Tineke Goessens

A volume averaging and overlapping domain decomposition technique to model mass transfer in textiles

A new three scale approach for textile models is suggested: a one-dimensional fiber model and a fabric model, with a meso-level in between, i.e. the yarn scale, Goessens?et?al.?(2012). For loose textile substrates this seems appropriate as the yarn level plays an important role. This is because the saturation vapor pressure will influence the release rate from the fibers, and its value will vary over the yarn cross section. Therefore, in this work we present two upscaling techniques for the three step multiscale model. The active component is tracked in the fiber, the yarn, and finally at the fabric level. At the fiber level a one-dimensional reduction to a non-linear diffusion equation is performed, and solved on an as needed basis. The outcome is upscaled via the volume averaging method and used as an input for the yarn level. At this level a one-dimensional model can be applied to calculate the concentration, which on its turn is upscaled using the overlapping domain decomposition as an input for the fabric level model.

Characteristic times for multiscale diffusion of active ingredients in coated textiles

A three-scale approach for textile models was given in 5]: a one-dimensional fiber model and a room model, with a meso-level in between, which is the yarn scale. To analyze and simplify the model, its characteristic times are investigated here. At these times the fiber and yarn model and the yarn and room model, respectively, tend to reach a partial equilibrium concentration. The identification of these characteristic times is key to reducing the model to its variously scaled components when simplifying it.

Bayesian inference in the uncertain EEG problem including local information and a sensor correlation matrix

We present a framework based on Bayesian inference to combine expert judgment and the problem of an uncertain conductivity in the electroencephalography (EEG) inverse problem. A three layer spherical head model with different and random layer conductivities is considered. The randomness is modeled by Legendre Polynomial Chaos. Using this Polynomial Chaos we build on previous work to obtain a correlation matrix for the error used in the likelihood function of the Bayesian procedure. We compare with a classical isotropic correlation.

Modeling Textile Fabric Used in Pest Control with a 3 Scale Domain Decomposition Method

In this paper we present a model to simulate textile as used in pest control. For this application, textile is coated with a repellent, protecting the user from insect bites, and one wants to determine optimal material properties. The model extends an existing 3 scale method to allow for simulations in saturated conditions. This is achieved with the addition of an overlapping domain decomposition approach for the fiber-yarn interaction.

An extended virtual location method for yarn cross-section construction

The first step in the evaluation of physical phenomena in yarns consists of creating a suitable representation of the yarn. Here, we focus on the creation of a realistic grid for the yarn cross-section, which is suitable for mass and heat transfer models. We extend and adapt the virtual location method (VLM) which allows the quick creation of a range of 2D yarn–fiber layouts. We overcome two of its main disadvantages: the presence of too much regularity, and the inability to produce yarn–fiber layouts when blends of fibers with different sizes are present. Our method is based on the standard ring configuration VLM, creating two sets of virtual locations per fiber type, which causes some overlap of the fibers. The overlap is removed with an iteration scheme based on induced movement. The final result is a realistic 2D cross-section of a yarn. A reference implementation is available, and it is shown how the layout can be used to create a grid.

Model-based determination of the influence of textile fabric on bioassay analysis and the effectiveness of a textile slow-release system of DEET in mosquito control

BACKGROUND Determining the effectiveness of a product in repelling mosquitoes or other flying insects is a difficult task. One approach is to use a bioassay with textile fabric. We investigated the role of the textile substrate in a bioassay with a numerical model, and compared the outcome with known results for DEET. The model was then used to determine the effectiveness of textile slow-release formulations based on coatings, and results were compared with those of a field study in the Cameroon. Slow-release formulations are difficult to evaluate with standard tests, as the compound needs a build-up time not present in these tests. RESULTS We found excellent correspondence between the model and the known DEET results without matching parameters. Slow-release approaches are deemed possible but have several drawbacks. Modelling can help in identifying optimal use conditions. The field test with a slow-release system performed better than anticipated by the model, with initially more than 90% repellency. DEET-coated textile was considered not to be marketable, however. CONCLUSION We advise that bioassays characterise in more detail the type of textile fabric used so as to allow conclusions to be drawn by textile modelling. As regards coated-textile slow-release systems, more research is needed. We nevertheless advise usage mainly at entry points, e.g. as scrims.