Model Boundary Conditions Research Articles

Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA.

Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently.

Towards early response to desert locust swarming in eastern Africa by estimating timing of hatching

Desert locust (Schistocerca gregaria) plagues threaten agricultural production, food security and the environment across Africa, the Middle East, and Southwest Asia. Control methods targeting adult desert locusts present significant challenges and financial costs. Recognizing this, we developed a ground-breaking fuzzy set Mamdani type inference model that provides an innovative solution for early warning alerts. The model aids in predicting the juvenile stages of locust development, thereby preventing wide-scale locust swarming and mitigating its extensive damages and socioeconomic costs. The novelty of our approach lies in our unique application of environmental variables relevant for locust breeding to estimate the timing and location of desert locust hatching. Additionally, we improved the algorithmic handling of these variables, with localized desert locust bands data used as a proxy for hatching timing with a temporal offset of 35 days. The model's boundary conditions were determined using a training area in Sudan, where comprehensive ground data was available. This rule set was subsequently applied to Turkana County in Kenya, a data-scarce region, demonstrating the model's applicability and success in different contexts. The model's accuracy, assessed by data from the Sudan training site, demonstrated a remarkable score of 82% for true predictions. Furthermore, the model correctly identified the months of highest hatching probabilities in Turkana during 2020, demonstrating its real-world effectiveness and practical value. A correlation analysis affirmed that hatching was associated with increases in chlorophyll levels and precipitation accumulations. Our study marks a significant advancement in predicting the timing of hatching using fuzzy logic in data-scarce environments. By operationalizing more targeted early responses to desert locust infestations, our model facilitates more effective locust control. This study stands as an important contribution to locust management strategies, with substantial implications for agricultural production and food security in affected regions.

3D modelling of surface spreading and underground dam groundwater recharge: Egri Creek Subbasin, Turkey.

This study investigated surface spreading and underground dam recharge methods to replenish groundwater in Turkey's Egri Creek Sub-basin of the Kucuk Menderes River Basin. A three-dimensional numerical model was employed for this purpose. Field and lab data are provided to the model for realistic simulations. Pumping test results were used to determine the aquifer parameters. The laboratory works involved sieve analysis, permeability tests, and porosity and water content prediction. The numerical model's boundary conditions were determined from the geological and hydrogeological characteristics of the study area. Initial conditions were expressed regarding water content and pressure head in the vadose zone. The numerical model was satisfactorily validated by simulating water levels in three different pumping wells in the study area. Seven different scenarios, each having a different pool size, were investigated for the surface spreading recharge method. The results showed that a pool size of 30 × 30m with a 6-m depth basin was the most optimal choice, raising the groundwater level to about 29.3m. On the other hand, it was found that an underground dam could raise the levels by an average of 9.5m, which might not be significant to warrant the construction.

Numerically Detection Fluid Characteristic Effects in Porous Media for Plastic Manufacturing Process Reconstruction

Research work aimed to prove the cooling effectiveness and efficiency of heating numerically. It was carried out by reconstructing the mold to reduce condensation while keeping the production cycle time. The simulation was done using CFD Fluent, Finite Volume Method (FVM) with a solution method of pressure-velocity, SIMPLE coupling, and second-order upwind discretization scheme. The model's boundary condition represents plastic packaging's production process. The simulation was done to optimize performance during production. Reconstruction and simulation were undertaken. The initial investigation was to produce data for cooling time versus the whole production time. The research result indicated that the average cooling time was 3/5 of the total production time. There were 15.5706 of 26.4206 sec and 15.641 of 26.491 sec, respectively. Further investigation was focused on investigating heat absorption and transfer through cells in the matrix. The porous effect showed temperature gradient reduction between cooled and cooler temperatures by simulation in color. The phenomenon predicted that the Ra contributed to the rate of temperature decrease revealed. There is also an apparent increase in temperature interfaces is evidence. The porous media application with The Rayleigh Number ≥ 105 retained the potential cooling effectivity and heating efficiency. By simulation, a series of discussions of the result of research, the potential may apply in the plastic mold to reconstruct the space and its methods using filling-porous. It is relevant because the heating ability in the phase change of fluid that creates particular characteristics has the potential to make effective cooling.

