Reasonable Simulation Research Articles

Modelling the frost cracking behavior in clayey soils: A peridynamic approach

Frost cracking is one of the primary causes of deterioration in frozen soil structures, yet few relevant numerical studies have been reported, and the simulation of frost cracking in soils remains challenging due to inadequate consideration of reasonable simulation algorithms. Numerous experimental studies have identified frost heave and desiccation shrinkage as the principal cause of frozen soil cracking. On this basis, this study presents a peridynamic (PD) model that considers the coupled effects of frost heaving and desiccation shrinkage for simulating frost cracking in soils during freezing process. The heat conduction equation is reformulated using the peridynamic differential operator (PDDO). The variation of thermal parameters for soils is addressed using the thermal enthalpy method, equivalent homogeneous method, and linear release assumption of latent heat. The frost-heaving load induced by pore water is represented using an equivalent displacement load. The multiphysics solution using PDDO and bond-based peridynamics (BBPD) considering freezing heave and desiccation shrinkage is developed for the first time. By simulating the frost cracking of a two-dimensional soil strip after model validations, the resulting crack pattern closely resembles the experimental observation. It indicates that the present model can capture the phase transition interface (PTI) and cracking behaviors of frozen soils.

Comparison of different elastic strain definitions for largely deformed SEI of chemo-mechanically coupled silicon battery particles

Amorphous silicon is a highly promising anode material for next-generation lithium-ion batteries. Large volume changes of the silicon particle have a critical effect on the surrounding solid-electrolyte interphase (SEI) due to repeated fracture and healing during cycling. Based on a thermodynamically consistent chemo-elasto-plastic continuum model we investigate the stress development inside the particle and the SEI. Using the example of a particle with SEI, we apply a higher order finite element method together with a variable-step, variable-order time integration scheme on a nonlinear system of partial differential equations. Starting from a single silicon particle setting, the surrounding SEI is added in a first step with the typically used elastic Green–St-Venant (GSV) strain definition for a purely elastic deformation. For this type of deformation, the definition of the elastic strain is crucial to get reasonable simulation results. In case of the elastic GSV strain, the simulation aborts. We overcome the simulation failure by using the definition of the logarithmic Hencky strain. However, the particle remains unaffected by the elastic strain definitions in the particle domain. Compared to GSV, plastic deformation with the Hencky strain is straightforward to take into account. For the plastic SEI deformation, a rate-independent and a rate-dependent plastic deformation are newly introduced and numerically compared for three half cycles for the example of a radial symmetric particle.

Differentiating Children's Reading Materials with Artificial Intelligence: Exploring Possibilities for Personalized Learning

AbstractArtificial intelligence is the simulation or development of reasoning and other human‐like cognitive abilities by computers. Currently, there is limited exploration of applications of artificial intelligence tools for teaching reading to children in the early childhood and elementary grades, including how artificial intelligence might be used to personalize reading materials for children. This article draws on teachers' experiences with artificial intelligence tools to provide tips on how to use artificial intelligence to create differentiated reading materials for children.

Tree-ring based forest model calibrations with a deep learning algorithm

Process-based forest growth models are important tools for forest management and studies for forest growth, carbon sequestration, and forest dynamics. However, their reliable simulations rely not only on the quality of model construction but also on accurate parameters to appropriately depict various physiological and biophysical processes in the models. While explicit physiological measurements are excellent sources for model parameterization, they are not always readily available. It would be ideal to use easy-to-measure tree-ring data as a benchmark for parametrization, but such applications have been rare. Here we present a new approach to reasonably parameterize a stand growth model based on tree-ring data using deep learning algorithms. We integrated a stable carbon isotope (δ13C) version of a simple process-based stand growth model, 3-PG, into a recurrent neural network (RNN). The new 3PG-RNN model trained the RNN network and calibrated 3-PG parameters to minimize the mean squared error between the 3-PG outputs and targeted values. We then tested 3PG-RNN based on two Abies grandis stands located in Northern Idaho, USA. The results showed that the 3PG-RNN model was efficient in calibrating key parameters related to gas-exchange processes, including specifically quantum yield, maximum canopy conductance, and the slope function for stomatal responses to vapor pressure deficit. These calibrated parameters provided reasonable simulations for gas-exchanges similar to those constrained by explicit physiological observations. This was particularly true when both long-term diameter growth (estimated from tree-ring width) and tree-ring δ13C were used for model training. The RNN tool made it possible to use tree ring data as the key benchmark to calibrate the forest model and provide unbiased simulations for gas exchanges and forest growth with the RNN approach, which would greatly facilitate forest studies and management.

