superconductors." Physical Review D 109.5 (2024): 055024.

Venturelli, Davide↗

DOE OSTI2018

Advancing the Authenticatable Container Tracking System (ACTS)

The Authenticatable Container Tracking System (ACTS) is an extensible secure container tag that can be used to optimize chain-of-custody monitoring for packaged nuclear materials as they are being stored, processed, and transported. Based on the ultra–low power TI MSP430 mixed-signal microcontroller, ACTS is an active device that uses an almost-universal core platform that can be appropriately configured with expansion modules to provide the application-specific data acquisition, data logging, container sealing, and communications functions needed for 21st century material accountancy, monitoring, and tracking applications. This core architecture enables appropriately designed modules to be easily interfaced to the basic system, providing an integration path for current and new technologies. ACTS contains a built-in set of sensors and supports an expansion bus for up to six additional communications, tracking, authentication, encryption, sealing, location, or sensing modules that may enhance the monitoring and tracking of designated containers for specific applications. To date, testing has used an IEEE 802.15.4-2011 ultra-wideband–compliant wireless transceiver module that has additional capabilities for indoor positioning and proximity location. Proximity-based location (i.e., where an ACTS tag knows where it is relative to other ACTS tags) will be explored as a continuity-of-knowledge mechanism to determine unauthorized movement of containers in storage arrays of many items. Partners for this system are Oak Ridge National Laboratory, Argonne National Laboratory, Savannah River National Laboratory, Pacific Northwest National Laboratory and commercialization partner the Aquila Group.

Younkin, Jim↗

DOE OSTI2018

Optimizing Voltage Control for Large-Scale Solar

Learn how NREL researchers, working with GE’s Grid Solution business and Duke Energy, used an advanced distribution management system to better manage voltage swings from utility-scale photovoltaic systems. The research brought NREL’s extensive power-hardware-in-the-loop and modeling capabilities together with real data from Duke Energy and GE’s advanced distribution management system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Advancing the Authenticatable Container Tracking System (ACTS)

Younkin, James↗

DOE OSTI2023

Adaptively Optimizing Experiments: Past, Present, and Future

This is a presentation for the May 31 - June 3, 2023, SIAM conference with the goal to present advances in mathematical modeling and a case study as part of the Carbon Capture Simulation Initiative.

Dowling, Alexander↗

DOE OSTI2012

Sensitivity analysis and optimization of the nuclear fuel cycle

A sensitivity study has been conducted to assess the robustness of the conclusions presented in the MIT Fuel Cycle Study. The Once Through Cycle (OTC) is considered as the base-line case, while advanced technologies with fuel recycling characterize the alternative fuel cycles. The options include limited recycling in LWRs and full recycling in fast reactors and in high conversion LWRs. Fast reactor technologies studied include both oxide and metal fueled reactors. The analysis allowed optimization of the fast reactor conversion ratio with respect to desired fuel cycle performance characteristics. The following parameters were found to significantly affect the performance of recycling technologies and their penetration over time: Capacity Factors of the fuel cycle facilities, Spent Fuel Cooling Time, Thermal Reprocessing Introduction Date, and in core and Out-of-core TRU Inventory Requirements for recycling technology. An optimization scheme of the nuclear fuel cycle is proposed. Optimization criteria and metrics of interest for different stakeholders in the fuel cycle (economics, waste management, environmental impact, etc.) are utilized for two different optimization techniques (linear and stochastic). Preliminary results covering single and multi-variable and single and multi-objective optimization demonstrate the viability of the optimization scheme. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Acceleration Techniques using ADVANTG for the MDF Design and Optimization

The Spallation Neutron Source (SNS) facility at Oak Ridge National Laboratory (ORNL) is implementing a Moderator Demonstration Facility (MDF) to demonstrate the performance characteristics of advanced moderators central to the Second Target Station (STS) for SNS. The MDF will provide a small, short-pulsed neutron source for slow neutron production in which we intend to easily and quickly change from one moderator concept to another, characterizing the performance of each moderator in a reproducible and consistent fashion. The MDF will be unique and innovative amongst the world's moderator test facilities in that it will be intended to be very prototypic in terms of 'moderator illumination' - the spatial variation of the neutron flux entering the moderator itself - as well as capable of testing so-called high-brightness moderators in a wing configuration. During the design phase of the MDF, the Monte Carlo N-Particle transport code MCNPX v.2.7.0 is being used as the primary tool for the neutronic analysis of the facility and characterization of the moderator performance. The Automated Variance Reduction Generator (ADVANTG) code is being utilized to accelerate the MCNPX calculations. The ADVANTG code generates space- and energy-dependent weight window parameters for a successive MCNP run by calculating the adjoint flux with the Denovo discrete ordinates code based on the user defined MCNP tally. We have tested various strategies using ADVANTG to generate weight window parameters to efficiently calculate detailed moderator performance. Using point detector response functions for weight window parameter optimization with ADVANTG did not work well for improving the efficiency of Monte Carlo calculations of neutron emission into beam lines. However, using cell fluxes of the area of highest contributions to the point detector for the variance reduction parameter analysis worked very well and improved the FOM of the calculation of neutron emission into a beamline by a factor of 50 over the unbiased case. (authors)

