Hierarchical Multiscale Framework Research Articles

Multiscale modeling of granular dynamics on flowslide triggering and runout

AbstractA hierarchical multiscale modeling framework is proposed to simulate flowslide triggering and runout. It couples a system‐scale sliding‐consolidation model (SCM) resolving hydro‐mechanical feedbacks within a flowslide with a local‐scale solver based on the discrete element method (DEM) replicating the sand deformation response in the liquefied regime. This coupling allows for the simulation of a seamless transition from solid‐ to fluid‐like behavior following liquefaction, which is controlled by the grain‐scale dynamics. To investigate the role of grain‐scale interactions, the DEM simulations replace the constitutive model within the SCM framework, enabling the capture of the emergent rate‐dependent behavior of the sand during the inertial regime of motion. For this purpose, a novel algorithm is proposed to ensure the accurate passage of the strain rate from the global analysis to the local DEM solver under both quasi‐static (pre‐triggering) and dynamic (post‐triggering) regimes of motion. Our findings demonstrate that the specifics of the coupling algorithm do not bear significant consequences to the triggering analysis, in that the grain‐scale dynamics is negligible. By contrast, major differences between the results obtained with traditional algorithms and the proposed algorithm are found for the post‐triggering stage. Specifically, the existing algorithms suffer from loss of convergence and require proper numerical treatment to capture the micro‐inertial effects arising from the post‐liquefaction particle agitation responsible for viscous‐like effects that spontaneously regulate the flowslide velocity. These findings emphasize the important role of rate‐dependent feedback for the analysis of natural hazards involving granular materials, especially for post‐failure propagation analysis.

Prediction of broken areas in murals based on MLP-fused long-range semantics

Predicting the broken areas in murals plays a key role in mural virtual restoration. Mural damage may arise for various reasons and the broken areas also vary greatly in terms of type. The existing methods, however, are limited to predicting a single type of damage and often struggle to identify the dispersed damage with accuracy. Moreover, these methods make it difficult to capture the global information in the broken areas for their insufficient understanding of contexts. To fully use the features at different scales, we propose a novel hierarchical multi-scale encoder-decoder framework termed as Mixer of Dual Attention and Convolution (DACMixer). With the introduction of an attention-convolution dual-branch module in the encoder, DACMixer can not only improve its ability to extract intricate features of small broken areas but also capture long-range dependencies of independent broken areas. Within DACMixer, the MFF (Multi-layer perceptron-based feature fusion) module integrates both local and global information in the broken areas, facilitating efficient and explicit modeling image hierarchies in the global and local range. Contrary to the encoder, DACMixer uses only lightweight multi-level decoder to decode the features of the broken masks, thus reducing the computational cost. Additionally, DACMixer preserves skip-connection to effectively integrate features from different levels of the MFF module. Furthermore, we provide a diversified mural dataset with elaborated broken annotation, which is named YMDA [YMDA denotes our dataset Yunnan_Murals_Dataset_Aug.], to further improve DACMixer’s generalization ability to predict the broken areas. The experimental results demonstrate that DACMixer is capable of predicting the texture, edges, and details of the broken areas in murals with complex backgrounds. DACMixer outperforms the conventional methods with superb results: it achieves 78.3% broken areas IoU (Intersection over Union), 87.5% MIoU (Mean Intersection over Union), and 85.7% Dice coefficient.

Hierarchical multi-scale parametric optimization of deep neural networks

AbstractTraditionally, sensitivity analysis has been utilized to determine the importance of input variables to a deep neural network (DNN). However, the quantification of sensitivity for each neuron in a network presents a significant challenge. In this article, a selective method for calculating neuron sensitivity in layers of neurons concerning network output is proposed. This approach incorporates scaling factors that facilitate the evaluation and comparison of neuron importance. Additionally, a hierarchical multi-scale optimization framework is proposed, where layers with high-importance neurons are selectively optimized. Unlike the traditional backpropagation method that optimizes the whole network at once, this alternative approach focuses on optimizing the more important layers. This paper provides fundamental theoretical analysis and motivating case study results for the proposed neural network treatment. The framework is shown to be effective in network optimization when applied to simulated and UCI Machine Learning Repository datasets. This alternative training generates local minima close to or even better than those obtained with the backpropagation method, utilizing the same starting points for comparative purposes within a multi-start optimization procedure. Moreover, the proposed approach is observed to be more efficient for large-scale DNNs. These results validate the proposed algorithmic framework as a rigorous and robust new optimization methodology for training (fitting) neural networks to input/output data series of any given system. Graphical Abstract

Inductive Determination of Rate-Reaction Equation Parameters for Dislocation Structure Formation Using Artificial Neural Network

The reaction-diffusion equation approach, which solves differential equations of the development of density distributions of mobile and immobile dislocations under mutual interactions, is a method widely used to model the dislocation structure formation. A challenge in the approach is the difficulty in the determination of appropriate parameters in the governing equations because deductive (bottom-up) determination for such a phenomenological model is problematic. To circumvent this problem, we propose an inductive approach utilizing the machine-learning method to search a parameter set that produces simulation results consistent with experiments. Using a thin film model, we performed numerical simulations based on the reaction-diffusion equations for various sets of input parameters to obtain dislocation patterns. The resulting patterns are represented by the following two parameters; the number of dislocation walls (p2), and the average width of the walls (p3). Then, we constructed an artificial neural network (ANN) model to map between the input parameters and the output dislocation patterns. The constructed ANN model was found to be able to predict dislocation patterns; i.e., average errors in p2 and p3 for test data having 10% deviation from the training data were within 7% of the average magnitude of p2 and p3. The proposed scheme enables us to find appropriate constitutive laws that lead to reasonable simulation results, once realistic observations of the phenomenon in question are provided. This approach provides a new scheme to bridge models for different length scales in the hierarchical multiscale simulation framework.

CCCs off-axial orientation sensitivity analysis in hole pin-bearing failure via hierarchical multiscale simulation framework

In current investigation, a sequential multiscale modelling strategy together with corresponding experimental validation aimed to evaluate the influence of off-axial orientation on three-dimensional orthogonal woven carbon/carbon composites (3DOWCCCs) pin-bearing failure behavior. A hierarchical numerical simulation methodology at micro−/meso−/macroscales was developed to obtain the predictions. With the intention of acquiring precise mechanical responses of unnotched 3DOWCCCs, fiber and void stochastic arrangements in micro-level representative volume elements (RVEs) were generated using Random Sequential Adsorption (RSA) algorithm, meanwhile the geometrical reconstruction of meso-level RVE was accomplished via X-ray computed tomography (X-CT). Moreover, off-axial angle sensitivity analysis was presented through the comparison between macroscale modelling and testing results. The stress interaction and coupling impacts were embedded in material constitutive laws, which combined Murakami-Ohno theory for yarn based on continuum damage mechanics, and exponential damage rule for carbon matrix, incorporating trilinear cohesive zone model (CZM) for interface, and the achieved predictions agreed reasonably well with the experimental results. It is concluded that, for on-axial specimen, the warp yarns fracture and material out-of-plane swelling were the primary damage mechanism, while the material out-of-plane swelling in conjunction with yarns in-plane rotation dominated the structural failure under off-axial cases.

A multiscale computational framework for wear prediction in knee replacement implants

Wear damage represents a long-term material failure that affects the service life of joint implants. Prediction of wear in bearing inserts under arbitrary joint kinematics and kinetics holds an important key to improving the performance of entire joint implant device through new material development and customized geometry design. In this work, a hierarchical multiscale computational framework that predicts the wear rate of ultra-high molecular weight polyethylene (UHMWPE) insert by resolving the microstructure details and microscale deformation mechanisms is developed. A fully dynamic finite element simulation is carried out at the macroscale level to reproduce the interactions between femoral component and UHMWPE insert according to the knee simulator input profiles. The stress evolution history from the initial wear site is extracted and applied back to the representative volume element (RVE) to further study the microscale deformation mechanisms that are related to wear damage. Activation of different slip systems in the crystalline phase as well as the interplay between the crystalline phase and amorphous phase are captured through a semi-crystal plasticity model. Results shown that improving wear resistance requires minimizing the plastic deformation upon the same external loading while maintaining a load-sharing balance between the crystalline phase and the amorphous phase. A combination of high crystallinity, well-dispersed crystallite with small size and texture alignment along the axial load direction can promote activation of chain pull slip that leads to improved wear resistance.

Multiscale modeling of coupled thermo-mechanical behavior of granular media in large deformation and flow

Heat generation and transfer in a granular material can be intricately coupled with their mechanical responses, playing a key role in causing excessive large deformation, flow and failure of the material. The coupling may manifest in various forms, including thermal induced stress, mechanically induced heat and thermally induced melting in granular media. We propose a novel hierarchical multiscale modeling framework, TM-DEMPM, to model the coupled thermo-mechanical behavior in granular media which may undergo large deformation and flow. Material Point Method (MPM) is hierarchically coupled with Discrete Element Method (DEM) to offer physics-based, natural scale-crossing simulations of thermo-mechanical granular responses without assuming complicated phenomenological constitutive models. To offer speedup for the numerical solution, hybrid OpenMP and GPU-based parallelization is proposed to take advantage of the hierarchical computing structure of the framework. The proposed framework may provide an effective and efficient pathway to next-generation simulation of engineering-scale large-deformation problems that involve complicated thermo-mechanical coupling in granular media.

Enhancing Hierarchical Multiscale Off-Road Mobility Model by Neural Network Surrogate Model

Abstract A hierarchical multiscale off-road mobility model is enhanced through the development of an artificial neural network (ANN) surrogate model that captures the complex material behavior of deformable terrain. By exploiting the learning capability of neural networks, the incremental stress and strain relationship of granular terrain is predicted by the ANN representative volume elements (RVE) at various states of the stress and strain. A systematic training procedure for ANN RVEs is developed with a virtual tire test rig model for producing training data from the discrete-element (DE) RVEs without relying on computationally intensive full vehicle simulations on deformable terrain. The ANN surrogate RVEs are then integrated into the hierarchical multiscale computational framework as a lower-scale model with the scalable parallel computing capability, while the macroscale terrain deformation is described by the finite element (FE) approach. It is demonstrated with several numerical examples that off-road vehicle mobility performances predicted by the proposed FE-ANN multiscale terrain model are in good agreement with those of the FE-DE multiscale model while achieving a substantial computational time reduction. The accuracy and robustness of the ANN RVE for fine-grain sand terrain are discussed for scenarios not considered in training datasets. Furthermore, a drawbar pull test simulation is presented with the ANN RVE developed with data in the cornering scenario and validated against the full-scale vehicle test data. The numerical results confirm the predictive ability of the FE-ANN multiscale terrain model for off-road mobility simulations.

A hierarchical multiscale strategy for analyzing the impact response of 3D braided composites

A hierarchical multiscale framework has been developed to predict the impact response of 3D braided composites, in which a bridging model(unit cell model), was employed as a bridge between micro- and macro- scale. A paraboloidal elastic-to-plastic damage model for the epoxy, including the hydrostatic effect, was conducted, where a robust algorithm has been implemented to enhance the convergence and accuracy. At the microscale, nine RVEs (representative volume elements) with different fibers distributions were generated in order to enhance reliability and to reduce the size effect for the micromechanical analysis; At the macroscale, firstly, a unit cell model was proposed as a virtual meso-scale based on the geometric topology of 3D braided composites, and then the unit cell model was implicitly introduced into the macromodel based on the transformation matrix. The homogenization method was adopted for transferring the mechanical properties between scales. These constitutive models were implemented by two user defined subroutines (UMAT and VUMAT) in ABAQUS/Standard and ABAQUS/Explicit for micromechanical analysis and impact simulation, respectively. Furthermore, the failure envelop was carried out for matching a better strength criteria for the braiding yarn. Finally, the high-speed impact simulation was employed to validate the effectiveness of the multiscale framework. The results show that the numerical results are agreed with the experiments well.

Energetically-consistent multiscale analysis of fracture in composites materials

Two distinct length scale transition methodologies are developed to establish effective traction-separation relations for fracture in composite materials within a hierarchical multiscale framework. The two methodologies, one kinetics-based and the other kinematics-based, specify effective fracture properties that satisfy a surface-based Hill-Mandel consistency condition. Correspondingly, the total amount of energy dissipated is the same whether a crack is described in detail with micro quantities or in terms of an effective macroscopic crack. Though both methods guarantee consistency in terms of energy rates across length scales, they provide in general distinct effective traction-separation relations. Several representative samples of fiber reinforced composites are analyzed numerically, including the formation and propagation of cracks at mid-ply locations as well as (idealized) ply interfaces. Through post-processing of the microscale results, it is shown that the kinematics-based averaging method provides a macroscopic traction that is prone to rapid fluctuations while the kinetics-based averaging method shows a more smooth response but with openings that can deviate from the surface average of the microscale openings. The two methods are also compared with a previously-proposed scale transition methodology, which is a hybrid method that only satisfies the Hill-Mandel condition approximately. The suitability of the three methods is discussed in light of the results obtained from the simulations.

From Smart-Cities to Smart-Communities

Nowadays, through ICT supports and their applications, the concept of smart cities has evolved into smart communities, where the collaborative relationship between citizens and public administration generates multi-dimensional impacts: urban sites are living labs and agents of innovation and inclusion. As a first step, this article aims to critically review the state of the art of the assessment methods of these impacts through a set of synthetic indicators; the second step is to elaborate a specific framework to evaluate quality of life through a set of impact indicators for smart communities and inclusive urban processes. According to some referenced authors, cities and communities are smart if they perform well in six smart categories: smart economy; smart people; smart governance; smart mobility; smart environment; and smart living. Considering a recent experiment carried out in Turin (Italy), the authors propose a methodology, whose trial is ongoing, based on a hierarchical multiscale framework defining a set of smart community indicators.

Peridynamics with Corrected Boundary Conditions and Its Implementation in Multiscale Modeling of Rolling Contact Fatigue

There is a boundary effect due to incomplete horizons of boundary or near-boundary points in peridynamics. In this paper, we propose to attach “fictitious walls” to boundary surfaces so that the boundary effect can be reduced or eliminated. Differing from the concept of “fictitious material layers”, which is only attached to displacement boundary surfaces, “fictitious walls” are attached to both displacement and force boundary surfaces. Three types of fictitious walls are considered in this paper: undeformed, deformed, and periodic. It is recommended to attach “undeformed fictitious walls” to displacement boundaries and “deformed fictitious walls” to force boundaries. “Periodic fictitious walls” are suggested for use in peristatics only. In addition, peridynamics with corrected boundary conditions is then implemented in a hierarchical multiscale method to study rolling contact fatigue. In this hierarchical multiscale framework, the coefficient of friction is passed from molecular dynamics simulations to peridynamics, which models crack initiation and propagation in rolling contact simulations.

A Hierarchical Multiscale Modeling Investigation on the Behavior of Microtextured Regions in Ti-6242 α/β Processing

Ti-6242 is a near alpha titanium alloy, which has excellent high-temperature creep resistance and is widely used in jet engine compressors. This alloy is susceptible to creep fatigue failure under dwell loading below 473 K. The existence of microtextured regions (MTRs) contributes significantly to this fast crack propagation. Mechanical processing in the alpha + beta region has been employed to eliminate MTRs, but the efficiency depends significantly on the applied strain path. Previous investigations based on crystal plasticity finite element (CPFE) simulations have demonstrated the relationship between breakdown efficiency and loading direction. Therein, MTRs with regular geometry and pure initial orientation were used to isolate the effect of loading direction from initial microstructure. In this paper, the behavior of MTRs with realistic initial microstructure was investigated using a hierarchical multiscale modeling framework, and the microscale results were analyzed in detail to understand the behavior of MTRs under different loading conditions. It was shown that a hierarchical multiscale model with realistic initial microstructure at the microscale can reflect the influences from different strain paths, initial orientation distributions, and positions of the region simultaneously. The combined effect of initial orientation distribution and loading direction on the MTR breakdown efficiency is discussed in detail.

Multiscale modeling of the interface effects in CNT-epoxy nanocomposites

This paper presents a hierarchical multiscale simulation framework for investigating the interface effects in polymer nanocomposites. In this framework, the load transfer ability of the interface in carbon nanotube (CNT)-epoxy nanocomposites is evaluated using molecular dynamics (MD) simulations by adopting an atomistic graphene-polymer interface model in which the cured epoxy matrix with various crosslink densities is constructed by using a dynamic crosslinking algorithm. The interfacial behavior between CNTs and the epoxy matrix has been characterized in both normal opening mode and sliding mode separation in terms of the force-separation responses at the nanoscale. Key factors, e.g. the crosslink density of the epoxy network in the matrix, the system temperature, the separation mode and functionalization, has been investigated on their effects on the load transfer ability of the CNT-epoxy interface. Further, by employing embedded cohesive zone model in finite element analysis, the macroscale effective material properties of the CNT-epoxy nanocomposites have been evaluated under the nanoscale interface effects. It is observed that covalent functionalization between CNT and polymer matrix can dramatically improve the load transfer ability of the interface at the nanocale, thereby enhancing the effective mechanical properties of the nanocomposites at the microscale. This work will assist in deepening our knowledge about the load transfer ability of the interface and the corresponding strengthening mechanisms in CNT reinforced epoxy nanocomposites.

Accelerating multi-scale sheet forming simulations by exploiting local macroscopic quasi-homogeneities

Multi-scale simulations are computationally expensive if a two-way coupling is employed. In the context of sheet metal forming simulations, a fine-scale representative volume element (RVE) crystal plasticity (CP) model would supply the Finite Element analysis with plastic properties, taking into account the evolution of crystallographic texture and other microstructural features. The main bottleneck is that the fine-scale model must be evaluated at virtually every integration point in the macroscopic FE mesh. We propose to address this issue by exploiting a verifiable assumption that fine-scale state variables of similar RVEs, as well as the derived properties, subjected to similar macroscopic boundary conditions evolve along nearly identical trajectories. Furthermore, the macroscopic field variables primarily responsible for the evolution of fine-scale state variables often feature local quasi-homogeneities. Adjacent integration points in the FE mesh can be then clustered together in the regions where the field responsible for the evolution shows low variance. This way the fine-scale evolution is tracked only at a limited number of material points and the derived plastic properties are propagated to the surrounding integration points subjected to similar deformation. Optimal configurations of the clusters vary in time as the local deformation conditions may change during the forming process, so the clusters must be periodically adapted. We consider two operations on the clusters of integration points: splitting (refinement) and merging (unrefinement). The concept is tested in the Hierarchical Multi-Scale (HMS) framework [1] that computes macroscopic deformations by means of the FEM, whereas the micro-structural evolution at the individual FE integration points is predicted by a CP model. The HMS locally and adaptively approximates homogenized stress responses of the CP model by means of analytical plastic potential or yield criterion function. Our earlier work investigated simple test cases [2]. In this contribution we present a deep drawing process simulated using the HMS framework improved to exploit local quasi-homogeneities. We conclude that large performance gains (e.g. a speedup of 25) are obtained at the expense of introducing only a minor (e.g. below 1%) modelling error compared to the HMS simulation with no clustering of integration points.

Nonlinear structural design using multiscale topology optimization. Part II: Transient formulation

We extend the hierarchical multiscale design framework of Nakshatrala et al. (2013) to nonlinear elastodynamics wherein we use topology optimization to design material micro-structures to achieve desired energy propagation in nonlinear elastic material systems subjected to impact loading. As in Part I, a well-posed topology optimization problem is obtained via (a) relaxation to design the macroscale which requires homogenization theory to relate the macroscopic homogenized response to its micro-structure and (b) via restriction to design the microscale to obtain a well-defined micro-structural length scale. It is assumed that the primary wavelengths of interest are much longer than the micro-structural length scale and hence the effective properties are computed using the static homogenization theory. An adjoint sensitivity analysis is performed to compute the derivatives of the objective function with respect to the micro-structural design parameters and a gradient-based optimization algorithm is used to update the design. The numerical implementation of the computationally challenging terminal-value adjoint problems is discussed and a structural design example for tailored energy propagation is provided.

Diagnosing problems produced by flow regulation and other disturbances in Southern European Rivers: the Porma and Curueño Rivers (Duero Basin, NW Spain)

This research presents an analysis of river responses to flow regulation and other disturbances over time. The study was conducted in the Porma and Curueno rivers, using the hierarchical multi-scale process-based framework developed within the European REFORM Project. The characterisation of the rivers at the landscape unit, segment and reach scales under current and past conditions by different hydro-morphological indicators has provided a useful approach to (1) identify where and how main channel adjustments have occurred, (2) establish causal relationships at different scales and (3) discuss potential future scenarios and restoration strategies. Reduction of mean annual flow at the catchment scale has been observed and associated with forest expansion in the uplands. Channel narrowing, braiding reduction and riparian vegetation encroachment have occurred along both rivers, although with variable intensity among the reaches as a result of their landscape setting. Damming and flow regulation of the Porma River have been directly associated with locally accelerated narrowing, vertical bank profiles, the absence of bare gravel bars and no Salicaceae recruitment observed in certain reaches. The vertical bank profiles and the absence of in-channel bare gravel deposition forms are good indicators of sediment starvation downstream from the dam. The findings from this study suggest the importance of multi-scale approaches to assess the impacts of human-induced, short-term changes (e.g., flow regulation by dams and reservoirs) in the context of other disturbances at broader scales (e.g., climate and land cover changes) and their utility in the diagnosing problems and the proposal of restoration measures.

The interplay between anisotropy and strain localisation in granular soils: a multiscale insight

This paper presents a multiscale investigation on the interplay among inherent anisotropy, fabric evolution and strain localisation in granular soils, based on a hierarchical multiscale framework with rigorous coupling of the finite-element method (FEM) and discrete-element method (DEM). DEM assemblies with elongated particles are generated to simulate inherent anisotropy and are embedded to the Gauss points of the FEM mesh to derive the required constitutive relation. Specimens prepared with different bedding plane angles are subjected to biaxial shear under either smooth or rough loading platens. Key factors and physical mechanisms contributing towards the occurrence and development of strain localisation are examined. The competing evolutions of two sources of anisotropy, one related to particle orientations and the other related to contact normals, are found to underpin the development of the shear band. A single band pattern is observed under smooth boundary conditions, and its orientation relative to the bedding plane depends critically on the relative dominance between the two anisotropies. Under rough boundary conditions, the non-coaxial material response and the boundary constraint jointly lead to cross-shaped double shear bands. The multiscale simulations indicate that the DEM assemblies inside the shear band(s) undergo extensive shearing, fabric evolution and particle rotation, and may reach the critical state, while those located outside the shear band(s) experience mild loading followed by unloading. The particle-orientation-based fabric anisotropy needs significantly larger shear and dilation for mobilisation than the contact-normal based one. The asynchrony in evolution of the two fabric anisotropies can cause non-coaxial responses for initially coaxial packings, which directly triggers strain localisation.

An evolving plane stress yield criterion based on crystal plasticity virtual experiments

This paper presents a new hierarchical multi-scale framework that allows taking into account evolution of the plastic anisotropy during sheet forming processes. The evolution of crystallographic texture, which is identified as the main source of the plastic anisotropy, is predicted by the ALAMEL crystal plasticity model. An extension to the phenomenological anisotropic plane-stress yield criterion BBC2008 is proposed, which provides adaptive updates of the local anisotropy in the integration points of the macroscopic finite element model. To this end, the BBC2008 is systematically recalibrated to data provided by the crystal plasticity virtual experiment framework (VEF). An enhanced identification algorithm is proposed. The new algorithm exploits comprehensive material characterization delivered by the VEF.The deep drawing of cylindrical cups is used as a benchmark case. An industrial-grade aluminium alloy AA6016 is chosen for the test case. The experimental part of the study includes X-ray diffraction measurements of the texture as well as mechanical testing, which comprises uniaxial tensile tests, a bulge test and the deep drawing of cylindrical cups. A noticeable through-thickness gradient in terms of both the texture and the plastic anisotropy is identified in the initial material. This issue is taken into consideration in the study.Three groups of simulations have been performed, in which (i) the BBC2008 was calibrated by means of the mechanical tests, (ii) the BBC2008 was calibrated by means of the VEF based on the initial texture, but kept constant throughout the simulation, and (iii) the BBC2008 was systematically recalibrated by means of the VEF to reflect the effects of texture evolution. The earing profiles measured in the experimentally drawn cups are compared to the ones predicted by the simulations. It is found that the cup profile predictions by (i) are clearly inferior compared to both (ii) and (iii). However, in the considered process (iii) provides only a moderate improvement over the results of (ii). The explanation of this limited impact of the anisotropy evolution is based on the analysis of the evolved texture and associated plastic anisotropy.

Building large microkinetic models with first-principles׳ accuracy at reduced computational cost

We present a systematic hierarchical multiscale framework for parameterization of large microkinetic models that delivers first-principles׳ accuracy at significantly reduced computational cost. The framework leverages recently introduced first-principles-based semi-empirical methods (FPSEM), such as group additivity and Brønsted–Evans–Polanyi (BEP) relations, for surface reactions, local sensitivity analysis, and a heuristic classification of the order of corrections to produce a hierarchy or family of models of improved accuracy. We demonstrate this approach to the moderate size ethanol steam reforming mechanism on Pt, consisting of 67 species (14 gas, 53 surface) and 160 reversible elementary-like reactions, for which the ‘exact’ density functional theory (DFT)-based model is available. We find that the majority of refined parameters are surface species free energies and lateral interactions, underscoring the importance of thermodynamics in kinetic mechanisms.