Lattice-Boltzmann simulation of incompressible fluid flow past immersed bodies: models and boundary conditions

PurposeThe main purpose of this paper was to investigate Lattice Boltzmann (LB) models for the bulk incompressible flow past immersed bodies and to find the set of boundary conditions (BCs) that can be considered suitable for modeling the borders of the numerical simulation domain in such a way as to avoid any effect of these BC on the flow trail that is formed behind the body.Design/methodology/approachThree different models of the Lattice Boltzmann equation (LBE) and six different sets of BCs are tested. In addition to the classical LBE based on the Bhatnagar–Gross–Krook (BGK) single relaxation time collision model, a moments-based model and a model with two relaxation times were investigated.FindingsThe flow pattern and its macroscopic effects on the aerodynamic coefficients appear to be very dependent on the set of BC models used for the borders of the numerical domain. The imposition of pressure at the exit results in pressure perturbations, giving rise to sound waves that propagate back into the simulation domain, producing perturbations on the upwind flow. In the same way, the free-slip BC for the lateral bords appears to affect the trail of vortices behind the body in this range of Reynolds number (Re = 1,000).Originality/valueThe paper investigates incompressible flow past immersed bodies and presents the set of BCs that can be considered suitable for modeling the borders of the numerical simulation domain in such a way as to avoid any effect of these BCs on the flow trail that is formed behind the body.

Temperature Analysis of Friction Stir Welding (AA6061-T6) with Coupled Eulerian-Lagrangian Approach.

For Friction Stir Welding (FSW), several simplified FEM models have been developed to explain key parts of thermal-mechanical phenomena. In this study, to simulate the FSW process of the AA6061-T6 aluminum alloy, a thermo-mechanical 3-D finite element model based on the Coupled Eulerian-Lagrangian (CEL) approach is used. The CEL method combines the benefits of both the Lagrangian and Eulerian approaches, allowing it to solve large-scale deformation issues. The Abaqus/CAE software is used to simulate the stages of the FSW process using the Johnson–Cook material law and the Johnson-Cook failure model. The CEL approach is used to estimate process parameters based on the model's boundary conditions of the 3-D rigid tool. Thermocouples were installed in the appropriate positions on the AA6061-T6 plate, and temperature is correlated with the simulation results. The temperature generated during the plunging, dwelling, and welding stages of the FSW of AA6061-T6 is 80% to 90% of the melting temperature being evaluated. The experimental data was obtained from the HMT made CNC (Siemens) operated VMC and the same is compared with the results obtained from the overall FEM simulation.

Influence of nonlinear thermal radiation on mixed convective hybrid nanofluid flow about a rotating sphere

AbstractThe present study investigates the mixed convective hybrid nanofluid flow over a rotating sphere under the impact of nonlinear thermal radiation. A model is built to examine the heat transport performance of ferrimagnetic magnetite and copper nanoparticles over a rotating sphere. Nonsimilar transformations are used to nondimensionalize the coupled nonlinear governing equations and the flow model's boundary conditions. Furthermore, the nondimensional governing equations were solved using implicit finite difference approximation and the quasilinearization technique. The impacts of the flow regime on many controlling parameters are then thoroughly addressed. Temperature patterns improve when nonlinear thermal radiation and hybrid nanofluid values increase. The fluid velocity and skin friction coefficient increase in the streamwise direction while decreasing in the rotating direction. The separation of the boundary layer is delayed as the sphere's rotation weakens. The stationary sphere has a larger boundary layer separation than the revolving sphere. The velocity distribution improves with increasing rotation parameter values while decreasing with increasing combined convection parameter values in the rotating direction. An increase in the temperature ratio parameter makes the fluid get hotter, and the Nusselt number goes down simultaneously. Nusselt number and skin friction coefficient in the rotation direction increase, while skin friction coefficient in streamwise direction reduces for increasing values of hybrid nanofluid. The velocity of the fluid enhances in the stream‐wise direction while reducing in the rotational direction with the increasing values of the combined convection parameter.

Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains

Physics-informed neural networks (PINNs) are increasingly employed to replace/augment traditional numerical methods in solving partial differential equations (PDEs). While state-of-the-art PINNs have many attractive features, they approximate a specific realization of a PDE system and hence are problem-specific. That is, the model needs to be re-trained each time the boundary conditions (BCs) and domain shape/size change. This limitation prohibits the application of PINNs to realistic or large-scale engineering problems especially since the costs and efforts associated with their training are considerable.We introduce a transferable framework for solving boundary value problems (BVPs) via deep neural networks which can be trained once and used forever for various unseen domains and BCs. We first introduce genomic flow network (GFNet), a neural network that can infer the solution of a BVP across arbitrary BCs on a small square domain called genome. Then, we propose mosaic flow (MF) predictor, a novel iterative algorithm that assembles the GFNet’s inferences for BVPs on large domains with unseen sizes/shapes and BCs while preserving the spatial regularity of the solution. We demonstrate that our framework can estimate the solution of Laplace and Navier–Stokes equations in domains of unseen shapes and BCs that are, respectively, 1200 and 12 times larger than the training domains. Since our framework eliminates the need to re-train models for unseen domains and BCs, it demonstrates up to 3 orders-of-magnitude speedups compared to the state-of-the-art.

Physiologically personalized coronary blood flow model to improve the estimation of noninvasive fractional flow reserve.

Coronary outlet resistance is influenced by the quantification and distribution of resting coronary blood flow. It is crucial for a more physiologically accurate estimation of fractional flow reserve (FFR) derived from computed tomography angiography (CTA), referred to as FFRCT. This study presents a physiologically personalized (PP)-based coronary blood flow model involving the outlet boundary condition (BC) and a standardized outlet truncation strategy to estimate the outlet resistance and FFRCT. In this study, a total of 274 vessels were retrospectively collected from 221 patients who underwent coronary CTA and invasive FFR within 14 days. For FFRCT determination, we have employed a PP-based outlet BC model involving personalized physiological parameters and left ventricular mass (LVM) to quantify resting coronary blood flow. We evaluated the improvement achieved in the diagnostic performance of FFRCT by using the PP-based outlet BC model relative to the LVM-based model, with respect to the invasive FFR. Additionally, in order to evaluate the impact of the outlet truncation strategy on FFRCT, 68 vessels were randomly selected and analyzed independently by two operators, by using two different outlet truncation strategies at 1-month intervals. The per-vessel diagnostic performance of the PP-based outlet BC model was improved, based on invasive FFR as reference, compared to the LVM-based model: (i) accuracy/sensitivity/specificity: 91.2%/90.4%/91.8% versus 86.5%/84.6%/87.6%, for the entire dataset of 274 vessels, (ii) accuracy/sensitivity/specificity: 88.7%/82.4%/90.4% versus 82.4%/ 76.5%/84.0%, for moderately stenosis lesions. The standardized outlet truncation strategy showed good repeatability with the Kappa coefficient of 0.908. It has been shown that our PP-based outlet BC model and standardized outlet truncation strategy can improve the diagnostic performance and repeatability of FFRCT.

Moments-based method for boundary conditions in the lattice Boltzmann framework: A comparative analysis for the lid driven cavity flow

Dealing with boundary conditions (BC) was ever considered a puzzling question in the lattice Boltzmann (LB) method. The most popular BC models are based on Ad-Hoc rules and, although these BC models were shown to be suitable for low-order LB equations, their extension to high-order LB was shown to be a very difficult problem and, at authors knowledge, never solved with satisfaction. The main question to be solved is how to deal with a problem when the number of unknowns (the particle populations coming from the outside part of the numerical domain) is greater than the number of equations at our disposal at each boundary site. Recently, BC models based on the regularization of the LB equation, or moments-based models, were proposed. These moments replace the discrete populations as unknowns, independently of the number of discrete velocities that are needed for solving a given problem. The full set of moments-based BC leads, nevertheless, to an overdetermined system of equations, and what distinguishes one model from another is the way this system is solved. In contrast with previous work, we base our approach on second-order moments. Four versions of this model are compared with previous moments-based models considering, in addition to the accuracy, some main model attributes such as global and local mass conservation, rates of convergence, and stability. For this purpose, the complex flow patterns displayed in a two-dimensional lid-driven cavity are investigated.

A novel algorithm to solve the nonlinear differential equation of the motion function of a lithium-battery assembly machine

In this study, a stochastic differential equation capable of describing (using the motion function) the automatic manufacturing process of a lithium battery with a sleeve shell is introduced. The boundary-condition modeling method for this type of motion is an ordinary differential equation. The nonlinear equation is found using a dynamic method. The equations of the motions for the assembly process are derived by reducing the order of terms and separating the variables. Both the derivatives and integrals of the motion functions are derived and applied to describe the assembly process for a lithium battery. By using a characteristic constitutive assembly model, a new method to calculate the nonlinear terms is proposed. The main advantage of this method is that it can simplify the problem, which is done by finding the algebraic error in the region space of the motion function. The reliability and the applicability of the method were confirmed using a real-life example. Finally, by solving and improving the Euler model, the fourth-order Kutta method and Taylor series are applied to verify the correctness of the algorithm, and the convergence order of the function and the optimization of the assembly model are discussed. Through a one-dimensional search of the convergence domain, a method is found to make the analytical solution approximate the exact solution and reduce the calculation cost. This method in dynamics and mathematical engineering.

Boundary-condition models of film-cooling holes for large-eddy simulation of turbine vanes

In many industrial applications, the mechanical integrity of a surface operating under large thermal loads is ensured by injecting a cold fluid through a series of hole along the surface, forming a thin film of cool fluid shielding the solid surface from external heat. An accurate prediction of the heat transfer provided by these so-called film-cooling systems is crucial to ensure the durability of the cooled surfaces. However, the large-eddy simulation of film-cooling systems is complex and expensive because the in-hole flow must be meshed and simulated. To address this difficulty, the modelling of the film-cooling jet by mean of a dedicated boundary condition has recently been proposed. This paper investigates several potential improvements for this type of model in four geometries: an inclined cylindrical hole, a fanshaped hole and two fanshaped laidback holes. The analysis focuses on the comparison of a spatially uniform injection to a model taking into account velocity and temperature spatial variations at the hole exit. The study also compares a non-turbulent injection to a model with synthetic turbulence injection. The comparisons are first performed using a fine mesh to validate the approach, then using a coarse mesh representative of a mesh that could be used to simulate a cooled nozzle guide vane. The results show that both spatial inhomogeneity and turbulence injection significantly improve the cooling effectiveness predictions in a wide variety of cases. The spatial inhomogeneity is especially crucial for the near-hole behaviour of the flow while turbulence injection is particularly important when the destabilisation of the jet by the crossflow is not sufficient to immediately trigger transition. Using a very coarse mesh, the turbulent mixing is observed to be underestimated with all examined boundary-condition models and the behaviour of the jet is not correctly described for some of the configurations investigated. Although not sufficient, non-negligible improvements are nevertheless obtained with an inhomogeneous turbulent injection compared to the baseline uniform model.

Improved Preservation Technology for Non-Instantaneous Deteriorating Inventory Using Boundary Condition Estimation

Various forms of preservation technology nowadays allow businesses to handle valuable perishable items with greater flexibility. Even with a wide variety of preservation techniques, the mathematical modelling of its implementation in EOQ literature remains rigid. The paper aims to integrate an improved preservation technology in a non-instantaneous deteriorating inventory model for businesses maximizing their average total cycle profit. The improved preservation technology furthers the delay to the time within the cycle where deterioration begins and enhances the durability of inventory that allows operators to employ a less prudent holding facility. Another improvement in this area is the accurate accumulation of preservation cost depending on the inventory level at hand. The conventional EOQ method of forming the objective function before choosing the optimal values for our two decision variables (Cycle time and level of preservation) is undertaken. The cycle time is split in two, differing in their inventory process (deterioration beginning in the second period). The time when deterioration begins is derived using the model's boundary conditions, a first attempt within the area. The optimal solution set is solved for a numerical example using an algorithm to demonstrate the model and prove the global nature of the solution. An investigation into the gains from the improved preservation technology is conducted by dissecting the effects within each individual component within the objective function. 3 separate channels by which this improved preservation technology modelling benefits the business model is found namely shifting to the higher profitable period, effects towards preservation affected costs and the returns to scale from successively increasing preservation levels. Sensitivity analysis is conducted to demonstrate and confirm the findings. The paper discovers great benefits from such an improved modelling that warrants further attention within the scope of preserved inventory models, especially on how levels of preservation could influence the traditional decision variable optimized such as cycle time or ordering frequency. Findings of the paper would have significant benefits to different inventory models with its own delay before deterioration and holding facility requirement.

Assessment of boundary conditions for CFD simulation in human carotid artery.

Computational fluid dynamics (CFD) is an increasingly used method for investigation of hemodynamic parameters and their alterations under pathological conditions, which are important indicators for diagnosis of cardiovascular disease. In hemodynamic simulation models, the employment of appropriate boundary conditions (BCs) determines the computational accuracy of the CFD simulation in comparison with pressure and velocity measurements. In this study, we have first assessed the influence of inlet boundary conditions on hemodynamic CFD simulations. We selected two typical patients suspected of carotid artery disease, with mild stenosis and severe stenosis. Both patients underwent digital subtraction angiography (DSA), magnetic resonance angiography, and the invasive pressure guide wire measured pressure profile. We have performed computational experiments to (1) study the hemodynamic simulation outcomes of distributions of wall shear stress, pressure, pressure gradient and (2) determine the differences in hemodynamic performances caused by inlet BCs derived from DSA and Womersley analytical solution. Our study has found that the difference is related to the severity of the stenosis; the greater the stenosis, the more the difference ensues. Further, in our study, the two typical subjects with invasively measured pressure profile and thirty subjects with ultrasound Doppler velocimeter (UDV) measurement served as the criteria to evaluate the hemodynamic outcomes of wall shear stress, pressure, pressure gradient and velocity due to different outlet BCs based on the Windkessel model, structured-tree model, and fully developed flow model. According to the pressure profiles, the fully developed model appeared to have more fluctuations compared with the other two models. The Windkessel model had more singularities before convergence. The three outlet BCs models also showed good correlation with the UDV measurement, while the Windkessel model appeared to be slightly better ([Formula: see text]). The structured-tree model was seen to have the best performance in terms of available computational cost and accuracy. The results of our numerical simulation and the good correlation with the computed pressure and velocity with their measurements have highlighted the effectiveness of CFD simulation in patient-specific human carotid artery with suspected stenosis.

A functionally personalized boundary condition model to improve estimates of fractional flow reserve with CT (CT-FFR).

The purpose of this study is to develop and evaluate a functionally personalized boundary condition (BC) model for estimating the fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA) using flow simulation (CT-FFR). The CCTA data of 90 subjects with subsequent invasive FFR in 123 lesions within 21 days (range: 0-83) were retrospectively collected. We developed a functionally personalized BC model accounting specifically for the coronary microvascular resistance dependency on the coronary outlets pressure suggested by several physiological studies. We used the proposed model to estimate the hemodynamic significance of coronary lesions with an open-loop physics-based flow simulation. We generated three-dimensional (3D) coronary tree geometries using automatic software and corrected manually where required. We evaluated the improvement in CT-FFR estimates achieved using a functionally personalized BC model over anatomically personalized BC model using k-fold cross-validation. The functionally personalized BC model slightly improved CT-FFR specificity in determining hemodynamic significance of lesions with intermediate diameter stenosis (30%-70%, N = 72), compared to the anatomically personalized model lesions with invasive FFR measurements as the reference (sensitivity/specificity: 0.882/0.79 vs 0.882/0.763). For the entire set of 123 coronary lesions, the functionally personalized BC model improved only the area under the curve (AUC) but not the sensitivity/specificity in determining the hemodynamic significance of lesions, compared to the anatomically personalized model (AUC: 0.884 vs 0.875, sensitivity/specificity: 0.848/0.805). The functionally personalized BC model has the potential to improve the quality of CT-FFR estimates compared to an anatomically personalized BC model.

Biomechanics of the classic metaphyseal lesion: finite element analysis.

The classic metaphyseal lesion (CML) is strongly associated with infant abuse, but the biomechanics responsible for this injury have not been rigorously studied. Radiologic and CT-pathological correlates show that the distal tibial CML always involves the cortex near the subperiosteal bone collar, with variable extension of the fracture into the medullary cavity. Therefore, it is reasonable to assume that the primary site of bone failure is cortical, rather than intramedullary. This study focuses on the strain patterns generated from finite element modeling to identify loading scenarios and regions of the cortex that are susceptible to bone failure. A geometric model was constructed from a normal 3-month-old infant's distal tibia and fibula. The model's boundary conditions were set to mimic forceful manipulation of the ankle with eight load modalities (tension, compression, internal rotation, external rotation, dorsiflexion, plantar flexion, valgus bending and varus bending). For all modalities except internal and external rotation, simulations showed increased cortical strains near the subperiosteal bone collar. Tension generated the largest magnitude of cortical strain (24%) that was uniformly distributed near the subperiosteal bone collar. Compression generated the same distribution of strain but to a lesser magnitude overall (15%). Dorsiflexion and plantar flexion generated high (22%) and moderate (14%) localized cortical strains, respectively, near the subperiosteal bone collar. Lower cortical strains resulted from valgus bending, varus bending, internal rotation and external rotation (8-10%). The highest valgus and varus bending cortical strains occurred medially. These simulations suggest that the likelihood of the initial cortical bone failure of the CML is higher along the margin of the subperiosteal bone collar when the ankle is under tension, compression, valgus bending, varus bending, dorsiflexion and plantar flexion, but not under internal and external rotation. Focal cortical strains along the medial margins of the subperiosteal bone collar with varus and valgus bending may explain the known tendency for focal distal tibial CMLs to occur medially. Further research is needed to determine the threshold of applied forces required to produce this strong indicator of infant abuse.

A numerical shallow-water model for gravity currents for a wide range of density differences

Gravity currents with various contrasting densities play a role in mass transport in a number of geophysical situations. The ratio of the density of the current, ρc, to the density of the ambient fluid, ρa, can vary between 100 and 103. In this paper, we present a numerical method of simulating gravity currents for a wide range of ρc/ρa using a shallow-water model. In the model, the effects of varying ρc/ρa are taken into account via the front condition (i.e., factors describing the balance between the driving pressure and the ambient resistance pressure at the flow front). Previously, two types of numerical models have been proposed to solve the front condition. These are referred to here as the Boundary Condition (BC) model and the Artificial Bed (AB) model. The front condition is calculated as a boundary condition at each time step in the BC model, whereas it is calculated by setting a thin artificial bed ahead of the front in the AB model. We assessed the BC and AB models by comparing their numerical results with the analytical results for a simple case of homogeneous currents. The results from the BC model agree well with the analytical results when ρc/ρa≲102, but the model tends to overestimate the speed of the front position when rho _{mathrm {c}}/rho _{mathrm {a}}gtrsim 10^{2}. In contrast, the AB model generates good approximations of the analytical results for rho _{mathrm {c}}/rho _{mathrm {a}}gtrsim 10^{2}, given a sufficiently small artificial bed thickness, but fails to reproduce the analytical results when ρc/ρa≲102. Therefore, we propose a numerical method in which the BC model is used for currents with ρc/ρa≲102 and the AB model is used for currents with rho _{mathrm {c}}/rho _{mathrm {a}}gtrsim 10^{2}.

Characteristic boundary conditions for magnetohydrodynamics: The Brio–Wu shock tube

In the present paper we develop and test a characteristic-based boundary condition (BC) scheme for the compressible magnetohydrodynamic equations, as the extension of a characteristic gasdynamics BC model. We use a Harten–Yee finite volume scheme for the spatial discretization of the domain, and a TVD Backward Euler time integrator for the sake of robustness. First we verify that the scheme works correctly for gasdynamic initial conditions (i.e., when B=0), comparing with both analytical and experimental data. We then test the BC scheme with the Brio and Wu shock tube for two different types of boundaries: an open end and solid walls. We present a comparison between results obtained with the zeroth-order extrapolated BC scheme and the characteristic scheme developed. For a solid wall condition, we found discrepancies between both schemes when perturbations in the transverse magnetic field component (By) reach the boundaries. Also for the open end condition some discrepancies appear between the characteristic and extrapolated schemes, presenting the latter some instabilities. The results obtained with the characteristic scheme are smoother but presented a different wave pattern, which we believe is physical.

Implementation of Simulation-Based Virtual Sensors Using Radiance and Java

Building performance simulation is traditionally used to support the building design process. However, it can also be used during building operation phase by providing relevant data to building automation systems. We refer to monitoring and metering data generated by computational simulation (virtual) models as data from virtual sensors. Virtual sensors, if reliably and effectively incorporated, can expand the reach of real sensors. A continuous supply of virtual sensor data can support near real-time representation of both primary environmental variables (e.g., air temperature, relative humidity) as well as complex performance indicators (e.g., thermal comfort indices, visual glare). Such information, especially in large multi-zone buildings, would be otherwise expensive to obtain from real sensors due to high capital costs of the sensors, their installation, networking, and maintenance. Virtual sensors can increase the resolution of sensory information, as they imply little monetary expenditures and are not limited in view of numbers and location.Conventional building energy simulation uses a model of the building and creates boundary conditions using historic weather data and predefined schedules for lighting and equipment load. This method is suitable for evaluation of building designs but not suitable for monitoring and control processes during building operation phase, as such processes usually require actual real-time information regarding boundary conditions. Thus, capability to automatically update simulation model's boundary conditions in real-time using monitored data is required. Toward this end, a supervisory software application is developed, in Java programming language, to implement and illustrate the concept of virtual sensors using Radiance simulation program. This application creates virtual illuminance sensors in Radiance model and then automates the process of regularly updating simulation model's external and internal boundary conditions (solar irradiance, state of electrical lighting elements) by using real-time monitored data from on-site weather station and building management system (BMS). As a result, the simulation kernel can continuously generate virtual sensor readings that can be utilized by building monitoring and/or control systems. This paper discusses the general architecture of the application, to further illustrate the concept of virtual sensors. Moreover, it discusses the accuracy of virtual illuminance sensors by comparing their output with monitored illuminance data in realistic test scenarios.

QCT/FEA predictions of femoral stiffness are strongly affected by boundary condition modeling

Quantitative computed tomography-based finite element models of proximal femora must be validated with cadaveric experiments before using them to assess fracture risk in osteoporotic patients. During validation, it is essential to carefully assess whether the boundary condition (BC) modeling matches the experimental conditions. This study evaluated proximal femur stiffness results predicted by six different BC methods on a sample of 30 cadaveric femora and compared the predictions with experimental data. The average stiffness varied by 280% among the six BCs. Compared with experimental data, the predictions ranged from overestimating the average stiffness by 65% to underestimating it by 41%. In addition, we found that the BC that distributed the load to the contact surfaces similar to the expected contact mechanics predictions had the best agreement with experimental stiffness. We concluded that BC modeling introduced large variations in proximal femora stiffness predictions.