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This paper presents the optimization of a flow injection analysis (FIA) biosensor with respect to its design and operational parameters such as flow cell geometry, microfluidic channel dimensions, and flow rate. Since it is time consuming and costly to investigate the effect of each factor on the biosensor performance by building it, computational fluid dynamics (CFD) theory is presented as a great tool for finding optimal parameter values. This modeling approach has a high potential in the design of high accuracy FIA-biosensors, regardless of the chosen enzyme substrate system. As an example the optimal design for a glucose/glucose oxidase FIA biosensor is calculated with the CFD theory

Gas filled intercellular spaces are considered the predominant pathways for gas transport through plant organs and, as such, are greatly related to the characteristics of gas exchange. To understand the transport mechanisms of gas diffusion of fruit tissue, a microscale model for the transport of O2 in the intercellular spaces, the cell wall network and the intracellular liquid phase was introduced. The objective of this study was (1) to verify the applicability of the microscale model of the gas transport at tissue level and (2) to quantify the pathways of gas transport in relation to the microstructure of fruit tissue. The 2D microstructure of pear tissue was modelled from light microscopy images using the ellipse tessellation method. Gas transport properties of the gas and liquid phases and the cell membrane were determined from available literature data. The O2 transfer in each of intercellular space, cell wall and cytoplasm was applied using diffusion laws and irreversible thermodynamics. The model was solved using the finite element method. The results showed that the microscale model can be applied to study the gas transport in fruit tissue. The model quantified the pathways of gas transport in fruit tissue. The O2 transport was mainly by means of the intercellular space, the cell wall network and less through the intracellular liquid. The results have important consequences for respiration–related disorders of fruits. ispartof: 13th World Congress of Food Sciences & Technology location:Nantes, France date:17 Sep - 21 Sep 2006 status: published

Modeling plant microstructure is of great interest to food engineers to simulate the behavior of the physical properties (e.g., concerning mass transfer, mechanics) of plant tissues at the cellular level. The generation of geometrical models of microscopic structures is considered a prime requirement to develop microscale models to study and describe these properties. For this end, this paper presents a novel ellipse tessellation algorithm to generate a 2D geometrical model of apple tissue. Ellipses are used to quantify the orientation and aspect ratio of cells on a microscopic image. The cell areas and centroids of each cell are also determined by means of a numerical procedure. These characteristic quantities are then described by means of probability density functions. The model tissue geometry is generated from the ellipses which are truncated when neighboring areas overlap. As a result, a virtual microstructure consisting of truncated ellipses fills up the entire space with the same number of cells as that of microscopic images and with similar area, orientation and aspect ratio distribution. The spatial variability of the geometric characteristics (cell area size, cell shape, cell orientation and tissue porosity) of the virtual cellular structure was also evaluated and compared to that of the microscopic images. Statistical analysis showed that the virtual geometry generated with this approach yields spatially equivalent geometries to that of real fruit microstructures. Compared to the more common algorithm of Voronoi diagrams, ellipse tesselation is superior for generating the microstructure of tissue. The extension of the algorithm to 3D is straightforward. These representative tissues will be exported into a finite element environment via interfacing codes to perform in silico experiments for estimating gas and moisture diffusivities and investigating their relation with fruit microstructure.

A diffusion model based on Fick's second law was used to simulate water transport in pear fruit at various conditions (20 ◦ C and 75% RH; 1 ◦ C and 60% RH). The finite element method was used to discretise the governing differential equations over the actual 3D pear geometry. For the first time, water transport in Conference pear fruits was described at the mesoscale level by incorporating different tissues (cuticle, inner and outer cortex) with different diffusion properties. The validated model explained water transport well as validated through nuclear magnetic resonance imaging techniques and was able to predict mass loss of intact pear during postharvest conditions. It was noticed that, at high temperature conditions, the model can be improved further by taking into account respiration and shrinkage effects.  2006 Society of Chemical Industry

Drying processes are considered on an empirical basis in industrial applications. The results presented in this paper concern theoretical and experimental data, obtained during the drying of semolina pasta. The moisture concentration is modelled for semolina pasta, made from durum wheat, by assuming the mass transfer between the pasta and the air. As the transport kinetics are entirely controlled by the internal transport resistance, Fick’s law applies. The moisture concentrations of durum pasta are predicted in the model as a function of pasta (initial moisture concentration, thickness and food composition) and air properties (air temperature and relative humidity). The model was validated by additional measurements, corresponding well with the modelled moisture concentrations. Finally a parameter study was performed to study the impact of the model parameters on the moisture concentration.

