Deep Neural Network and Monte Carlo Tree Search applied to Fluid-Structure Topology Optimization

This paper shows the application of Deep Neural Network algorithms for Fluid-Structure Topology Optimization. The strategy offered is a new concept which can be added to the current process used to study Topology Optimization with Cellular Automata, Adjoint and Level-Set methods. The design space is described by a computational grid where every cell can be in two states: fluid or solid. The system does not require human intervention and learns through an algorithm based on Deep Neural Network and Monte Carlo Tree Search. In this work the objective function for the optimization is an incompressible fluid solver but the overall optimization process is independent from the solver. The test case used is a standard duct with back facing step where the optimizer aims at minimizing the pressure losses between inlet and outlet. The results obtained with the proposed approach are compared to the solution via a classical adjoint topology optimization code

Neural network topology for wind turbine analysis

In this work Artificial Neural Networks (ANN) are used for a multi-target optimization of the aerodynamics of a wind turbine blade. The Artificial Neural Network is used to build a meta-model of the blade, which is then optimized according to the imposed criteria. The neural networks are trained with a data set built by a series of CFD simulations and their configuration (number of neurons and layers) selected to improve performances and avoid over-fitting. The basic configuration of the airfoil is the profile S809, which is commonly used in horizontal axis wind turbines (HAWT), equipped with a Coanda jet. The design position and momentum of the jet are optimized to maximize aerodynamic efficiency and minimize the power required to activate the Coanda Jet

Local bi-fidelity field approximation with knowledge based neural networks for computational fluid dynamics

This work presents a machine learning based method for bi-fidelity modelling. The method, a Knowledge Based Neural Network (KBaNN), performs a local, additive correction to the outputs of a coarse computational model and can be used to emulate either experimental data or the output of a more accurate, but expensive, computational model. An advantage of the method is that it can scale easily with the number of input and output features. This allows bi-fidelity modelling approaches to be applied to a wide variety of problems, for instance in the bi-fidelity modelling of fields. We demonstrate this aspect in this work through an application to Computational Fluid Dynamics, in which local corrections to a velocity field are performed by the KBaNN to account for mesh effects. KBaNNs were trained to make corrections to the free-stream velocity field and the boundary layer. They were trained on a limited data-set consisting of simple two-dimensional flows. The KBaNNs were then tested on a flow over a more complex geometry, a NACA 2412 airfoil. It was demonstrated that the KBaNNs were still able to provide a local correction to the velocity field which improved its accuracy. The ability of the KBaNNs to generalise to flows around new geometries that share similar physics is encouraging. Through knowledge based neural networks it may be possible to develop a system for bi-fidelity, computer based design which uses data from past simulations to inform its predictions

Measurements of the Complex Conductivity of NbxSi1-x Alloys on the Insulating Side of the Metal-Insulator Transition

We have conducted temperature and frequency dependent transport measurements in amorphous Nb_x Si_{1-x} samples in the insulating regime. We find a temperature dependent dc conductivity consistent with variable range hopping in a Coulomb glass. The frequency dependent response in the millimeter-wave frequency range can be described by the expression $sigma(omega) \propto (-\imath omega)^alpha$ with the exponent somewhat smaller than one. Our ac results are not consistent with extant theories for the hopping transport.Comment: 4 pages with 3 figures; published version has a different title from original (was: "Electrodynamics in a Coulomb glass"

Spin-Waves in the Mid-Infrared Spectrum of Antiferromagnetic YBa2_2Cu3_3O6.0_{6.0}

The mid-infrared spin-wave spectrum of antiferromagnetic YBa$_2$Cu$_3$O$_{6.0}$\ was determined by infrared transmission and reflection measurements (\bbox{k} \!\! \parallel c) at $T\!=\!10\!$~K.\@ Excitation of single magnons of the optical branch was observed at $E_{\text{op}}\!=\!178.0\!$~meV.\@ Two further peaks at $346\!$~meV ($\approx\!1.94\,E_{\text{op}}$) and $470\!$~meV ($\approx\!2.6\,E_{\text{op}}$) both belong to the two-magnon spectrum. Linear spin wave theory is in good agreement with the measured two-magnon spectrum, and allows to determine the exchange constant $J$ to be about $120\!$~meV, whereas the intrabilayer coupling $J_{12}$ is approximately $0.55\,J$.Comment: 3 figures in uuencoded for

