Framework For Development Of Models Research Articles

Model-driven fatigue crack characterization and growth prediction: A two-step, 3-D fatigue damage modeling framework for structural health monitoring

Prevailing fatigue damage evaluation approaches that make use of the acoustic nonlinearity of guided ultrasonic waves (GUWs) are sustained by simplified models, most of which depict three-dimensional (3-D) fatigue damage in a two-dimensional (2-D) domain [1]. Such approximation risks the evaluation accuracy. With such motivation, this study aspires to a new, two-step modeling framework, aimed at accurately characterizing and continuously monitoring fatigue damage, from its embryonic initiation, through progressive growth to formation of macroscopic crack. In the first step, a 3-D, analytical model based on the theory of elastodynamics sheds light on the generation of contact acoustic nonlinearity in GUWs under the modulation of `breathing' behavior of a non-penetrating fatigue crack, on which basis a crack-area-dependent nonlinear damage index is yielded. In the second step, a 3-D fatigue crack growth model predicts the continuous growth of the identified fatigue crack in the length and depth along crack front. The framework is validated using numerical simulation, followed with experiment, in both of which the initiation and progressive growth of a real corner fatigue crack is monitored, with continuous prediction of the crack growth in length and depth. Results have demonstrated the accuracy and precision of the developed modeling framework for characterizing embryonic fatigue damage.

Inter-Stage Dynamic Performance of an Axial Compressor of a Twin-Shaft Industrial Gas Turbine

In this study, the inter-stage dynamic performance of a multistage axial compressor is simulated through a semi-empirical model constructed in the Matlab Simulink environment. A semi-empirical 1-D compressor model developed in a previous study has been integrated with a 0-D twin-shaft gas turbine model developed in the Simulink environment. Inter-stage performance data generated through a high-fidelity design tool and based on throughflow analysis are considered for the development of the inter-stage modeling framework. Inter-stage performance data comprise pressure ratio at various speeds with nominal variable stator guide vane (VGV) positions and with hypothetical offsets to them with respect to the gas generator speed (GGS). Compressor discharge pressure, fuel flow demand, GGS and power turbine speed measured during the operation of a twin-shaft industrial gas turbine are considered for the dynamic model validation. The dynamic performance of the axial-compressor, simulated by the developed modeling framework, is represented on the overall compressor map and individual stage characteristic maps. The effect of extracting air through the bleed port in the engine center-casing on transient performance represented on overall compressor map and stage performance maps is also presented. In addition, the dynamic performance of the axial-compressor with an offset in VGV position is represented on the overall compressor map and individual stage characteristic maps. The study couples the fundamental principles of axial compressors and a semi-empirical modeling architecture in a complementary manner. The developed modeling framework can provide a deeper understanding of the factors that affect the dynamic performance of axial compressors.

A self-similar model for fretting wear contact with the third body in gross slip

A continuum mechanics approach is applied to model fretting wear in gross-slip regime with accounting for the third-body layer, which is formed by wear debris trapped within contact interface. The developed modeling framework employs the known concept of a third-body conversion factor and utilizes it in a self-similar form. A two-dimensional Hertzian type contact geometry (cylinder on a plate of relatively large thickness) is assumed, though a major part of the presented analysis is curried out for an arbitrary initial gap function. A Winkler foundation model is used to describe the contact deformation of the third-body layer. The effect of elasticity is neglected in describing the self-similar evolution of the contact pressure profile in the advanced stage of fretting wear. A comparison with the FEM-based simulations available in the literature is given. The fact that the elasticity effect plays a minor role has been verified a posteriori in the considered example. Simple formulas are derived to estimate the continuous variation of the third body layer profile, thickness, and volume.

