Chemical kinetic model uncertainty minimization through laminar flame speed measurements
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Park, Okjoo (ORCID:0000000336349776)↗
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SAM-I-Am: Semantic boosting for zero-shot atomic-scale electron micrograph segmentation
Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics — where are boundaries located? what segments are logically similar? — change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming; domain images (micrographs) can be exponentially diverse; and data sharing (for third-party retraining) is restricted. To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations. Here, we apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, serves as a post-processing engine that extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations. We demonstrate a zero-shot performance increase of (absolute) +21.35%, +12.6%, +5.27% in mean IoU, and a -9.91%, -18.42%, -4.06% drop in mean false positive masks across images of three difficulty classes over vanilla SAM (ViT-L).
36 MATERIALS SCIENCE↗
Equation-based and data-driven modeling: Open-source software current state and future directions
Here, a review of current trends in scientific computing reveals a broad shift to open-source and higher-level programming languages such as Python and growing career opportunities over the next decade. Open-source modeling tools accelerate innovation in equation-based and data-driven applications. Significant resources have been deployed to develop data-driven tools (PyTorch, TensorFlow, Scikit-learn) from tech companies that rely on machine learning services to meet business needs while keeping the foundational tools open. Open-source equation-based tools such as Pyomo, CasADi, Gekko, and JuMP are also gaining momentum according to user community and development pace metrics. Integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to radically accelerate progress. However, long-term support mechanisms are still necessary to sustain the momentum and maintenance of critical foundational packages.
97 MATHEMATICS AND COMPUTING↗
Harnessing glycosylation to improve cellulase activity
Cellulases and hemicellulases are responsible for the turnover of plant cell wall polysaccharides in the biosphere, and thus form the foundation of enzyme engineering efforts in biofuels research. Many of these carbohydrate-active enzymes from filamentous fungi contain both N-linked and O-linked glycosylation, the extent and heterogeneity of which depends on growth conditions, expression host, and the presence of glycan trimming enzymes in the secretome, all of which in turn impacts enzyme activity. As the roles of glycosylation in enzyme function have not been fully elucidated, here we discuss the potential roles of glycosylation on glycoside hydrolase enzyme structure and function after secretion. We posit that glycosylation, instead of hindering cellulase engineering, can be used as an additional tool to enhance enzyme activity, given deeper understanding of its molecular-level role in biomass deconstruction.
Cellulase, Glycosylation, Filamentous fungi↗
Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance
Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.
42 ENGINEERING↗
Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study
BACKGROUND:We previously established a 53-gene prognostic signature for overall survival (OS) of gastric cancer patients. This retrospective multi-center study aimed to develop a clinically applicable gene expression detection assay and to investigate the prognostic value of this signature. METHODS:A TCGA gastric adenocarcinoma cohort (TCGA-STAD) was used for comparing 53-gene signature with other gene signatures. A high-throughput mRNA hybridization gene expression assay was developed to quantify the expression of 53-genes in formalin-fixed paraffin-embedded tissues of 540 patients enrolled from three hospitals. 180 patents were randomly selected from two hospitals to build a prognostic prediction model based on the 53-gene signature using leave-p-out (one-third out) cross-validation method together with Cox regression and Kaplan-Meier analysis, and the model was assessed on three validation cohorts. FINDINGS:In the evaluation phase, studies based on TCGA-STAD showed that the 53-gene signature was significantly superior to other three prognostic signatures and was independent of TCGA molecular subtypes and clinical factors. For clinical validation and utility, the prognostic scores were generated using the newly developed assay, which was reliable and sensitive, in 100 sampling training sets and were significantly associated with OS in 100 sampling validation sets. The scores were significantly associated with OS in three independent and combined validation cohorts, and in patients with stages II and III/IV. The multivariate Cox regression demonstrated that the prognostic power of the score was independent of clinical factors, consistent with those findings in the TCGA dataset. Finally, patients with good prognostic scores exhibited significantly a better 5-year OS rate from adjuvant FOLFOX chemotherapy after surgery than from other chemotherapies. INTERPRETATION:The 53-gene prognostic score system is clinically applicable for predicting the OS of patients independent of clinical factors in gastric cancers, which could also be a promising predictive biomarker for FOLFOX regimen. FUNDING:Chinese National Science and Technology, National Natural Science Foundation and Natural Science Foundation of Jiangsu Province.