In order to improve flatbed rice dryers, which have been used in Mekong delta (MD), Vietnam, A prototype model was set up and investigated. A mathematical model was developed to compute the local airflow at different positions in the flatbed dryer model and investigate the effect of dryer geometry. The model solves the Reynolds-averaged Navier- Stokes equations for the airflow with a k- a model for turbulence. A good qualitative agreement was obtained between measured and simulated velocity profiles in the model.

This paper presents a transient CFD model of heat and mass transfer in porous bulks of chicory roots. The model consists of the full system of conservation equations of momentum, energy and mass for the two-phase air-product matrix through bulk of chicory roots. A finite volume code (CFX, Harwell, UK) is used to solve the model equations, which is then validated against experimental data. The results show a good agreement between the model and the experiments. Differences between predicted and measured weight loss only amount to maximum 0.3% after the initial cooling period. The non-ideal experimental conditions, the various sizes of the products and the small scale of the porous region compared to the size of the product contribute to the observed differences between experiment and simulation, but do represent reality. Humidity and air velocity have a strong effect on the cooling rate and the weight-loss of the product. The results led to an improved understanding of the cooling process with different airflow conditions. The model can be applied to study the cooling process in an industrial cold store to find the optimal process settings to improve the product quality and reduce product weight.

Magnitude and patterns of airflow inside a microwave oven and resulting surface heat transfer coefficients were studied using a computational fluid dynamics (CFD) model of the process. The cavity of the oven and the cylindrical food were modelled in 3D. The governing Navier-Stokes equations were solved for laminar flow with one inlet and one outlet. Both natural and forced convection modes of heat transfer were studied. The magnitude and distribution of surface heat transfer coefficients on the food surface were calculated and compared for the different modes of heat transfer. Calculated values were in the same range as used in previous studies.

Transport of CO(2) in leaves was investigated by combining a 2-D, microscale CO(2) transport model with photosynthesis kinetics in wheat (Triticum aestivum L.) leaves. The biophysical microscale model for gas exchange featured an accurate geometric representation of the actual 2-D leaf tissue microstructure and accounted for diffusive mass exchange of CO(2.) The resulting gas transport equations were coupled to the biochemical Farquhar-von Caemmerer-Berry model for photosynthesis. The combined model was evaluated using gas exchange and chlorophyll fluorescence measurements on wheat leaves. In general a good agreement between model predictions and measurements was obtained, but a discrepancy was observed for the mesophyll conductance at high CO(2) levels and low irradiance levels. This may indicate that some physiological processes related to photosynthesis are not incorporated in the model. The model provided detailed insight into the mechanisms of gas exchange and the effects of changes in ambient CO(2) concentration or photon flux density on stomatal and mesophyll conductance. It represents an important step forward to study CO(2) diffusion coupled to photosynthesis at the leaf tissue level, taking into account the leaf's actual microstructure.

Speed is an important parameter of an inspection system. Inline computed tomography systems exist but are generally expensive. Moreover, their throughput is limited by the speed of the reconstruction algorithm. In this work, we propose a Neural Network-based Hilbert transform Filtered Backprojection (NN-hFBP) method to reconstruct objects in an inline scanning environment in a fast and accurate way. Experiments based on apple X-ray scans show that the NN-hFBP method allows to reconstruct images with a substantially better tradeoff between image quality and reconstruction time.

Spray drift continues to be a major problem in applying agricultural pesticides. This paper summarizes the results of a 5 year study about drift from field crop sprayers using a unique integrated approach.

