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At least 937 records · Page 52

Software Validation Work With The ZPPR-15 Data

The analysis activities for fast reactors involve using many different pieces of software that are relied upon for their predictive capabilities. For this software to be considered reliable, documented proof that the predictions of the software are accurate is required. In this manuscript, the validation work that covers some of the Argonne software used in fast reactor design activities is discussed and displayed. This validation work includes neutron and gamma flux distributions, reaction rate distributions, and reactivity worth. In an ideal world, a reactor development program would have access to a comprehensive set of experimental facilities to help inform the design aspects of the reactor itself. While thermal-hydraulics experiments, and to a limited degree mechanical experiments, can be carried out today for validation needs, neutronics related experimental facilities are rather impractical because of the lack of experimental facilities. Given the desired time table for construction of new reactors, the reconstitution or creation of new neutronic experimental facilities is untenable and thus those reactor development programs must rely upon any available experimental measurements that are qualitatively similar to the design. While a methodology has been proposed to assess the similarity between the past experimental measurements and the reactor itself, that aspect is beyond the scope of this manuscript. In this manuscript, the focus is entirely placed on the analysis results for a series of experiments carried out at the ZPPR facility in Idaho in the mid-1980s. In this regard, this manuscript only shows the validation of the stated neutronics software for specific loadings of the ZPPR reactor. Because of the fuel form, its proposed enrichment, and the material content of the reactor core, the ZPPR-15 experiments were identified as potential validation data for the reactor. The ZPPR-15 experiments were intended as mockups of a 330 MWe Integral Fast Reactor program which was a follow on program to the Clinch River Breeder Reactor. In the ZPPR-15 series of experiments, measurements of the neutron spectrum, control rod worth, sodium void worth, foil reaction rate distributions, Doppler worth of heated samples, gamma dose, and axial expansion worth were all carried out and published. In many cases, these reactivity coefficients are good candidates to validate the reactivity coefficient calculation scheme used by the analysis software and included in the safety analysis activities of fast reactor development projects today. This manuscript discusses the modeling methodology and accuracy of the calculated experimental results using the LANL software MCNP and the ANL software package ARC (Argonne Reactor Codes). As will be shown, for many of the experimental measurements, the two software packages are found to be good predictive analysis tools for those experiments. In other cases, problems with the analysis methodology or underlying cross section data are exposed which indicates where predictive analysis is not as reliable. Finally, in some of the measurements the conclusion is reached that the experimental measurement cannot be reproduced with the analysis software as it is simply too difficult.

Aliberti, Gerardo↗

Exploring Properties and Correlations of Fatal Events in a Large-Scale HPC System

In this study, we explore potential correlations of fatal system events for one of the most powerful supercomputers-IBM Blue Gene/Q Mira, which is deployed at Argonne National Laboratory, based on its 5-year reliability, availability, and serviceability (RAS) log. Our contribution is two-fold. (1) We design an efficient log analysis tool, namely LogAider, with a novel filtering method to effectively extract fatal events from masses of system messages that are heavily duplicated in the log. LogAider exhibits a very precise detection of temporal-correlation with a high similarity (up to 95 percent) to the ground-truth (i.e., compared to the failure records reported by the administrators). The total number of fatal events can be reduced to about 1,255 compared with originally 2.6 million duplicated fatal messages. (2) We analyze the 5-year RAS log of the MIRA system using LogAider, and summarize six important "takeaways" which can help system vendors and administrators better understand an extreme-scale system's fatal events. Specifically, we find that the distribution or proportion of the fatal system events follow a Pareto-like principle in general. The temporal correlation among fatal events is much stronger than that of warn messages and info messages, and the correlated events tend to constitute a few clusters. The mean time between fatal events (MTBFE) of the Mira system is about 1.3 days from the perspective of the system, and the MTTI is 2-4 days from the perspective of users. The most error-prone item value with respect to any key attribute appears likely in the log every 2-10 days. Weibull, Gamma, and Pearson6 are the three best-fit distributions for the fatal event intervals. The overall correlation of fatal events on the 5D torus network is not prominent, whereas the small-region locality correlation (e.g., the fatal events inside racks) is relatively strong. We believe our work will be interesting to large-scale HPC system administrators and vendors and to fault tolerance researchers, enabling them to better understand fatal events and mitigate such events accordingly.