60 APPLIED LIFE SCIENCES↗
Opening new institutional spaces for grappling with uncertainty: A constructivist perspective
In the context of an increasing reliance on predictive computer simulation models to calculate potential project impacts, it has become common practice in impact assessment (IA) to call on proponents to disclose uncertainties in assumptions and conclusions assembled in support of a development project. Understandably, it is assumed that such disclosures lead to greater scrutiny and better policy decisions. This paper questions this assumption. Drawing on constructivist theories of knowledge and an analysis of the role of narratives in managing uncertainty, I argue that the disclosure of uncertainty can obscure as much as it reveals about the impacts of a development project. It is proposed that the opening up of institutional spaces that can facilitate the negotiation and deliberation of foundational assumptions and parameters that feed into predictive models could engender greater legitimacy and credibility for IA outcomes. - Highlights: Black-Right-Pointing-Pointer A reliance on supposedly objective disclosure is unreliable in the predictive model context in which IA is now embedded. Black-Right-Pointing-Pointer A reliance on disclosure runs the risk of reductionism and leaves unexamined the social-interactive aspects of uncertainty. Black-Right-Pointing-Pointer Opening new institutional spaces could facilitate deliberation on foundational predictive model assumptions.
54 ENVIRONMENTAL SCIENCES↗
Social vulnerability indicators as a sustainable planning tool
In the face of global warming and environmental change, the conventional strategy of resource centralization will not be able to cope with a future of increasingly extreme climate events and related disasters. It may even contribute to inter-regional disparities as a result of these events. To promote sustainable development, this study offers a case study of developmental planning in Chiayi, Taiwan and a review of the relevant literature to propose a framework of social vulnerability indicators at the township level. The proposed framework can not only be used to measure the social vulnerability of individual townships in Chiayi, but also be used to capture the spatial developmental of Chiayi. Seventeen social vulnerability indicators provide information in five dimensions. Owing to limited access to relevant data, the values of only 13 indicators were calculated. By simply summarizing indicators without using weightings and by using zero-mean normalization to standardize the indicators, this study calculates social vulnerability scores for each township. To make social vulnerability indicators more useful, this study performs an overlay analysis of social vulnerability and patterns of risk associated with national disasters. The social vulnerability analysis draws on secondary data for 2012 from Taiwan's National Geographic Information System. The second layer of analysis consists of the flood potential ratings of the Taiwan Water Resources Agency as an index of biophysical vulnerability. The third layer consists of township-level administrative boundaries. Analytical results reveal that four out of the 18 townships in Chiayi not only are vulnerable to large-scale flooding during serious flood events, but also have the highest degree of social vulnerability. Administrative boundaries, on which social vulnerability is based, do not correspond precisely to “cross-administrative boundaries,” which are characteristics of the natural environment. This study adopts an exploratory approach that provides Chiayi and other government agencies with a foundation for sustainable strategic planning for environmental change. The final section offers four suggestions concerning the implications of social vulnerability for local development planning. -- Highlights: • This study proposes a framework of social vulnerability indicators at the township level in Chiayi County, Taiwan. • Seventeen social vulnerability indicators are categorized into four dimensions. • This study performs a three-layer overlay analysis of social vulnerability and natural disaster risk patterns. • 4 out of the 18 townships not only have potential for large-scale flooding, but also high degree of social vulnerability. • This study provides a foundation for sustainable strategic planning to deal with environmental change. • Four suggestions are proposed regarding the implications of social vulnerability for local development planning.
54 ENVIRONMENTAL SCIENCES↗
Developing micro-level urban ecosystem indicators for sustainability assessment
Sustainability assessment is increasingly being viewed as an important tool to aid in the shift towards sustainable urban ecosystems. An urban ecosystem is a dynamic system and requires regular monitoring and assessment through a set of relevant indicators. An indicator is a parameter which provides information about the state of the environment by producing a quantitative value. Indicator-based sustainability assessment needs to be considered on all spatial scales to provide efficient information of urban ecosystem sustainability. The detailed data is necessary to assess environmental change in urban ecosystems at local scale and easily transfer this information to the national and global scales. This paper proposes a set of key micro-level urban ecosystem indicators for monitoring the sustainability of residential developments. The proposed indicator framework measures the sustainability performance of urban ecosystem in 3 main categories including: natural environment, built environment, and socio-economic environment which are made up of 9 sub-categories, consisting of 23 indicators. This paper also describes theoretical foundations for the selection of each indicator with reference to the literature [Turkish] Highlights: • As the impacts of environmental problems have multi-scale characteristics, sustainability assessment needs to be considered on all scales. • The detailed data is necessary to assess local environmental change in urban ecosystems to provide insights into the national and global scales. • This paper proposes a set of key micro-level urban ecosystem indicators for monitoring the sustainability of residential developments. • This paper also describes theoretical foundations for the selection of each indicator with reference to the literature.
54 ENVIRONMENTAL SCIENCES↗
A critical evaluation of the NCRP COMMENTARY 27 endorsement of the linear no-threshold model of radiation effects
Highlights: • The linear no-threshold (LNT) model of radiation effects has fractured the scientific community. • This paper analyzes the latest review of the LNT model, NCRP Commentary 27. • Expert reviews of the LNT model rely on institutional inertia and numerous logical fallacies. • Recommendations to advance this debate in an objective future review are provided. Regulatory policy to protect the public and the environment from radiation is universally based on the linear, no-threshold model (LNT) of radiation effects. This model has been controversial since its inception over nine decades ago, and remains so to this day, but it has proved remarkably resistant to challenge from the scientific community. The LNT model has been repeatedly endorsed by expert advisory bodies, and regulatory agencies in turn adopt policies that reflect this advice. Unfortunately, these endorsements rest on a foundation of institutional inertia and numerous logical fallacies. These include most significantly setting the LNT as the null hypothesis, and shifting the burden of proof onto LNT skeptics. Other examples include arbitrary exclusion of alternative hypotheses, ignoring criticisms of the LNT, cherry-picking evidence, and making policy judgements without foundation. This paper presents an evaluation of the National Council on Radiation Protection and Measurements’ (NCRP) Commentary 27, which concluded that recent epidemiological studies are compatible with the continued use of the LNT model for radiation protection. While this report will likely provide political cover for regulators’ continued reliance on the LNT, it is a missed opportunity to advance the scientific discussion of the effects of low dose, low dose-rate radiation exposure. Due to its Congressionally chartered mission, no organization is better positioned than the NCRP to move this debate forward, and recommendations for doing so in future reviews are provided.
54 ENVIRONMENTAL SCIENCES↗
Site heterogeneity and broad surface-binding isotherms in modern catalysis: Building intuition beyond the Sabatier principle
Learning the science of heterogeneous catalysis and electrocatalysis always starts with the simple case of a flat, uniform surface with an ideal adsorbate. It has of course been recognized for a century that real catalysts are more complicated. For the increasingly complex catalysts of the 21st century, this Perspective argues that surface heterogeneity and non-ideal binding isotherms are central features, and their implications need to be incorporated in current thinking. A variety of systems are described herein where catalyst complexity leads to broad, non-Langmuirian surface isotherms for the binding of hydrogen atoms – and this occurs even for ideal, flat Pt(111) surfaces. Modern catalysis employs nanoscale materials whose surfaces have substantial step, edge, corner, impurity, and other defect sites, and they increasingly have both metallic and non-metallic elements M n X m , including metal oxides, chalcogenides, pnictides, carbides, doped carbons, etc. The surfaces of such catalysts are often not crystal facets of the bulk phase underneath, and they typically have a variety of potential active sites. Catalytic surfaces in operando are often non-stoichiometric, amorphous, dynamic, and impure, and often vary from one part of the surface to another. Understanding of the issues that arise at such nanoscale, multi-element catalysts is just beginning to emerge. Yet these catalysts are widely discussed using Brønsted/Bell-Evans-Polanyi (BEP) relations, volcano plots, Tafel slopes, the Butler-Volmer equation, and other linear free energy relations (LFERs), which all depend on the implicit assumption that the active sites are “similar” and that surface adsorption is close to ideal. These assumptions underly the ubiquitous intuition based on the Sabatier Principle, that the fastest catalysis will occur when key intermediates have free energies of adsorption that are not too strong nor too weak. Current catalysis research often aims to minimize the complexity of non-ideal isotherms through experimental and computational design (e.g., the use of single crystal surfaces), and these studies are the foundation of the field. In contrast, this Perspective argues that the heterogeneity of binding sites and binding energies is an inherent strength of these catalysts. Here, this diversity makes many nanoscale catalysts inherently a high-throughput screen wrapped in a tiny package. Only by making the heterogeneity part of the foundation of catalysis models, sorting the types of active sites and dissecting non-ideal binding isotherms, will modern catalysis learn to harness the inherent diversity of real catalysts. Controlling and exploiting diversity rather than avoiding it will help to optimize complex modern catalysts and catalytic conditions.
Mayer, James M.↗
Characterization of ferredoxins involved in electron transfer pathways for nitrogen fixation implicates differences in electronic structure in tuning 2[4Fe 4S] Fd activity
Ferredoxins (Fds) are small proteins which shuttle electrons to pathways like biological nitrogen fixation. Physical properties tune the reactivity of Fds with different pathways, but knowledge on how these properties can be manipulated to engineer new electron transfer pathways is lacking. Recently, we showed that an evolved strain of Rhodopseudomonas palustris uses a new electron transfer pathway for nitrogen fixation. This pathway involves a variant of the primary Fd of nitrogen fixation in R. palustris, Fer1, in which threonine at position 11 is substituted for isoleucine (Fer1 T11I ). To understand why this substitution in Fer1 enables more efficient electron transfer, we used in vivo and in vitro methods to characterize Fer1 and Fer1 T11I . Electrochemical characterization revealed both Fer1 and Fer1 T11I have similar redox transitions (–480 mV and – 550 mV), indicating the reduction potential was unaffected despite the proximity of T11 to an iron-sulfur (Fe—S) cluster of Fer1. Additionally, disruption of hydrogen bonding around an Fe—S cluster in Fer1 by substituting threonine with alanine (T11A) or valine (T11V) did not increase nitrogenase activity, indicating that disruption of hydrogen bonding does not explain the difference in activity observed for Fer1 T11I . Electron paramagnetic resonance spectroscopy studies revealed key differences in the electronic structure of Fer1 and Fer1 T11I , which indicate changes to the high spin states and/or spin-spin coupling between the Fe—S clusters of Fer1. Finally, our data implicates these electronic structure differences in facilitating electron flow and sets a foundation for further investigations to understand the connection between these properties and intermolecular electron transfer.
59 BASIC BIOLOGICAL SCIENCES↗
X-ray observation of individual Ti-6Al-4V spherical powder particle impact in an in-situ operando laser directed energy deposition system
Understanding the particle capture mechanism in the laser directed energy deposition additive manufacturing process provides a foundation for improving productivity and reducing material loss. Capturing this phenomenon is challenging due to its highly transient time interval. To analyze the impact behavior of spherical Ti-6Al-4V powder, a custom laser directed energy deposition system was designed to control the deposition of individual particles. Synchrotron X-ray imaging at 24 kHz monitored the impact dynamics of individual powder particles. A multi-physics model based on the Smoothed-Particle Hydrodynamics scheme supported the analysis by providing the temperature and velocity fields of the molten pool. Results revealed that in laser directed energy deposition, surface tension forces dominated the powder–molten pool interaction. Hydrophobic Ti-6Al-4V powder particles had an equilibrium contact angle of 121 degrees with their molten state. Furthermore, Ti-6Al-4V powder particles required a high impact velocity (6 to 12 m/s) to transition from oscillation to submergence behavior. Lastly, oscillating powder particles that impacted near the location of the laser beam exhibited faster melting, highlighting the contribution of the laser beam to the melting mechanism. This work lays the foundation for investigations of powder particle impact in laser directed deposition and supports the validation of numerical models of powder–molten pool interactions.
36 MATERIALS SCIENCE↗
Crystal Structures of the S. cerevisiae Spt6 Core and C-terminal Tandem SH2 Domain
The conserved and essential eukaryotic protein Spt6 functions in transcription elongation, chromatin maintenance, and RNA processing. Spt6 has three characterized functions. It is a histone chaperone capable of reassembling nucleosomes, a central component of transcription elongation complexes, and is required for recruitment of RNA processing factors to elongating RNA polymerase II (RNAPII). Here, we report multiple crystal structures of the 168-kDa Spt6 protein from Saccharomyces cerevisiae that together represent essentially all of the ordered sequence. Our two structures of the {approx} 900-residue core region reveal a series of putative nucleic acid and protein-protein interaction domains that fold into an elongated form that resembles the bacterial protein Tex. The similarity to a bacterial transcription factor suggests that the core domain performs nucleosome-independent activities, and as with Tex, we find that Spt6 binds DNA. Unlike Tex, however, the Spt6 S1 domain does not contribute to this activity. Crystal structures of the Spt6 C-terminal region reveal a tandem SH2 domain structure composed of two closely associated SH2 folds. One of these SH2 folds is cryptic, while the other shares striking structural similarity with metazoan SH2 domains and possesses structural features associated with the ability to bind phosphorylated substrates including phosphotyrosine. Binding studies with phosphopeptides that mimic the RNAPII C-terminal domain revealed affinities typical of other RNAPII C-terminal domain-binding proteins but did not indicate a specific interaction. Overall, these findings provide a structural foundation for understanding how Spt6 encodes several distinct functions within a single polypeptide chain.
59 BASIC BIOLOGICAL SCIENCES↗
Effects of Composition and Oxidation States on the Structures of Chromium-Containing Sodium Silicate Glasses: Molecular Dynamics Simulations using Machine Learning Interatomic Potentials
Chromium represents a significant challenge for the vitrification of high-level nuclear waste into silicate and borosilicate glasses due to its low solubility and variable oxidation states, which can limit the waste loading due to promotion of crystallization or phase separation during processing. In this study, we modeled chromium containing silicate glasses using molecular dynamics simulations with three machine learning interatomic potentials (MLIPs), MACE, CHGNet, and PFP were employed, to gain insights on glass composition and oxidation states on the structures of these glasses. One of the goals is to evaluate their ability of these MLIPs to accurately represent the general structure of silicate glasses and chromium local environments as a function of chromium oxidation states. Density Functional Theory (DFT) based calculations and experimental data such as neutron structure factors were used to validate the structural models. It was found that the foundation models of all three MLIPs are able to reproduce general structural features of the sodium silicate glass structure consistent with experimental and DFT data, but only CHGNet and PFP can accurately capture the oxidation states and local environment of chromium: tetrahedral for Cr6+ and octahedral for Cr3+. Furthermore, we studied the effect of varying Cr3+/ Cr6+ (Cr3+/Crtotal) ratio and total chromium content using PFP. Our results show that Cr6+ enhances network polymerization by reducing non-bridging oxygens through Na? charge compensation required due to the formation of chromate (CrO42-) species, while Cr³? acts as a network modifier that disrupts connectivity. System size effects on the structural characteristics and chromium environments were also tested using the PFP potential. This work highlights the importance of careful validation on the precision, transferability, and potential of MLIPs for modeling glasses containing transition metal elements that can exist in multiple oxidation states. It is also encouraging to see the foundational models are all three MLFFs are able to reproduce the basic sodium silicate glass structures, while suggesting additional training or refining is needed to improve the description of more complex systems containing transition metals.
