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Finite orbit width effects in large aspect ratio stellarators

New orbit-averaged equations for low collisionality neoclassical fluxes in large aspect ratio stellarators with mirror ratios close to unity are derived. The equations retain finite orbit width effects by employing the second adiabatic invariant $J$ as a velocity-space coordinate and they have been implemented in the orbit-averaged neoclassical code KNOSOS (Velasco et al. , J. Comput. Phys. , vol. 418, 2020, 109512; Velasco et al. , Nucl. Fusion , vol. 61, 2021, 116013). The equations are used to study the $1/\nu$ regime and the lower collisionality regimes. For generic large aspect ratio stellarators with mirror ratios close to unity, as the collision frequency decreases, the $1/\nu$ regime transitions directly into the $\nu$ regime, without passing through a $\sqrt {\nu }$ regime. An explicit formula for the neoclassical fluxes in the $\nu$ regime is obtained. The formula includes the effect of particles that transition between different types of wells. While these transitions produce stochastic scattering independent of the value of the collision frequency in velocity space, the diffusion in real space remains proportional to the collision frequency. The $\sqrt {\nu }$ regime is only recovered in large aspect ratio stellarators close to omnigeneity: large aspect ratio stellarators with large mirror ratios and optimized large aspect ratio stellarators with mirror ratios close to unity. Neoclassical transport in large aspect ratio stellarators with large mirror ratios can be calculated with the orbit-averaged equations derived by Calvo et al. ( Plasma Phys. Control. Fusion , vol. 59, 2017, 055014). In these stellarators, the $\sqrt {\nu }$ regime exists in the collisionality interval $(a/R) \ln (R/a) \ll \nu _{ii} R a/\rho _i v_{ti} \ll R/a$ . In optimized large aspect ratio stellarators with mirror ratios close to unity, the $\sqrt {\nu }$ regime occurs in an interval of collisionality that depends on the deviation from omnigeneity $\delta$ : $\delta ^{2} |\ln \delta | \ll \nu _{ii} R a/\rho _i v_{ti} \ll 1$ . Here, $\nu _{ii}$ is the ion–ion collision frequency, $\rho _i$ and $v_{ti}$ are the ion gyroradius and thermal speed, and $a$ and $R$ are the minor and major radii.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A New LMP-Sensitivity-Based Heterogeneous Decomposition for Transmission and Distribution Coordinated Economic Dispatch

Transmission and distribution coordinated economic dispatch (TDCED) will be necessary to settle the problems caused by high penetrations of distributed generators in future grids. An efficient heterogeneous decomposition algorithm (HGD) was proposed to solve the TDCED, but it may diverge occasionally. To overcome this shortcoming, a new HGD (N-HGD) with enhanced convergency is proposed and studied in this paper. Compared with the previous HGD, the N-HGD similarly decomposes the TDCED into transmission ED and distribution ED subproblems, but it considers the sensitivity of the boundary locational marginal price to load injections to enhance convergency and avoid divergence. Two kinds of sensitivity computing methods, M-c and M-p, are studied. M-c is based on a centralized sensitivity equation, executed on the transmission side. M-p is based on a probing mechanism and can be executed distributedly on the distribution side. The reason for the N-HGD's improved convergency is theoretically analyzed, and the optimality is proven. Numerical tests show that N-HGD performs better than the original HGD, as well as a penalty-based modified HGD that needs a careful parametertuning process. Furthermore, compared with M-c, an M-p-based N-HGD is preferable because it has a much lower computational cost and no additional communicational cost.

distribution↗

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Enabling Scalable and Extensible Memory-mapped Datastores in Userspace

Exascale workloads are expected to incorporate data-intensive processing in close coordination with traditional physics simulations. These emerging scientific, data-analytics and machine learning applications need to access a wide variety of datastores in flat files and structured databases. Programmer productivity is greatly enhanced by mapping datastores into the application process's virtual memory space to provide a unified “in-memory” interface. Currently, memory mapping is provided by system software primarily designed for generality and reliability. However, scalability at high concurrency is a formidable challenge on exascale systems. Also, there is a need for extensibility to support new datastores potentially requiring HPC data transfer services. In this article, we present UMap , a scalable and extensible userspace service for memory-mapping datastores. Furthermore, through decoupled queue management, concurrency aware adaptation, and dynamic load balancing, UMap enables application performance to scale even at high concurrency. We evaluate UMap in data-intensive applications, including sorting, graph traversal, database operations, and metagenomic analytics. Our results show that UMap as a userspace service outperforms an optimized kernel-based service across a wide range of intra-node concurrency by 1.22-1.9 × . We performed two case studies to demonstrate UMap 's extensibility. First, a new datastore residing in remote memory is incorporated into UMap as an application-specific plugin. Second, we present a persistent memory allocator Metall built atop UMap for unified storage/memory.