97 MATHEMATICS AND COMPUTING↗

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗

Economic and Performance Analysis of Gear Box Failures. Cooperative Research and Development Final Report, CRADA Number CRD-07-236

The National Renewable Energy Laboratory (NREL) in Golden, CO, USA is forming a collaborative within the wind energy industry to address reliability issues on wind turbines. The National Wind Technology Center (NWTC), leading the collaborative effort, will allow gearbox manufacturers, bearing manufacturers, wind turbine owner/operators, and wind turbine manufacturers to team up for joint projects that address specific issues regarding design and reliability of wind turbine gearboxes. The primary means of investigation will be through full scale testing and analysis of actual gearboxes, both in the field and in the NREL 2.5 MW dynamometer test facility. These teams would contribute resources into a campaign that is intended to serve the mutual interests of a majority of the wind and gearbox industry stakeholders in addition to the specific project team objectives. that address specific issues regarding design and reliability of wind turbine gearboxes. The primary means of investigation will be through full scale testing and analysis of actual gearboxes, both in the field and in the NREL 2.5 MW dynamometer test facility. These teams would contribute resources into a campaign that is intended to serve the mutual interests of a majority of the wind and gearbox industry stakeholders in addition to the specific project team objectives.

17 WIND ENERGY↗

Investigation of the MTC noise estimation with a coupled neutronic/thermal-hydraulic dedicated model - 'Closing the loop'

This paper investigates the reliability of different noise estimators aimed at determining the Moderator Temperature Coefficient (MTC) of reactivity in Pressurized Water Reactors. By monitoring the inherent fluctuations in the neutron flux and moderator temperature, an on-line monitoring of the MTC without perturbing reactor operation is possible. In order to get an accurate estimation of the MTC by noise analysis, the point-kinetic component of the neutron noise and the core-averaged moderator temperature noise have to be used. Because of the scarcity of the in-core instrumentation, the determination of these quantities is difficult, and several possibilities thus exist for estimating the MTC by noise analysis. Furthermore, the effect of feedback has to be negligible at the frequency chosen for estimating the MTC in order to get a proper determination of the MTC. By using an integrated neutronic/thermal- hydraulic model specifically developed for estimating the three-dimensional distributions of the fluctuations in neutron flux, moderator properties, and fuel temperature, different approaches for estimating the MTC by noise analysis can be tested individually. It is demonstrated that a reliable MTC estimation can only be provided if the core is equipped with a sufficient number of both neutron detectors and temperature sensors, i.e. if the core contain in-core detectors monitoring both the axial and radial distributions of the fluctuations in neutron flux and moderator temperature. It is further proven that the effect of feedback is negligible for frequencies higher than 0.1 Hz, and thus the MTC noise estimations have to be performed at higher frequencies. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Life-cycle cost-benefit analysis of a novel self-heating pavement made from coal-derived solid carbon

Winter weather events challenge the safety, efficiency, and sustainability of roadway networks, particularly road bridges vulnerable to climate impacts. Although effective, conventional de-icing methods incur high expenses and cause significant environmental contamination. To address these longstanding issues, a low-cost novel coal-derived carbon enabled smart pavement (CDC-SP) de-icing system was developed, aiming to alleviate traffic delays, severe corrosion, compromised safety, and environmental pollution. However, the economic performance of CDC-SP has not been quantified to guide decision-making. To this end, this paper presents a Life-Cycle Cost-Benefit Analysis (LCCBA) to assess the economic feasibility of the CDC-SP de-icing system for road bridges in five representative urban and rural areas across cool humid and cold humid climate zones. Given the variations and uncertainties of several factors that determine the costs and benefits, a sensitivity analysis was conducted to determine the system’s economic reliability. Additionally, a Monte Carlo simulation (MCS) was performed to quantify the impacts of important factors and identify the most beneficial scenarios. Furthermore, the CDC-SP de-icing systems have demonstrated substantial economic benefits and short payback periods, showing great applicability for rural and urban road bridges with different service levels across multiple climate zones as compared to conventional de-icing methods.

