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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 361 records · Page 20

CdTe Core: Final Technical Report (FTR)

CdTe is presently the cost-leading thin-film PV technology, directly competing with Si at scale, even when domestically manufactured. While an impressive technology, its efficiency remains much below the detailed balance limit with the largest cause due to its low photovoltage and fill factor. To realize gains, the carrier concentration, minority carrier lifetime, and interface recombination all need to be improved simultaneously over historic levels. Using a new defect chemistry (group V doping instead of copper) has been identified as a viable route using single crystal systems. This project focused on implementing this new defect chemistry in scalable, polycrystalline thin-film photovoltaic CdTe devices with tasks focusing improvements to the front interface, absorber, and rear interface as well as capability development & stakeholder engagement. The goal of the project was to establish a strategy using devices, test structures, detailed characterization, and modeling to quantify the sources of losses in state-of-the-art CdTe photovoltaic devices. Using this strategy and advanced synthesis, losses at the front interface, absorber, and rear interface were worked on in parallel. The final objective was to significantly improve the voltage deficit in CdTe devices to enable improvements in photovoltage and efficiency that can be implemented by industry in the near-term. Over the course of the project, the team developed new characterization techniques, analysis, and modeling which were then applied to state-of-the-art materials generated internally and collaboratively. In particular to enable rapid progress, NREL worked closely with First Solar where NREL grew complete devices as well as ones that interleaved process steps where First Solar had completed different steps such as absorber growth or absorber growth and activation using their baseline methods. Using detailed characterization and analysis including photoemission, photoluminescence, and scanning probe techniques enabled understanding of the loss pathways and area for improvements in our own and First Solar s materials. Ultimately, this contributed to the first series of new world record CdTe efficiencies since 2016, culminating in a 23.1% certified cell that was P-doped along with As-doped cells of similar performance. Internally, NREL improved the statistical variation in baseline As-doped devices and improved average photovoltage by over 100 mV. This was done through an improvement in absorber quality, changed front interface, and improved back contact. In addition to materially improving the fabrication processes at NREL, characterization, analysis, and modeling were developed and disseminated. NREL also played a pivotal role in community building over the course of this project working closely with the Cadmium Telluride Accelerator Consortium. NREL worked in a series of collaborations with academic and industry partners, leveraging knowledge and innovations from this project, as well as helped organize a series of workshops to ensure rapid progress in the field. Working closely with the academic community has led to a dissemination of knowledge; working with First Solar as increased US competitiveness First Solar expanded domestic production to ~10 GW and opened new facilities.

14 SOLAR ENERGY↗

IEA Wind TCP Task 49: Reference Site Conditions for Floating Wind Arrays

This report, prepared within Work package 1 of IEA Wind Task 49, presents reference site conditions for floating wind arrays to serve as a design basis for the techno-economic design of reference floating wind arrays. Data of the reference sites presented here are publicly available in an open database and thus support fast development and comparable design of floating wind arrays for various relevant conditions. The development of these reference sites drew on existing open access datasets and ongoing research projects of task participants. Six classes were identified that describe relevant key conditions for the design and development of floating wind arrays: met-ocean conditions, seabed conditions, coastal infrastructure, environmental impact, socio-economic impact, as well as regulations and permissions. The reference sites for the techno-economic design of floating wind arrays are based on a concept with building blocks to synthesize purpose-built site representations. In each of the identified classes with influencing design factors, building blocks are used to describe the characteristic properties and their spread. However, the latter three classes (i.e., environmental impact, socio-economic impact, regulations and permissions) are not included in the reference site conditions due to limited knowledge and lack of reliable criteria to quantify their impact on the techno-economic design in numeric parameters. Building blocks with key parameters for the techno-economic design of floating wind arrays are provided for met-ocean conditions, seabed conditions, and coastal infrastructure. For met-ocean conditions, multiple sites were selected for detailed analysis that represent a range of conditions across the pipeline of floating wind projects. Wind conditions and sea states are separated, and each location considers both the severity of wind and waves e.g. one site may have a moderate wave condition but severe wind condition. From this pipeline, eleven representative sites were selected where both site-specific analysis was available within the consortium, and where they represent different parts of the global pipeline. The eleven sites are: Hannibal (Italy), Humboldt (US), Ulsan (South Korea), MoneyPoint One (Ireland), Havbredey (UK), Fukushima (Japan), Utsira Nord (Norway), Gulf of Maine (US), Sud de la Bretagne II (France), Sorlige Nordsjo II (Norway). Each of these sites is summarized in the main report while more details about the studies and analyses behind the datasets are provided in the appendix. For seabed conditions, general information about the geotechnical parameters is provided and a baseline is established for the geotechnical parameters and stratigraphy that may be encountered on the sites. A set of six 'synthetic cases' is defined as building blocks providing the different parameters required for design under each case/soil condition. For the coastal infrastructure, general information about the main requirements is provided that a port should comply with to provide a satisfactory service during the construction of floating offshore wind arrays. Minimum port infrastructural requirements are provided for three types of ports.