97 MATHEMATICS AND COMPUTING↗

ElectroCat: Platinum-Group-Metal-Free Engineered Framework Nano-Structure Catalyst (Final Technical Report)

Traditional synthesis methodologies that produce durable, highly active PGM-free electrocatalysts typically consist of a top-down approach utilizing a nitrogen-coordinating precursor, a metal precursor, and a support material. Nitrogen-coordinated transition metal complexes typically are absorbed onto a support material, which is then activated via high-temperature pyrolysis. Selection of the support and the nitrogen coordinating precursor has long been the focal point of efforts to maximize the number of accessible, catalytically active sites. In this project, GWE in collaboration with SRNL and NU will develop durable, highly active, low-cost, PGM-free electrocatalysts for polymer electrolyte membrane fuel cells designed and synthesized using a unique, bottom-up, rationally designed approach. Electrocatalysts are produced via polymerization reaction followed by a high-temperature pyrolysis step. Additionally, this project focuses on catalyst site and ORR mechanistic modeling to better understand the structure-property relationship.

08 HYDROGEN↗

Structural optimization of adaptaquin, a HIF prolyl hydroxylase inhibitor

The structural optimization of adaptaquin, viz., 7-(4-chlorophenyl)[(3-hydroxypyridin-2-yl)amino]methylquinolin-8-ol, a HIF (hypoxia inducible factor) prolyl hydroxylase inhibitor, was carried out. This was done by cell-based screening using cell lines stably expressing a reporter construct consisting of the fusion protein of HIF oxygen-dependent domain and firefly luciferase (HIF1 ODD-luc). More than 100 structural analogs of adaptaquin were screened and the structures active in the submicromolar range were selected. The highest activity was found for analogs containing isobutyl, isopropyl, trifluoromethyl, and nitro group or halogen atom in the para-position of the benzene ring, but not a dimethylamino group or more bulky substituents. The lack of dependence on the donor-acceptor properties of substituents attests to predominance of the steric effect. The presence of methyl group in positions 3 and 5 of the pyridine ring provides additional activation. The 2-methyl group present in the hydroxyquinoline scaffold (50 compounds in the set) prevents coordination of the inhibitor in the active site and significantly reduces the activation. According to the real-time PCR results, the newly improved adaptaquin analog induces HIF target genes 15-times more efficiently than the FG-4592 inhibitor developed by Fibrogen.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Vapor Phase Infiltration of Titanium Oxide into P3HT to Create Organic–Inorganic Hybrid Photocatalysts

Herein, we report for the first time the use of vapor phase infiltration (VPI) to infuse conducting polymers with inorganic metal oxide clusters that together form a photocatalytic material. While vapor infiltration has previously been used to electrically dope conjugated polymers, this is the first time, to our knowledge, that the resultant hybrid material has been demonstrated to have photocatalytic properties. The system studied is poly(3-hexylthiophene-2,5-diyl) (P3HT) vapor infiltrated with TiCl 4 and H 2 O to create P3HT-TiO x organic–inorganic hybrid photocatalytic materials. X-ray photoelectron spectroscopy analysis shows that P3HT-TiO x VPI films consist of a partially oxidized P3HT matrix, and the infiltrated titanium inorganic is in a 4+ oxidation state with mostly oxide coordination. Upon visible light illumination, these P3HT-TiO x hybrids degrade methylene blue dye molecules. The P3HT-TiO x hybrids are 4.6× more photocatalytically active than either the P3HT or TiO 2 individually or when sequentially deposited (e.g., P3HT on TiO 2 ). On a per surface area basis, these hybrid photocatalysts are comparable or better than other best in class polymer semiconductor photocatalysts. VPI of TiCl 4 + H 2 O into P3HT makes a unique hybrid structure and idealized photocatalyst architecture by creating nanoscale TiO x clusters concentrated toward the surface achieving extremely high catalytic rates. The mechanism for this enhanced photocatalytic rate is understood using photoluminescence spectroscopy, which shows significant quenching of excitons in P3HT-TiO x as compared to neat P3HT, indicating that P3HT acts as a photosensitizer for the TiO x catalyst sites in the hybrid material. This work introduces a new approach to designing and synthesizing organic–inorganic hybrid photocatalytic materials, with expansive opportunities for further exploration and optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Q-Learning-Based Approach for Distributed Beam Scheduling in mmWave Networks