This article presents a transient 3D CFD model of heat and mass transfer in bulks of chicory roots. The model consists of the system of conservation equations of momentum, energy, and mass for the air phase, and the energy and mass for the product phase without considering the internal gradient in temperature and moisture in the product phase. The interaction between the airflow and the porous media is described by an Ergun-type equation based on experimental data. Heat of respiration is included in the model as an empirically derived function of temperature. A finite volume code is used to solve the model equations. The results show a good agreement between the model and the experiments. Differences between predicted and measured weight loss only amount to a maximum 10% after the initial cooling period. The non-ideal experimental conditions (high velocity, low relative humidity of the air), the various sizes of the products, the small scale of the porous region compared to the size of the product, and the estimation of transfer correlations contribute to the observed differences between experiment and simulation. The model can be applied to study the cooling process in an industrial cold store to find the optimal process settings to improve product quality and reduce product weight loss.

The propagation of parameter uncertainties which fluctuate in space and time through a thermal sterilization process of a conductive heated food was calculated by means of a Monte Carlo and variance propagation algorithm. The parameters were modelled as autoregressive random waves. A good agreement between both methods was obtained for the mean and variances of the temperature field inside the can. The variance propagation algorithm was advantageous in terms of computer time. It is shown that high frequency fluctuations in space and time of the retort temperature and the surface heat transfer coefficient are attenuated because of the thermal inertia of the food.

The magnitude of surface heat and mass transfer coefficients in microwave ovens is important to control food surface temperature and moisture and are a result of the faint airflow present in the oven cavity and of surface radiation. Magnitude and patterns of airflow inside a microwave oven and the resulting surface heat transfer coefficients were studied using a computational fluid dynamics model of the process. The governing Navier–Stokes and energy equations were solved for both natural, forced and combined convection. The magnitude and distribution of surface heat transfer coefficients on the food surface were computed for a 3-D oven cavity with one inlet and one outlet and a cylindrical food placed inside the oven. Calculated convective heat transfer coefficient values were found to be in the same range as has been used in the literature. A combined convection regime proves beneficial for heat transfer uniformity and the reduction of moisture accumulation inside the oven. Radiation heat transfer coefficients for energy exchange between food surface and oven interior were calculated and shown to be of the same order of magnitude as the convection heat transfer coefficients.

A steady state variance propagation algorithm is derived to investigate the effect of stochastic air temperature disturbances on the variability of the temperature field inside refrigerated fruits during cold storage conditions. The disturbances are modelled by continuous-time autoregressive processes of variable order. The algorithm is based on the finite element formulation of the heat conduction equation and involves the numerical solution of matrix equations of the Lyapunov and Sylvester type. As an example, the cold storage of pineapple has been considered. It is shown that disturbances of different order but with the same scale of fluctuation result in comparable centre temperature variances.

The quality of agricultural sprays plays an important role in the application of plant protection products. For 13 nozzle-pressure combinations, droplet size and velocity characteristics were measured 0.50 m below the nozzle using a PDPA laser-based measurement setup. Nozzles were mounted on a transporter to sample the whole of the spray fan. The effects of nozzle type (standard, low-drift, and air-inclusion), nozzle size (ISO 02, 03, 04, and 06) and operating pressure (2.0, 3.0, and 4.0 bar) were tested. Measured droplet sizes and velocities were related, and both were affected by nozzle type, size, and operating pressure. Droplet velocities at 0.50 m were determined by their size and initial ejection velocity. In general, bigger droplet sizes correspond with higher droplet velocities, and smaller droplets with lower droplet velocities. Important differences in velocities were observed depending on the nozzle type and size, both affecting the ejection velocity. For the same droplet size, droplet velocities were highest for the flat-fan nozzles, followed by the low-drift nozzles and the air-inclusion nozzles (because of the lower ejection velocities caused by pre-orifice and Venturi effects). Similarly, the bigger the ISO nozzle size, the faster were the droplets of the same size. Droplet velocities of the larger droplet sizes (>400 µm) varied from about 4.5 to 8.5 m s-1 depending on the nozzle type and size. Below 400 µm, droplet velocities consistently decreased with the decrease in droplet size, and vary from 0.5 to 2 m s-1 depending on the nozzle type and size. All this information is very useful with regard to crop penetration, the risk of spray drift, and the quantity and distribution of the deposit on the target.