Understanding quotation extraction and attribution: towards automatic extraction of public figure’s statements for journalism in Indonesia

PurposeExtracting information from unstructured data becomes a challenging task for computational linguistics. Public figure’s statement attributed by journalists in a story is one type of information that can be processed into structured data. Therefore, having the knowledge base about this data will be very beneficial for further use, such as for opinion mining, claim detection and fact-checking. This study aims to understand statement extraction tasks and the models that have already been applied to formulate a framework for further study.Design/methodology/approachThis paper presents a literature review from selected previous research that specifically addresses the topics of quotation extraction and quotation attribution. Research works that discuss corpus development related to quotation extraction and quotation attribution are also considered. The findings of the review will be used as a basis for proposing a framework to direct further research.FindingsThere are three findings in this study. Firstly, the extraction process still consists of two main tasks, namely, the extraction of quotations and the attribution of quotations. Secondly, most extraction algorithms rely on a rule-based algorithm or traditional machine learning. And last, the availability of corpus, which is limited in quantity and depth. Based on these findings, a statement extraction framework for Indonesian language corpus and model development is proposed.Originality/valueThe paper serves as a guideline to formulate a framework for statement extraction based on the findings from the literature study. The proposed framework includes a corpus development in the Indonesian language and a model for public figure statement extraction. Furthermore, this study could be used as a reference to produce a similar framework for other languages.

Merlin—A unified modeling framework for data analysis and methods development in Stata

The challenges in statistics and data science are rapidly growing because access to a multitude of data types continues to increase, as well as the sheer quantity of data. Analysts are now presented with multivariate data, sometimes measured repeatedly, and often requiring the ability to model nonlinear relationships and hierarchical structures. In this article, I present the merlin command, which attempts to provide an extremely general framework for data analysis. From simple settings such as fitting a linear regression model or a Weibull survival model to more complex settings such as fitting a three-level logistic mixed-effects model or a multivariate joint model of multiple longitudinal outcomes (of different types) and a recurrent event and survival with nonlinear effects, merlin can fit them all. I will take a single dataset and attempt to show you the full range of capabilities of merlin and discuss some future directions for the implementation in Stata.

Development and testing of volume models for Pinus nigra Arn., Fagus sylvatica L., and Quercus pubescens Willd.

Volume models were developed and tested for Pinus nigra Arn., Fagus sylvatica L., and Quercus pubescens Willd. to accurately predict the woody stem volume at tree level, since despite the economic importance of these species in the Mediterranean type of forests standard models do not apply. Using a total non-destructive sample of 471 trees in mountain forests of northern Greece, two fundamental volume models, the Hohenadl–Krenn and the standard equation were fitted using a generalised least squares (GLS) estimator along with a power function, to regulate the unequal variance and the normality assumption of the residuals. The results showed that both models presented good fitting performance explaining the largest part of stem volume variance and therefore they can be used in operational forest management planning. In addition, the normality assumption of the distribution of the residuals can be satisfied through weighting techniques for reducing the potential heteroscedastic trends during the fitting procedure. The GLS may provide the necessary framework for stem volume model development on an accurate and unbiased basis.

Microstructural Rearrangements and Their Rheological Signature in Coarsening of Cocontinuous Polymer Blends

Domains of cocontinuous polymer blends coarsen during annealing. In general, coarsening undergoes two regimes: linear growth followed by slower nonlinear growth. While the linear regime is well understood by several theories, the number of studies on the nonlinear regime is scarce and often inconclusive. Herein, we examine the entire spectrum of the coarsening of cocontinuous polymer blends using an in situ high-temperature confocal rheology technique. By linking 4-dimensional microscopic details (time evolution of three-dimensional microscopic details) with the rheological properties, we demonstrate that the transition to the nonlinear regime is associated with the onset of droplet formation during the coarsening of these blends. Such transitions and morphological changes occurred only in the blends with high interfacial tension to zero-shear viscosity ratio (Γ/η₀). This phenomenon provides a framework for comprehensive model development. By analyzing the data from the literature, we propose that there exists a critical Γ/η₀ ratio beyond which the system undergoes such transitions.

A modelling framework for developing early warning systems of COPD emergency admissions

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of mortality worldwide and is a major contributor to the number of emergency admissions in the UK. We introduce a modelling framework for the development of early warning systems for COPD emergency admissions. We analyse the number of COPD emergency admissions using a Poisson generalised linear mixed model. We group risk factors into three main groups, namely pollution, weather and deprivation. We then carry out variable selection within each of the three domains of COPD risk. Based on a threshold of incidence rate, we then identify the model giving the highest sensitivity and specificity through the use of exceedance probabilities. The developed modelling framework provides a principled likelihood-based approach for detecting the exceedance of thresholds in COPD emergency admissions. Our results indicate that socio-economic risk factors are key to enhance the predictive power of the model.

