Modeling Scheme Research Articles

Efficient beam-based model for reinforced concrete walls considering shear-flexure interaction

This paper presents an efficient beam-based modelling scheme for the seismic analysis of reinforced concrete structural walls. The model combines a force-based beam element with a fibre section for flexural response and a zero-length element for shear response. The fibre-based element simulates the nonlinear flexural behaviour through uniaxial material laws that account for concrete cracking, concrete crushing, and yielding and rupture of reinforcing bars. The zero-length element represents the shear behaviour with a trilinear lateral force-displacement curve representing, in a phenomenological way, nonlinear deformations caused by diagonal cracking. The reduction of shear resistance caused by inelastic flexural deformations is accounted for in the model to reproduce failures due to shear-flexure interaction. The model has been validated using data from 52 tests on wall specimens exhibiting flexure, shear and mixed shear-flexure modes from experimental campaigns reported in the literature, showing good accuracy in predicting the effective stiffness, maximum strength and displacement capacities obtained in the tests. Model results for ultimate displacement capacity correlate better with experimental results than simplified code-oriented expressions in performance-based evaluation standards and recommendations. Considering its balanced accuracy and computational efficiency, it is concluded that the proposed modelling scheme can effectively be used for performance-based seismic design and assessment of RC wall buildings.

Characterizing Splitting Failure of Concrete Influenced by Material Heterogeneity Based on Digital Image Processing Techniques

Concrete, as a composite material, is subject to heterogeneity in its mechanical properties and damage characteristics responding to load. In this paper, a numerical approach for analyzing the heterogeneous characteristics and the mechanical behavior of concrete specimens in tensile splitting tests using DIP techniques is introduced. The experiment involves the preparation of three types of concrete specimens with different strengths and performances of the tensile splitting test. The contour and position information of the different components in the split surface of a concrete specimen are reflected in the numerical model using the DIP techniques and the fracture of the split surface is realized by three types of cohesive elements in the finite element software ABAQUS. The results of the proposed numerical model are highly consistent with the experimental results with a maximum error of 4.77%, whereby the evolution of the splitting process is discussed. The simulation shows that the concrete fracture develops from the periphery towards the center of the concrete and the ITZ region splits first at similar strain levels, followed by the mortar region and finally the aggregate region. In addition, a simplified modeling scheme with faster computational efficiency and higher accuracy is proposed, which indicates that the shape of the heterogeneous components in concrete has a low effect on mechanical strength. The proposed model can accurately reflect the splitting fracture process of concrete which is instantaneous in the actual process, contributing to the understanding of the mechanism of the splitting fracture process and proposing a new methodology for simulating the fracture process of heterogeneous materials (e.g., concrete, rock). This work contributes to the understanding of the effect of material heterogeneity on concrete’s mechanical behavior and fracturing process and provides valuable hints for the research on the non-destructive prediction of concrete strength.

Accelerating particle-in-cell kinetic plasma simulations via reduced-order modeling of space-charge dynamics using dynamic mode decomposition.

We present a data-driven reduced-order modeling of the space-charge dynamics for electromagnetic particle-in-cell (EMPIC) plasma simulations based on dynamic mode decomposition (DMD). The dynamics of the charged particles in kinetic plasma simulations such as EMPIC is manifested through the plasma current density defined along the edges of the spatial mesh. We showcase the efficacy of DMD in modeling the time evolution of current density through a low-dimensional feature space. Not only do such DMD-based predictive reduced-order models help accelerate EMPIC simulations, they also have the potential to facilitate investigative analysis and control applications. We demonstrate the proposed DMD-EMPIC scheme for reduced-order modeling of current density and speedup in EMPIC simulations involving electron beam under the influence of magnetic field, virtual cathode oscillations, and backward wave oscillator.

A novel method for assessing water quality status using MODIS images: A case study of large lakes and reservoirs in China

