Level Of Model Complexity Research Articles

Transdimensional Bayesian inversion of magnetotelluric data in anisotropic layered media with galvanic distortion correction

SUMMARYPresence of electrical anisotropy in the lithosphere can provide useful constraints on regional structure patterns and dynamics of tectonic processes, and they can be imaged by magnetotelluric (MT) data. However, Inversion of MT data for anisotropic structures using standard gradient-based approaches requires subjective choices of model regularization for constraining structure and anisotropy complexity. Furthermore, the ubiquitous presence of galvanic distortion due to small-scale near-surface conductivity inhomogeneities prevents accurate imaging of subsurface structures if ignored or not properly removed. Here, we present a transdimensional Bayesian approach for inverting MT data in layered anisotropic media. The algorithm allows flexible model parametrization, in which both the number of layers and model parameters of each layer are treated as unknowns. In this manner, the presence or absence of anisotropy within the layers, as well as the level of model complexity, is determined adaptively by the data. In addition, to account for the effects of galvanic distortion, three frequency-independent distortion parameters resulting from the distortion decomposition are treated as additional variables during the inversion. We demonstrate the efficiency of the algorithm to resolve both isotropic and anisotropic structures with synthetic and field MT data sets affected by galvanic distortion effects. The transdimensional inversion results for the field data are compatible with results from previous studies, and our results improve the constraints on the magnitude and the azimuth (i.e. most conductive direction) of electrically anisotropic structures. For practical applications, the validity of 1-D anisotropic approximation should be first tested prior to the use of our approach. Otherwise it may produce spurious anisotropic structures due to the inapplicability of the anisotropic 1-D inversion for MT data affected by 2-D or 3-D electrical resistivity structures.

How suitable are satellite rainfall estimates in simulating high flows and actual evapotranspiration in MelkaKunitre catchment, Upper Awash Basin, Ethiopia?

Understanding the suitability of Satellite Rainfall Estimates (SREs) in simulating high flows and Actual Evapotranspiration (AET) is crucial for developing flood monitoring systems. Therefore, this study aims to assess i) the suitability of SREs in simulating both high flows and AET for different levels of model complexity, and ii) the effect of streamflow calibration on simulating AET for different rainfall inputs in Melkakunitre catchment, Upper Awash Basin, Ethiopia. Three state-of-the-art SREs (TRMM 3B42v7, IMERG v06B, and TAMSAT v3) were used and their usefulness in simulating high flows (Q5), daily streamflow, and wet season flows (from June to September) was assessed using the HBV-light model for the period 2003–2015. The model was set up for two levels of complexity: with and without considering the effect of orography on rainfall and temperature. Moreover, the water balance derived AET was compared against three remotely sensed AET products, MOD 16A2, GLEAM v3, and SSEBob, so as to examine the effect of streamflow calibration on AET simulation. Results show that rainfall inputs and model complexity have a strong impact on simulating streamflow and AET. For all rainfall forcing datasets, the performance of the hydrological model improves when we consider the effects of orography on rainfall and temperature. The IMERG v06B and TAMSAT v3 products showed the highest and least performances in simulating all the three flow conditions, respectively. Moreover, the MODIS-AET is the best remotely sensed AET product in reproducing the water balance-derived AET for all rainfall inputs except TAMSAT v3. The HBV-light model parameters calibrated with streamflow provided better results for simulating AET as well. On average, the usefulness of the IMERG v06B product for simulating high flows and AET is outstanding and can be thus used for developing flood monitoring and management systems in the study catchment.

Grey-box modelling of lithium-ion batteries using neural ordinary differential equations

Grey-box modelling combines physical and data-driven models to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling, as differential equations given by physical laws and neural networks can be combined in a single modelling framework. This simplifies the simulation and optimization and allows to consider irregularly-sampled data during training and evaluation of the model. We demonstrate this approach using two levels of model complexity; first, a simple parallel resistor-capacitor circuit; and second, an equivalent circuit model of a lithium-ion battery cell, where the change of the voltage drop over the resistor-capacitor circuit including its dependence on current and State-of-Charge is implemented as NODE. After training, both models show good agreement with analytical solutions respectively with experimental data.