Coupled fluid-structure-interaction simulation approach for correction of the thermal tool elongation to improve the milling precision

During milling operation, the position of the tool centre point (TCP) is affected by structural displacements, which are caused by force loads and heat input inside the machine as well as machining induced thermal loads. These thermal loads result in considerable thermal deformations of tool holder, tool, and accordingly the TCP position. They can be summed up to about tens of microns and compromise the dimensional accuracy. The objective was to develop an integrated, numerical simulation-based approach for future TCP correction for a milling process using characteristic diagrams. For this, a complex Fluid-Structure-Interaction (FSI) simulation model predicts the uni-axial displacement in the longitudinal direction of the TCP due to a specified process heat source at the tool tip. As a partial result, the simulation has reached its performance limits, under the restriction of a reasonable simulation time in order to produce characteristic diagrams for application on a milling machine. The calculated thermally induced displacement can be further processed with the aid of Design of Experiments (DoE) and response surface methodology (RSM), depending on the thermal process load and coolant volume flow rate. That results in characteristic diagrams for the displacement as a function of process parameters. In this study the calculated value for thermally induced TCP displacement covers a span from 10 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m to 80 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m, with a strongly nonlinear behaviour. Subsequently, this forms the basis for a future implementation of characteristic diagrams in the machine control system for online correction of thermal tool errors.

Design and Analysis of Orthogonal Polarization Point Diffraction Pinhole Plate

The pinhole plate is a key component of the point diffraction interferometer (PDI). The reasonable improvement and simulation of this device would enhance the application of point diffraction interferometry technology during the measurement of wavefronts. The traditional point diffraction interferometry measurement method is easily disturbed by environmental noise, making it difficult to obtain high-precision dynamic measurements. This paper introduces a four-step phase-shift PDI that can be employed in a common optical path. By using the principle of the finite-difference time-domain method (FDTD), a simulation model of the orthogonal polarization point diffraction pinhole plate (OP-PDPP) structure is established. The results show that when Cr is used as the film material in the pinhole plate, the parameters include a film thickness of 150 nm, a pinhole diameter of 2 μm, a wire grid period of 150 nm, and a wire grid width of 100 nm; in addition, the comprehensive extinction ratio of the pinhole plate is the greatest and the diffraction wavefront error is the smallest. Finally, the constructed experimental system is used to test the wavefront of a flat sample with a 25.4 mm aperture, and the test results are compared with those of the ZYGO interferometer. The difference in the peak-to-valley (PV) value between the OP-PDI and the ZYGO interferometer measurement is 0.0028λ, with an RMS value difference of 0.0011λ; this verifies the feasibility of the scheme proposed in this paper. The experimental results show that the proposed OP-PDPP is an effective tool for high-precision dynamic measurement.

Design and operation of an adiabatic compressed air energy storage system incorporating a detailed heat exchanger model

Heat exchangers (HEXs) are among the key components of adiabatic compressed air energy storage (A-CAES) systems. However, the existing HEX models applied in the A-CAES systems are overly simplistic, limiting research regarding design and operational simulation. For the first time, this study incorporates a comprehensive HEX model, including calculations for geometric dimensioning, heat transfer, and pressure drop, into the A-CAES system model; in addition, a novel layered, interactive system design and operational simulation algorithms are developed. The developed models and algorithms can facilitate the design process and yield reasonable operational simulation results with only basic parameters. The design algorithm can determine all key parameters of the system and assess the effect of the HEX pressure drop and heat transfer area on the system performance. The operational simulation algorithm considers the performance of all key equipment under off-design conditions. The design exergy efficiency and daily net income of the system are 73.15 % and $26,998, respectively; however, these values decrease during operation to 72.15 % and $25,034, respectively. A comprehensive analysis of the key parameters during operation revealed that the primary causes for the degradation were the sliding pressure in the energy storage process and variations in the air storage tank parameters.

