Mechanistic Prediction Research Articles

Mechanistic prediction of Westinghouse TRITON11® BWR fuel critical power with MEFISTO-T subchannel analysis code

The TRITON11® fuel design is the latest Boiling Water Reactor (BWR) fuel product developed by Westinghouse, based on an 11 × 11 optimized fuel rod lattice, including mixing vane spacer grids, three large water rods and 18 part-length rods. The design offers larger fuel cycle cost saving, improved fuel reliability and increased thermal margin over previous Westinghouse fuel products. The critical power performances of the TRITON11 fuel design were assessed at the Westinghouse thermal–hydraulic FRIGG loop using a full-scale test bundle covering a wide range of BWR core conditions (covering normal operation and Anticipated Operational Occurrences) with various radial and two axial power distributions. The resulting FRIGG steady state database was simulated with Westinghouse subchannel analysis code MEFISTO-T based on a two-phase three-field approach of annular two-phase flow, accounting for the drop deposition enhancement provided by the spacer grids. A new model of local film entrainment was introduced due to the liquid “scrapping off” effects provided by the frame and side vanes of the spacer grids along the fuel channel and water rods. After spacer grid effect calibration, the steady state critical power is simulated mechanistically by power iterations up to complete local film dryout. The MEFISTO-T code can successfully predict the critical power performance of the Westinghouse TRITON11 BWR fuel design, including on part-length rods, with almost no bias and trend, within a standard deviation of about 5 %.

Mechanistic definition and prediction of the mass exchange coefficient between rivers and hyporheic zones: The [formula omitted] of two [formula omitted]s

Solute transport in interconnected rivers and hyporheic zones is typically modeled through dual-domain models where first-order solute mass transfer between the two domains, ΩR and ΩHZ, is represented by a coefficient α. The transient storage model (TSM) is an example of such an approach. In practice, α is determined by fitting the tails of solute tracer breakthrough curves using a TSM. This approach has led to ambiguity regarding α’s physical meaning and transferability. Here, we investigated the physical basis for α and tested it with virtual experiments through the fully coupled multiphysics model hyporheicFoam for the ΩR−ΩHZ system. hyporheicFoam explicitly simulated coupled flow and solute transport over a kilometer with centimeter-scale resolution. Model results were analyzed to calculate α following its theoretical definition directly. Using the determined α within a TSM enables accurate reproduction of solute transport, underscoring α’s physical relevance and precision.

Hybrid modelling of nitrogen removal by biofiltration using high-frequent operational data

ABSTRACT In this research, a parallel hybrid model is presented for the simulation of nitrogen removal by submerged biofiltration of a very large-size wastewater treatment plant. This hybrid model consists of a machine learning model and a mechanistic model that are combined to produce accurate predictions of water quality variables. The models are calibrated/validated using detailed and quality-controlled operational data collected over a period of 3.5 months in 2020. The mechanistic model applied is a modified activated sludge model that describes the biological, physical and chemical processes taking place in a biofilm reactor based on the domain knowledge of these processes. A three-layer feed-forward artificial neural network with a rectified linear activation function that aims to reduce the mechanistic model's residual error and then correct its output. The results show how the hybrid model outperforms and significantly reduces the size of the mechanistic model's prediction errors of the effluent nitrate concentration from a relative mean error of 12% for the mechanistic model to 2% for the hybrid model during training. The error on nitrate simulations increases to 8% during hybrid model testing, still significantly lower than the error of the mechanistic model. These results support future operational applications of hybrid biofilm models.

A Mechanistic Prediction Model of Resistance to Uprooting of Coniferous Trees in Heilongjiang Province, China.

Coarse roots and the root plate play an important role in tree resistance to uprooting. In this study, a qualitative mechanistic model was developed to analyze coniferous tree resistance to uprooting in relation to tree morphological characteristics. The sizes of the crown, stem, and root plate of twenty sample spruces and twenty sample Korean pines were individually measured for this purpose. Using Ground Penetrating Radar (GPR), the coarse root distribution and root plate size were detected. In the qualitative mechanistic model, a larger crown area increased the overturning moment, while higher DBH and root plate mass increased the resistance moment. The resistance coefficient (Rm) was calculated by comparing resistive and overturning moments, classifying samples into three uprooting hazard levels. Trees with smaller crown areas, larger stems, and root plates tend to have higher resistance to uprooting, as indicated by higher Rm values. This qualitative mechanistic model provides a useful tool for assessing coniferous standing tree uprooting resistance.