The metallic state in disordered quasi-one-dimensional conductors

The unusual metallic state in conjugated polymers and single-walled carbon nanotubes is studied by dielectric spectroscopy (8--600 GHz). We have found an intriguing correlation between scattering time and plasma frequency. This relation excludes percolation models of the metallic state. Instead, the carrier dynamics can be understood in terms of the low density of delocalized states around the Fermi level, which arises from the competion between disorder-induced localization and interchain-interactions-induced delocalization.Comment: 4 pages including 4 figure

Determining factors of thermoelectric properties of semiconductor nanowires

It is widely accepted that low dimensionality of semiconductor heterostructures and nanostructures can significantly improve their thermoelectric efficiency. However, what is less well understood is the precise role of electronic and lattice transport coefficients in the improvement. We differentiate and analyze the electronic and lattice contributions to the enhancement by using a nearly parameter-free theory of the thermoelectric properties of semiconductor nanowires. By combining molecular dynamics, density functional theory, and Boltzmann transport theory methods, we provide a complete picture for the competing factors of thermoelectric figure of merit. As an example, we study the thermoelectric properties of ZnO and Si nanowires. We find that the figure of merit can be increased as much as 30 times in 8-Å-diameter ZnO nanowires and 20 times in 12-Å-diameter Si nanowires, compared with the bulk. Decoupling of thermoelectric contributions reveals that the reduction of lattice thermal conductivity is the predominant factor in the improvement of thermoelectric properties in nanowires. While the lattice contribution to the efficiency enhancement consistently becomes larger with decreasing size of nanowires, the electronic contribution is relatively small in ZnO and disadvantageous in Si

Three-dimensional fluid topology optimization for heat transfer

In this work, an in house topology optimization (TO) solver is developed to optimize a conjugate heat transfer problem: realizing more complex and efficient coolant systems by minimizing pressure losses and maximizing the heat transfer. The TO method consists in an idealized sedimentation process in which a design variable, in this case impermeability, is iteratively updated across the domain. The optimal solution is the solidified region uniquely defined by the final distribution of impermeability. Due to the geometrical complexity of the optimal solutions obtained, this design method is not always suitable for classic manufacturing methods (molding, stamping....) On the contrary, it can be thought as an approach to better and fully exploit the flexibility offered by additive manufacturing (AM), still often used on old and less efficient design techniques. In the present article, the proposed method is developed using a Lagrangian optimization approach to minimize stagnation pressure dissipation while maximizing heat transfer between fluid and solid region. An impermeability dependent thermal conductivity is included and a smoother operator is adopted to bound thermal diffusivity gradients across solid and fluid. Simulations are performed on a straight squared duct domain. The variability of the results is shown on the basis of different weights of the objective functions. The solver builds automatically three-dimensional structures enhancing the heat transfer level between the walls and the flow through the generation of pairs of counter rotating vortices. This is consistent to solution proposed in literature like v-shaped ribs, even if the geometry generated is more complex and more efficient. It is possible to define the desired level of heat transfer and losses and obtain the closest optimal solution. It is the first time that a conjugate heat transfer optimization problem, with these constraints, has been tackled with this approach for three-dimensional geometries

A hybrid approach combining DNS and RANS simulations to quantify uncertainties in turbulence modelling

Uncertainty quantification (UQ) has recently become an important part of the design process of countless engineering applications. However, up to now in computational fluid dynamics (CFD) the errors introduced by the turbulent viscosity models in Reynolds-Averaged Navier Stokes (RANS) models have often been neglected in UQ studies. Although Direct Numerical Simulations (DNS) are physically correct, obtaining a large enough set of DNS data for UQ studies is currently computationally intractable. UQ based only on RANS simulations or on DNS thus leads to physical and statistical inaccuracies in the output probability distribution functions (PDF). Therefore, three hybrid methods combining both RANS simulations and DNS to perform non-intrusive UQ are suggested in this work. Low-fidelity RANS simulations and high-fidelity DNS are combined to give an approximation of an output PDF using the advantages of both data sets: the physical accuracy via the DNS and the statistical accuracy via the RANS simulations. The hybrid methods are applied to the flow over 2D periodically arranged hills. It is shown that the Gaussian CoKriging (GCK) method is the best hybrid method and that a non-intrusive hybrid UQ approach combining both DNS and RANS simulations is possible, with both physically more accurate and statistically better PDF