The changing climate and economic development have exerted significant pressure on the water quality of global lakes and reservoirs (hereinafter referred to as lakes). Existing sporadic in-situ monitoring limits a comprehensive understanding of water quality changes at large spatiotemporal scales. Although remote sensing techniques offer efficient water quality observations, they have primarily focused on monitoring single optical active substances, making it difficult to assess water quality changes caused by chemical indicators such as total phosphorus and total nitrogen. However, changes in chemical indicators within a certain range can cause responses to physical indicators, which are comprehensively reflected in the water-leaving reflectance. Based on this physical mechanism, this study obtained the water quality status categories (from excellent to severe pollution) by calculating the water quality index (WQI) and developed a new remote sensing assessment model of water quality status using moderate resolution imaging spectroradiometer (MODIS) imagery. This model, combining fractional-order derivatives and various-dimensional spectral indices, significantly enhanced the sensitivity of spectral reflectance to water quality states, achieving a maximum correlation of 0.72. To address the applicability of the model at large spatiotemporal scales, this study proposed a classification modeling scheme based on the optimal optical water categories, resulting in a 20.69%–34.48% improvement in model accuracy after classification. Additionally, this study proposed an improved ensemble learning model combining the concepts of Stacking and Bagging, achieving an average accuracy of 82% and enhancing accuracy by 5%–15% compared to the original single model. Subsequently, the model was used for the first time to assess the water quality status of 180 large lakes in China from 2000 to 2022. The results revealed that 76.11% of the lakes exhibited excellent and good water quality, with a spatial distribution pattern showing a “better in the west, worse in the east” pattern. Over the 23-year period, 28.33% and 58.89% of the lakes showed improvement and stability trends in water quality status, with stability and improvement predominating in the western and eastern regions, respectively. The research results provide technical support for the rapid assessment of water quality status and sustainable resource management, highlighting the potential of remote sensing technology in water quality monitoring.

A knowledge graph-based approach to modeling & representation for machining process design intent

Numerical control machining process design, which connects product design and modern manufacturing, is the extension of the design process in the process field. The process design intent, derived from in-depth part analysis, embodies the cognitive and strategic thinking of technologists in response to the specific design requirements during the process design. It encapsulates interpretable experiential knowledge and provides valuable insights for subsequent process design stages. However, the process design intent is the tacit experiential knowledge of technologists that implicitly exists in the process design, lacking a reasonable organizational framework and effective method to effectively represent and capture it. To fill this gap, a process design intent model for representing and capturing useful design intent knowledge for future reuse is developed and evaluated in this study. Firstly, this paper analyzes the existence of process design intent in process design and gives a brief description of the working scheme of the process design intent model. Then, the key modules of the model are introduced in detail and a knowledge graph of process design intent is built based on the model structure. Moreover, the process design intent annotator is developed to effectively capture process design intent. Finally, the example for capturing and representing process design intent is shown and the model is evaluated and discussed by the example.

Ab initio property characterisation of thousands of previously unexplored 2D materials

We perform extensive density functional theory calculations to determine the stability and elementary properties of 4249 previously unexplored monolayer crystals. The monolayers comprise the most stable subset (energy within 0.1 eV/atom of the convex hull) of a larger portfolio of two-dimensional (2D) materials recently discovered using a deep generative model and systematic lattice decoration schemes. The relaxed 2D structures are run through the basic property workflow of the Computational 2D Materials Database (C2DB) to evaluate the dynamical stability and obtain the stiffness tensor, piezoelectric tensor, deformation potentials, Born and Bader charges, electronic band structure, effective masses, plasma frequency, Fermi surface, projected density of states, magnetic moments, magnetic exchange couplings, magnetic anisotropy, topological indices, optical- and infrared polarisability. We provide statistical overviews of the property data and highlight a few specific examples of interesting materials. Our work exposes previously unknown parts of the 2D chemical space and provides a basis for the discovery of 2D materials with specific properties. All data is available in the C2DB.

Probabilistic analysis of seepage and temperature fields in geothermal extraction process based on discrete fracture network (DFN) model

Enhanced geothermal system is currently the most efficient technology of exploiting geothermal energy from high-temperature rock reservior in deep earth. However, due to the uncertainty of geological structural discontinuities, such as natural or artificial fractures, great challenges are encountered in conducting the precise evaluation of heat extraction within enhanced geothermal system. In this paper, a probability analysis is carried out to incorporate the uncertainty of the fracture network into the analysis of the spatial-time evolution on the seepage and temperature fields, based on a discrete fracture network based modeling scheme developed by the authors. The developed finite element model of the fractured reservior exhibits two-phase structure consisting of rock matrix and fractures, which are practically discretized into triangular elements and zero-thickness elements, respectively. The approach demonstrates a favorable comparison with the results obtained from the thereotical and other numerical solution. Monte Carlo simulation technique is then employed to probabilistically analyze the evolution of coupled thermal–hydraulic behavior, which involves 1000 different realizations considering the uncertainty of fracture structure. The results illustrate that the coefficient of variation of the seepage field decreases from 0 to 0.29 at 1 a to 0.06 to 0.15 at 50 a, averaging at 0.07, and the peak coefficient of variation of the temperature field decreases from 0.61 to 0.52, averaging at 0.27. The uncertainty of the temperature field is varying against time and prominent around the proximity to the advancing cold front, upper and lower boundaries, primarily associated with variations in fractures and constraint of boundary conditions. The existence of highly permeable fractures leads to a pronounced heterogeneity in the seepage and temperature fields within enhanced geothermal system. Furthermore, the importance of taking the uncertainty of the fracture structure into account for geothermal reservoir evaluation is highlighted, to enhance the reliability of numerical modeling for geothermal process prediction.

Macroscopic finite-difference scheme and modified equationsof the general propagation multiple-relaxation-time lattice Boltzmann model.

In this paper we first present the general propagation multiple-relaxation-time lattice Boltzmann (GPMRT-LB) model and obtain the corresponding macroscopic finite-difference (GPMFD) scheme on conservative moments. Then based on the Maxwell iteration method, we conduct the analysis on the truncation errors and modified equations(MEs) of the GPMRT-LB model and GPMFD scheme at both diffusive and acoustic scalings. For the nonlinear anisotropic convection-diffusion equation(NACDE) and Navier-Stokes equations(NSEs), we also derive the first- and second-order MEs of the GPMRT-LB model and GPMFD scheme. In particular, for the one-dimensional convection-diffusion equation(CDE) with the constant velocity and diffusion coefficient, we can develop a fourth-order GPMRT-LB (F-GPMRT-LB) model and the corresponding fourth-order GPMFD (F-GPMFD) scheme at the diffusive scaling. Finally, three benchmark problems, the Gauss hill problem, the CDE with nonlinear convection and diffusion terms, and the Taylor-Green vortex flow in two-dimensional space, are used to test the GPMRT-LB model and GPMFD scheme, and it is found that the numerical results not only are in good agreement with corresponding analytical solutions, but also have a second-order convergence rate in space. Additionally, a numerical study on one-dimensional CDE also demonstrates that the F-GPMRT-LB model and F-GPMFD scheme can achieve a fourth-order accuracy in space, which is consistent with our theoretical analysis.

Improved local time-stepping schemes for storm surge modeling on unstructured grids

This paper presents improved explicit local time-stepping (LTS) schemes of both first and second order accuracy for storm surge modeling. The two-dimensional shallow water equations are numerically solved on unstructured triangular meshes using finite volume method with Roe’s approximate Riemann solver. The LTS algorithms are designed based on explicit Euler and strong stability preserving Runge–Kutta time integration methods. A single-layer interface prediction–correction scheme is adopted to combine coarse and fine time discretization, further enhancing the stability of the LTS schemes, particularly at higher LTS levels and during long time simulations. An ideal numerical test validates the efficiency of the improved LTS models, revealing their capability to improve computational speed while preserving conservation properties and reducing accuracy loss as LTS levels increase. We further apply the LTS models to cross-scale simulations of storm surges in the Northwest Pacific. Results show that compared to the global time-stepping (GTS) models, the LTS models significantly boost computational speed by up to 37%, all while delivering equally reliable computational outcomes. With expanding high-resolution coastal data and the need for high-resolution modeling, the improved LTS models show great potential for cross-scale storm surge modeling.

Application of a Novel Weighted Essentially Non-Oscillatory Scheme for Reynolds-Averaged Stress Model and Reynolds-Averaged Stress Model/Large Eddy Simulation (RANS/LES) Coupled Simulations in Turbomachinery Flows

In numerical simulations, achieving high accuracy without significantly increasing computational cost is often a challenge. To address this issue, this paper proposes an improved finite volume Weighted Essentially Non-Oscillatory (WENO) scheme for structured grids. By employing a single-point quadrature rule to perform flux integration on the control volume faces, this scheme is designed for use in NUAA-Turbo three-dimensional fluid solvers based on structured grids, utilizing RANS and RANS/LES coupling to simulate turbomachinery flows. Firstly, the new WENO scheme is validated against classical numerical test cases to evaluate its stability and reliability in handling discontinuities, double Mach reflection problems, and Rayleigh–Taylor (RT) instability. Compared to the original scheme, this improved finite-volume WENO scheme demonstrates better stability near discontinuities and more effectively resolves flow features at the same grid resolution. Next, for engineering applications related to turbomachinery, such as compressor and turbine characteristics, calculations using RANS are performed and the results obtained with WENO-ZQ3 and WENO-JS3 are compared. Finally, the new fifth-order WENO scheme is applied to RANS/LES coupling simulations of turbine wake and film cooling. The results indicate that the improved finite-volume WENO scheme provides better stability and accuracy in engineering applications. For instance, the average error in calculating compressor efficiency characteristics is reduced from 0.76% to 0.05%, the error in turbine vane pressure distribution compared to the experimental values is within 1%, and the error in film cooling efficiency centerline distribution compared to the experimental values is within 3%. Additionally, the qualitative results of turbine wake and film cooling show that even with a small number of grid points, more detailed flow physics can be captured, thereby reducing computational costs in aerodynamic applications.

Investigating Directional Invariance in an Item Response Tree Model for Extreme Response Style and Trait-Based Unfolding Responses.

Item response tree (IRTree) approaches have received increasing attention in the response style literature due to their capability to partial out response style latent traits from content-related latent traits by considering separate decisions for agreement and level of agreement. Additionally, it has shown that the functioning of the intensity of agreement decision may depend upon the agreement decision with an item, so that the item parameters and person parameters may differ by direction of agreement; when the parameters across direction are the same, this is called directional invariance. Furthermore, for non-cognitive psychological constructs, it has been argued that the response process may be best described as following an unfolding process. In this study, a family of IRTree models to handle unfolding responses with the agreement decision following the hyperbolic cosine model and the intensity of agreement decision following a graded response model is investigated. This model family also allows for investigation of item- and person-level directional invariance. A simulation study is conducted to evaluate parameter recovery; model parameters are estimated with a fully Bayesian approach using JAGS (Just Another Gibbs Sampler). The proposed modeling scheme is demonstrated with two data examples with multiple model comparisons allowing for varying levels of directional invariance and unfolding versus dominance processes. An approach to visualizing the final model item response functioning is also developed. The article closes with a short discussion about the results.

Limited data on infectious disease distribution exposes ambiguity in epidemic modeling choices

Traditional disease transmission models assume that the infectious period is exponentially distributed with a recovery rate fixed in time and across individuals. This assumption provides analytical and computational advantages, however, it is often unrealistic when compared to empirical data. Current efforts in modeling nonexponentially distributed infectious periods are either limited to special cases or lead to unsolvable models. Also, the link between empirical data (the infectious period distribution) and the modeling needs (the definition of the corresponding recovery rates) lacks a clear understanding. Here we introduce a mapping of an arbitrary distribution of infectious periods into a distribution of recovery rates. Under the Markovian assumption to ensure analytical tractability, we show that the same infectious period distribution at the population level can be reproduced by two modeling schemes that we call and , depending on the individual response to the infection, and aggregated empirical data cannot easily discriminate the correct scheme. Besides being conceptually different, the two schemes also lead to different epidemic trajectories. Although sharing the same behavior close to the disease-free equilibrium, the scheme deviates from the expected epidemic when reaching the endemic equilibrium of a susceptible-infectious-susceptible transmission model, while the scheme turns out to be equivalent to assuming a homogeneous recovery rate. We show this through analytical computations and stochastic epidemic simulations on a contact network, using both generative network models and empirical contact data. It is therefore possible to reproduce heterogeneous infectious periods in network-based transmission models, however, the resulting prevalence is sensitive to the modeling choice for the interpretation of the empirically collected data on the length of the infectious period. In the absence of higher resolution data, studies should acknowledge such deviations in the epidemic predictions. Published by the American Physical Society 2024

An efficient scheme for nonlinear shock wave model in a fractal domain under Caputo fractional operator.

This paper introduces a refined approach for obtaining the analytical solution of the nonlinear shock wave model incorporating fractal derivatives. The Fractal Yang Variational Iteration Strategy (FYVIS) is utilized to obtain the approximate solution of a fractal model in the form of a series under Caputo fractional operator. The suggested method is the composition of the fractal Yang transform and the variational iteration approach. By using the two-scale fractal theory, we transform the fractal model into its traditional problem and then apply the yang transform to generate a recurrence relation. The variational iteration approach is now suitable to handle this recurrence relation without imposing any hypotheses or restrictions on variables. The derived results by the proposed scheme are shown in terms of series solution. Numerical calculations verify the accuracy and consistency of the suggested approach, demonstrating its excellent performance. The dynamic behavior of fractal components is explored by evaluating absolute errors and presenting two-dimensional diagrams across the fractal domain. This investigation underscores that the suggested technique offers an efficient and user-friendly solution for solving the nonlinear shock wave model involving fractal derivatives.

Quasi-Newton positivity-preserving scheme for the Spalart-Allmaras turbulence model using unstructured grids

A new implicit method for solving the Spalart-Allmaras (SA) turbulence model is proposed using a formal second-order upwind scheme on unstructured grids for steady-state flows. The mean-flow set of equations and the SA model equation are solved in a loosely coupled manner. While the Jacobian of the mean-flow equations is derived from a low-order approximation of the residual (i.e. the defect-correction approach), the SA Jacobian is obtained by modifying a direct linearization of the second-order residual. The modified Jacobian is designed to form an M-matrix without the artificial time derivative commonly used for steady-state problems. Namely, no time step is involved in solving the SA model equation. Thanks to a special design of the M-matrix, the positivity of the SA model working variable and the convergence of the SA model linearized problem (fixed-point iterative problem) is guaranteed. To further enhance the overall stability of the flow solver, the Runge-Kutta implicit algorithm is employed, allowing CFL numbers as high as one to two thousand for the mean-flow equations. A second-order upwind scheme is achieved with a least-square method using a limiter to suppress oscillation in the solution. However, second-order upwind scheme discretization of turbulence models may lack robustness, especially when using unstructured grids. The limiter can hinder the convergence of the non-linear iterations, especially of the turbulence model. A square root transformation is applied to the dependent variable of a baseline SA model to improve the non-linear convergence characteristics. Three variants of the SA model are implemented and tested together with two limiters. Numerical simulations of a broad range of aerodynamic applications are conducted. The transformed variant exhibits the least sensitivity to the limiter type.

Quasi-localization and Wannier obstruction in partially flat bands

The localized nature of a flat band is understood by the existence of a compact localized eigenstate. However, the localization properties of a partially flat band, ubiquitous in surface modes of topological semimetals, have been unknown. We show that the partially flat band is characterized by a non-normalizable quasi-compact localized state (Q-CLS), which is compactly localized along several directions but extended in at least one direction. The partially flat band develops at momenta where normalizable Bloch wave functions can be obtained from a linear combination of the non-normalizable Q-CLSs. Outside this momentum region, a ghost flat band, unseen from the band structure, is introduced based on a counting argument. Then, we demonstrate that the Wannier function corresponding to the partially flat band exhibits an algebraic decay behavior. Namely, one can have the Wannier obstruction in a band with a vanishing Chern number if it is partially flat. Finally, we develop the construction scheme of a tight-binding model for a topological semimetal by designing a Q-CLS.

A high-order upwind compact difference scheme for solving the streamfunction-velocity formulation of the unsteady incompressible Navier–Stokes equations

In this paper, we propose an upwind compact difference method with fourth-order spatial accuracy and second-order temporal accuracy for solving the streamfunction-velocity formulation of the two-dimensional unsteady incompressible Navier–Stokes equations. The streamfunction and its first-order partial derivatives (velocities) are treated as unknown variables. Three types of compact difference schemes are employed to discretize the first-order partial derivatives of the streamfunction. Specifically, these schemes include the fourth-order symmetric scheme, the fifth-order upwind scheme, and the sixth-order symmetric scheme derived by combining the two parts of the fifth-order upwind scheme. As a result, the fourth-order spatial discretization schemes are established for the Laplacian term, the biharmonic term, and the nonlinear convective term, along with the Crank–Nicolson scheme for the temporal discretization. The unconditional stability characteristic of the scheme for the linear model is proved by discrete von Neumann analysis. Moreover, six numerical experiments involving three test problems with the analytic solutions, and three flow problems including doubly periodic double shear layer, lid-driven cavity flow, and dipole-wall interaction are carried out to demonstrate the accuracy, robustness, and efficiency of the present method. The results indicate that the present method not only has good numerical performance but also exhibits quite efficiency.

Power Network Vulnerability Assessment Based on Electric Power System Cyber Security Simulation Platform

Abstract This paper introduces a modeling approach for the integration of electric power and information networks, along with the creation of an electric power system network simulation platform. The system, achieved through the deep integration of computation, communication, and control technologies, offers new insights into power grid intelligence. The paper discusses the concept of power system vulnerability and presents a modeling scheme that effectively simulates and analyzes power systems. Using the Analytic Hierarchy Process (AHP), the impact of cyber attacks under different load conditions is evaluated, revealing that attacks have more severe consequences under heavy loads. In conclusion, this research contributes to enhancing the security and stability of power systems.

Simulating the Thermal Regime and Surface Energy Balance of a Permafrost‐Underlain Forest in Mongolia

AbstractForests overlap with large parts of the northern hemisphere permafrost area, and representing canopy processes is therefore crucial for simulating thermal and hydrological conditions in these regions. Forests impact permafrost through the modulation of radiative fluxes and exchange of turbulent fluxes, precipitation interception and regulation of transpiration. Forests also feature distinct soil layers of litter and organic matter, which play central roles for the infiltration and evaporation of water, while also providing thermal insulation for deeper ground layers. In this study, we present a new module within the CryoGrid community model to simulate forest ecosystems and their impact on the surface water and energy balance. The module includes a big‐leaf vegetation scheme with adaptations for canopy heat storage and transpiration. Furthermore, we account for the effect of surface litter layers on water and energy transfer. We show that the model is capable of simulating radiation, snow cover and ground temperatures below a deciduous needleleaf forest on a north‐facing slope in the Khentii Mountains in Central Mongolia. A sensitivity analysis of topographic aspect and ecosystem configuration confirms the important role of the litter layers for the energy and water balance of the ground. Furthermore, it suggests that the presence of permafrost is primarily linked to topographic aspect rather than the presence of forest at this site. The presented model scheme can be used to study the development of the ground thermal regime in forests, including the state of permafrost, under different climate, ecosystem, and land use scenarios.

MixFormer: End-to-End Tracking With Iterative Mixed Attention.

Visual object tracking often employs a multi-stage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, in this paper, we present a compact tracking framework, termed as MixFormer, built upon transformers. Our core design is to utilize the flexibility of attention operations, and we propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration. This synchronous modeling scheme allows us to extract target-specific discriminative features and perform extensive communication between target and search area. Based on MAM, we build our MixFormer trackers simply by stacking multiple MAMs and placing a localization head on top. Specifically, we instantiate two types of MixFormer trackers, a hierarchical tracker MixCvT, and a non-hierarchical simple tracker MixViT. For these two trackers, we investigate a series of pre-training methods and uncover the different behaviors between supervised pre-training and self-supervised pre-training in our MixFormer trackers. We also extend the masked autoencoder pre-training to our MixFormer trackers and design the new competitive TrackMAE pre-training technique. Finally, to handle multiple target templates during online tracking, we devise an asymmetric attention scheme in MAM to reduce computational cost, and propose an effective score prediction module to select high-quality templates. Our MixFormer trackers set a new state-of-the-art performance on seven tracking benchmarks, including LaSOT, TrackingNet, VOT2020, GOT-10 k, OTB100, TOTB and UAV123. In particular, our MixViT-L achieves AUC scores of 73.3% on LaSOT, 86.1% on TrackingNet and 82.8% on TOTB.

Utilizing integrated neutron diffraction and elastoplastic self-consistent crystal plasticity model to quantitatively assess the strengthening mechanism in Al–12.5Ce and Al–12.5Ce–0.4Mg alloys

An integrated in-situ neutron diffraction and elastic plastic self-consistent crystal plasticity (EPSC-CP) modeling scheme is performed on a binary Al–12Ce alloy and a ternary Al–12Ce–0.4Mg alloys. Using this scheme, the constitutive parameters, i.e. elastic constants and slip system parameters of individual phases can be calibrated which can be used in microstructure-based CP models to predict materials performance. From this study, it is shown that the elastic constants of Al11Ce3 intermetallics calculated from density function theory calculation in the literature are rather accurate. When applied to the EPSC-CP model, the lattice strains of both the binary and ternary alloys are correctly predicted as compared with experiments, and large lattice strain differences between Al (100) plane and Al11Ce3 (010) plane are demonstrated. The slip system parameters calibrated by the scheme shows that the addition of 0.4 wt% Mg in the alloy has little influence on the critical resolved shear stress of initial dislocation glide in the Al matrix which caused plastic yield in the material. This can be explained by the very dilute Mg solute content in the Al solid solution, causing large spacing of Al–Mg lattice misfit sites and little impact on resistance of dislocation glide at initial yield. The 0.4 wt% Mg addition, on the other hand, has a large influence on the hardening term in the slip system parameters, indicating those Al–Mg misfit sites do help dislocation accumulation during the deformation. The impact of dilute Mg addition on the Al slip system parameters is also reflected in the flow behavior of the ternary alloy: little impact on the yield stress, but a large impact on working hardening and tensile strength of the materials which is consistent with the literature.