Mixed Effects of Item Parceling on Performance of Factor Mixture Modeling

ABSTRACT Factor mixture modeling (FMM) is generally complex with both unobserved categorical and unobserved continuous variables. We explore the potential of item parceling to reduce the model complexity of FMM and improve convergence and class enumeration accordingly. To this end, we conduct Monte Carlo simulations with three types of data, continuous, polytomous, and binary under two levels of model complexity, constrained FMM under strict invariance and relaxed FMM under scalar or metric invariance. The results show that item parceling could be advantageous for FMM with binary items but not with continuous or polytomous items.

Patient-specific simulation of stent-graft deployment in type B aortic dissection: model development and validation

Thoracic endovascular aortic repair (TEVAR) has been accepted as the mainstream treatment for type B aortic dissection, but post-TEVAR biomechanical-related complications are still a major drawback. Unfortunately, the stent-graft (SG) configuration after implantation and biomechanical interactions between the SG and local aorta are usually unknown prior to a TEVAR procedure. The ability to obtain such information via personalised computational simulation would greatly assist clinicians in pre-surgical planning. In this study, a virtual SG deployment simulation framework was developed for the treatment for a complicated aortic dissection case. It incorporates patient-specific anatomical information based on pre-TEVAR CT angiographic images, details of the SG design and the mechanical properties of the stent wire, graft and dissected aorta. Hyperelastic material parameters for the aortic wall were determined based on uniaxial tensile testing performed on aortic tissue samples taken from type B aortic dissection patients. Pre-stress conditions of the aortic wall and the action of blood pressure were also accounted for. The simulated post-TEVAR configuration was compared with follow-up CT scans, demonstrating good agreement with mean deviations of 5.8% in local open area and 4.6 mm in stent strut position. Deployment of the SG increased the maximum principal stress by 24.30 kPa in the narrowed true lumen but reduced the stress by 31.38 kPa in the entry tear region where there was an aneurysmal expansion. Comparisons of simulation results with different levels of model complexity suggested that pre-stress of the aortic wall and blood pressure inside the SG should be included in order to accurately predict the deformation of the deployed SG.

Modelling of energy metabolism and analysis of pH variations in postmortem muscle

A kinetic model structure was developed to describe the major variations in energy metabolism and to gain further understanding of pH changes in postmortem muscle experimentally observed with an in vitro glycolytic system. Comparison with experiments showed that the model could describe the kinetics of major metabolites and pH under varied conditions. Optimized model parameters definitively and consistently showed the observed effects of mitochondria, indicating a desirable level of model complexity. Simulation and analysis of pH variations based on the model suggested that phosphofructokinase activity has the strongest impact on the rate and extent of postmortem pH decline. Postmortem pH is also influenced by rates of ATP hydrolysis and glycolysis, and to a much lesser extent, pH buffering capacity. Other reactions, including those mediated by creatine kinase, adenylate kinase, and AMP deaminase, have minimal effects on postmortem pH decline.

Solar shading and multi-zone thermal simulation: Parsimonious modelling at urban scale

With the growing interest in urban scale simulation, urban building energy modelling tools (UBEM) are being developed. The purpose of these UBEM tools is to tackle multiple issues that can range from buildings retrofit potential to the sizing of thermal and/or electrical network or to the assessment of renewable energy sources potential. Unlike the modelling of a single building, collecting accurate building information data at district scale is nearly impossible and therefore the uncertainties increase. In this context, the selection of the most relevant and adequate models with regard to the available data and simulation objectives is challenging, and often a trade-off between accuracy and parametrization feasibility has to be found. A novel approach is proposed to choose the right level of model complexity for thermal heating demand simulation in buildings at district or city scale, by assessing in particular the impact of different thermal zoning methods with the UBEM tool DIMOSIM. This method for assessing the parsimony in modelling is applied to a selection of models from the literature in order to find a compromise between the available data, the different modelling levels of detail, the expected output and the computation time, according to district and buildings characteristics. By applying this approach, it has been found that a division per floor for building modelling is in most cases the most parsimonious choice for thermal heating demand simulation at district scale. Eventually, the coupling between thermal zoning models and solar shading models is analysed through the same methodology for assessing the modelling parsimony. The results show that the combination of simpler models (in particular a thermal zone per floor or mono-zone buildings for thermal zoning models, and a solar shading coefficient calculated per floor or facade for solar models) can be parsimonious enough to be used to determine the annual heating demand, speeding up strongly the simulation at the same time.

Impact of rupture-plane uncertainty on earthquake hazard: observations from the 30 october 2020 Samos earthquake

We assess the significance of rupture-plane uncertainty in the estimated ground-motion intensity measures (IMs) by using the centroid moment-tensor and fault-plane solutions as well as the ground-motion recordings of the 30 October 2020 Samos Earthquake. We sampled ground-motion fields using stochastically generated rupture planes by considering the uncertainties imposed from alternative fault-plane solutions to reach our objective. Our observations indicate that the compromise between rupture-plane uncertainty and variability in the predicted ground-motion IMs depends on the modeling complexity of the ground-motion predictive model (GMPM). It also depends on the spatial location of the site relative to the ruptured fault. This conclusive remark is important for modelers who perform regional or site-specific seismic hazard and risk analyses. The presented case studies are also useful for GMPM developers because the ground-motion models contain predictor parameters, the most ubiquitous one is source-to-site distance, that are affected from rupture-plane geometry. Depending on the level of model complexity, the number of predictor parameters affected from rupture-plane geometry can increase and therefore the estimated ground-motion IMs can become more prone to rupture-plane uncertainty. Confined to our case-specific observations, we intend to make some suggestions to hazard, risk, and GMPM modelers for the consideration of rupture-plane uncertainty at the end of the paper.

Upgrading waste material flow analysis with process models: The case of anaerobic digestion

Material flow analysis (MFA) has been shown to be a valuable tool for goal-oriented decision making of waste management systems. When used to evaluate or compare waste management scenarios, it relies on applying transfer coefficients to model how the flows of goods and substances are transformed through waste treatment processes. Although transfer coefficients provide a useful simplification, their applicability to represent complex processes such as anaerobic digestion (AD) is limited. In this study, three levels of model complexity were compared to upgrade an MFA: a material-specific empirical transfer coefficient method using the biochemical methane potential, a kinetic method using a first-order model, and a process-based method using the Anaerobic Digestion Model No. 1 (ADM1). The three models were integrated in an MFA and compared based on their prediction of methane production and nitrogen recovery for different waste compositions, reactor operating conditions and simulation time steps. The results showed that the simpler models were sufficient to predict the methane production, but that only complex models such as ADM1 could predict behaviour regarding the loss of nitrogen in the liquid digestate. The results also showed the advantages of integrating the model in transient state for nitrogen modelling. An MFA upgraded with a process model such as ADM1 allows to better implement the analysis on both the levels of goods and substances and can be useful for the case of waste treatment processes other than AD.

Breast cancer histopathology using infrared spectroscopic imaging: The impact of instrumental configurations

Digital analysis of cancer specimens using spectroscopic imaging coupled to machine learning is an emerging area that links spatially localized spectral signatures to tissue structure and disease. In this study, we examine the role of spatial-spectral tradeoffs in infrared spectroscopic imaging configurations for probing tumors and the associated microenvironment profiles at different levels of model complexity. We image breast tissue using standard and high-definition Fourier Transform Infrared (FT-IR) imaging and systematically examine the localization, spectral origins, and utility of data for classification. Results demonstrate that higher spatial detail provides high sensitivity and specificity of tissue segmentation, despite the increased subcellular variability. High definition imaging also allows accurate analysis of complex, multiclass models of breast tissue without compromising accuracy. A comparison of results also highlights the key differences in the data distributions and classification performance across modalities to better guide decision making for acquiring and analyzing IR imaging data for specific histopathological models.

High Inter-Patient Variability in Sepsis Evolution: A Hidden Markov Model Analysis

BackgroundSevere sepsis and septic shock are common in the intensive care unit (ICU) and contribute significantly to cost and mortality. Early treatment is critical but is confounded by the difficulty of real-time diagnosis. This study uses hidden Markov models (HMMs) to examine whether the time evolution of sepsis can add diagnostic accuracy or value using a proven set of bio-signals. MethodsClinical data (N=36 patients; 6071 hours), including an hourly personalised insulin sensitivity metric. A two hidden state HMM is created to discriminate diagnosed cases (Severe Sepsis, Septic Shock) from controls (SIRS, Sepsis) states. Diagnostic performance is measured by ROC curves, likelihood ratios (LHRs), sensitivity/specificity, and diagnostic odds-ratios (DOR), for a best-case resubstitution estimate and a worst-case 80/20% repeated holdout analysis. ResultsThe HMM delivered near perfect results (95% Sensitivity; 96% Specificity) for best-case resubstitution estimates, but was comparatively poor (59% Sensitivity; 61% Specificity) for worst-case repeated holdout estimations. Adding the time evolution of sepsis did not add to the accuracy of diagnosis from using the signals alone without time history. ConclusionsThese potentially surprising results indicate significant inter-patient variability in the time evolution of sepsis, preventing effective diagnosis in the context of the bio-signals, data, and HMM topology used. Efforts for improved real-time, early sepsis diagnosis should concentrate on the robustness and efficacy of the bio-signals and data used, as well as the level of model complexity, to create more effective real-time classifiers.

Neutron-gamma discrimination method based on blind source separation and machine learning

The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks. Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimination. However, their performances are often associated with certain factors, such as experimental requirements and resulting mixed signals. The main purpose of this study is to achieve fast and accurate neutron-gamma discrimination without a priori information on the signal to be analyzed, as well as the experimental setup. Here, a novel method is proposed based on two concepts. The first method exploits the power of nonnegative tensor factorization (NTF) as a blind source separation method to extract the original components from the mixture signals recorded at the output of the stilbene scintillator detector. The second one is based on the principles of support vector machine (SVM) to identify and discriminate these components. In addition to these two main methods, we adopted the Mexican-hat function as a continuous wavelet transform to characterize the components extracted using the NTF model. The resulting scalograms are processed as colored images, which are segmented into two distinct classes using the Otsu thresholding method to extract the features of interest of the neutrons and gamma-ray components from the background noise. We subsequently used principal component analysis to select the most significant of these features wich are used in the training and testing datasets for SVM. Bias-variance analysis is used to optimize the SVM model by finding the optimal level of model complexity with the highest possible generalization performance. In this framework, the obtained results have verified a suitable bias–variance trade-off value. We achieved an operational SVM prediction model for neutron-gamma classification with a high true-positive rate. The accuracy and performance of the SVM based on the NTF was evaluated and validated by comparing it to the charge comparison method via figure of merit. The results indicate that the proposed approach has a superior discrimination quality (figure of merit of 2.20).

The benefits of increasing resolution in global and regional climate simulations for European climate extremes

Abstract. Many climate extremes, including heatwaves and heavy precipitation events, are projected to worsen under climate change, with important impacts for society. Future projections required for adaptation are often based on climate model simulations. Given finite resources, trade-offs must be made concerning model resolution, ensemble size, and level of model complexity. Here we focus on the resolution component. A given resolution can be achieved over a region using either global climate models (GCMs) or at lower cost using regional climate models (RCMs) that dynamically downscale coarser GCMs. Both approaches to increasing resolution may better capture small-scale processes and features (downscaling effect), but increased GCM resolution may also improve the representation of the large-scale atmospheric circulation (upscaling effect). The size of this upscaling effect is therefore important for deciding modelling strategies. Here we evaluate the benefits of increased model resolution for both global and regional climate models for simulating temperature, precipitation, and wind extremes over Europe at resolutions that could currently be realistically used for coordinated sets of climate projections at the pan-European scale. First we examine the benefits of regional downscaling by comparing EURO-CORDEX simulations at 12.5 and 50 km resolution to their coarser CMIP5 driving simulations. Secondly, we compare global-scale HadGEM3-A simulations at three resolutions (130, 60, and 25 km). Finally, we separate out resolution-dependent differences for HadGEM3-A into downscaling and upscaling components using a circulation analogue technique. Results suggest limited benefits of increased resolution for heatwaves, except in reducing hot biases over mountainous regions. Precipitation extremes are sensitive to resolution, particularly over complex orography, with larger totals and heavier tails of the distribution at higher resolution, particularly in the CORDEX vs. CMIP5 analysis. CMIP5 models underestimate precipitation extremes, whilst CORDEX simulations overestimate compared to E-OBS, particularly at 12.5 km, but results are sensitive to the observational dataset used, with the MESAN reanalysis giving higher totals and heavier tails than E-OBS. Wind extremes are somewhat stronger and heavier tailed at higher resolution, except in coastal regions where large coastal grid boxes spread strong ocean winds further over land. The circulation analogue analysis suggests that differences with resolution for the HadGEM3-A GCM are primarily due to downscaling effects.