17 WIND ENERGY↗

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗

Collaborative: in situ visual analytics technologies for extreme scale combustion simulations

This project aims to drastically enhance the usability of in situ analysis and visualization for extreme-scale scientific simulations. Current exascale computing capabilities promise to offer greater predictive ability of simulations and to further push the frontiers of science and technology. However, to validate the simulation output at extreme scale, examine the modeled phenomena, and discover previously unknowns from the output data, the output must be reduced or transformed in situ as it is being generated during the simulation such that the amount of data to examine and store is kept to a minimum. Such in situ approaches allow us to process and analyze the data and any embedded geometry to an extent that would be prohibitively expensive, if not impossible, to perform as a post hoc task. While in situ processing has been demonstrated to be a feasible and promising approach, its full potential has not yet been leveraged. In this project, we have developed comprehensive enhancements to in situ technology based on probability distributions in data. Our research focuses on jointly developing new ways of interacting with massive statistical samples while creatively utilizing new state-of-the-art computational resources to push the boundaries of in situ exploration. Moreover, we have developed new time-dependent techniques to enable previously unattainable capabilities in areas such as intelligent simulation steering and precise feature identification. We have experimentally studied our design and implementation at NERSC and OLCF, and are able to leverage existing in situ infrastructures whenever possible. While the exemplar in this project is combustion, many other fields for which turbulent transport is important, e.g., fusion, climate, astrophysics among others, encounter similar issues as simulations scale up to the exascale. This project shows its potential to generate high impact on DOE missions since the resulting technology promises to improve scientists’ ability to rapidly and correctly interpret and tune extreme-scale simulations, leading to new scientific understanding and advancements.

97 MATHEMATICS AND COMPUTING↗

Beneficial Use of Harvested Ponded Fly Ash and Landfilled FGD Materials for High-Volume Surface Mine Reclamation

The overall motivation of this project was to demonstrate at laboratory, bench-scale, and full-scale demonstration levels that (a) coal ash surface impoundments can go through closure by removal as per USEPA and state regulations so that the material can be used as is (other than draining free water using CCRs piles) in high-volume beneficial applications, (b) FGD material from closed out FGD facilities can be excavated and recompacted for coal mine reclamation, and (c) harvested CCRs can be beneficially utilized (providing a net environmental gain) in large-volumes for reclamation at abandoned coal mine sites across the US, especially in the Eastern and Midwest coal mining regions. The objectives of this project were to: 1) promote the safe and cost-effective closure by removal of coal ash impoundments, 2) harvest landfilled FGD, and 3) promote the high-volume beneficial use of these harvested CCRs in the reclamation of abandoned surface coal mine sites across the eastern and midwestern coal mining regions of the United States. The major tasks carried out for this project are summarized below: 1) Conesville Full-Scale Demonstration Project: About 2 million tons of harvested CCR materials from the closure by removal of an inactive fly ash pond and an adjacent old FGD landfill were used for the full-scale demonstration project to fully reclaim a nearby partially completed abandoned surface coal mine. Site monitoring for the project duration was carried out and results are discussed. 2) Laboratory Testing: Geotechnical and environmental testing of harvested ponded fly ash and landfilled FGD material at the former Conesville power plant were carried out. Completing the laboratory testing allowed for QA/QC for the full-scale site construction and informed the formulation of the risk analysis. 3) Risk Analysis: We developed a reliable computational model for fate and transport. We used these models and the rich set of monitored data for the Conesville site to analyze risks to human health and ecological risks associated with high-volume surface mine reclamation using harvested CCRs. 4) GIS Siting Study: A Geographic Information System (GIS) study was carried out for three states in the Eastern coal mining region and two states in the Midwest coal region. This effort provided site specific GIS information for five states and allowed us to establish protocols that other states can follow in implementing their own state specific GIS study.

01 COAL, LIGNITE, AND PEAT↗

Important Human Actions for Advanced Reactors: Implications for Human Factors

As advanced reactor platforms continue to develop and gain traction in the energy sector there is a need for risk-informed, scalable regulations that match that progress. This is a core component of the U.S. Nuclear Regulatory Commission’s proposed Part 53 Rule Making; the Accelerating Deployment of Versatile, Advanced Nuclear for Clean Energy (ADVANCE) Act; and other efforts that seek to update nuclear power regulations. This paper covers one key aspect of that regulatory evolution: Important Human Actions (IHA). In this paper, we discuss how the understanding and definitions of IHAs have changed and what that means for human factors engagement through the process of developing these technologies. Instead of a narrow focus on control actions that led to an increase in core damage risk, the new focus is on IHAs is “wherever they occur.” What this means is that having a highly automated or passive safety system does not eliminate IHAs. Rather, it shifts the focus point to all the actions that enable these systems. Everything from maintenance, to design, to training can be considered an IHA and that dramatically shifts the efforts and level of engagement necessary for human factors to enable these technologies. We discuss the notions of risk-informed human factors that underpin these efforts, give several examples, and briefly describe the risk assessment methodologies that will be needed. In the past, IHAs were identified and then became a focus point of human factors engineering (HFE) activities to ensure a robust evaluation of the task was completed. The future is less clear. HFE for nuclear energy will need to evolve and become more integrated in technology development than ever before.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Testing lepton flavor universality at future Z factories

As one of the hypothetical principles in the Standard Model (SM), lepton flavor universality (LFU) should be tested with a precision as high as possible such that the physics violating this principle can be fully examined. The run of a Z factory at a future e + e − collider, such as the Circular Electron-Positron Collider or Future Circular Collider (electron/positron), provides a great opportunity to perform this task because of the large statistics and high reconstruction efficiencies for b hadrons at the Z pole. In this paper, we present a systematic study on the LFU test in future Z factories. The goal is threefold. First, we study the sensitivities of measuring the LFU-violating observables of b → c τ ν , i.e., R J / ψ , R D s , R D s * , and R Λ c , where τ decays muonically. For this purpose, we develop the strategies for event reconstruction, based on the track information significantly. Second, we explore the sensitivity robustness against detector performance and its potential improvement with the message of event shape or beyond the b -hadron decays. A picture is drawn on the variation of analysis sensitivities with the detector tracking resolution and soft photon detectability, and the impact of Fox-Wolfram moments is studied on the measurement of relevant flavor events. Finally, we interpret the projected sensitivities in the SM effective field theory, by combining the LFU tests of b → c τ ν and the measurements of b → s τ + τ − and b → s ν ¯ ν . We show that the limits on the LFU-violating energy scale can be pushed up to ∼ O ( 10 ) TeV for ≲ O ( 1 ) Wilson coefficients at Tera- Z . Published by the American Physical Society 2024

Astronomy & Astrophysics↗

Software Quality Assurance for the MOOSE-Based Open-Source Multiphysics Code Cardinal - An Expanded CI Testing Suite

Cardinal is a wrapping of the GPU-oriented spectral element Computational Fluid Dynamics (CFD) code NekRS and the Monte Carlo particle transport code OpenMC within the Multiphysics Object-Oriented Simulation Environment (MOOSE). Cardinal provides high-resolution thermal-hydraulics and/or radiation transport feedback to MOOSE multiphysics simulations. Multiphysics feedback is implemented in a geometry-agnostic manner which eliminates the need for rigid one-to-one mappings. A generic data transfer implementation also allows NekRS and OpenMC to couple to any MOOSE application, enabling a broad set of multiphysics capabilities. Cardinal simulations can also leverage combinations of MPI, OpenMP, and GPU resources. Cardinal continuous development and improvement efforts have led to the software being considered as a high-fidelity design and licensing tool for key areas of nuclear reactor relevant physics, including neutron transport, fluid flow, heat transfer, and mechanical processes. The fast development and expansion of the software from a pure R&D framework towards its application in the nuclear industry and regulation require a focus on developing, enhancing and, maintaining Cardinal’s software quality through strict adherence to a Software Quality Assurance (SQA) framework and SQA program. To facilitate compliance with SQA standards, the Cardinal SQA Program has been initiated during Fiscal Year 2023 (FY23). During the development of the Cardinal SQA Program, multiple gaps have been identified. These gaps are primarily related to model verification and code pedigree as they relate to the use of Cardinal as a safety analysis tool. These gaps have been captured in a report published in 2023. A second report highlighted the progress made during Fiscal Year 2024 (FY24) and described Argonne’s effort to document and integrate software verification within Cardinal’s software development process. This report documents a snapshot of the verification test cases currently available for Cardinal and NekRS in their assimilation into a Continuous Integration (CI) platform. Following the CI practice permits the integrating of source code changes frequently and ensuring that the integrated codebase clears the verification testing for the software. It should be noted that the SQA program itself, including the program plans, procedures, configuration management, and testing strategies, need to be developed in a future step of this task.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Improved Fundamental Understanding of Aluminum Chemistry and Interactions of Aluminate Anion with Co-Anions: ORNL Project Progress Report

The U.S. Department of Energy (DOE)’s Hanford Site in Washington State houses 177 underground storage tanks containing millions of gallons of nuclear and chemical waste with high aluminum content. Aluminum (Al) salt reactions with strong bases produce aluminum hydroxides, like boehmite (γ-AlOOH), which are main components of insoluble nuclear waste sludge. Understanding the morphology, crystallinity, and dissolution behavior of boehmite under waste tank conditions is necessary for improving waste management and mitigation strategies. The ORNL team uses in situ multimodal analysis strategy to study Al chemistry of simulated tank waste using microfluidic reactors and advanced chemical imaging and mass spectrometry (MS) techniques. Because the SALVI device is vacuum compatible and transferrable among different platforms, we can use it in scanning electron microscopy (SEM), vacuum ultraviolet single photon mass spectrometry (VUV SPI-MS), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) to study the chemical speciation and colloid stability in liquids in this project. Additionally, ex situ transmission electron microscopy (TEM) and x-ray diffraction (XRD) spectroscopy can be used to verify particle phase to further the understanding of the Al-bearing phases. This report gives a summary of the technical progress of the ORNL tasks. A plan for year 2 performance is recommended in the summary.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Errant Beam Prognostics with Machine Leaning at SNS Accelerator

Particle Accelerators are complex machine with many pieces of equipment running in synchronization to deliver required beam. However, faults in particle accelerators reduce the availability of the beam for experiments affecting the overall science output. To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability. Many researchers have adopted semi-supervised Machine Learning (ML) methods such as auto-encoders and variational auto-encoders for such tasks. However, supervised ML techniques designed for similarity learning such as Siamese Neural Network (SNN) can outperform semi-supervised or unsupervised methods for anomaly prediction. One of the challenges associated with application of ML models to particle accelerators is the variability in observed data over time due to system configuration changes. We employ conditional models such as Conditional Siamese Neural Networks (CSNN), and Conditional-VAE (CVAE) to learn the variability in the data by using beam configuration parameters as conditional input. We apply these models for errant beam prediction at Spallation Neutron Source accelerator under different system configurations and compare their performance. We demonstrate that CSNN outperforms CVAE in our application. This talk will present the data source, collection, analysis, data-preparation, model development, hyper-parameter studies and the results.

Rajput, Kishansingh↗

Machine learning models for segmentation and classification of cyanobacterial cells

Abstract Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.

Huffine, Clair A.↗

Photovoltaic Cable Connectors: A Comparative Assessment of the Present State of the Industry [Slides]

The consequences of failure for balance of systems (BoS) components (such as PV cable connectors) include offline module string(s); low system voltage; arc, ground, insulation, and over-temperature faults; triggered fuse(s); system shutdown; and fire. The degradation modes for connectors are studied here through an industry survey and its subsequent examination, which are compared to field-degraded specimens. 117 specimens were obtained from a variety of locations and climates or accelerated tests. A failure analysis for connectors from PV installations was developed (and applied to 54 specimens) including nondestructive examinations (photography, a custom resistance-current scan, and X-ray computed tomography) and destructive examinations (featuring milling of the external plastic, extraction of the internal convolute spring, and potting and polishing in cross-section). Surface and through-thickness composition of the metal pins and springs was quantified using scanning electron microscopy with energy-dispersive X-ray spectroscopy. Fourier-transform infrared spectroscopy was used to verify the base polymer materials and compare the chemical structure of the connector body, bushing, end nut, and o-ring. Thermogravimetric analysis and differential scanning calorimetry were used to further verify the degradation of the same polymeric components. Updated from 2023 NIST/UL Workshop on Photovoltaic Materials Durability (website: https://events.ul.com/WPMogn?rt=aAuoWsl4E0KaORLCMeOgfA) and 2024 PVRW workshop (https://pvrw.nrel.gov/past-proceedings).

14 SOLAR ENERGY↗

Task Sharing of Proton Incorporation in Vertically Aligned Nanocomposite Triple Conductors: Growth, Structure, and Surface Exchange Kinetics

As protonic ceramic electrolysis cells emerge for efficient H 2 production, there is a need to develop air electrode materials enabling fast, durable steam splitting and proton incorporation. Single-phase triple conductors may fail to satisfy the myriad performance/stability requirements, and their critical charge-carriers (holes, oxygen vacancies, and protons) are in competition, limiting their concentrations. Instead, we propose task-sharing, vertically aligned nanocomposites (VANs), comprising a proton conductor (BaZr 0.9 Y 0.1 O 3-δ ) and a redox-active mixed ionic electronic conductor (Ce 0.9 Pr 0.1 O 2-δ ), that may enable rapid proton surface exchange at the solid–gas interface and transport along the solid–solid heterointerfaces. We grew VANs by pulsed laser deposition and investigated the interplay between their processing conditions, structure, and proton and oxygen surface exchange kinetics. We varied the substrate temperature, laser repetition rate, laser fluence, and processing oxygen pressure. The crystallinity and phases were characterized by grazing-incidence X-ray diffraction, and the strain and structural order as a function of depth were evaluated by angle-dependent synchrotron X-ray pair distribution function analysis. To evaluate the potential for interdiffusion, the formation energies of substitutional defects were simulated with density functional theory. Corresponding structural analysis and elemental mapping were performed by scanning/transmission electron microscopy, energy-dispersive X-ray spectroscopy, and electron energy-loss spectroscopy, indicating distinct nanoscale compositional regions with a hierarchical structure embedded in individual VANs columns and minimal interdiffusion across a bilayer film. Proton and oxygen surface exchange coefficients (k H , k O ) and polarization resistances were evaluated by electrical and optical relaxations and impedance spectroscopy of VAN-incorporated protonic ceramic electrochemical cells, respectively, at 400–500 °C, demonstrating values comparable to some of the best-known triple and mixed conductors.

36 MATERIALS SCIENCE↗

Wavelet flow for extragalactic foreground simulations

Extragalactic foregrounds in cosmic microwave background (CMB) observations are both a source of cosmological and astrophysical information and a nuisance to the CMB. Effective field-level modeling that captures their non-Gaussian statistical distributions is increasingly important for optimal information extraction, particularly given the low-noise observations from current and upcoming experiments. Here, we explore the use of Wavelet Flow (WF) models to tackle the novel task of modeling the field-level probability distributions of multi-component CMB secondaries and foregrounds. Specifically, we jointly train correlated CMB lensing convergence (κ) and cosmic infrared background (CIB) maps with a WF model and obtain a network that statistically recovers the input to high accuracy — the trained network generates samples of κ and CIB fields whose average power spectra are within a few percent of the inputs across all scales, and whose Minkowski functionals are similarly accurate compared to the inputs. Leveraging the multiscale architecture of these models, we fine-tune both the model parameters and the priors at each scale independently, optimizing performance across different resolutions. These results demonstrate that WF models can accurately simulate correlated components of CMB secondaries, supporting improved analysis of cosmological data. Our code and trained models can be found on this GitHub repo.

cosmological simulations↗

Recovery Simulator and Analysis Formulation: Mathematical Framework for Enhanced Resilience and Resource Allocation

This report introduces recovery simulator and analysis (RSA), a framework aimed at enhancing the resilience of electrical grids post-disruption. The RSA model leverages an optimization problem formulation that focuses on maximizing the load served (or optionally customers served) through a coordinated and cooptimized recovery of non-black start generation, transmission lines, feeders and substations subject to labor budget constraints. By integrating advanced linear programming techniques, the simulator selects efficient reocovery pathways, optimizing both short-term and long-term grid recovery strategies. The mathematical framework guides decision-making through a comprehensive evaluation of potential recovery actions, factoring in the trade-offs between labor constraints and load (or optionally customer) restoration efficacy. This enables grid operators to simulate diverse outage scenarios and delineate optimal recovery pathways, thereby prioritizing critical repair tasks and ensuring resource allocation is both economical and effective. The intended use case of RSA is to allow planners to explore many recovery scenarios quickly and determine assets most critical across a wide range of scenarios, and therefore strong candidates for hardening or additional investment. RSA might also be used in an operational setting, following a single event, for exploring efficient recovery pathways.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

Abstract Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

60 APPLIED LIFE SCIENCES↗

On the Training and Generalization of Deep Operator Networks

Here, we present a novel training method for deep operator networks (DeepONets), one of the most popular neural network models for operators. DeepONets are constructed by two subnetworks, namely the branch and trunk networks. Typically, the two subnetworks are trained simultaneously, which amounts to solving a complex optimization problem in a high dimensional space. In addition, the nonconvex and nonlinear nature makes training very challenging. To tackle such a challenge, we propose a two-step training method that trains the trunk network first and then sequentially trains the branch network. The core mechanism is motivated by the divide-and-conquer paradigm and is the decomposition of the entire complex training task into two subtasks with reduced complexity. Therein the Gram–Schmidt orthonormalization process is introduced which significantly improves stability and generalization ability. On the theoretical side, we establish a generalization error estimate in terms of the number of training data, the width of DeepONets, and the number of input and output sensors. Numerical examples are presented to demonstrate the effectiveness of the two-step training method, including Darcy flow in heterogeneous porous media.

deep operator networks↗