(Invited) Computational Models of Proton-Coupled Electron Transfer in Energy Conversion and Storage

Proton-coupled electron transfer (PCET) is a fundamental class of chemical reactions that is central to many electrochemical energy conversion and storage processes. In this talk, I will discuss recent theoretical model development for interfacial and intermolecular PCET reactions. First, I will describe a rate constant model for the reaction between reduced anatase TiO2 surfaces and a 4-MeO-TEMPO nitroxyl radical. The rate constants are modeled using vibronically nonadiabatic PCET theory, where the transferring proton and electron are treated quantum mechanically. Hybrid functional periodic density functional theory (DFT) calculations are used to derive model parameters such as driving forces, reorganization energies, and proton vibrational wavefunctions. This modeling strategy highlights the role of inner-sphere bond reorganization and excited vibronic states on the PCET reactivity of metal oxides. Next, I will show how similar computational approaches can be used to predict whether electrochemical PCET is likely to proceed through concerted electron–proton transfer or sequential electron transfer and proton transfer steps. Using PCET reactions between redox-active quinones and functionalized imidazole bases as an example, mechanistic predictions are informed by DFT-calculated potential energy surfaces. These PCET modeling strategies have broad applicability to kinetic studies of electrochemical energy conversion and storage processes.

Positron emission tomography quantifies crystal surface reactivity during sorption reactions

Mechanistic understanding and prediction of solute transport and surface reactions in the pore space of rocks and soils is critical for a variety of processes, including sediment diagenesis, contaminant remediation, and long-term waste storage strategies. Positron emission tomography (PET) using β+-emitting radiotracers is an established and reliable method for investigating advective flow and diffusive flux in porous geomaterials. Here we present a conceptual strategy for spatially resolved quantification of crystal surface reactivity for sorption reactions based on PET techniques. The deconvolution of tracer breakthrough curves quantifies the sorption of F− tracer on calcite crystal surfaces and identifies the varying surface reactivity in the complex porous material. Using an artificial sediment as a model system, we demonstrate the quantifiability of sorption effects down to 10 pmol/mm3. The proposed strategy outlines how spatially resolved surface reactivities and their temporal changes over time can generally be determined using PET.

Capturing the complexity of child behavior and caregiver-child interactions in the HEALthy Brain and Child Development (HBCD) Study using a rigorous and equitable approach

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. This article outlines methodological considerations and the decision-making process for measurement selection for child behavior, parenting/caregiver-child interactions, and the family/home environment for HBCD. The decision-making process is detailed, including formation of a national workgroup (WG-BEH) that focused on developmentally appropriate measures that take a rigorous and equitable approach and aligned with HBCD objectives. Multi-level-observational and caregiver-report measures were deemed necessary for capturing the desired constructs across multiple contexts while balancing the nuance of observational data with pragmatic considerations. WG-BEH prioritized developmentally sensitive, validated assessments with psychometrics supporting use in diverse populations and focused on mechanistic linkages and prediction of desired constructs. Other considerations included participant burden and retention, staff training needs, and cultural sensitivity. Innovation was permitted when it was grounded in evidence and filled key gaps. Finally, this article describes the rationale for the selected constructs (e.g., temperament, social-emotional development, parenting behaviors, family organization) and corresponding measures chosen for HBCD visits from early infancy through 17 months of age.

Monitoring atmospheric corrosion under multi-droplet conditions by electrical resistance sensor measurement

This paper presents a novel approach to investigate atmospheric corrosion kinetics of carbon steel under multi-droplet conditions. A homemade climate chamber has been developed to accurately control and monitor environmental conditions, including temperature (T) and relative humidity (RH), during exposure. Carbon steel corrosion kinetics are monitored with a custom-designed Electrical Resistance (ER) sensor pair. Savitzky-Golay (S-G) based filtering technique has been used for the corrosion signal processing. In parallel, top-view droplet temporal evolution has been recorded by microscopic imaging and analyzed for both droplet size distribution and the solid-liquid contact angle. The droplet size distribution can typically be described with a power-law form curve. The curve shows a decrease in height and a concurrent expansion in width with progressive drying. The introduction of NaCl into the electrolyte and surface roughness variations have also been identified to substantially influence the carbon steel corrosion rate. A strong correlation between the corrosion rate derived from the ER monitoring method and the RH can be observed. This correlation is further analyzed to incorporate the impact of droplet-based electrolyte conditions. This study offers valuable insights into the development of mechanistic and kinetic prediction models for atmospheric corrosion.