36 MATERIALS SCIENCE↗

Fluctuation localization imaging-based fluorescence in situ hybridization (fliFISH) for accurate detection and counting of RNA copies in single cells

Here, quantitative gene expression analysis in intact single cells can be achieved using single molecule-based fluorescence in situ hybridization (smFISH). This approach relies on fluorescence intensity to distinguish between true signals, emitted from an RNA copy hybridized with multiple oligonucleotide probes, and background noise. Thus, the precision in smFISH is often compromised by partial or nonspecific probe binding and tissue autofluorescence, especially when only a small number of probes can be fitted to the target transcript. Here we provide an accurate approach for setting quantitative thresholds between true and false signals, which relies on on-off duty cycles of photoswitchable dyes. This fluctuation localization imaging-based FISH (fliFISH) uses on-time fractions (measured over a series of exposures) collected from transcripts bound to as low as 8 probes, which are distinct from on-time fractions collected from nonspecifically bound probes or autofluorescence. Using multicolor fliFISH, we identified radial gene expression patterns in mouse pancreatic islets for insulin, the transcription factor, NKX2-2 and their ratio ( Nkx2 - 2/Ins2 ). These radial patterns, showing higher values in β cells at the islet core and lower values in peripheral cells, were lost in diabetic mouse islets. In summary, fliFISH provides an accurate, quantitative approach for detecting and counting true RNA copies and rejecting false signals by their distinct on-time fractions, laying the foundation for reliable single-cell transcriptomics.

59 BASIC BIOLOGICAL SCIENCES↗

Time-dependent behavioral data from zebrafish reveals novel signatures of chemical toxicity using point of departure analysis

High-content imaging of embryonic zebrafish behavior can be used as a screening approach to rapidly evaluate the relative potential for chemicals to cause toxicity. However, most statistical methods applied to these data so far transform movement values to incidence-based “hits” and calculate lowest effect levels (LELs), which loses individual fish resolution of behavior and defies hazard ranking due to reliance on applied dose levels. Here, we have developed and parallelized a workflow to calculate benchmark dose (BMD) values from dynamic, high-content zebrafish behavior data that scales for high-throughput chemical screening. To appropriately capture the dynamics of zebrafish response from light to dark stimulus, we summarized time-dependent data using both area under the curve and immediate change at the transition point into two novel metrics that characterized abnormal behavior as a function of chemical concentration. The BMD workflow was applied to calculate BMD 10 values of 1,060 ToxCast chemicals for 24 zebrafish endpoints, including behavior, mortality and morphology. The BMD 10 values provided better precision and separation than LELs for clustering chemicals since they were derived from models that best-fit their concentration-response curves. Clustering analysis revealed behavioral signatures as the most sensitive endpoints. High concordance in chemical activity was observed between ToxCast assays and zebrafish behavioral endpoints, however ToxPi analysis indicated that rankings based on in vitro data was not a reliable predictor of in vivo rankings for lower potency chemicals. The methods developed in this work will enable the use of high-content zebrafish behavioral screening data for BMD analysis in toxicological hazard assessment.