Finite–Discrete Element Method Simulation Study on Development of Water-Conducting Fractures in Fault-Bearing Roof under Repeated Mining of Extra-Thick Coal Seams

The formation of water-conducting fractures in overlying strata caused by underground coal mining not only leads to roof water inrush disasters, but also water-conducting fractures penetrate the aquifer, resulting in the occurrence of a mine-water-inrush disaster and the loss of water resources. It destroys the sustainability of surface water and underground aquifers. This phenomenon is particularly significant in extra-thick coal seams and fault-bearing areas. Numerical simulation is an effective method to predict the failure range of mining overburden rock with low cost and high efficiency. The key to its accuracy lies in a reasonable constitutive model and simulation program. In this study, considering that the three parts of penetrating cracks, non-penetrating cracks, and intact rock blocks are often formed after rock failure, the contact state criterion and shear friction relationship of discrete rock blocks and the mixed fracture displacement–damage–load relationship are established, respectively. Combined with the Mohr–Coulomb criterion, the constitutive model of mining rock mass deformation–discrete block motion and interaction is formed. On this basis, according to the engineering geological conditions of Yushupo Coal Mine, a numerical model for the development of water-conducting cracks in the roof with faults under repeated mining of extra-thick coal seams is established. The results show the following: The constitutive relation of the continuous deformation–discrete block interaction of overlying strata and the corresponding finite element–discrete element FDEM numerical program and VUSDFLD multi-coal seam continuous mining subroutine can numerically realize the formation process of faults and water flowing fractures in overlying strata under continuous mining of extra-thick multi-coal seams. The toughness of sand mudstone is low, and the fracture will be further developed under the repeated disturbance of multi-thick coal seam mining. Finally, it is stabilized at 216–226 m, and the ratio of fracture height to mining thickness is 14.1. When the working face advances to the fault, the stress concentration occurs in the fault and its overlying rock, which leads to the local fracture of the roof rock mass and the formation of cracks. The fault group makes this phenomenon more obvious. The results have been preliminarily applied and tested in Ningwu mining area, which provides theoretical support for further development of roof water disaster control under the condition of an extra-thick coal seam and avoids the loss of water resources in surface water and underground aquifers.

Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0

Abstract. Land ecosystems are important sources and sinks of atmospheric components. In turn, air pollutants affect the exchange rates of carbon and water fluxes between ecosystems and the atmosphere. However, these biogeochemical processes are usually not well presented in Earth system models, limiting the explorations of interactions between land ecosystems and air pollutants from regional to global scales. Here, we develop and validate the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) by upgrading the Yale Interactive Terrestrial Biosphere Model with process-based water cycles, fire emissions, wetland methane (CH4) emissions, and trait-based ozone (O3) damage. Within iMAPLE, soil moisture and temperature are dynamically calculated based on the water and energy balance in soil layers. Fire emissions are dependent on dryness, lightning, population, and fuel load. Wetland CH4 is produced but consumed through oxidation, ebullition, diffusion, and plant-mediated transport. The trait-based scheme unifies O3 sensitivity of different plant functional types (PFTs) with the leaf mass per area. Validations show correlation coefficients (R) of 0.59–0.86 for gross primary productivity (GPP) and 0.57–0.84 for evapotranspiration (ET) across the six PFTs at 201 flux tower sites and yield an average R of 0.68 for CH4 emissions at 44 sites. Simulated soil moisture and temperature match reanalysis data with high R above 0.86 and low normalized mean biases (NMBs) within 7 %, leading to reasonable simulations of global GPP (R=0.92, NMB=1.3 %) and ET (R=0.93, NMB=-10.4 %) against satellite-based observations for 2001–2013. The model predicts an annual global area burned of 507.1 Mha, close to the observations of 475.4 Mha with a spatial R of 0.66 for 1997–2016. The wetland CH4 emissions are estimated to be 153.45 Tg [CH4] yr−1 during 2000–2014, close to the multi-model mean of 148 Tg [CH4] yr−1. The model also shows reasonable responses of GPP and ET to the changes in diffuse radiation and yields mean O3 damage of 2.9 % to global GPP. iMAPLE provides an advanced tool for studying the interactions between land ecosystems and air pollutants.

Improving Supercooled Water Forecast Through Real‐Time Aerosol Input: A Case Study

AbstractAccurately simulating and forecasting supercooled water has been crucial for addressing the threat of aircraft icing. This study utilized the Weather Research and Forecast model and Aerosol‐Aware Thompson‐Eidhammer microphysics (TE14) scheme to simulate supercooled water properties. Three high‐resolution experiments were conducted to explore the impact of increased model resolution and different aerosol initial conditions. The synoptic‐scale characteristics and cloud extent were well‐matched between observations and the model simulation, which established a background for supercooled water comparisons. Microphysical characteristics of supercooled droplets and the formation of supercooled large drops demonstrated the TE14 scheme's reasonable simulation of supercooled water microphysics under different aerosol loadings. In situ aircraft measurements were used to validate the simulated supercooled water properties. Results highlighted the sensitivity of supercooled water simulation to aerosol number concentration via droplet activation in the scheme. The choice for the aerosol initial condition could result in a significant underestimation or overestimation of supercooled water number concentration, consequently leading to biased estimations of droplet size. Incorporating real‐time forecast aerosol data improved the simulation by increasing cloud droplet number concentration and reducing droplet size. These findings underscored the importance of aerosols via droplet activation in accurately simulating cloud water properties and emphasized their role in forecasting aircraft icing hazards.

Calibration for Improving the Medium-Range Soil Temperature Forecast of a Semiarid Region over Tibet: A Case Study

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions.

A Social Self-Awareness Agent with Embodied Reasoning

In the realm of multi-agent systems, establishing social self-awareness in agents stands as a critical challenge within the broader scope of artificial general intelligence research. Addressing this challenge, in this paper, we draw upon Tomasello’s social self-awareness theory as a foundational framework, and employ Non-Axiomatic Reasoning System (NARS), an artificial general intelligence model, to simulate analogical and recursive reasoning methods used by socially embodied agents. In this paper, we assume that an agent can possess sensorimotor capabilities and physical common sense, it explores the process of constructing a “core self” through embodied reasoning. On this basis, we explore deeper into the construction process of social self-awareness. As a result, we clarify how agents generate common knowledge about themselves through recursive reasoning regarding the mental states of others and communication dynamics. Following this initial step, metaphors are generated utilizing analogical reasoning, intertwining public knowledge with these metaphors to facilitate mutual comprehension between humans and machines. This paper delves into the simulation of perspective representation and recursive reasoning within social embodied agents, employing NARS– an overarching artificial intelligence platform. The experimental results substantiate the viability of constructing a social self-agent within a machine framework grounded in embodied reasoning.

Simulation-based process parameter optimization for wire arc additive manufacturing

AbstractDuring manufacturing of components using wire arc additive manufacturing, specific cooling times are required to prevent overheating of the structure and geometrical distortions. Currently, these cooling times are inserted based on experience at certain interlayer temperatures — which reduces the reproducibility, leads to unwanted component properties, and increases the process time. In this contribution, instationary thermal finite element simulations are applied to compute the temperature evolution of additively manufactured components using the inactive element method. This allows to optimize the process parameters, which are — in our considerations here — the welding velocity and the cooling time of each layer, to reduce the total process time while achieving sufficient component properties. The optimization is carried out with the gradient-free Nelder–Mead simplex algorithm, where certain constraints of the process parameters are considered via penalty functions. To obtain reasonable simulation results, the temperature-dependent heat transfer of the experimental setup is modeled and calibrated with experimental data beforehand. It becomes apparent that thermal finite element simulations combined with a gradient-free optimization procedure are a suitable numerical tool to perform the optimization of process parameters for wire arc additive manufacturing. The optimized process parameters fulfill certain requirements regarding the cooling of the manufactured component. Moreover, the optimized parameters can significantly reduce the process time compared to manually chosen parameters. In our example, this is around 48 %.

Experimental and numerical investigation on agglomeration of aluminum during combustion process of aluminized composite propellant

The addition of aluminum particles can effectively improve the energy characteristics of solid composite propellants in propulsion systems. However, these aluminum particles affecting propellant mixture viscosity, ignition delay time, burning time and specific impulse could also lead to undesirable effects. Therefore, it is a general practice that in the early stage of propellant formulation design that the particle size of condensed phase products is optimized/minimized through formulation adjustment to reduce its negative impact on the engine. In practice, it means that a costly manpower and materials are needed to obtain the size distribution of condensed phase products during the combustion process of propellants via experimental tests. Thus, there is a strong industrial need for a reasonable and feasible simulation method of agglomeration process during the combustion process of propellants. In this work, we propose and examine the implementation of molecular dynamics and a random packing algorithm on creating numerically models of aluminized composite propellants. Considering the pyrolysis of ammonium perchlorate (AP) and the combustion process of aluminum, we develop a more accurate agglomeration model basing on the classical Jackson model. Our proposed methods and simulations models are validated by our experimental results. It confirms that the present work provides a prediction tool for simulating and improving the agglomeration process, thus aiming at optimizing solid propellant formula.

