Thanks to the distinctive morphology of graphite particles in its microstructure, compacted graphite iron (CGI) exhibits excellent thermal conductivity together with high strength and durability. CGI is extensively used in many applications, e.g., engine cylinder heads and brakes. The structural integrity of such metal-matrix materials is controlled by the generation and growth of microcracks. Although the effects of the volume fraction and morphology of graphite inclusions on the tensile response of CGI were investigated in recent years, their influence on crack initiation is still unknown. Experimental studies of crack initiation require a considerable amount of time and resources due to the highly complicated geometries of graphite inclusions scattered throughout the metallic matrix. Therefore, developing a 2D computational framework for CGI with a random microstructure capable of predicting the crack initiation and path is desirable. In this work, an integrated numerical model is developed for the analysis of the effects of volume fraction and nodularity on the mechanical properties of CGI as well as its damage and failure behaviours. Finite-element models of random microstructure are generated using an in-house Python script. The determination of spacings between a graphite inclusion and its four adjacent particles is performed with a plugin, written in Java and implemented in ImageJ. To analyse the orientation effect of inclusions, a statistical analysis is implemented for representative elements in this research. Further, Johnson-Cook damage criteria are used to predict crack initiation in the developed models. The numerical simulations are validated with conventional tensile-test data. The created models can support the understanding of the fracture behaviour of CGI under mechanical load, and the proposed approach can be utilised to design metal-matrix composites with optimised mechanical properties and performance.

In vitro assays of ion transport are an essential tool for understanding molecular mechanisms associated with ATP-dependent pumps. Because ion transport is generally electrogenic, principles of electrophysiology are applicable, but conventional tools like patch-clamp are ineffective due to relatively low turnover rates of the pumps. Instead, assays have been developed to measure either voltage or current generated by transport activity of a population of molecules either in cell-derived membrane fragments or after reconstituting purified protein into proteoliposomes. In order to understand the nuances of these assays and to characterize effects of various operational parameters, we have developed a numerical model to simulate data produced by two relevant assays: fluorescence from voltage-sensitive dyes and current recorded by capacitive coupling on solid supported membranes. Parameters of the model, which has been implemented in Python, are described along with underlying principles of the computational algorithm. Experimental data from KdpFABC, a K+ pump associated with P-type ATPases, are presented, and model parameters have been adjusted to mimic these data. In addition, effects of key parameters such as nonselective leak conductance and turnover rate are demonstrated. Finally, simulated data are used to illustrate the effects of capacitive coupling on measured current and to compare alternative methods for quantification of raw data.

Among the many benefits of implementing numerical analysis on real objects, economic and environmental considerations are likely the most important ones. Nonetheless, it is also crucial to constrain the duration and space necessary for conducting experimental investigations. Although these benefits are clear, the applicability of such models must be appropriately verified. This research subjected validation of numerical models depicting the behavior of unstrengthened and strengthened laminated veneer lumber (LVL) beams. As a reinforcement, a carbon fiber reinforced polymer (CFRP) sheet and laminates were used. Experiments were conducted on full-scale members within the framework of the so-called four-point bending testing method. Numerical simulations were performed using the Abaqus software. Two types of material models were examined for laminated veneer lumber: linearly elastic and linearly elastic-perfectly plastic with Hill\'s yield criterion. A distinction was made in the material properties of carbon composites based on their location on the height of the cross-section. The outlined numerical models accurately depict the behavior of real structural elements. The precision of predicting load-bearing capacity amounts to a few percent for strengthened beams and a maximum of eleven percent for unstrengthened beams. The relative deviation between numerical and experimental values of bending stiffness was at a maximum of seven percent. Applying the elastic-plastic model enables accurate representation of the load versus deflection relation and the distribution of stress and deformation of strengthened beams. Based on the findings, directives were provided for further optimization of the positioning of composite reinforcement along the span of the beam. Reinforcement design of existing laminated veneer lumber members can be made using presented methodology.

Currently available numerical models that describe the fecal contamination of aquatic environments using Escherichia coli as an indicator bacterium did not consider its survival in sediments. We conducted a series of comparative experiments to reveal the independent and interactive effects of sediment factors, including temperature, pH, water-extractable total dissolved solids (TDSs), coexisting microbes, and sampling sites, in lake environments on E. coli survival. In experiments, E. coli survival was observed by controlling any two factors at a time. Consequently, the decrease in pH and presence of coexisting microbes enhanced E. coli die-off, whereas the addition of water-extractable TDSs promoted its growth. To select factors to be considered for modelling E. coli survival in sediments, the independent effects of each factor and the interaction effect of the two factors were statistically compared based on their effect sizes (η2). As a result, pH (η2 = 59.5-89.0%) affected E. coli survival most significantly, followed by coexisting microbes (1.7-48.4%). Among the interactions affecting E. coli survival, including pH or coexisting microbes-which had larger independent effects-relatively larger statistically significant interactions were observed between pH and coexisting microbes (31.1%), coexisting microbes and water-extractable TDSs (85.4%), and coexisting microbes and temperature (26.4%).

Numerical health risk assessment models have been developed to describe faecal contamination of water using Escherichia coli as an indicator bacterium. Although many previously established numerical models for E. coli in aquatic environments have only considered the effects of one or two water quality parameters such as temperature and sunlight, it is difficult to simulate E. coli survival with only one or two parameters because the aquatic environment is a complex system. This study conducted a series of comparative experiments to select water quality parameters that should be preferentially considered in a numerical model for E. coli survival in lakes. The parameters considered were temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), suspended solids (SS), coexisting microbes, and light intensity. In the laboratory experiments, the survival of E. coli was observed by controlling two of these seven parameters, and the effects of these parameters on the rate of E. coli population change were statistically compared. Consequently, light intensity affected the survival of E. coli most significantly, followed by the presence of coexisting microbes, temperature, pH, and TDS. However, DO and SS had smaller effects on survival than other parameters. High-impact interactions on E. coli survival were observed between temperature and TDS and temperature and coexisting microbes. These results suggest that existing numerical models for simulating E. coli survival in lakes should be modified to consider the independent and interactive effects of multiple parameters such as sunlight, coexisting microbes, temperature, pH, and TDS.

Underground coal storage bunkers serve as crucial infrastructural components in the coal mining industry, providing secure and accessible locations for the storage of mined coal. The interaction between stored coal and underground water in coal storage bunkers indeed poses significant challenges due to the unpredictable nature of the resulting coal-water mixture. This phenomenon is particularly prevalent in coal mines operating under water hazards, where groundwater infiltration into storage areas can lead to the formation of coal-water mixtures, altering the physical properties of the stored coal. The interaction between coal and water can result in the formation of coal-water mixtures (hydromixture), which exhibit complex rheological properties. These mixtures may vary in viscosity, density, and particle size distribution, making their behavior difficult to predict. Underground water may exert hydrostatic pressure on the stored coal, influencing its mechanical behavior and compaction properties. Changes in pressure can result in coal compaction or expansion, affecting bunker stability and the integrity of surrounding rock strata. The main goal of the paper was to determine the values of pressure field variations exerted by the flowing hydromixture within underground coal storage bunkers. This objective reflects a critical aspect of understanding the dynamic behavior of coal-water mixtures (hydromixture) under varying conditions, particularly in environments where water hazards pose significant challenges to storage and operational stability. The paper utilized computational fluid dynamics (CFD) methods to examine the changes in pressure within underground coal storage bunkers induced by the flow of coal-water mixtures. The examination of damage to an underground coal storage bunker due to stress distribution was conducted using the finite element method (FEM). This computational technique is widely utilized in engineering and structural analysis to model complex systems and predict the behavior of materials under various loading conditions The results of the CFD numerical simulation were compared with the mathematical models.

Tails play essential roles in functions related to locomotor stability and maneuverability among terrestrial and arboreal animals. In kangaroo rats, bipedal hopping rodents, tails are used as effective inertial appendages for stability in hopping, but also facilitate stability and maneuverability during predator escape leaps. The complexity of tail functionality shows great potential for bio-inspiration and robotic device design, as maneuvering is accomplished by a long and light-weight inertial appendage. To (i) further understand the mechanics of how kangaroo rats use their tails during aerial maneuvers, and to (ii) explore if we can achieve this behavior with a simplified tail-like appendage (i.e., template), we combined quantified animal observations, computational simulations, and experiments with a two degrees of freedom (2-DoF) tailed robot. We used video data from free-ranging kangaroo rats escaping from a simulated predator and analyzed body and tail motion for the airborne phase. To explain tail contributions to body orientation (i.e., spatial reorientation), we built a mid-air kangaroo rat computational model and demonstrate that three-dimensional body orientation of the model can be controlled by a simplified 2-DoF tail with a nonlinear control strategy. Resulting simulated trajectories show movement patterns similar to those observed in kangaroo rats. Our robot experiments show that a lightweight tail can generate a large yaw displacement and stabilize pitch and roll angles to zero, simultaneously. Our work contributes to better understanding of the form-function relationship of the kangaroo rat tail and lays out an important foundation for bio-inspiration in robotic devices that have lightweight tail-like appendages for mid-air maneuvering.

The organic pollutants disposed at the Sardas landfill in Sabiñánigo (Huesca, northeastern Spain) by the INQUINOSA lindane factory have reached the Gállego alluvial aquifer and could affect the Sabiñánigo reservoir. The daily oscillations of the reservoir water level produce a tidal effect on the piezometric heads of the aquifer. These oscillations are transmitted in a damped way with a time lag, thus attesting that the silting sediments of the reservoir and the natural silts of the Gállego alluvial are interposed between the reservoir water and the layer of sands and gravels. A 2D finite element groundwater flow and total dissolved hexachlorocyclohexane (HCH) transport model through the Gállego alluvial aquifer is presented here. The flow model was constructed to: (1) Quantify the tidal effect, produced by the daily fluctuations of the reservoir water level on the aquifer; (2) Estimate the hydrodynamic parameters of the layer of sands and gravels; and 3) Estimate the vertical hydraulic conductivity of the silting sediments and silts; and (4) Quantify aquifer/reservoir interactions. The flow model reproduces the dynamics of the tidal effect and attests that groundwater velocity and flow direction changes daily in response to the oscillations of the reservoir level. Model results reproduce the measured well hydrographs and the Darcy velocity derived from tracer tests and confirm the validity of the conceptual model. The transport model of total dissolved HCH simulates the time evolution of the contaminant plume. The computed concentrations of total dissolved HCH and the contaminant mass outflux are very sensitive to changes in the source terms and the distribution coefficient, Kd of HCH. The best fit to the measured HCH plumes in September 2010 and December 2020 is obtained with a Kd ranging from 1 to 3 L/kg. The computed flux of dissolved HCH leaving the Sardas site in 2020 towards the Sabiñánigo reservoir ranges from 0.6 kg/year for Kd = 3 L/kg to 3.1 kg/year for Kd = 1 L/kg. The findings of this study will be most useful for planning and designing remedial and containment actions at the Sardas site and other similar lindane-affected sites.

Cancer invasion and migration play a pivotal role in tumor malignancy, which is a major cause of most cancer deaths. Rotating magnetic field (RMF), one of the typical dynamic magnetic fields, can exert substantial mechanical influence on cells. However, studying the effects of RMF on cell is challenging due to its complex parameters, such as variation of magnetic field intensity and direction. Here, we developed a systematic simulation method to explore the influence of RMF on tumor invasion and migration, including a finite element method (FEM) model and a cell-based hybrid numerical model. Coupling with the data of magnetic field from FEM, the cell-based hybrid numerical model was established to simulate the tumor cell invasion and migration. This model employed partial differential equations (PDEs) and finite difference method to depict cellular activities and solve these equations in a discrete system. PDEs were used to depict cell activities, and finite difference method was used to solve the equations in discrete system. As a result, this study provides valuable insights into the potential applications of RMF in tumor treatment, and a series of in vitro experiments were performed to verify the simulation results, demonstrating the model\'s reliability and its capacity to predict experimental outcomes and identify pertinent factors. Furthermore, these findings shed new light on the mechanical and chemical interplay between cells and the ECM, offering new insights and providing a novel foundation for both experimental and theoretical advancements in tumor treatment by using RMF.

The membrane interface probe (MIP) is an efficient and economical in-situ tool for chlorinated hydrocarbon (CH) contaminated site investigation. Given that the interpretation of MIP test is currently limited to a qualitative level, a theoretical model considering multiphase flow and multifield coupling is firstly proposed to simulate MIP test process. This model can consider phase change, membrane effect, adsorption and dissolution of the CH liquid, gas diffusion, and evaporation. Then, the model is used to study the changes in soil temperature and soil CH concentration during MIP test, as well as the influences of soil CH concentration and soil properties (initial water saturation, soil intrinsic permeability, and thermal properties) on MIP response. Finally, a simplified MIP interpretation model is developed based on parametric analysis results and verified against field and laboratory test data. It is found that the soil CH concentration, rather than soil properties, dominates the MIP response. The simplified interpretation model can deliver practical prediction of the CH concentration through the detected results by MIP, which may improve the applicability of MIP.