We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to different service operators share the same unlicensed spectrum with no centralized control or explicit coordination among them. Our goal is to design efficient distributed beam scheduling and power allocation schemes such that the network-level payoff, defined as the weighted sum of the total throughput and a power penalization term, can be maximized. To this end, we propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent. As a baseline, we compare the proposed approach to the state-of-the-art non-cooperative game-based approach which was previously developed for the same problem. We conduct extensive experiments under various scenarios to verify the effect of multiple factors on the performance of both approaches. Experiment results show that the proposed approach adapts well to different interference conditions by learning from experience and thus can achieve higher payoff than the game-based approach. The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee. As a result, the weights in the payoff function can be automatically and optimally determined by the virtual queue values from the sub-problems derived from the Lyapunov optimization framework.

99 GENERAL AND MISCELLANEOUS↗

Technical Impacts of Light-Duty and Heavy-Duty Transportation Electrification on a Coordinated Transmission and Distribution System

In this study, we propose a strategy to model the required spatiotemporal charging demand from light-duty (LD) and medium- and heavy-duty (MHD) electric vehicles (EVs) using actual transportation data by mapping the demand for the required EV charging to a realistic and coordinated distribution and transmission electric grid at the predicted times of the day to study their impact on the power system in a variety of load, weather, and EV penetration scenarios. This work is the first study that includes the actual weather data and transportation data with realistic and coordinated distribution and transmission grid data in a large industry-scale level study. The main goal of this study is to identify possible issues and required upgrades in the electric grid, caused by an increase in EV integration. The transmission case study is a large grid with 6717 buses over a Texas footprint, and the distribution grid is over Houston, a city in Texas, covering over three million customers. The resulting overloads and voltage violations experienced in the system are discussed, and required planning upgrades to avoid these issues are suggested.

AC optimal power flow (AC-OPF)↗

Feasibility Studies on Process Coupling of Transesterification and Methanol Synthesis Using Cellulose and Bio-oil

The Sponsored Programs of WKU is the compliance management for the entire campus. They are responsible for both the fiscal compliance, in addition to the coordination and implementation of pre and post-award documentation. Dr. Wei-Ping Pan is the Principal Investigator of the project, and is responsible for the carrying out of the objectives outlined in this proposal. Dr. Yan Cao, serve as the Co-Principal Investigator, who is responsible for directing the team in carrying out the objectives outlined. This project investigates feasibilities of combining the transesterification bio-diesel production into the traditional methanol production process. In this process, the raw materials for process feedstock will be completely the biomass, include cellulose fractions of biomass and bio-oil fractions of biomass. The process product will be either the bio-diesel or mixed bio-diesel and bio-methanol, which both can be directly fed as the alterative liquid fuel and also has some improvement on fuel handling. Since its capability of the complete utilization of every part of biomass (both cellulose biomass and bio-oils) and capability of the in-site optimization of reaction heat transfer and thermodynamics equilibrium shift and instant separation of process product, the proposed process can better balance several drawbacks of individual process during production of bio-diesel. This is a totally “green” process with almost all aspects of process to embody the characterization of renewable, compatibility and economic.

10 SYNTHETIC FUELS↗

A genomics-informed study of oxalate and cellulase regulation by brown rot wood-degrading fungi