42 ENGINEERING↗

DOE OSTI2015

Bench-scale Development of an Advanced Solid Sorbent-based CO 2 Capture Process for Coal-fired Power Plants

It is increasingly clear that CO 2 capture and sequestration (CCS) must play a critical role in curbing worldwide CO 2 emissions to the atmosphere. Development of these technologies to cost-effectively remove CO 2 from coal-fired power plants is very important to mitigating the impact these power plants have within the world’s power generation portfolio. Currently, conventional CO 2 capture technologies, such as aqueous-monoethanolamine based solvent systems, are prohibitively expensive and if implemented could result in a 75 to 100% increase in the cost of electricity for consumers worldwide. Solid sorbent CO 2 capture processes – such as RTI’s Advanced Solid Sorbent CO 2 , Capture Process – are promising alternatives to conventional, liquid solvents. Supported amine sorbents – of the nature RTI has developed – are particularly attractive due to their high CO 2 loadings, low heat capacities, reduced corrosivity/volatility and the potential to reduce the regeneration energy needed to carry out CO 2 capture. Previous work in this area has failed to adequately address various technology challenges such as sorbent stability and regenerability, sorbent scale-up, improved physical strength and attrition-resistance, proper heat management and temperature control, proper solids handling and circulation control, as well as the proper coupling of process engineering advancements that are tailored for a promising sorbent technology. The remaining challenges for these sorbent processes have provided the framework for the project team’s research and development and target for advancing the technology beyond lab- and bench-scale testing. Under a cooperative agreement with the US Department of Energy, and part of NETL’s CO 2 Capture Program, RTI has led an effort to address and mitigate the challenges associated with solid sorbent CO 2 capture. The overall objective of this project was to mitigate the technical and economic risks associated with the scale-up of solid sorbent-based CO 2 capture processes, enabling subsequent larger pilot demonstrations and ultimately commercial deployment. An integrated development approach has been a key focus of this project in which process development, sorbent development, and economic analyses have informed each of the other development processes. Development efforts have focused on improving the performance stability of sorbent candidates, refining process engineering and design, and evaluating the viability of the technology through detailed economic analyses. Sorbent advancements have led to a next generation, commercially-viable CO 2 capture sorbent exhibiting performance stability in various gas environments and a physically strong fluidizable form. The team has reduced sorbent production costs and optimized the production process and scale-up of PEI-impregnated, fluidizable sorbents. Refinement of the process engineering and design, as well as the construction and operation of a bench-scale research unit has demonstrated promising CO 2 capture performance under simulated coal-fired flue gas conditions. Parametric testing has shown how CO 2 capture performance is impacted by changing process variables, such as Adsorber temperature, Regenerator temperature, superficial flue gas velocity, solids circulation rate, CO 2 partial pressure in the Regenerator, and many others. Long-term testing has generated data for the project team to set the process conditions needed to operate a solids-based system for optimal performance, with continuous 90% CO 2 capture, and no operational interruptions. Data collected from all phases of testing has been used to develop a detailed techno-economic assessment of RTI’s technology. These detailed analyses show that RTI’s technology has significant economic advantages over current amine scrubbing and potential to achieve the DOE’s Carbon Capture Program’s goal of >90% CO 2 capture rate at a cost of < $40/T-CO 2 captured by 2025. Through this integrated technology development approach, the project team has advanced RTI’s CO 2 capture technology to TRL-4 (nearly TRL-5, with the missing variable being testing on actual, coal-fired flue gas), according to the DOE/FE definitions for Technology Readiness Levels. At a broader level, this project has advanced the whole of the solid sorbent CO 2 capture field, with advancements in process engineering and design, technical risk mitigation, sorbent scale-up optimization, and an understanding of the commercial viability and applicability of solid sorbent CO 2 capture technologies for the U.S. existing fleet of coal-fired power plants.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2021