Unified constitutive model for granular–fluid mixture in quasi-static and dense flow regimes

Most granular materials encountered in nature and industry lie either in the quasi-static regime or the intermediate dense flow regime. Debris materials are a typical granular material with viscous interstitial fluid and show solid-like behaviors before failure and fluid-like behaviors after failure. Based on Bagnold’s pioneering work on granular–fluid flows, we propose a framework for constitutive model development, which has an additive form. Based on this framework, a unified constitutive model for granular–fluid material in the quasi-static and dense flow regimes is developed. The main intergranular interactions and granular–fluid interactions controlling the mechanical behaviors are taken into account using the Mohr–Coulomb model and a Bagnold-type relation. Dry granular flows in three simple configurations, i.e., plain shear, vertical chute flow and flow on an inclined plane, are studied. Analytical solutions based on the presented unified model are obtained. Comparisons between results from the presented model and the $$\mu (I)$$ model indicate that the explicit partition of frictional and collisional stress components provides insights into dense granular flows. In addition, the new model is used to predict the stress–strain relations in two annular shear tests. The applicability and advantages of the unified model are discussed.

Which risk predictors are more likely to indicate severe AKI in hospitalized patients?

ObjectivesAcute kidney injury (AKI) is a sudden episode of kidney failure or damage and the risk of AKI is determined by the complex interactions of patient factors. In this study, we aimed to find out which risk factors in hospitalized patients are more likely to indicate severe AKI. MethodsWe constructed a retrospective cohort of adult patients from all inpatient units of a tertiary care academic hospital between November 2007 and December 2016. AKI predictors included demographic information, admission and discharge dates, medications, laboratory values, past medical diagnoses and admission diagnosis. We developed a machine learning-based knowledge mining model and a screening framework to analyze which risk predictors are more likely to imply severe AKI in hospitalized populations. ResultsAmong the final analysis cohort of 76,957 hospital admissions, AKI occurred in 7,259 (9.43 %) with 6,396 (8.31 %) at stage 1, 678 (0.88 %) at stage 2, and 185 (0.24 %) at stage 3. We compared the non-AKI (without AKI) vs any AKI (stages 1–3), and mild AKI (stage 1) vs severe AKI (stages 2 and 3), where the best cross-validated area under the receiver operator characteristic curve (AUC) were 0.81 (95 % CI, 0.79−0.82) and 0.66 (95 % CI, 0.62−0.71), respectively. Using the developed knowledge mining model and screening framework, we identified 33 risk predictors indicating that severe AKI may occur. ConclusionsThis study screened out 33 risk predictors that are more likely to indicate severe AKI in hospitalized patients, which would help strengthen the early care and prevention of patients.

Non-local thermodynamic equilibrium transmission spectrum modelling of HD 209458b

Context. Exoplanetary upper atmospheres are low density environments where radiative processes can compete with collisional ones and introduce non-local thermodynamic equilibrium (NLTE) effects into transmission spectra. Aims. We develop a NLTE radiative transfer framework capable of modelling exoplanetary transmission spectra over a wide range of planetary properties. Methods. We adapted the NLTE spectral synthesis code Cloudy to produce an atmospheric structure and atomic transmission spectrum in both NLTE and local thermodynamic equilibrium (LTE) for the hot Jupiter HD 209458b, given a published T–P profile and assuming solar metallicity. Selected spectral features, including Hα, NaI D, HeI λ10 830, FeI and II ultra-violet (UV) bands, and C, O, and Si UV lines, are compared with literature observations and models where available. The strength of NLTE effects are measured for individual spectral lines to identify which features are most strongly affected. Results. The developed modelling framework that computes NLTE synthetic spectra reproduces literature results for the HeI λ10 830 triplet, the NaI D lines, and the forest of FeI lines in the optical. Individual spectral lines in the NLTE spectrum exhibit up to 40% stronger absorption relative to the LTE spectrum.