Advances in dissolved oxygen prediction and control methods in aquaculture: a review

Abstract In the aquaculture industry, maintaining stable levels of dissolved oxygen (DO) is crucial for ensuring the health of aquatic organisms and enhancing farming efficiency. This article delves into the challenges faced in predicting and controlling DO levels, such as the need for real-time monitoring and response, the complexity of systems, and limitations in technology and resources. The paper comprehensively reviews various methods for DO prediction and control, including mechanistic modeling prediction, machine learning techniques, and both classical and intelligent control strategies. It analyzes their advantages, limitations, and applicability in aquaculture environments. Through this review and analysis, the article provides more comprehensive insights and guidance for future research directions in DO prediction and control in aquaculture.

Fast, accurate and accessible calculations of leaf temperature and its physiological consequences with NicheMapR

Abstract Mechanistic predictions of ecological niches are fundamentally based on energy and mass exchange processes that, to be realistic, must be linked to microclimate and incorporate behaviour and physiology. Estimates of leaf temperature and associated transpiration and net photosynthesis rates are fundamental in ecology, agriculture and global change biology. Equations for calculating leaf energy budgets have been available for over 60 years and complex models of stomatal behaviour have been developed. However, user‐friendly ways of estimating leaf temperature under realistic microclimatic regimes, including soil moisture effects, remain limited. Here we show that the integrated microclimate and ectotherm functions of the NicheMapR package for the R programming environment can be used to make fast and effective estimates of leaf temperature and associated rates of water loss and photosynthesis for a wide range of leaf functional types, root depths and microhabitats. Stomatal conductance may be constant or simulated to change dynamically (a) as a function of vapour pressure deficit and CO2 concentration, (b) as a function of soil water potential and (c) as a thermoregulatory mechanism to avoid heat stress. We tested the approach against hourly leaf temperature observations in summer from an arid shrubland in Australia, a temperate woodland in Montana USA, along an elevational gradient in Colorado USA, and at a botanical garden in Florida, USA. National or global gridded data sets can provide the required forcing data but local observational data or some combination of local and gridded data can be used. The global ERA5 and NCEP datasets allow historical leaf energy budget calculations to be made anywhere in the world and a Shiny app has been developed to facilitate use of the model with these datasets. Situated leaf energy budget calculations can be used to design informative field programs to collect much needed detailed temporal data on leaf temperature under natural settings, enabling better predictions and interpretations of how plants respond to environmental change.

The Ecosystem as Super-Organ/ism, Revisited: Scaling Hydraulics to Forests under Climate Change.

Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth's ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity.

Influence of test paradigm on loading dynamics during proximal femur fracture tests simulating sideways falls

Fall-related hip fractures are a serious public health issue in older adults. As most mechanistic hip fracture risk prediction models incorporate tissue tolerance, test methods that can accurately characterize the fracture force of the femur (and factors that influence it) are imperative. While bone possesses viscoelastic properties, experimental characterization of rate-dependencies has been inconsistent in the whole-femur literature. The goal of this study was to investigate the influence of experimental paradigm on loading rate and fracture force (both means and variability) during mechanical tests simulating lateral fall loadings on the proximal femur. Six pairs of matched femurs were split randomly between two test paradigms: a ‘lower rate’ materials testing system (MTS) with a constant displacement rate of 60 mm/s, and a hip impact test system (HIT) comprised of a custom-built vertical drop tower utilizing an impact velocity of 4 m/s. The loading rate was 88-fold higher for the HIT (mean (SD) = 2465.49 (807.38) kN/s) compared to the MTS (27.78 (10.03) kN/s) paradigm. However, no difference in fracture force was observed between test paradigms (mean (SD) = 4096.4 (1272.6) N for HIT, and 3641.3 (1285.8) N for MTS). Within-paradigm variability was not significantly different across paradigms for either loading rate or fracture force (coefficients of variation ranging from 0.311 to 0.361). Within each test paradigm, significant positive relationships were observed between loading rate and fracture force (HIT adjusted R2 = 0.833, p = 0.007; MTS adjusted R2 = 0.983, p < 0.0001). Overall, this study provides evidence that energy-based impact simulators can be a valid method to measure femoral bone strength in the context of fall-related hip fractures. This study motivates future research to characterize potential non-linear relationships between loading rate and fracture threshold at both macro and microscales.

