Simulation Of Models Research Articles

Wave energy assessment and wave converter applicability at the Pacific coast of Central America

Nowadays, numerous governments have instituted diverse regulatory frameworks aimed at fostering the assimilation of sustainable energy sources characterized by reduced environmental footprints. Solar, wind, geothermal, and ocean energies were subject to extensive scrutiny, owing to their ecological merits. However, these sources exhibit pronounced temporal fluctuations. Notably, ocean dynamics offer vast energy reservoirs, with oceanic waves containing significant amounts of energy. In the Central American Pacific context, the exploration of wave energy resources is currently underway. Accurate numerical wave models are required for applied studies such as those focused on the estimation of exploitable wave power; and even more so in Central American region of the Pacific Ocean where existing numerical models simulations have so far relied on coarse resolution and limited validation field data. This work presents a high-resolution unstructured wave hindcast over the Central American Pacific region, implemented using the third-generation spectral wave model WAVEWATCH III over the period between 1979 and 2021. The results of the significant wave height have been bias-corrected on the basis of satellite information spanning 2005 to 2015, and further validation was performed using wave buoy and acoustic Doppler current profiler (ADCP) records located in the nearshore region of the Central America Pacific coast. After correction and validation of the wave hindcast, we employed the dataset for the evaluation and assessment of wave energy and its possible exploitation using different wave energy converters (WECs). This evaluation addressed the need to diverse the energy portfolio within the exclusive economic zones of Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, Panama, Colombia, and Ecuador in a sustainable manner. Moreover, a comprehensive analysis was carried out on the advantages of harnessing wave energy, juxtaposed with the imperative of regulatory frameworks and the current dearth of economic and environmental guidelines requisite for development within the region.

Communication Simulation: On the Development of the Interpersonal Model

The article is devoted to developing an interpersonal model of communicative simulations constituted by a series of communicative practices within which various forms of social interaction are reproduced. The theoretical basis of the work was the study of the concepts of “Model/Simulacrum” and “Mimesis” in philosophy and narratology. The methodological approach proposed within the study was aimed at parameterizing interpersonal interaction in a communicative simulation through establishing the structure of interaction, its participants and the roles they actualize. Based on the model proposed, a typology of communicative simulations was suggested. Three types of communicative simulations were identified: narrative simulation, simulation for entertainment purposes, and falsification. The first type of communicative simulations (the narrative one) is the indirect interaction between the sender of a message (concrete or abstract author, narrator) and its recipient (concrete or abstract reader). The communicative practices of simulations of the first type include ASMR videos and fake news. The second type (simulation for entertainment purposes) involves the interaction of communicants within a fictional world. The participants in the interaction are a particular sender/recipient of a message, an abstract sender/recipient of a message, and avatars (characters). The communicative practices of simulations of the second type include sexting and role-playing games. The third type (falsification of social practice) is the interaction in which one of the participants is unaware of being involved in a communicative simulation. The participants in the interaction are a specific author of the simulation, a specific recipient of the message, an abstract author/recipient of the message, and an avatar, which is a social mask of the author of the simulation. The communicative practices of simulations of the third type include telephone fraud.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of “rainfall and drainage pipeline siltation” were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

Separation and reattachment fixed Reynolds-stress model for iced airfoil and wing simulations

Differential Reynolds-stress models (RSMs) are the higher level of Reynolds-averaged Navier-Stokes (RANS) modeling and generally considered to have more potential than eddy viscosity models (EVMs) when applied to separated flows. Nevertheless, some peculiarities have been observed in simulating flows over iced wings. The first is the numerical stability problem caused by non-streamlined wall surfaces. To weaken this, a wall-boundary-natural scale λ=ω−1/8 is introduced into SSG/LRR RSM in this paper. The second is to remedy the problem of the unsatisfactory non-equilibrium characteristic in separating shear layer (SSL), a correction determined by anisotropy of Reynolds normal stresses is developed. The last focuses on the unphysical backbending of the streamlines near stagnation/reattachment (SR) points. Since the SSL correction will worsen this defect, a SR correction is carried out to blended with it. Four two-dimensional iced airfoils and a three-dimensional iced wing are simulated and then the above measures are confirmed to be the positive.

A review of resistance–capacitance thermal network model in urban building energy simulations

With the increasing population and urbanization promotion, energy consumption and carbon emissions have increased, and concern for inefficient energy use and deteriorating urban environments is growing. The prediction of building energy consumption and environmental evaluation, especially at large scales, are considered to be a major challenge confronting the research community. An outstanding strategy for mitigating energy consumption and carbon emissions resides in the field of energy modeling. As a simplified building energy modeling model, resistance–capacitance (RC) network model has the applications in fast predicting building energy consumption. Notably, in recent years, there has been an evident absence of thorough review endeavors related to RC model for urban building energy loads and climate. This review systematically explores the application of low-order models and reduction methods for urban or regional energy simulation starting from the typical RC model for building. In summation, the challenges associated with employing this model for urban building energy loads and climate can be succinctly summarized as follows: the absence of a unified platform for RC modeling; insufficient readily applicable urban datasets; the need for modeling tools that facilitate cross-platform analyses in conjunction with existing Geographic Information Systems (GIS) and urban thermal environment simulation research platforms, and the essential requirement for seamless integration with other complementary modules.

Development of a photocell-based system supported by simulations for road vehicle counting and particle emissions estimation

This work presents the design and implementation of an innovative system that utilizes photocells for road vehicle counting and computational simulations to estimate traffic-related particulate (PM10) emissions. The integration of photocell technology has resulted in a robust system with reduced counting errors, ensuring high accuracy in correlating traffic counts with emission values. The system was evaluated under various environmental conditions, demonstrating its effectiveness in providing reliable real-time data on the impact of road traffic on particle emissions. The methodology included the use of the ADMS-Urban model for emission estimation and simulations with Ansys Fluent, enabling the acquisition of dependable real-time data. The results indicate a strong agreement between measured and calculated PM10 concentrations, with the system maintaining precision comparable to direct observations. The observed values of 5.0 µg/m3 and 19.2% for absolute and relative errors, respectively, demonstrate the system's remarkable performance, supported by a robust correlation represented by R2 = 0.88. This strong correlation underscores the reliability of the employed model, suggesting its capability to capture and explain a substantial portion of the variability in PM10 concentrations. Validation campaigns conducted on a street in Portugal confirmed the system's ability to accurately count vehicles and estimate particle emissions. The analysis revealed a significant agreement between measured and calculated average PM10 concentrations, with standard deviation values of 7.0 µg/m3 for measured concentrations and 7.5 µg/m3 for calculated concentrations, suggesting consistency between the datasets. These conclusions confirm that the system is a valuable tool for researchers and policymakers, providing crucial insights for the management and improvement of urban air quality, contributing to the development of urban planning policies and environmental sustainability.

Numerical integration of mechanical forces in center-based models for biological cell populations

Center-based models are used to simulate the mechanical behavior of biological cells during embryonic development or cancer growth. To allow for the simulation of biological populations potentially growing from a few individual cells to many thousands or more, these models have to be numerically efficient, while being reasonably accurate on the level of individual cell trajectories. In this work, we increase the robustness, accuracy, and efficiency of the simulation of center-based models by choosing the time steps adaptively in the numerical method and comparing five different integration methods. We investigate the gain in using single rate time stepping based on local estimates of the numerical errors for the forward and backward Euler methods of first order accuracy and a Runge-Kutta method and the trapezoidal method of second order accuracy. Properties of the analytical solution such as convergence to steady state and conservation of the center of gravity are inherited by the numerical solutions. Furthermore, we propose a multirate time stepping scheme that simulates regions with high local force gradients (e.g. as they happen after cell division) with multiple smaller time steps within a larger single time step for regions with smoother forces. These methods are compared for a model system in numerical experiments. We conclude, for example, that the multirate forward Euler method performs better than the Runge-Kutta method for low accuracy requirements but for higher accuracy the latter method is preferred. Only with frequent cell divisions the method with a fixed time step is the best choice.

Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models

Abstract We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free $$\hbox {Mn}_4$$ Mn 4 N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression. Graphical abstract

Linearised Fokker–Planck collision model for gyrokinetic simulations

Abstract We introduce a gyrokinetic, linearised Fokker–Planck collision model that satisfies conservation laws and is accurate at arbitrary collisionalities. The differential test-particle component of the operator is exact; the integral field-particle component is approximated using a spherical harmonic and a modified Laguerre polynomial expansion developed by Hirshman and Sigmar (1976 Phys. Fluids 19 1532). The numerical methods of the implementation in the δf-gyrokinetic code stella (Barnes et al 2019 J. Comput. Phys. 391 365–80) are discussed, and conservation properties of the operator are demonstrated. The collision model is then benchmarked against the collision model of the gyrokinetic solver GS2 in the limiting cases of a reduced test-particle collision operator and energy- and momentum-conserving operator. The accuracy of the full collision model is investigated by solving the parallel Spitzer-Härm problem for the transport coefficients. It is shown that retaining collisional energy flux and higher-order terms in the field-particle operator reduces errors in the transport coefficients from 10%–25% for a simple momentum- and energy-conserving model to under 1%.

Behavior of LSTM and Transformer deep learning models in flood simulation considering South Asian tropical climate

ABSTRACT The imperative for a reliable and accurate flood forecasting procedure stems from the hazardous nature of the disaster. In response, researchers are increasingly turning to innovative approaches, particularly machine learning models, which offer enhanced accuracy compared to traditional methods. However, a notable gap exists in the literature concerning studies focused on the South Asian tropical region, which possesses distinct climate characteristics. This study investigates the applicability and behavior of long short-term memory (LSTM) and transformer models in flood simulation considering the Mahaweli catchment in Sri Lanka, which is mostly affected by the Northeast Monsoon. The importance of different input variables in the prediction was also a key focus of this study. Input features for the models included observed rainfall data collected from three nearby rain gauges, as well as historical discharge data from the target river gauge. Results showed that the use of past water level data denotes a higher impact on the output compared to the other input features such as rainfall, for both architectures. All models denoted satisfactory performances in simulating daily water levels, with Nash–Sutcliffe Efficiency (NSE) values greater than 0.77 while the transformer encoder model showed a superior performance compared to encoder–decoder models.

Simulating the Competitive Ion Pairing of Hydrated Electrons with Chaotropic Cations.

Experiments show that the absorption spectrum of the hydrated electron (ehyd-) blue-shifts in electrolyte solutions compared with what is seen in pure water. This shift has been assigned to the ehyd-'s competitive ion-pairing interactions with the salt cation relative to the salt anion based on the ions' positions on the Hofmeister series. Remarkably, little work has been done investigating the ehyd-'s behavior when the salts have chaotropic cations, which should greatly change the ion-pairing interactions given that the ehyd- is a champion chaotrope. In this work, we remedy this by using mixed quantum/classical simulations to analyze the behavior of two different models of the ehyd- in aqueous RbF and RbI electrolyte solutions as a function of salt concentration. We find that the magnitude of the salt-induced spectral blue-shift is determined by a combination of the number of chaotropic Rb+ cations near the ehyd- and the number of salt anions near those cations so that the spectrum of the ehyd- directly reflects its local environment. We also find that the use of a soft-cavity ehyd- model predicts stronger competitive interactions with Rb+ relative to I- than a more traditional hard cavity model, leading to different predicted spectral shifts that should provide a way to distinguish between the two models experimentally. Our simulations predict that at the same concentration, salts with chaotropic cations should produce larger spectral blue-shifts than salts with kosmotropic cations. We also found that at high salt concentrations with chaotropic cations, the predicted blue-shift is greater when the salt anion is kosmotropic instead of chaotropic. Our goal is for this work to inspire experimentalists to make such measurements, which will help provide a spectroscopic means to distinguish between simulations models that predict different hydration structures for the ehyd-.

3D Variability Analysis Reveals a Hidden Conformational Change Controlling Ammonia Transport in Human Asparagine Synthetase.

How motions in enzymes might be linked to catalytic function is of considerable general interest. Advances in X-ray crystallography and cryogenic electron microscopy (cryo-EM) offer the promise of elucidating functionally relevant conformational changes that are not easily studied by other biophysical methods. Here we use 3D variability analysis (3DVA) of the cryo-EM map for wild-type (WT) human asparagine synthetase (ASNS) to identify a functional role for the Arg-142 side chain as a gate that mediates ammonia access to a catalytically relevant intramolecular tunnel. Our 3DVA-derived hypothesis is assessed experimentally, using the R142I variant in which Arg-142 is replaced by isoleucine, and by molecular dynamics (MD) simulations on independent, computational models of the WT human ASNS monomer and its catalytically relevant, ternary complex with β-aspartyl-AMP and MgPPi. Residue fluctuations in the MD trajectories for the human ASNS monomer are consistent with those determined for 3DVA-derived structures. These MD simulations also indicate that the gating function of Arg-142 is separate from the molecular events that form a continuous tunnel linking the two active sites. Experimental support for Arg-142 playing a role in intramolecular ammonia translocation is provided by the glutamine-dependent synthetase activity of the R142 variant relative to WT ASNS. MD simulations of computational models for the R142I variant and the R142I/β-aspartyl-AMP/MgPPi ternary complex provide a possible molecular basis for this observation. Overall, the combination of 3DVA with MD simulations is a generally applicable approach to generate testable hypotheses of how conformational changes in buried side chains might regulate function in enzymes.

Identification of influencing factors and risk assessment of underground space flooding in the mountain city

As a consequence of the process of urbanization, there is an increasing reliance on and exploitation of underground spaces. Moreover, these areas are susceptible to urban waterlogging, which can be difficult to drain, potentially leading to significant risks. At present, there are few simulations in hydrodynamic models that consider underground spaces. The risk of inundation of underground spaces remains an uncertain phenomenon. This study conceptualized underground spaces as storage areas, with the aim of simulating the spatial distribution of flood risk for different return periods. We studied the flooding processes and characteristics of three underground space types. The results indicate that the flood evolution simulated by the simplified modeling method that incorporates underground space is more aligned with the actual situation. To further analyze the risk distribution of urban waterlogging, we choose the XGBoost model and SHapley Additive exPlanations (SHAP) method to assess the influence of specific indicators on the occurrence of urban waterlogging in mountainous regions. Our analysis revealed that key factors include density of road (DR), flow velocity (V), density of pipes (DP), elevation (DEM), and inundation height (H), with contribution rates of 23.13 %, 17.06 %, 14.67 %, 10.12 %, and 9.04 %, respectively. This analysis clarified the risk of underground inundation within the study area, which can serve as a reference point for the formulation of flood control and drainage strategies.

MicroMagnetic.jl: A Julia package for micromagnetic and atomistic simulations with GPU support

Abstract MicroMagnetic.jl is an open-source Julia package for micromagnetic and atomistic simulations. Using the features of the Julia programming language, MicroMagnetic.jl supports CPU and various GPU platforms, including NVIDIA, AMD, Intel, and Apple GPUs. Moreover, MicroMagnetic.jl supports Monte Carlo simulations for atomistic models and implements the nudged-elastic-band method for energy barrier computations. With built-in support for double and single precision modes and a design allowing easy extensibility to add new features, MicroMagnetic.jl provides a versatile toolset for researchers in micromagnetics and atomistic simulations.

Power-law correlation in the homogeneous disordered state of anisotropically self-propelled systems

Self-propelled particles display unique collective phenomena, due to the intrinsic coupling of density and polarity. For instance, the giant number fluctuation appears in the orientationally ordered state, and the motility-induced phase separation appears in systems with repulsion. Effects of strong noise typically lead to a homogeneous disordered state, in which the coupling of density and polarity can still play a significant role. Here we study universal properties of the homogeneous disordered state in two-dimensional systems with uniaxially anisotropic self-propulsion. Using hydrodynamic arguments, we propose that the density correlation and polarity correlation generically exhibit power-law decay with distinct exponents (−2 and −4, respectively) through the coupling of density and polarity. Simulations of self-propelled lattice gas models indeed show the predicted power-law correlations, regardless of whether the interaction type is repulsion or alignment. Further, by mapping the model to a two-component boson system and employing non-Hermitian perturbation theory, we obtain the analytical expression for the structure factors, the Fourier transform of the correlation functions. This reveals that even the first order of the interaction strength induces the power-law correlations. Published by the American Physical Society 2024

Effect of atmospheric response induced by preceding typhoon on movement of subsequent typhoon over Northwestern Pacific

This study investigates the atmospheric interactions between two closely located typhoons in 2019. Typhoons in the Western Pacific significantly impact Eastern and Southeastern Asian countries, leading to various damages. As global warming is expected to increase typhoon intensity, accurate track forecasting becomes crucial for coastal disaster management. Despite the existing knowledge about the influence of typhoon activities on the atmospheric background, limited research addresses the atmospheric response between two typhoons. The study focuses on the cases of LEKIMA and KROSA, occurring simultaneously in 2019, and utilizes the Weather Research and Forecasting (WRF) model for simulations. The experimental setup involves comparing two scenarios: one with both typhoons and one with LEKIMA removed. Results reveal LEKIMA-induced distinctive atmospheric responses, including the closure of the western North Pacific subtropical high (WNPSH) boundary and the formulation of a wave train, influencing KROSA’s stagnation. The absence of LEKIMA allows KROSA to move more freely along the steering flow. Furthermore, the study highlights the potential of atmospheric models for understanding typhoon effects at regional to mesoscale levels. A comprehensive analysis of similar cases could enhance typhoon predictions, contributing to better damage mitigation strategies.

On the use of reactive multiparticle collision dynamics to gather particulate level information from simulations of epidemic models

This paper discusses the application of reactive multiparticle collision (RMPC) dynamics, a particle-based method, to epidemic models. First, we consider a susceptible-infectious-recovered framework to obtain data on contacts of susceptibles with infectious people in a population. It is found that the number of contacts increases and the contact duration decreases with increases in the disease transmission rate and average population speed. Next, we obtain reinfection statistics for a general infectious disease from RMPC simulations of a susceptible-infectious-recovered-susceptible model. Finally, we simulate a susceptible-exposed-infectious-recovered model and gather the exposure, infection, and recovery time for the individuals in the population under consideration. It is worth mentioning that we can collect data in the form of average contact duration, average initial infection time, etc., from RMPC simulations of these models, which is not possible with population-based stochastic models, or deterministic systems. This study provides quantitative insights on the potential of RMPC to simulate epidemic models and motivates future efforts for its application in the field of mathematical epidemiology.

Domain Decomposition for Data-Driven Reduced Modeling of Large-Scale Systems

This paper focuses on the construction of accurate and predictive data-driven reduced models of large-scale numerical simulations with complex dynamics and sparse training datasets. In these settings, standard, single-domain approaches may be too inaccurate or may overfit and hence generalize poorly. Moreover, processing large-scale datasets typically requires significant memory and computing resources, which can render single-domain approaches computationally prohibitive. To address these challenges, we introduce a domain-decomposition formulation into the construction of a data-driven reduced model. In doing so, the basis functions used in the reduced model approximation become localized in space, which can increase the accuracy of the domain-decomposed approximation of the complex dynamics. The decomposition furthermore reduces the memory and computing requirements to process the underlying large-scale training dataset. We demonstrate the effectiveness and scalability of our approach in a large-scale three-dimensional unsteady rotating-detonation rocket engine simulation scenario with more than 75 million degrees of freedom and a sparse training dataset. Our results show that compared to the single-domain approach, the domain-decomposed version reduces both the training and prediction errors for pressure by up to 13% and up to 5% for other key quantities, such as temperature, and fuel, and oxidizer mass fractions. Lastly, our approach decreases the memory requirements for processing by almost a factor of four, which in turn reduces the computing requirements as well.

The role of the thermal properties of electrons on the dispersion properties of Alfvén waves in space plasmas

Context. The transition from left-hand to right-hand polarised Alfvén waves depends on the wavenumber, the ratio of kinetic to magnetic pressure, β, the temperature anisotropy, and the ion composition of the plasma. Along with the temperature anisotropy, the electron-to-proton temperature ratio, Te/Tp, is of great relevance for the characterisation of the thermal properties of a plasma. This ratio varies significantly between different space plasma environments. Thus, studying how variations in this ratio affect the polarisation properties of electromagnetic waves becomes highly relevant for our understanding of the dynamics of space plasmas. Aims. We present an extensive study on the effect of the thermal properties of electrons on the behaviour and characteristics of Alfvénic waves in fully kinetic linear theory, as well as on the transition from the electromagnetic ion-cyclotron wave to the kinetic Alfvén wave. Methods. We solved the fully kinetic dispersion relation for oblique electromagnetic waves of the Alfvén branch in a homogenous Maxwellian electron-proton plasma. We quantified the effect of the thermal properties of electrons by varying the electron-to-proton temperature ratio for different configurations of the propagation angle, βp = 8πnkTp/B2, and wavenumber. Results. We show that the temperature ratio, Te/Tp, has strong and non-trivial effects on the polarisation of the Alfvénic modes, especially at kinetic scales (k⊥ρL > 1, where k⊥ = k sin θ, θ is the propagation angle, and ρL = cs/Ωp, with cs the plasma sound speed and Ωp the proton’s gyrofrequency) and βe + βp > 0.5. We conclude that electron inertia plays an important role in the kinetic scale physics of the kinetic Alfvén wave in the warm plasma regime, and thus cannot be excluded in hybrid models for computer simulations.

Revisiting scale effect on joint roughness coefficient and shear strength considering sampling methods and geometric characteristics

The scale effect on shear strength of rock joints is well-documented. However, whether scale effects are negative, positive, or even exist or not is still controversial. Joint roughness significantly influences the shear strength of rock joints. Compared to the shear tests, using the joint roughness coefficient (JRC) and its roughness parameters offers a more convenient method for describing the scale effect on shear strength. However, it is crucial to understand that the scale effect mechanisms of JRC are distinct from those of shear strength. Therefore, this paper aims to clarify these distinct mechanisms. By digitally extracting roughness parameters from granite samples, it's found that the scale effect of roughness parameters mainly comes from the sampling methods and the geometric characteristics of parameters. Furthermore, a full data sampling method considering heterogeneity is proposed to obtain more representative roughness parameters. To reveal the scale effect mechanisms of shear strength, Gaussian filtering is firstly used to separate the waviness and unevenness components of roughness, facilitating a deeper understanding of the geometric characteristics of roughness. It is suggested that the wavelength of the waviness component can reflect the scale effect on shear strength. Secondly, numerical simulations of ideal artificial joint models are conducted to validate that the wavelength of the waviness component serves as the dividing point between positive and negative scale effects. The mechanical mechanisms of positive and negative scale effects are also interpreted. Finally, these mechanisms successfully elucidate the occurrence patterns of the scale effect on natural joint profiles.