Investigating mass balance of Parvati glacier in Himalaya using satellite imagery based model

Accurate assessment of glacier mass loss is essential for understanding the glacier sensitivity to climate change and the ramifications of glacier retreat or surge. The glacier melt affects the runoff and water availability, on which the drinking and irrigation water supplies and generation of hydroelectric energy depend upon. The excessive glacial retreat may cause flood, glacial lake outburst flood, avalanches and sea level rise which are likely to affect the lives and livelihood of the people and damage the infrastructure. Here, we present a remote sensing based modeling framework to improve the understanding of accumulation and ablation processes and to quantify the glacier mass balance using multispectral satellite imageries, as several glacierized regions of the world are still poorly monitored because the field measurements for continuous monitoring on a large scale or in a complex harsh terrain are costly, time consuming and difficult. The developed modeling framework has been applied to the Parvati glacier in the western Himalaya to investigate glaciological processes and estimate the surface mass loss using 19 years of satellite images from 1998 to 2016. It spreads over 425.318 km2 and more than 50% of the area is accumulation area. The study shows that the Parvati glacier is not in equilibrium and its behavioural response changes year to year characterized with high rate of mass loss. The value of accumulation area ratio varies between 0.33 and 0.70 with an average value of 0.55, indicating a negative mass loss. The mean specific mass loss is − 0.49 ± 0.11 m w.e. and the total mass loss is 3.95 Gt., indicating strong influence of climate change and effect on river flows and water availability.

Reservoir characterization and static earth model for potential carbon dioxide storage in Upper Pennsylvanian cyclothems, Nebraska, United States

This study estimates the carbon storage potential of interbedded shales and carbonate rocks (cyclothems) in the Pennsylvanian Lansing and Kansas City groups (LKC) on the Cambridge arch in southwestern Nebraska. This effort is essential to the development of a CO2 storage strategy for the Integrated Midcontinent Stacked Carbon Storage Hub project as part of the Department of Energy–National Energy Technology Laboratory’s Carbon Storage Assurance Facility Enterprise initiative. We present a static earth (SE) model representing the 250-ft (76-m)-thick LKCs. This model is based on vintage (mostly pre-1970) well logs from the Sleepy Hollow field (Red Willow County, Nebraska) as well as a new (June 2019) stratigraphic test well drilled expressly for the purpose of the present study. Interpretations of advanced petrophysical logs and cores from this new well were crucial ingredients in the development of the geologic framework for SE model development. Gamma-ray (GR) logs readily differentiate carbonate and mudstone units within the LKC, allowing the differentiation of three GR facies for use in a facies model. Carbonate rock units, which are composed of multiple textures, were correlated across the field. We capture the heterogeneity of these carbonates during petrophysical modeling using effective porosity logs along with Gaussian random function simulation conditioned by the three-dimensional facies model. The SE model was used in computing carbon storage estimates for each LKC carbonate zone over an area of 1 mi2. In total, supercritical CO2 storage is estimated at 602,157 t/mi2 (232,494 t/km2) when using a deterministic saline storage efficiency factor of 0.1.

Towards efficient Low Impact Development: A multi-scale simulation-optimization approach for nutrient removal at the urban watershed

In this study, we proposed a multi-scale modelling framework for Low Impact Development (LID) by integrating (i) local-scale LID cost-effectiveness analysis, (ii) landscape-scale Management Category (MC) classification and LID selection, and (iii) macro-scale integrative decision making. The framework was used for sitting LID to reduce Total Phosphorus (TP) loss in the City Kunming regional watershed, which discharges massive urban Non-Point Source (NPS) pollution to Lake Dianchi, one of the three most eutrophic large lakes in China. Local-scale simulation-optimization was performed to optimize the sizes and quantities of the candidate LID practices with the System for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) tool. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to solve the optimization problem. The local-scale LID strategies were ultimately integrated at the macro scale in compliance with the water quality standards. Our analysis suggests that the marginal benefits of LID implementation decline dramatically with increasing TP abatement attributed to the law of Diminishing Marginal Benefits. The areal cost-effectiveness curves of different scenarios resemble each other for the same MC since the Coefficients of Variation (CVs) of unit costs are mostly below 10%. Spatial variability of LID implementation is more prominently affected by disparities of landscapes than drainage area sizes because the coverage ratios among different MCs vary from 2.3% to 24.7% whereas they resemble each other among different area sizes. The multi-scale decision-making framework could dramatically improve computational efficiency by reducing the model runs from 923 to 23 compared to the conventional full-watershed simulation-optimization.