59 BASIC BIOLOGICAL SCIENCES↗

Synchrophasor Visual Integration and Event Evaluation for Utilities (SynchroVIEEU) with High Penetration of Renewables

The Hawaiian Electric Companies (Companies), operating independent grid systems on the islands of Oahu, Hawaii, Maui, Molokai and Lanai, have been contending with some of the highest levels of renewable generation from both utility-scale and distributed generation resources. Renewable penetration levels exceeding 50% of generation are some of the highest in the nation and are changing the system characteristics and requiring operational changes to grid management. The Companies have aggressively leveraged federal, multi-state and industry grant funding to demonstrate new technologies for monitoring, visualization, forecasting and analysis to inform transformation needs and to improve operational awareness, especially at the distribution level. As part of the American Recovery and Reinvestment Act (ARRA) supported efforts to facilitate integration of renewables, synchrophasor devices were deployed on the islands of Hawaii and Maui in 2010. The devices have potential to provide Hawaii Electric Light Company (HELCO) on Hawaii Island and Maui Electric Company (MECO) with additional information to support post-event analysis and to help quantify observed area-wide changes in voltage and frequency on the systems. Consistent with the U.S. Department of Energy’s (DOE) Office of Electricity Delivery and Energy Reliability (OE) FOA objectives, the goal of this project was to accelerate the integration of synchrophasor information into production grade data visualization and analysis platforms/models to inform timely, efficient and reliable operations of a modern grid in light of high penetrations of renewable resources. The project comprised a two-year development effort to investigate the integration and visualization of synchrophasor field data into production-ready transmission and distribution proactive modeling efforts and a common data analysis platform application currently being used by the Companies. Conceptually, the project was about getting the right data to the right people at the right time in an easily accessible and timely environment.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Datasets for Widespread Residential Space Heating Electrification in Texas

In this experiment, we explore long term patterns in electricity demand driven by the dual effects of full electrification of space heating in Texas (by adoption of electric heat pumps), and climate change. We use a predictive model of electricity demand, climate projections, and an open source nodal power system (DC Optimal Power Flow) model of the Electric Reliability Council of Texas (ERCOT) system. Heat pumps are a more energy efficient way of providing space heating and cooling in homes. We attempt to exhaustively investigate the impacts of full residential space heating electrification by adoption of heat pumps for the segment of Texas households that currently rely on fossil fuels (about 40%), while simultaneously incorporating climate change meteorological variables. We explore a range of scenarios of heat pump efficiency and climate uncertainty over a long period of future years (2020-2099). In total, the simulation experiment generates 1,280 simulation years of hourly data. We report and analyze results in form of impacts on residential load, total load, peak load, seasonality of peaking, and reliability measured by occurrence and frequency of loss of load events. While the experiment is for ERCOT, the insights and approach can be applied to other regions. The results from the analysis can inform system planners on a range of potential capacity requirements/ reliability implications and/or risks of full space heating electrification via the adoption of electric heat pumps, given the uncertainty in the scenarios/ climate futures. The dataset includes model output for residential, non residential and total load, and the results from the GO ERCOT model runs for 4 RCP Scenarios (RCP 4.5 Cooler, RCP 4.5 Hotter, RCP 8.5 Cooler, RCP 8.5 Hotter), 4 Heating electrification Scenarios (Base , Standard Efficiency HP, High Efficiency HP, Ultra-High Efficiency HP) over 80 years (2020-2099). The metrological variables at BA scale were weighted weighted using population projections consistent with the SSP3 scenario.

Climate Change↗

Microgrid optimized resource dispatch for public-purpose resiliency and sustainability. The Olney Town Center Microgrid Project