Spectral clustering based demand-oriented representative days selection method for power system expansion planning

Due to the computational burden of the system expansion planning (SEP) models, it is a common approach to select representative days by clustering to make the SEP models tractable. However, the performance of the selection of representative days is unsatisfactory, as the conventional methods fail to properly capture the demand for adequacy and flexibility in system scheduling and to effectively cluster the feature vectors that are commonly high-dimensional and highly non-hyperspherical distributed. This paper proposes a spectral clustering based demand-oriented (SCDOR) representative days selection method, which extracts the daily net load duration curve (NLDC) and net load ramp duration curve (NLRDC) as a feature vector to accurately capture demand for the adequacy and flexibility. Additionally, the feature vectors are regarded as connected vertices in a graph, and the representative days are obtained through the graph partition by spectral clustering that is adaptable to different data distribution. The superior performance of SCDOR is demonstrated through the case studies with different levels of modeling complexity on the Texas power system and IEEE RTS-79 system.

Examining modelling ability before educational reforms: findings of cross-country comparisons of grade 8 data from TIMSS 2011

ABSTRACT Modelling ability is one of the essential elements of the latest educational reforms, and Trends in International Mathematics and Science Study (TIMSS) is a curriculum-based assessment which allows educational systems worldwide to inspect the curricular influences. The aims of this study were to examine the role of modelling ability in the generalized Grade 8 (G8) curriculum via TIMSS 2011 and to compare the G8 students’ modelling ability among the four top-performing countries (i.e., Japan, Korea, Singapore, and Taiwan) in Asia before the latest educational reforms. This study developed Modelling Ability Analytic Index-TIMSS (MAAI-T) to analyse TIMSS 2011 G8 items. There are two dimensions of MAAI-T, stages of modelling process (i.e., selection, construction, validation, analysis and application, and deployment) and levels of model complexity (i.e., Levels 1 through 4, from single factor to extended relation). The findings showed: TIMSS 2011 G8 items centred on the stage of modelling selection, and analysis and application and mainly measured Level 3 (simple relation among factors). The highest-performing country (Singapore) demonstrated Level 3 in all stages and Level 4 of model selection and deployment; while the lowest-performing country (Korea) demonstrated Level 3 only in the stages of model selection, validation, and analysis and application.

Temporal transferability of marine distribution models in a multispecies context

Abstract Transferability of species distribution models is crucial for supporting biological conservation and management planning in the face of climate changes; however, there is a lack of understanding in how to achieve transference spatially and temporally, especially in a multispecies circumstance. This study evaluated the roles of model complexity and time scope in temporal transferability for multispecies distribution modelling. We collected data of multispecies distribution from a seven-year marine survey in coastal Yellow Sea, China, and used the data to evaluate the predictive performances of an array of multi-species distribution models (constrained linear ordination, constrained additive ordination, hierarchical models of species communities, multivariate random forests, multivariate tree boosting model, and multivariate artificial neural network), characterized by different structures, levels of complexity and time spans with 36 models and 50 scenarios. Our study showed that model transference was proper for some species but limited at the community level in general, and no specific modelling algorithm (regression models/machine-learning) or level of model complexity was found to be superior. Instead, model structure, features of target species, and time scope contributed substantially and influenced transferability in a contingent way. Temporal transferability tended to increase with the length of training periods and decay with the time gaps of prediction, whereas the magnitude varied among modelling algorithms. In addition, prolonging the time series of survey programs alone may have limited effectiveness on community-level transferability. This study contributes to the understanding of temporal transferability of multi-species distribution models and inform their applications in biological conservation and management from a temporal perspective.

Effect of Biophysical Model Complexity on Predictions of Volume of Tissue Activated (VTA) during Deep Brain Stimulation.

Deep brain stimulation (DBS) has evolved to an important treatment for several drug-resistant neurological and psychiatric disorders, such as epilepsy, Parkinson's disease, essential tremor and dystonia. Despite general effectiveness of DBS, however, its mechanisms of action are not completely understood. Simulations are commonly used to predict the volume of tissue activated (VTA) around DBS electrodes, which in turn helps interpreting clinical outcomes and understand therapeutic mechanisms. Computational models are commonly used to visualize the extend of volume of activated tissue (VTA) for different stimulation schemes, which in turn helps interpreting and understanding the outcomes. The degree of model complexity, however, can affect the predicted VTA. In this work we investigate the effect of volume conductor model complexity on the predicted VTA, when the VTA is estimated from activation function field metrics. Our results can help clinicians to decide what level of model complexity is suitable for their specific need.