Diisopropyl Methylphosphonate and Sarin Decomposition on Pristine vs Hydroxylated Alumina Surfaces: Mechanistic Predictions from Ab Initio Molecular Dynamics

Diisopropyl Methylphosphonate and Sarin Decomposition on Pristine vs Hydroxylated Alumina Surfaces: Mechanistic Predictions from Ab Initio Molecular Dynamics

Mechanistic prediction and validation of Brevilin A Therapeutic effects in Lung Cancer

BackgroundTraditional Chinese medicine (TCM) has been found widespread application in neoplasm treatment, yielding promising therapeutic candidates. Previous studies have revealed the anti-cancer properties of Brevilin A, a naturally occurring sesquiterpene lactone derived from Centipeda minima (L.) A.Br. (C. minima), a TCM herb, specifically against lung cancer. However, the underlying mechanisms of its effects remain elusive. This study employs network pharmacology and experimental analyses to unravel the molecular mechanisms of Brevilin A in lung cancer.MethodsThe Batman-TCM, Swiss Target Prediction, Pharmmapper, SuperPred, and BindingDB databases were screened to identify Brevilin A targets. Lung cancer-related targets were sourced from GEO, Genecards, OMIM, TTD, and Drugbank databases. Utilizing Cytoscape software, a protein-protein interaction (PPI) network was established. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis (GSEA), and gene-pathway correlation analysis were conducted using R software. To validate network pharmacology results, molecular docking, molecular dynamics simulations, and in vitro experiments were performed.ResultsWe identified 599 Brevilin A-associated targets and 3864 lung cancer-related targets, with 155 overlapping genes considered as candidate targets for Brevilin A against lung cancer. The PPI network highlighted STAT3, TNF, HIF1A, PTEN, ESR1, and MTOR as potential therapeutic targets. GO and KEGG analyses revealed 2893 enriched GO terms and 157 enriched KEGG pathways, including the PI3K-Akt signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway. GSEA demonstrated a close association between hub genes and lung cancer. Gene-pathway correlation analysis indicated significant associations between hub genes and the cellular response to hypoxia pathway. Molecular docking and dynamics simulations confirmed Brevilin A’s interaction with PTEN and HIF1A, respectively. In vitro experiments demonstrated Brevilin A-induced dose- and time-dependent cell death in A549 cells. Notably, Brevilin A treatment significantly reduced HIF-1α mRNA expression while increasing PTEN mRNA levels.ConclusionsThis study demonstrates that Brevilin A exerts anti-cancer effects in treating lung cancer through a multi-target and multi-pathway manner, with the HIF pathway potentially being involved. These results lay a theoretical foundation for the prospective clinical application of Brevilin A.

Global Challenges Facing Plant Pathology: A Review on Multidisciplinary Approaches to Meet the Food Security

Plant disease outbreaks cause a decline in primary productivity and biodiversity, which negatively impacts the socioeconomic and environmental circumstances in the afflicted areas. They also pose significant threats to the environment's sustainability and the world's food security. Climate change increases the risk of outbreaks by altering host-pathogen interactions, altering the development of pathogens, and encouraging the emergence of new pathogenic strains. Changes in the range of pathogens can cause plant diseases to spread more quickly in new areas. In this review, we examine the potential effects of future climatic scenarios on plant disease pressures and the resulting effects on plant productivity in agricultural and wild environments. We study the effects of climate change, both now and in the future, on disease incidence and severity, pathogen biogeography, natural ecosystems, agriculture, and food production. To mitigate future disease outbreaks, we propose modifying the current conceptual framework and integrating eco-evolutionary theories into studies to improve our mechanistic comprehension and prediction of pathogen spread in future climates. We stress the need of an interface between science and policy that works closely with relevant intergovernmental organisations to provide effective monitoring and management of plant disease under future climate scenarios. This will be necessary to ensure long-term food and nutrient security as well as the sustainability of natural ecosystems.