Wood-degrading fungi that selectively remove carbohydrates (brown rot) combine Fenton-based oxidation and enzymatic hydrolysis to degrade wood. These two steps are incompatible in close proximity. To explain this, brown rot fungi may stagger oxidative reactions ahead of hydrolysis, but the scale and environmental controls for such a mechanism have not been resolved in solid wood. Here, we focused on one reaction control parameter, oxalate. In coordination with Fe 3+ -reducing compounds (e.g., 2,5-dimethoxyhydroquinone), oxalate can either promote Fenton chemistry by mobilizing Fe 3+ as mono-oxalates (facilitative) or inhibit Fenton chemistry (protective) by restricting reducibility and the formation of Fenton’s reagent as Fe 3+ /Fe 2 -(oxalate) 2,3 . We sectioned wood wafers colonized directionally by Postia placenta and Gloeophyllum trabeum to map end-to-end the expression of oxalate synthesis genes and to overlay enzyme activities, metabolites, and wood modifications. Near advancing hyphal fronts, oxaloacetase expression was up upregulated for both fungi, while regulation patterns of paralogous of isocitrate lyases and glyoxylate dehydrogenases varied, suggesting different physiological roles. Oxalate decarboxylase (ODC) expression in G. trabeum was induced in more decayed wood behind the hyphal front, but was constitutively expressed in all P. placenta sections. Relative ODC activities increased and oxalate levels stabilized in more decayed wood behind the hyphal front. Endoglucanase (EG) activity, on the other hand, peaked for both fungi in later decay stages. These oxalate optimization patterns are in line with previous whole-block ‘spiking’ experiments tracking oxalate, but we provide here information on its genetic controls across a spatial gradient. As a complement, we also demonstrate in vitro the plausibility of a protective role for oxalate, to emphasize that these fungi might be optimizing oxalate at a given level to maximize Fenton reactions but to minimize oxidative damage.

59 BASIC BIOLOGICAL SCIENCES↗

Preclinical Risk Evaluation of Normal Tissue Injury With Novel Radiosensitizers

Genotoxic damage induced by radiation triggers a highly coordinated DNA damage response, and molecular inhibitors of key nodes within this complex response network can profoundly enhance the antitumor efficacy of radiation. This is especially true for drugs targeting the catalytic subunit of DNA-dependent protein kinase, which is a core component of the nonhomologous end-joining DNA repair pathway, and ataxia telangiectasia mutated, which coordinates cell cycle arrest, apoptosis, and DNA repair functionalities after radiation exposure. Unlike the more modest in vitro radiosensitizing effects seen with classic sensitizing agents such as cisplatin, 5-fluorouracil, or taxanes, DNA-dependent protein kinase or ataxia telangiectasia mutated inhibitors provide much more robust sensitizing effects in vitro, as might be anticipated from targeting these key DNA repair modulators. However, patients with homozygous inactivating mutations of ataxia telangiectasia mutated or mice with homozygous defects in DNA-dependent protein kinase (severe combined immunodeficiency) have profoundly enhanced acute normal tissue radiation reactions. Therefore, there is significant potential that the combination of small molecule inhibitors of these kinases with radiation could cause similar dose-limiting acute normal tissue toxicities. Similarly, although less understood, inhibition of these DNA repair response pathways could markedly increase the risk of late radiation toxicities. Because these potent radiosensitizers could be highly useful to improve local control of otherwise radiation-resistant tumors, understanding the potential for elevated risks of radiation injury is essential for optimizing therapeutic ratio and developing safe and informative clinical trials. In this review, we will discuss 2 straightforward models to assess the potential for enhanced mucosal toxicity in the oral cavity and small intestine established in our laboratories. We also will discuss similar strategies for evaluating potential drug–radiation interactions with regard to increased risks of debilitating late effects.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Steady-State Gyrokinetics Transport Code (SSGKT), A Scientific Application Partnership with the Framework Application for Core-Edge Transport Simulations, Final Report

This project initiated the development of TGYRO - a steady-state Gyrokinetic transport code (SSGKT) that integrates micro-scale GYRO turbulence simulations into a framework for practical multi-scale simulation of conventional tokamaks as well as future reactors. Using a lightweight master transport code, multiple independent (each massively parallel) gyrokinetic simulations are coordinated. The capability to evolve profiles using the TGLF model was also added to TGYRO and represents a more typical use-case for TGYRO. The goal of the project was to develop a steady-state Gyrokinetic transport code (SSGKT) that integrates micro-scale gyrokinetic turbulence simulations into a framework for practical multi-scale simulation of a burning plasma core ? the International Thermonuclear Experimental Reactor (ITER) in particular. This multi-scale simulation capability will be used to predict the performance (the fusion energy gain, Q) given the H-mode pedestal temperature and density. At present, projections of this type rely on transport models like GLF23, which are based on rather approximate fits to the results of linear and nonlinear simulations. Our goal is to make these performance projections with precise nonlinear gyrokinetic simulations. The method of approach is to use a lightweight master transport code to coordinate multiple independent (each massively parallel) gyrokinetic simulations using the GYRO code. This project targets the practical multi-scale simulation of a reactor core plasma in order to predict the core temperature and density profiles given the H-mode pedestal temperature and density. A master transport code will provide feedback to O(16) independent gyrokinetic simulations (each massively parallel). A successful feedback scheme offers a novel approach to predictive modeling of an important national and international problem. Success in this area of fusion simulations will allow US scientists to direct the research path of ITER over the next two decades. The design of an efficient feedback algorithm is a serious numerical challenge. Although the power source and transport balance coding in the master are standard, it is nontrivial to design a feedback loop that can cope with outputs that are both intermittent and extremely expensive. A prototypical feedback scheme has already been successfully demonstrated for a single global GYRO simulation, although the robustness and efficiency are likely far from optimal. Once the transport feedback scheme is perfected, it could, in principle, be embedded into any of the more elaborate transport codes (ONETWO, TRANSP, and CORSICA), or adopted by other FSP-related multi-scale projects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

WRPS Hanford Update [Slides]

The purpose of this report is to coordinate with the staff from WRPS, the current contract holder for operations at the Hanford site to better inform design decisions for the Autonomous systems for Hanford waste tank handling project. The project goals are to support risk reduction associated with monitoring, inspection and mapping of Hanford tank underground pits and includes: 1. A pit mock-up demonstration of Idaho National Laboratory’s (INL) Autonomous Pit Exploration System (APES) technology, which has two configurations: A. The Autonomous Robotic Arm, which is a proposed collaboration with Florida International University (FIU) and has capabilities to visually inspect, monitor, map and conduct simple tool manipulation tasks in the tank pits, and B. The robotic crawler configuration, which has capabilities to deploy to the bottom of the pits and conduct closer and bottom-up visual inspections. 2. An Implementation Plan that identifies Hanford site-wide application of the INL remote robotics technologies to enhance the performance of the tank farm systems and the characterization of the waste they contain to mitigate risk and optimize the overall waste mission. 3. A first-of-a-kind environmental digital twin will be produced. Digital twins to date require permanently installed sensors in order to produce asset specific predictions. This project will use the intermittent signals during inspections from deployed sensors on robotic systems to produce the data sets used by AI/ML to enable predictions of issues on tanks.

42 - ENGINEERING↗

An MILP-Based Distributed Energy Management for Coordination of Networked Microgrids

An MILP-based distributed energy management for the coordination of networked microgrids is proposed in this paper. Multiple microgrids and the utility grid are coordinated through iteratively adjusted price signals. Based on the price signals received, the microgrid controllers (MCs) and distribution management system (DMS) update their schedules separately. Then, the price signals are updated according to the generation–load mismatch and distributed to MCs and DMS for the next iteration. The iteration continues until the generation–load mismatch is small enough, i.e., the generation and load are balanced under agreed price signals. Through the proposed distributed energy management, various microgrids and the utility grid with different economic, resilient, emission and socio-economic objectives are coordinated with generation–load balance guaranteed and the microgrid customers’ privacy preserved. In particular, a piecewise linearization technique is employed to approximate the augmented Lagrange term in the alternating direction method of multipliers (ADMM) algorithm. Thus, the subproblems are transformed into mixed integer linear programming (MILP) problems and efficiently solved by open-source MILP solvers, which would accelerate the adoption and deployment of microgrids and promote clean energy. The proposed MILP-based distributed energy management is demonstrated through various case studies on a networked microgrids test system with three microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Identifying materials-level sources of performance variation in superconducting transmon qubits

The Superconducting Quantum Materials and Systems Center, a U. S. Department of Energy National Quantum Information Science Research Center, has conducted a comprehensive and coordinated study using superconducting transmon qubit chips with known performance metrics to identify the underlying materials-level sources of device-to-device performance variation. Following qubit coherence measurements, these qubits of varying base superconducting metals and substrates have been examined with various non-destructive and invasive material characterization techniques at Northwestern University, Ames National Laboratory, and Fermilab as part of a blind study. We find trends in variations of the depth of the etched substrate trench, the thickness of the surface oxide, and the geometry of the sidewall, which when combined, lead to correlations with the T1 lifetime across different qubits on the same chip. In addition, we provide a list of features that varied from device to device, for which the impact on performance requires further studies. Finally, we identify two low-temperature characterization techniques that may potentially serve as proxy tools for qubit measurements. These insights provide materials-oriented solutions to not only reduce performance variations across neighboring devices but also to engineer and fabricate devices with optimal geometries to achieve performance metrics beyond the state-of-the-art values.

Murthy, Akshay A. [Fermilab] (ORCID:00000001767768↗

Multi-agent AI collaboration for digital twin development and assessment

Developing a digital twin (DT) model involves different steps that encompass formulating requirements, model development, implementation, and assessment with respect to real applications. Human expertise is required to coordinate and implement different steps in the DT development and assessment process. However, certain parts of this process can be automated using artificial intelligence (AI) agents for efficient workflow development. In this work, we test and analyze a multiagent AI collaboration with humans in the loop to automate different elements of the DT development and assessment process. To implement the workflow for multiagent AI DT development and assessment, we use Autogen, a multiagent framework developed by Microsoft. Autogen offers a modular and flexible framework for configuring and designing task-specific multiagent workflows. In this framework, large language models (LLMs) form the core intelligence of the AI agents where the quality and performance of the automated element is governed by the inherent capabilities and knowledge base of the LLM. We use retrieval augmented generation to supplement the LLM with relevant domain-specific information for DT requirement formulation. We illustrate this multiagent workflow using a case study on a thermal energy storage system, focusing on how AI agents can collaborate with humans to expedite and optimize different elements of DT development and assessment process.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Photoluminescence properties and efficient energy transfer of Ce3+/Eu2+ activated K2Ba7Si16O40 phosphors

Highlights: • K{sub 2}Ba{sub 7}Si{sub 16}O{sub 40}:Ce{sup 3+}/Eu{sup 2+} phosphors have been synthesized by a high temperature solid-state method with CO atmosphere. • The resonance-type energy transfer by dipole-dipole interaction from Ce{sup 3+} to Eu{sup 2+} in K{sub 2}Ba{sub 7}Si{sub 16}O{sub 40} is demonstrated. • The critical distance between Ce{sup 3+} and Eu{sup 2+} ions is calculated to be 25.06 Å. • The thermal activation energy of K{sub 2}Ba{sub 7}Si{sub 16}O{sub 40} is calculated to be 0.26 eV. - Abstract: Ce{sup 3+}/Eu{sup 2+} activated K{sub 2}Ba{sub 7}Si{sub 16}O{sub 40} phosphors have been synthesized via a high-temperature solid-state method with CO atmosphere. Under ultraviolet (UV) light pump, the photoluminescence (PL) spectra of K{sub 2}Ba{sub 7}Si{sub 16}O{sub 40}: Ce{sup 3+} is composed of a broadband from 300 nm to 500 nm ascribed to the spin-allowed 5d–4f transition of Ce{sup 3+} ions. The optimal concentration is 8% for Ce{sup 3+} single doped K{sub 2}Ba{sub 7}Si{sub 16}O{sub 40} due to the ernergy dispersion between Ce{sup 3+} ions and the critical distance between Ce{sup 3+} ions is 15.05 Å. K{sub 2}Ba{sub 7}Si{sub 16}O{sub 40}: Eu{sup 2+} emits a broad green emission peaking at 498 nm. The spectral overlap between the emission of Ce{sup 3+} and the excitation of Eu{sup 2+} suggests the resonance-type energy transfer from Ce{sup 3+} to Eu{sup 2+}, which is further demonstrated by the decay curves of K{sub 2}Ba{sub 7}Si{sub 16}O{sub 40}: 8 mol% Ce{sup 3+}, m Eu{sup 2+}. The critical distance between Ce{sup 3+} and Eu{sup 2+} ions is calculated to be 25.06 Å by Dexter’s theory. Furthermore, the energy transfer mechanism is dipole-dipole interaction. In addition, the thermal activation energy is calculated to be 0.26 eV. The CIE coordinates can be tuned from blue to green by increasing Eu{sup 2+} ions. All results suggest that K{sub 2}Ba{sub 7}Si{sub 16}O{sub 40}: Ce{sup 3+}, Eu{sup 2+} is potentially useful for near-UV pumped white LEDs.

36 MATERIALS SCIENCE↗