Disentangling model complexity in green roof hydrological analysis: A Bayesian perspective

Green Roofs (GRs) have proven to be a sustainable solution to stormwater management in urban areas. To boost their adoption at the large scale, there is a need to develop numerical models, which are accurate, computationally cheap, and as complex as needed to reproduce the hydrological behavior of GRs. Alternative conceptual and mechanistic approaches have been proposed and tested, however the most appropriate level of model complexity for GRs’ analysis is still unknown. To cover this scientific gap, we provide a Bayesian comprehensive perspective of GR hydrological modeling, which includes a statistically rigorous Bayesian comparison of one conceptual and multiple Richards-based mechanistic GR models, and a probabilistic assessment of the information content of different observations. The analysis of the marginal likelihoods reveals that the conceptual and the unimodal van Genuchten – Mualem models are the most appropriate parameterizations, and that further layers of model complexity are not fully supported by the measurements. In addition to that, the estimated Kullback-Leibler divergences suggest that the measured volumetric water content outperforms the measured subsurface outflow and tracer concentrations in terms of informativeness, leading to the lowest model predictive uncertainty for the simulation of water fluxes. The findings of this study represent a first step to clarify the role of model complexity in GRs’ analysis, and open new perspective on GRs’ model-based experimental design.

Comparison of variable selection methods in partial least squares regression

AbstractThrough the remarkable progress in technology, it is getting easier and easier to generate vast amounts of variables from a given sample. The selection of variables is imperative for data reduction and for understanding the modeled relationship. Partial least squares (PLS) regression is among the modeling approaches that address high throughput data. A considerable list of variable selection methods has been introduced in PLS. Most of these methods have been reviewed in a recently conducted study. Motivated by this, we have therefore conducted a comparison of available methods for variable selection within PLS. The main focus of this study was to reveal patterns of dependencies between variable selection method and data properties, which can guide the choice of method in practical data analysis. To this aim, a simulation study was conducted with data sets having diverse properties like the number of variables, the number of samples, model complexity level, and information content. The results indicate that the above factors like the number of variables, number of samples, model complexity level, information content and variant of PLS methods, and their mutual higher‐order interactions all significantly define the prediction capabilities of the model and the choice of variable selection strategy.

Heat transfer calculation methods in three-dimensional CFD model for pulverized coal-fired boilers

Computational fluid dynamics (CFD) has been used extensively to study the combustion characteristics of coal-fired boilers. However, very limited of the boiler CFD studies were used to quantitatively predict the heat transfer distributions in practical boiler applications. This paper presents a systematic description on the heat transfer calculation methods in three-dimensional CFD model for coal-fired boilers. The primary objective is to provide a set of heat transfer submodels that can be easily employed to solve and analyze large scale practical boiler problems. All types of boiler heating surfaces are considered with particular focus on the heat transfer calculation of furnace water wall. It is implemented in the CFD model in terms of a thermal boundary condition (B.C.) describing the energy balance between the boiler’s fire side heat transfer and water side heat absorption. The physical significance of the associated B.C. parameters and their accurate prescription are discussed in detail in this paper. It is found that furnace wall heat transfer is largely affected by the wall ash deposit conditions such that the heat transfer coefficient of furnace wall can be determined based on the thermal properties of ash. Although ash thickness may vary dramatically over furnace wall and is extremely difficult to predict, its thermal conductance was found to only vary within a small range for normally operated coal-fired boilers. Establishing the connection between the thermal conductance of wall ash deposits and the heat transfer calculation of furnace wall bypasses the tremendous difficulty and uncertainty incurred in predicting the ash thickness, and thereby, greatly simplifies the wall heat transfer calculation process. The heat transfer calculation methods introduced in this study embody the key physics associated with boiler’s heat transfer process while making a reasonable balance between the level of model complexity and their applicability to practical boiler problems. In this paper they are illustrated and validated on a 330 MW tangentially-fired subcritical boiler. The results show that the predicted boiler heat transfer distributions are in close agreement with the boiler’s operating data.