Damage failure prediction study of carbon fiber metal laminates in hot stamping process

In addressing the difficulty of forming Fiber Metal Laminates (FMLs), a laminate composed of a 0.1 mm-thick 304 Stainless Steel Foil (SSF) and Carbon Fiber Reinforced Plastics (CFRP) as the base material is prepared using a specific process. Subsequently, forming limit diagrams are obtained through hemispherical bulge forming experiments to investigate the forming performance of FMLs laminates. The damage phenomena of various forming specimens during the thermal stamping process are analyzed. Based on different damage forms, corresponding theoretical models are established to simulate and predict the forming damage modes of FMLs through simulation. The research results indicate that the stamping formability of FMLs is influenced by the damage tolerance of the thin stainless steel foil. During the thermal stamping process, FMLs may experience delamination, fracture/crack of the thin stainless steel foil, and rupture/tearing/wrinkling of the prepreg. To address these issues, cohesive elements, GTN-M-K damage, and Hashin damage models are established, providing a reasonable simulation prediction of the stamping damage phenomena of FMLs.

Simulation and performance analysis of quantum error correction with a rotated surface code under a realistic noise model

The demonstration of quantum error correction (QEC) is one of the most important milestones in the realization of fully-fledged quantum computers. Toward this, QEC experiments using the surface codes have recently been actively conducted. However, it has not yet been realized to protect logical quantum information beyond the physical coherence time. In this work, we performed a full simulation of QEC for the rotated surface codes with a code distance 5, which employs 49 qubits and is within reach of the current state-of-the-art quantum computers. In particular, we evaluate the logical error probability in a realistic noise model that incorporates not only stochastic Pauli errors but also coherent errors due to a systematic control error or unintended interactions. While a straightforward simulation of 49 qubits is not tractable within a reasonable computational time, we reduced the number of qubits required to 26 qubits by delaying the syndrome measurement in simulation. This and a fast quantum computer simulator, , implemented on GPU allows us to simulate full QEC with an arbitrary local noise within reasonable simulation time. Based on the numerical results, we also construct and verify an effective model to incorporate the effect of the coherent error into a stochastic noise model. This allows us to understand what the effect coherent error has on the logical error probability on a large scale without full simulation based on the detailed full simulation of a small scale. The present simulation framework and effective model, which can handle arbitrary local noise, will play a vital role in clarifying the physical parameters that future experimental QEC should target. Published by the American Physical Society 2024

Effect of Horizontal Resolution on a Meso‐β‐Scale Vortex Simulation in an Extreme Rainstorm on 22 May 2020 Over South China: A Contrastive Study Based on Different‐Resolution Ensembles

AbstractA transient extreme rainstorm that occurred over Guangzhou city, China, on 22 May 2020, has been investigated based on two ensemble prediction systems (EPSs), one with 9‐km (TRAMS9km‐EPS) and the other with 3‐km (TRAMS3km‐EPS) grid spacings, respectively. The results show that the better performance in the rainstorm event of TRAMS3km‐EPS than that of TRAMS9km‐EPS has been attributed to the reasonable simulation of an eastward propagating meso‐β‐scale convective vortex (MβCV). The composite evolution of the good‐performing members for the two EPSs has verified the important role of the MβCV in modulating the structure of low‐level jet (LLJ) to converge toward the Guangzhou city, facilitating the formation of the rainstorm. In comparison to the gravity wave disturbance when LLJ passed through the mountains in TRAMS3km‐EPS, the topographic effect induced the moist air to ascend deeply in TRAMS9km‐EPS, which directly led to earlier outbreaks of convection and rainstorm processes and affected the structural intensity of the upstream MβCV. Quantitative PV diagnosis has demonstrated that the dispersedly strong high‐PV systems in TRAMS3km‐EPS were caused by positive feedback effect between meso‐scale convective systems and diabatic heating, and were favorable for the entire cyclonic development of MβCV. The unsymmetrical PV tendency around the moving MβCV under the combined effect of diabatic heating and advection process in TRAMS9km‐EPS resulted in a rapid propagation of this system. However, the symmetrically developed PV system within the MβCV in TRAMS3km‐EPS allowed the enhanced cyclonic circulation to persistently affect Guangzhou city, inducing extreme rainstorms similar to the observations.