Pyomo.GDP: an ecosystem for logic based modeling and optimization development

We present three core principles for engineering-oriented integrated modeling and optimization tool sets—intuitive modeling contexts, systematic computer-aided reformulations, and flexible solution strategies—and describe how new developments in Pyomo.GDP for Generalized Disjunctive Programming (GDP) advance this vision. We describe a new logical expression system implementation for Pyomo.GDP allowing for a more intuitive description of logical propositions. The logical expression system supports automated reformulation of these logical constraints to linear constraints. We also describe two new logic-based global optimization solver implementations built on Pyomo.GDP that exploit logical structure to avoid “zero-flow” numerical difficulties that arise in nonlinear network design problems when nodes or streams disappear. These new solvers also demonstrate the capability to link to external libraries for expanded functionality within an integrated implementation. We present these new solvers in the context of a flexible array of solution paths available to GDP models. Finally, we present results on a new library of GDP models demonstrating the value of multiple solution approaches.

42 ENGINEERING↗

DOE OSTI2018

Development of a Cold Start Fuel Penalty Metric for Evaluating the Impact of Fuel Composition Changes on SI Engine Emissions Control

The U.S. Department of Energy’s Co-Optimization of Fuels and Engines initiative (Co-Optima) aims to simultaneously transform both transportation fuels and engines to maximize performance and energy efficiency. Researchers from across the DOE national laboratories are working within Co-Optima to develop merit functions for evaluating the impact of fuel formulations on the performance of advanced engines. The merit functions relate overall engine efficiency to specific measurable fuel properties and will serve as key tools in the fuel/engine co-optimization process. This work focused on developing a term for the Co-Optima light-duty boosted spark ignition (SI) engine merit function that captures the effects of fuel composition on emissions control system performance. For stoichiometric light-duty SI engines, the majority of NOx, NMOG, and CO emissions occur during cold start, before the three-way catalyst (TWC) has reached its “light-off” temperature. This light-off temperature depends on the exhaust composition, which changes with fuel formulation. Thus, the time to achieve light-off, and therefore the cold start emissions, depends on fuel composition. Since the time to reach light-off must be minimized to meet emissions regulations, modern vehicles employ cold start strategies that intentionally release fuel energy into the engine exhaust to heat the TWC above the light-off temperature as quickly as possible. The fuel penalty associated with catalyst heating during cold start provides a link between fuel properties, emissions performance, and engine efficiency. This paper describes the derivation of the Co-Optima SI engine merit function term for emissions control, which is based on the cold start fuel penalty for catalyst heating and the catalytic light-off temperature for a particular fuel composition. It also discusses the process used for extracting cold start fuel penalties from chassis dynamometer data sets previously collected at ORNL. The form of the merit function term is simple, but it is only intended as a starting point for comparing the potential impacts of changes in fuel formulation on emissions control system performance.

Pihl, Josh↗

DOE OSTI2024

Benchmarking Optimizers for Qumode State Preparation with Variational Quantum Algorithms

Quantum state preparation involves preparing a target state from an initial system, a process integral to applications such as quantum machine learning and solving systems of linear equations. Recently, there has been a growing interest in qumodes due to advancements in the field and their potential applications. However there is a notable gap in the literature specifically addressing this area. This paper aims to bridge this gap by providing performance benchmarks of various optimizers used in state preparation with Variational Quantum Algorithms. We conducted extensive testing across multiple scenarios, including different target states, both ideal and sampling simulations, and varying numbers of basis gate layers. Our evaluations offer insights into the complexity of learning each type of target state and demonstrate that some optimizers perform better than others in this context. Notably, the Powell optimizer was found to be exceptionally robust against sampling errors, making it a preferred choice in scenarios prone to such inaccuracies. Additionally, the Simultaneous Perturbation Stochastic Approximation optimizer was distinguished for its efficiency and ability to handle increased parameter dimensionality effectively.