Artificial neural network modeling of sliding wear

A widely used type of artificial neural networks, called multilayer perceptron, is applied for data-driven modeling of the wear coefficient in sliding wear under constant testing conditions. The integral and differential forms of wear equation are utilized for designing an artificial neural network-based model for the wear rate. The developed artificial neural network modeling framework can be utilized in studies of wearing-in period and the so-called true wear coefficient. Examples of the use of the developed approach are given based on the experimental data published recently.

Deep learning convolutional neural network in rainfall–runoff modelling

Abstract Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in the water cycle among precipitation and evapotranspiration processes, and also geophysical characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to difficulties in developing physical and analytical models when traditional statistical methods cannot simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which can capture the nonlinear relationship between prediction and predictors, have been rapidly developed in the last decades and have many applications in the field of water resources. This study attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying two convolutional filters in parallel to separate time series, which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and LSTM have better performance than the traditional models, and the statistical performance of the CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network is suitable for regression-type problems and can effectively learn dependencies in and between the series without the need for a long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.

Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment

Abstract. The increasing demand for high-resolution climate information has attracted growing attention to statistical downscaling (SDS) methods, due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness for purpose for many local-scale applications. As a result, a plethora of SDS methods is nowadays available to climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE initiative (http://www.value-cost.eu, last access: 29 March 2020). The systematic intercomparison of a large number of SDS techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. In this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (model output statistics – including the so-called “bias correction” methods – and perfect prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al., 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SDS model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in VALUE, placing an emphasis on the most technically complex stages of perfect-prognosis model calibration (predictor screening, cross-validation, and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook.

An atlas of human metabolism.

Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.

An integrated model of coupled heat and power sectors for large-scale energy system analyses

In Europe and other regions of the world, heat and power sectors are highly coupled. Thus, both sectors are interdependent which poses a challenge when modeling these systems. This paper presents a novel and integrated model framework to analyze coupled heat and power sectors. Said framework is designed to assess large-scale/multi-national systems such as the European continent’s one. The integral model character derives from a model sequence that first models heat demand at individual district heating network level, then determines the heat supply schedules of individual heat generation units in these networks and finally uses an extended version of a pre-existing large-scale market model, the WILMAR Joint Market Model, for electricity market simulations. All models are embedded in a common database environment. In order to validate the model framework, a historical year is chosen, and model outputs are compared to historical market outcomes. For comparison, two alternative, frequently used modeling approaches are equally tested. Results show that the developed model framework is far superior to the alternative modeling approaches. This statement concerns the reproduction of heat demands, power plant dispatch, electricity prices and net electricity exports. The results also suggest that the developed model can yield improved intra-zonal exchange schedules, which are one major driver for congested transmission network elements.

A simplified modelling framework facilitates more complex representations of plant circadian clocks.

The circadian clock orchestrates biological processes so that they occur at specific times of the day, thereby facilitating adaptation to diurnal and seasonal environmental changes. In plants, mathematical modelling has been comprehensively integrated with experimental studies to gain a better mechanistic understanding of the complex genetic regulatory network comprising the clock. However, with an increasing number of circadian genes being discovered, there is a pressing need for methods facilitating the expansion of computational models to incorporate these newly-discovered components. Conventionally, plant clock models have comprised differential equation systems based on Michaelis-Menten kinetics. However, the difficulties associated with modifying interactions using this approach—and the concomitant problem of robustly identifying regulation types—has contributed to a complexity bottleneck, with quantitative fits to experimental data rapidly becoming computationally intractable for models possessing more than ≈50 parameters. Here, we address these issues by constructing the first plant clock models based on the S-System formalism originally developed by Savageau for analysing biochemical networks. We show that despite its relative simplicity, this approach yields clock models with comparable accuracy to the conventional Michaelis-Menten formalism. The S-System formulation also confers several key advantages in terms of model construction and expansion. In particular, it simplifies the inclusion of new interactions, whilst also facilitating the modification of regulation types, thereby making it well-suited to network inference. Furthermore, S-System models mitigate the issue of parameter identifiability. Finally, by applying linear systems theory to the models considered, we provide some justification for the increased use of aggregated protein equations in recent plant clock modelling, replacing the separate cytoplasmic/nuclear protein compartments that were characteristic of the earlier models. We conclude that as well as providing a simplified framework for model development, the S-System formalism also possesses significant potential as a robust modelling method for designing synthetic gene circuits.