Performance of thermally conductive NR composites reinforced with stearic acid‐modified microcrystalline cellulose: A combined experimental and molecular dynamic simulation study

AbstractWith the increasing environmental requirements for improving products and materials using renewable and sustainable resources, cellulose has been seen as one of the most attractive and promising alternatives to traditional inorganic fillers. We developed a new modification method to improve the interface compatibility between natural rubber and sisal cellulose, to improve the mechanical and thermal conductivity properties of rubber composite. Microcrystalline cellulose (MCC) was extracted from sisal cellulose and then the hydrophobic cellulose (SA‐MCC) was prepared by grafting stearic acid on the surface of MCC. MCC and SA‐MCC were added to the thermal conductivity composite material composed of natural rubber and boron nitride. The results showed that the thermal conductivity and tensile properties of the natural rubber composites increased by 17.3% and 20%, respectively, under the addition of 1.8 wt% SA‐MCC. Furthermore, the interfacial interaction between the components in the composite was studied by molecular dynamics simulation. The solubility parameters, free volume fraction, and molecular binding energy were calculated to verify the effectiveness of the modification. This work is expected to provide a theoretical basis for the design and preparation of high‐performance natural rubber composite materials.Highlights Cellulose microcrystals were successfully extracted from sisal fiber and modified. Through the combination of boron nitride and the modified cellulose microcrystals, the thermal conductivity and tensile properties of thermal rubber were successfully improved synchronously. A reasonable molecular simulation model was established to analysis the strengthening mechanism from the microscopic point of view. The molecular simulation conclusions on interface enhancement are in good agreement with the experimental properties.

Establishment and applicability verification of hysteretic model of shear wall bottom corner dampers

In this study, the experimental results of a shear wall bottom corner damper (SWBCD) were investigated. It was found that the effects of the corrugated shear panel thickness, corrugated shear panel length, and flange plate thickness on the seismic performance from large to small. The failure process of the specimens was summarized; it was found that the high stiffness at the bend contributed to failure of cross-cracks in each long corrugated band of the corrugated shear panel in all specimens due to the geometric shape constraints of the corrugated shear panel. According to the calculation method for the skeleton curve parameters and the hysteretic rules of the SWBCD, a multilinear hysteretic model of the SWBCD was established. Using a corrugated steel plate-reinforced concrete composite shear wall with bottom corner dampers (RSPCW) as the application background, based on the layer shell model, a reasonable equivalent simulation method for an embedded corrugated steel plate based on the tension strip model was proposed. The hysteretic model of the SWBCD was modified according to the deformation requirements of the bottom corner damper. The twoNodeLink element representing the SWBCD was placed in the height range under the boundary element at both ends of the RSPCW. The ability of the hysteretic model to accurately reflect the nonlinear analysis of the SWBCD was verified.

Characterizing uncertainty in process-based hydraulic modeling, exemplified in a semiarid Inner Mongolia steppe

Assessing root sources of three uncertainties – parameterization of soil hydraulic characteristics, boundary conditions, and estimation of source/sink terms – is a significant challenge in soil water transport modeling. This study aims to evaluate the uncertainty of three each widely-used parameter estimation methods affecting plot-scale water dynamics. The study employs HYDRUS, a process-based hydrologic model, to incorporate these uncertainties and compare model predictions to measured values in a semiarid Inner Mongolia steppe, China. Soil hydraulic parameters are determined using two direct methods (laboratory-derived approach and evaporation method) and one indirect method (neural network). While each hydraulic parameter method generally simulates soil moisture dynamics, the evaporation method performed better, especially under dry conditions. This suggests that measuring the intensity properties, such as unsaturated hydraulic conductivity, with the evaporation method is crucial for reasonable soil moisture simulation. The study also demonstrates the impact of different applied boundary conditions on simulated soil moisture, specifically the partitioning of reference FAO evapotranspiration via one direct method (soil fraction cover) and two indirect methods (leaf area index and crop height). The partitioning via soil fraction cover reflected a better simulation. Additionally, the study compares the uncertainties of root water uptake function with root growth parameters and constant root depth referenced to grass and pasture, and finds no significant difference among them. Comparing three sources of uncertainty in predicting soil moisture, the study concludes that the input soil hydraulic parameter is more sensitive than evapotranspiration partitioning or representation of root water uptake function. Our study highlights that measuring soil intensity properties can better reflect the effects of land use change, such as compaction, on field water transports.