Puga, Christina L.↗
Nuclear Data Uncertainty Quantification: Past, Present and Future
An historical overview is provided of the mathematical foundations of uncertainty quantification and the roles played in the more recent past by nuclear data uncertainties in nuclear data evaluations and nuclear applications. Significant advances that have established the mathematical framework for contemporary nuclear data evaluation methods, as well as the use of uncertainty information in nuclear data evaluation and nuclear applications, are described. This is followed by a brief examination of the current status concerning nuclear data evaluation methodology, covariance data generation, and the application of evaluated nuclear data uncertainties in contemporary nuclear technology. A few possible areas for future investigation of this subject are also suggested.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Generalist multimodal AI: A review of architectures, challenges and opportunities
Multimodal models are expected to be a critical component to future advances in artificial intelligence. Here, this field is starting to grow rapidly with a surge of new design elements motivated by the success of foundation models in natural language processing (NLP) and vision. It is widely hoped that further extending the foundation models to multiple modalities (e.g., text, image, video, sensor, time series, graph, etc.) will ultimately lead to generalist multimodal models, i.e. one model across different data modalities and tasks. However, there is little research that systematically analyzes recent multimodal models (particularly the ones that work beyond text and vision) with respect to the underling architecture proposed. Therefore, this work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy. This includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs. The review further highlights key challenges and prospects for the field and guide the researchers into the new advancements.
Artificial intelligence (AI)↗
A model to assess Zircaloy’s mechanical property changes following a transient beyond critical heat flux
Maintaining the integrity of nuclear fuel rods is essential for ensuring public health and safety in nuclear power generation. During reactor operation, this integrity is confirmed by demonstrating compliance with established regulatory acceptance criteria. For moderate-frequency events, such as limiting transients and anticipated operational occurrences (AOOs), the current fuel integrity criterion is based on preventing boiling transition. This criterion assumes that prevention of boiling transition will prevent excessive cladding heating and, thus, fuel failure during normal operations. While conservative, this approach places significant constraints on core design, fuel cycle economics, and a plant’s ability to perform major power uprates, leading to suboptimal fuel utilization and inefficient carbon-free energy production. A more efficient approach could be achieved by revising the failure criterion to a material-specific limit rather than strictly preventing the boiling transition, since boiling transition per se is not a cause of fuel cladding failure. Here, as a result, a new licensing framework based on material properties, termed time-at-temperature (t@T), is needed. This approach would allow for brief periods of post–critical heat flux operation during an AOO without compromising safety. Implementing the t@T licensing strategy requires a robust technical foundation in material properties, which must be established through comprehensive data collection on both unirradiated and irradiated fuel and cladding materials. This foundation would enable the development of a safety basis that ensures safe operation while providing greater flexibility and efficiency for reactor operation. This paper documents a thorough review of the available data to establish a baseline knowledge that can inform the development of cladding mechanical models, as well as identify experimental data gaps that need to be addressed in future research. Machine learning and data informatics were utilized to extract the importance of parameters on the t@T parameter. Industry tools were used to perform baseline analyses to define the relevant transient conditions for data analysis. The subsequent review successfully identified applicable experimental data, as well as sufficient data to evaluate changes in cladding mechanical properties following an AOO transient. Rather than developing new models, this work coupled existing irradiation annealing and recrystallization models to calculate changes in hardness, yield stress, and ultimate tensile stress following an AOO event. The findings from this review were summarized to highlight the experimental data needs required to fill remaining gaps and support the development of future t@T licensing methodologies.
Cladding performance↗