Communities in Atlantic coastal regions have in recent years sought to improve the resiliency of their critical infrastructure and public services, especially to protect against hurricanes and other events capable of causing widespread damage and disruption. As the backbone of any community’s critical functions, the electricity distribution system requires high resiliency in order to maintain local energy delivery services. Against this backdrop, the Project sought to develop a resilient energy microgrid control system capable of integrating distributed renewable energy resources, natural gas CHP units, energy storage, and demand-side management technologies in near-real-time optimization schemes for the community of Olney, Md. The Montgomery County Planning Board in 2005 established the Olney Town Center area as a “civic center/town commons,” in part because it serves as a key point of interaction in the community – but also because it contains numerous vital community assets. With a total peak electrical load of about 8 MW (including Montgomery General Hospital with a 2.4 MW peak), the Project area is a business and essential services area, directly serving a suburban population of more than 33,000 residents. It contains a hospital, police station, two fire stations, two schools, grocery stores, and gas stations, and the community’s water tower, among other things. Moreover, the location stands at the crossroads of two state highways that represent major regional arteries for commerce and public safety in Montgomery County. These characteristics made the Project area an appropriate setting for considering microgrid deployment. It presented a model of a typical Maryland suburban community, with geographic dispersion of vital assets over a sizeable area, and a combination of overhead distribution lines and underground cables serving those critical loads. Such a representative model helped to ensure the solutions developed and the scenarios tested would be readily applicable to other communities in the state and the region. Further, the Project’s outcomes and lessons provide insights to guide community microgrid design and development in many locations. To achieve Project objectives – including those established by the U.S. Department of Energy (DOE) National Energy Technology Laboratory – the Project team researched, developed, and tested in simulation a set of microgrid controls capable of maintaining electricity supplies for critical community loads in the event of a regional utility outage lasting many days or even weeks. Testing and analysis showed that the microgrid would be capable of maintaining electricity supply to critical loads essentially indefinitely in most outage scenarios, while also substantially improving overall reliability for microgrid customers. With targeted improvements in local utility distribution infrastructure, test analysis showed that the microgrid would be capable of reducing annual electricity outages for critical loads by 98%. Further, to help achieve environmental and efficiency policy goals established by both the State of Maryland and the federal government, the team designed the system to reduce the annual carbon footprint of served loads by 20%, and to improve system energy efficiency for those loads by at least 20%. Testing showed that, as designed, the system is capable of meeting these performance requirements, with potential for further improvements through more effective thermal energy utilization. This Final Report, comprised of four volumes and 11 annexes, presents the results of these project efforts, including feasibility assessment (section F) and guidance for decision-makers considering prospective deployment of public-purpose microgrid systems in Maryland communities.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Efficient and robust analysis of complex scattering data under noise in microwave resonators

Superconducting microwave resonators are reliable circuits widely used for detection and as test devices for material research. A reliable determination of their external and internal quality factors is crucial for many modern applications, which either require fast measurements or operate in the single photon regime with small signal to noise ratios. Here, we use the circle fit technique with diameter correction and provide a step by step guide for implementing an algorithm for robust fitting and calibration of complex resonator scattering data in the presence of noise. The speedup and robustness of the analysis are achieved by employing an algebraic rather than an iterative fit technique for the resonance circle.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Minimizing treatment planning errors in proton therapy using failure mode and effects analysis

Purpose: Failure mode and effects analysis (FMEA) is a widely used tool to evaluate safety or reliability in conventional photon radiation therapy. However, reports about FMEA application in proton therapy are scarce. The purpose of this study is to apply FMEA in safety improvement of proton treatment planning at their center. Methods: The authors performed an FMEA analysis of their proton therapy treatment planning process using uniform scanning proton beams. The authors identified possible failure modes in various planning processes, including image fusion, contouring, beam arrangement, dose calculation, plan export, documents, billing, and so on. For each error, the authors estimated the frequency of occurrence, the likelihood of being undetected, and the severity of the error if it went undetected and calculated the risk priority number (RPN). The FMEA results were used to design their quality management program. In addition, the authors created a database to track the identified dosimetric errors. Periodically, the authors reevaluated the risk of errors by reviewing the internal error database and improved their quality assurance program as needed. Results: In total, the authors identified over 36 possible treatment planning related failure modes and estimated the associated occurrence, detectability, and severity to calculate the overall risk priority number. Based on the FMEA, the authors implemented various safety improvement procedures into their practice, such as education, peer review, and automatic check tools. The ongoing error tracking database provided realistic data on the frequency of occurrence with which to reevaluate the RPNs for various failure modes. Conclusions: The FMEA technique provides a systematic method for identifying and evaluating potential errors in proton treatment planning before they result in an error in patient dose delivery. The application of FMEA framework and the implementation of an ongoing error tracking system at their clinic have proven to be useful in error reduction in proton treatment planning, thus improving the effectiveness and safety of proton therapy.