Kan, Shuwen [Fordham University]↗

DOE OSTI2017

Co-Optimization of Fuels & Engines (Co-Optima) Initiative: Recent Progress on Light-Duty Boosted Spark-Ignition Fuels/Engines

This presentation reports recent progress on light-duty boosted spark-ignition fuels/engines being developed under the Co-Optimization of Fuels and Engines initiative (Co-Optima). Co-Optima is focused on identifying fuel properties that optimize engine performance, independent of composition, allowing the market to define the best means to blend and provide these fuels. However, in support of this, we are pursuing a systematic study of blendstocks to identify a broad range of feasible options, with the objective of identifying blendstocks that can provide target ranges of key fuel properties, identifying trade-offs on consistent and comprehensive basis, and sharing information with stakeholders.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Molten sodium batteries: advances in chemistries, electrolytes, and interfaces

The need for clean, renewable energy has driven the expansion of renewable energy generators, such as wind and solar. However, to achieve a robust and responsive electrical grid based on such inherently intermittent renewable energy sources, grid-scale energy storage is essential. The unmet need for this critical component has motivated extensive grid-scale battery research, especially exploring chemistries “beyond Li-ion”. Among others, molten sodium (Na) batteries, which date back to the 1960s with Na-S, have seen a strong revival, owing mostly to raw material abundance and the excellent electrochemical properties of Na metal. Recently, many groups have demonstrated important advances in battery chemistries, electrolytes, and interfaces to lower material and operating costs, enhance cyclability, and understand key mechanisms that drive failure in molten Na batteries. For widespread implementation of molten Na batteries, though, further optimization, cost reduction, and mechanistic insight is necessary. In this light, this work provides a brief history of mature molten Na technologies, a comprehensive review of recent progress, and explores possibilities for future advancements.

25 ENERGY STORAGE↗

DOE OSTI2020

Optimal carbon storage reservoir management through deep reinforcement learning

Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decision-making problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Finally, experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

4th Big Data for Nuclear Power Plants Workshop 2023

The Ohio State University and Idaho National Laboratory organized the 4 th Big Data for Nuclear Power Plants Workshop in November, 2023 in Columbus, Ohio. Workshop topics were chosen to understand the challenges and gaps that need to be addressed to maximize the impact of data on the nuclear industry, as well as the associated applications and risks. Discussions were focused around six specific application areas: Operation and Maintenance; Machine Learning in Nuclear Materials and Advanced Manufacturing; Cybersecurity; High-Performance Computing and Massive Computation; Big Data and Digital Twins; and Nuclear Non-Proliferation. The opportunities, challenges, and risks identified in the six focus areas explored in this workshop are diverse, but some common themes emerge, such as the importance of data integrity, quality, coverage, privacy, and traceability. Big data and AI/ML tools can be leveraged to reduce costs, optimize human tasking, and reduce human error across various application areas. In order for the nuclear industry to benefit from big data and advanced analytic capabilities, it is essential to address challenges and risks, such as data privacy, model reliability, and computational resource availability. Learning from other industries that have successfully implemented big data and AI/ML technologies, like the aerospace industry, can help the nuclear industry successfully integrate these technologies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

NRIC Final Water Management Options

The purpose of this paper is to inform advanced reactor developers of the conditions and requirements needed to manage the water used to reject heat from advanced reactor demonstrations at the INL. While there are many advanced reactor concepts, all of them require heat rejection, and some plan to use water for hear rejection. Cooling water for heat rejection is frequently the largest fresh-water load for a nuclear reactor, and therefore the focus of water management. The type of reactor and its purpose, the physical conditions at the INL, and the regulatory structure of the demonstration also influence the optimal method of heat rejection. Choosing the best method of rejecting heat will significantly influence the cost and probability of success for any advanced reactor demonstration. One of the important considerations in the deployment of demonstration plants at the INL site is water use and its implications. Advanced reactor developers need to consider the limitations of the water consumption based on the state and federal regulations. The water consumption may become a limitation when the demonstration reactor capacities reach to conventional nuclear power plants.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

A promising intersection of excited‐state‐specific methods from quantum chemistry and quantum Monte Carlo

Abstract We present a discussion of recent progress in excited‐state‐specific quantum chemistry and quantum Monte Carlo alongside a demonstration of how a combination of methods from these two fields can offer reliably accurate excited state predictions across singly excited, doubly excited, and charge transfer states. Both of these fields have seen important advances supporting excited state simulation in recent years, including the introduction of more effective excited‐state‐specific optimization methods, improved handling of complicated wave function forms, and ways of explicitly balancing the quality of wave functions for ground and excited states. To emphasize the promise that exists at this intersection, we provide demonstrations using a combination of excited‐state‐specific complete active space self‐consistent field theory, selected configuration interaction, and state‐specific variance minimization. These demonstrations show that combining excited‐state‐specific quantum chemistry and variational Monte Carlo can be more reliably accurate than either equation of motion coupled cluster theory or multi‐reference perturbation theory, and that it can offer new clarity in cases where existing high‐level methods do not agree. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry

Otis, Leon↗