Spray drift continues to be a major problem in applying agricultural pesticides. This article summarizes the results of a five-year study of drift from field crop sprayers using a unique integrated approach. Indirect (spray quality and wind tunnel measurements) and direct (field) drift experiments were performed, and drift models were developed to study the effect of spray application technique, droplet characteristics, buffer zones, meteorological conditions, spray liquid properties, border structures, and crop characteristics on drift from field crop sprayers. It was found that indirect drift measurements can be a valuable alternative to field drift experiments. A validated 3-D computational fluid dynamics (CFD) mechanistic drift model was developed, which can be used for a systemic study of different influencing factors. This model was reduced to a fast 2-D diffusion advection model, which is useful as a hands-on drift prediction tool. From the experiments as well as from the models, the fraction of small droplets and the spray boom height were found to be the most influential spray application factors. Moreover, meteorological conditions as well as crop characteristics have an important effect on the amount of spray drift, which can be reduced significantly using intercepting screens or buffer zones. From this study, drift protocols, data, and models are made available, which help to understand and reduce the complex phenomena of spray drift.

A first-order perturbation algorithm has been used to evaluate the effect of parameter uncertainties of the random variable and random field type on the temperature inside a can during a typical thermal sterilization process. The algorithm is based on the finite element formulation of the heat conduction equation and is considerably faster than a Monte C lo algorithm for a comparable accuracy. The perturbation algorithm is, however, only applicable when the coefficient of variation of the random parameters is smaller than 20%. In the case of random field parameters the finite elements should be smaller than half the scale of fluctuation. It was shown that, in the case of random field parameters, the magnitude of the temperature fluctuations in the can increases with increasing scale of fluctuation. If the scale of fluctuation becomes very large, the random field degenerates to a random variable and the variance of the temperature at an arbitrary position and time is maximal. For a typical sterilization process it appears that the thermophysical properties are the most important sources of variability.

An improved finite element enthalpy method was developed and implemented for solving non–linear phase change heat transfer problems such as freezing or thawing of foods with arbitrary 3D geometries. By simultaneously applying the enthalpy and Kirchhoff transforms, all non–linearities caused by the temperature–dependent thermophysical properties were incorporated in a functional relationship between the volumetric specific enthalpy and the Kirchhoff function. Such a problem reformulation results in a much more convenient solution procedure and avoids the possibility of missing the apparent specific heat capacity peak and the abrupt thermal conductivity change. Algorithms for the finite element solution of the resulting transformed equation were developed and programmed in Matlab. As the transformed equation is linear, the finite element matrices are constant and have to be calculated only once. This greatly improves the execution speed of the code, as compared to traditional finite element algorithms based on the original non–linear or enthalpy–transformed Fourier equation. As a consequence, the method is well suited for computationally intensive applications, such as numerical optimization or Monte Carlo uncertainty propagation analysis, in which typically a large number of phase change heat transfer problems must be solved. As an illustration, the freezing of a cylinder filled with tylose was investigated. A good agreement between measurements and model predictions was obtained.

A first-order perturbation algorithm for the computation of mean values and variances of transient temperature and moisture fields during coupled heat and mass transfer problems with random field parameters has been developed and implemented. The algorithm is based on the Galerkin finite-element discretization of Luikov's heat and mass transfer equations for capillary porous bodies and is computationally less demanding than the Monte Carlo method. The algorithm has been programmed in MATLAB and applied to a published test case of a drying process for soybean kernels. The simulations indicate that the stochastic fluctuations of the thermophysical properties and the process conditions may cause a considerable level of uncertainty in the predicted temperatures and moisture contents inside the product.