60 APPLIED LIFE SCIENCES↗

Dynamic Event Tree Analysis Through RAVEN

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (D-PRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed in a high modular and pluggable way in order to enable easy integration of different programming languages (i.e., C++, Python) and coupling with other application including the ones based on the MOOSE framework, developed by INL as well. RAVEN performs two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, RAVEN also models stochastic events, such as components failures, and performs uncertainty quantification. Such stochastic modeling is employed by using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This paper focuses on the first task and shows how it is possible to perform the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, the Dynamic PRA analysis, using Dynamic Event Tree, of a simplified pressurized water reactor for a Station Black-Out scenario is presented.

42 ENGINEERING↗

Transfer learning for analysis of collective and non-collective Thomson scattering spectra

Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental plasma parameters, such as electron density (⁠n e ) and electron temperature (⁠T e ⁠). Deep neural networks can provide accurate estimates of ⁠n e and T e when conventional fitting algorithms may fail, such as when TS spectra are dominated by noise, or when fast analysis is required for real-time operation. Although deep neural networks typically require large training sets, transfer learning can improve model performance on a target task with limited data by leveraging pre-trained models from related source tasks, where select hidden layers are further trained using target data. We present five architecturally diverse deep neural networks, pre-trained on synthetic TS data and adapted for experimentally measured TS data, to evaluate the efficacy of transfer learning in estimating n e and T e in both the collective and non-collective scattering regimes. We evaluate errors in n e and T e estimates as a function of training set size for models trained with and without transfer learning, and we observe decreases in model error from transfer learning when the training set contains ≲ 200 experimentally measured spectra.

Artificial neural networks↗

Thermal Reliability Study of Bypass Diodes in Photovoltaic Modules (Poster)

This paper presents the result of high-temperature durability and thermal cycling testing and analysis for the selected diodes to study the detail of the thermal design and relative long-term reliability of the bypass diodes used to limit the detrimental effects of module hot-spot susceptibility.

14 SOLAR ENERGY↗

Digital Tools for the Preventive Conservation of Built Heritage: The Church of Santa Ana in Seville

Historic Building Information Modelling (HBIM) plays a pivotal role in heritage conservation endeavours, offering a robust framework for digitally documenting existing structures and supporting conservation practices. However, HBIM’s efficacy hinges upon the implementation of case-specific approaches to address the requirements and resources of each individual asset and context. This paper defines a flexible and generalisable workflow that encompasses various aspects (i.e., documentation, surveying, vulnerability assessment) to support risk-informed decision making in heritage management tailored to the peculiar conservation needs of the structure. This methodology includes an initial investigation covering historical data collection, metric and condition surveys and non-destructive testing. The second stage includes Finite Element Method (FEM) modelling and structural analysis. All data generated and processed are managed in a multi-purpose HBIM model. The methodology is tested on a relevant case study, namely, the church of Santa Ana in Seville, chosen for its historical significance, intricacy and susceptibility to seismic action. The defined level of detail of the HBIM model is sufficient to inform the structural analysis, being balanced by a more accurate representation of the alterations, through linked orthophotos and a comprehensive list of alphanumerical parameters. This ensures an adequate level of information, optimising the trade-off between model complexity, investigation time requirements, computational burden and reliability in the decision-making process. Field testing and FEM analysis provide valuable insight into the main sources of vulnerability in the building, including the connection between the tower and nave and the slenderness of the columns.

Chaves, Estefanía↗