Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method

In situ chemical oxidation using permanganate as an oxidant is a remediation technique often used to treat contaminated groundwater. In this paper, groundwater flow with a full hydraulic conductivity tensor and remediation process through in situ chemical oxidation are simulated. The numerical approach was verified with a physical sandbox experiment and analytical solution for 2D advection-diffusion with a first-order decay rate constant. The numerical results were in good agreement with the results of physical sandbox model and the analytical solution. The developed model was applied to two different studies, using multi-objective genetic algorithm to optimise remediation design. In order to reach the optimised design, three objectives considering three constraints were defined. The time to reach the desired concentration and remediation cost regarding the number of required oxidant sources in the optimised design was less than any arbitrary design

Mathematical modelling of the dynamic response of an implantable enhanced capacitive glaucoma pressure sensor

Glaucoma as an eye disease influences the optic nerve, resulting in progressive vision loss and, thus, blindness. For this disease, the most important risk factor is high intraocular pressure. Therefore, it is important to accurately measure the intraocular pressure. The present work aimes to present a mathematical description of a capacitive pressure sensor based on a Micro-Electro-Mechanical-Systems (MEMS) to measure intraocular pressure (IOP). The relatively high working bias voltage of MEMS capacitive pressure sensors restricts their potential applications as implantable sensors. Hence, Polydimethylsiloxane (PDMS) is employed as a porous elastomeric substance between the deformable and fixed electrodes of the capacitor. With a low young modulus and a higher dielectric constant, it reduces the sensor's working bias voltage. The PDMS's permittivity and young modulus are a function of the porosity volume fraction based on displacement in terms of a power law with fractional power constant. The dynamic equation of the microplate's transversal motion is used in the developed model, taking mid-plane stre-tching into account along with the generated force owing to the PDMS film squeezing. To decompose the governing nonlinear equation, a weak formulation is used with appropriate basis functions, thus integrating the attained ordinary differential equations over time. The sensor response to static pressure and step-wise alteration of the applied pressure is examined by dynamic and static analysis. The results of pull-in voltage reveal that using the PDMS as a dielectric causes a considerable reduction. Additionally, the effect of the PDMS elasticity on the capacitance and displacement was assessed along with the effects of the geometrical parameters on the sensor response

Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem

Abstract Physics-informed neural networks (PINNs) leverage data and knowledge about a problem. They provide a nonnumerical pathway to solving partial differential equations by expressing the field solution as an artificial neural network. This approach has been applied successfully to various types of differential equations. A major area of research on PINNs is the application to coupled partial differential equations in particular, and a general breakthrough is still lacking. In coupled equations, the optimization operates in a critical conflict between boundary conditions and the underlying equations, which often requires either many iterations or complex schemes to avoid trivial solutions and to achieve convergence. We provide empirical evidence for the mitigation of bad initial conditioning in PINNs for solving one-dimensional consolidation problems of porous media through the introduction of affine transformations after the classical output layer of artificial neural network architectures, effectively accelerating the training process. These affine physics-informed neural networks (AfPINNs) then produce nontrivial and accurate field solutions even in parameter spaces with diverging orders of magnitude. On average, AfPINNs show the ability to improve the $${\mathscr {L}}_2$$ L 2 relative error by $$64.84\%$$ 64.84 % after 25,000 epochs for a one-dimensional consolidation problem based on Biot’s theory, and an average improvement by $$58.80\%$$ 58.80 % with a transfer approach to the theory of porous media