Microbial competition for phosphorus limits the CO2 response of a mature forest

The capacity for terrestrial ecosystems to sequester additional carbon (C) with rising CO2 concentrations depends on soil nutrient availability1,2. Previous evidence suggested that mature forests growing on phosphorus (P)-deprived soils had limited capacity to sequester extra biomass under elevated CO2 (refs. 3–6), but uncertainty about ecosystem P cycling and its CO2 response represents a crucial bottleneck for mechanistic prediction of the land C sink under climate change7. Here, by compiling the first comprehensive P budget for a P-limited mature forest exposed to elevated CO2, we show a high likelihood that P captured by soil microorganisms constrains ecosystem P recycling and availability for plant uptake. Trees used P efficiently, but microbial pre-emption of mineralized soil P seemed to limit the capacity of trees for increased P uptake and assimilation under elevated CO2 and, therefore, their capacity to sequester extra C. Plant strategies to stimulate microbial P cycling and plant P uptake, such as increasing rhizosphere C release to soil, will probably be necessary for P-limited forests to increase C capture into new biomass. Our results identify the key mechanisms by which P availability limits CO2 fertilization of tree growth and will guide the development of Earth system models to predict future long-term C storage.

Proximal termination generates a transcriptional state that determines the rate of establishment of Polycomb silencing

The mechanisms and timescales controlling de novo establishment of chromatin-mediated transcriptional silencing by Polycomb repressive complex 2 (PRC2) are unclear. Here, we investigate PRC2 silencing at Arabidopsis FLOWERING LOCUS C (FLC), known to involve co-transcriptional RNA processing, histone demethylation activity, and PRC2 function, but so far not mechanistically connected. We develop and test a computational model describing proximal polyadenylation/termination mediated by the RNA-binding protein FCA that induces H3K4me1 removal by the histone demethylase FLD. H3K4me1 removal feeds back to reduce RNA polymerase II (RNA Pol II) processivity and thus enhance early termination, thereby repressing productive transcription. The model predicts that this transcription-coupled repression controls the level of transcriptional antagonism to PRC2 action. Thus, the effectiveness of this repression dictates the timescale for establishment of PRC2/H3K27me3 silencing. We experimentally validate these mechanistic model predictions, revealing that co-transcriptional processing sets the level of productive transcription at the locus, which then determines the rate of the ON-to-OFF switch to PRC2 silencing.

Predicting fatigue crack growth rate of cement-based and asphalt paving materials based on indirect tensile cyclic loading tests

Fatigue crack growth rate (FCGR) is a critical indicator that reflects the rate at which material deterioration occurs under cyclic loads. However, there is currently a lack of a mechanistic quantitative analysis and prediction of the FCGR of paving materials under external indirect tensile (IDT) cyclic loads. To address this issue, this study aims to develop a mechanistic model for quantitative analysis and prediction of the FCGR in paving materials subjected to external IDT cyclic loads based on the J-integral based Paris’ law. Two types of paving materials, namely cement-treated aggregates and asphalt mixtures, were selected to demonstrate the results and model of the FCGR. The results indicate that the derived J-integral serves as the driving force for crack growth and is influenced by horizontal tensile stress, initial resilient modulus, damage density, specimen size, and surface energy of the paving materials. The fatigue life of cement-treated aggregates is primarily influenced by the crack initiation stage, while for asphalt mixtures, the crack growth stage plays a more significant role. The application of Paris’ law is found to be effective in predicting the FCGR at stable crack growth stage. The coefficients of Paris’ law exhibit minimal variation across different stress levels and loading frequencies, and can be utilized to evaluate the fatigue crack resistance of different paving materials.

Predicted thermophysical properties of UN, PuN, and (U,Pu)N

Molecular dynamics and density functional theory simulations are used to predict the lattice and electronic contributions of thermophysical properties for UN, PuN, and mixed (U,Pu)N systems. The properties predicted include the lattice parameter, linear thermal expansion, enthalpy, and specific heat capacity, as a function of temperature. The simulation predictions for high temperature specific heat capacity are compared against experimental measurements to understand the behavior, and why differences in the experimental measurements are observed. The influence of adding U vacancies, N interstitials, and Pu to UN is also examined. For this, a new PuN potential parameter set is developed and used with the Kocevski UN potential, enabling the dynamics of mixed (U,Pu)N systems to be studied. How defects impact the thermophysical properties is important for understanding fuel behavior under different reactor conditions, and these mechanistic predictions can be used to support fuel performance codes where data is scarce.

Context-aware knowledge selection and reliable model recommendation with ACCORDION

New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: comprehensive, retrieving relevant knowledge from a range of literature sources through machine reading engines; very effective, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; selective, recommending only the most relevant, context-specific, and useful subset (15%–20%) of candidate knowledge in literature; diverse, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions.