28 research outputs found
Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography
We study the feasibility of data based machine learning applied to ultrasound
tomography to estimate water-saturated porous material parameters. In this
work, the data to train the neural networks is simulated by solving wave
propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the
forward model, we consider a high-order discontinuous Galerkin method while
deep convolutional neural networks are used to solve the parameter estimation
problem. In the numerical experiment, we estimate the material porosity and
tortuosity while the remaining parameters which are of less interest are
successfully marginalized in the neural networks-based inversion. Computational
examples confirms the feasibility and accuracy of this approach
Characterising poroelastic materials in the ultrasonic range - A Bayesian approach
Acoustic fields scattered by poroelastic materials contain key information
about the materials' pore structure and elastic properties. Therefore, such
materials are often characterised with inverse methods that use acoustic
measurements. However, it has been shown that results from many existing
inverse characterisation methods agree poorly. One reason is that inverse
methods are typically sensitive to even small uncertainties in a measurement
setup, but these uncertainties are difficult to model and hence often
neglected. In this paper, we study characterising poroelastic materials in the
Bayesian framework, where measurement uncertainties can be taken into account,
and which allows us to quantify uncertainty in the results. Using the finite
element method, we simulate measurements where ultrasonic waves are incident on
a water-saturated poroelastic material in normal and oblique angles. We
consider uncertainties in the incidence angle and level of measurement noise,
and then explore the solution of the Bayesian inverse problem, the posterior
density, with an adaptive parallel tempering Markov chain Monte Carlo
algorithm. Results show that both the elastic and pore structure parameters can
be feasibly estimated from ultrasonic measurements.Comment: Published in JSV. https://doi.org/10.1016/j.jsv.2019.05.02
A High-Order Ultra-Weak Variational Formulation for Electromagnetic Waves Utilizing Curved Elements
The Ultra Weak Variational Formulation (UWVF) is a special Trefftz
discontinuous Galerkin method, here applied to the time-harmonic Maxwell's
equations. The method uses superpositions of plane waves to represent solutions
element by element on a finite element mesh. We discuss the use of our parallel
UWVF implementation called ParMax, and concentrate on methods for obtaining
high order solutions in the presence of scatterers with piecewise smooth
boundaries. In particular, we show how curved surface triangles can be
incorporated in the UWVF. This requires quadrature to assemble the system
matrices. We also show how to implement a total field and scattered field
approach, together with the transmission conditions across an interface to
handle resistive sheets. We note also that a wide variety of element shapes can
be used, that the elements can be large compared to the wavelength of the
radiation, and that a matrix free version is easy to implement (although
computationally costly). Our contributions are illustrated by several numerical
examples showing that curved elements can improve the efficiency of the UWVF,
and that the method accurately handles resistive screens as well as PEC and
penetrable scatterers. Using large curved elements and the matrix free
approach, we are able to simulate scattering from an aircraft at X-band
frequencies. The innovations here demonstrate the applicability of the UWVF for
industrial examples
Tomography-assisted control for the microwave drying process of polymer foams
This paper presents the integration of electrical capacitance tomography (ECT) with a moisture controller for the microwave drying of polymer foam. The proportional–integral (PI) control and the linear quadratic Gaussian (LQG) control are employed in designing the controller. The control objective in this process is that the moisture of polymer foam after the drying process reaches the desired set point. The permittivity distribution of polymer foam after the drying process is estimated in real-time using a designed ECT sensor and transferred as feedback to the controller. Since the permittivity and the moisture are strongly correlated, the material moisture can be controlled by controlling the permittivity. A state-space model is derived for the microwave drying process based on a system identification approach using the experimental data from the process. The derived model is employed in designing the LQG controller and adjusting the parameters of the PI controller. The designed controllers are implemented on a testbed microwave oven, and the experimental results show that the designed controllers are able to follow the desired set point moisture. The performance of the system with both controllers is compared, and their advantages and disadvantages are discussed. Moreover, the benefits of having a moisture controller for the microwave drying process are shown in simulation studies compared to an uncontrolled system
An electromagnetic time-reversal imaging algorithm for moisture detection in polymer foam in an industrial microwave drying system
Microwave tomography (MWT) based control is a novel idea in industrial heating systems for drying polymer foam. In this work, an X-band MWT module is designed and developed using a fixed antenna array configuration and integrated with the HEPHAISTOS industrial heating system. A decomposition of the time-reversal operator (DORT) algorithm with a proper Green’s function of multilayered media is utilized to localize the moisture location. The derived Green’s function can be applied to the media with low or high contrast layers. It is shown that the time-reversal imaging (TRI) with the proposed Green’s function can be applied to the multilayered media with a moderately rough surface. Moreover, a single frequency TRI is proposed to decrease the measurement time. Numerical results for different moisture scenarios are presented to demonstrate the efficacy of the proposed method. The developed method is then tested on the experimental data for different moisture scenarios from our developed MWT experimental prototype. Image reconstruction results show promising capabilities of the TRI algorithm in estimating the moisture location in the polymer foam
Monitoring of water content in a porous reservoir by seismic data: A 3D simulation study
A potential framework to estimate the amount of water stored in a porous
storage reservoir from seismic data is neural networks. In this study, the
water storage reservoir system is modeled as a coupled
poroviscoelastic-viscoelastic medium, and the underlying wave propagation
problem is solved using a three-dimensional discontinuous Galerkin method
coupled with an Adams-Bashforth time stepping scheme. The wave problem solver
is used to generate databases for the neural network-based machine learning
model to estimate the water content. In the numerical examples, we investigate
a deconvolution-based approach to normalize the effect from the source wavelet
in addition to the network's tolerance for noise levels. We also apply the
SHapley Additive exPlanations method to obtain greater insight into which part
of the input data contributes the most to the water content estimation. The
numerical results demonstrate the capacity of the fully connected neural
network to estimate the amount of water stored in the porous storage reservoir