95 research outputs found
Relation between Morphology and Chiroptical Properties in Chiral Conducting Polymer Films: A Case Study in Chiral PEDOT
The electronic properties of conducting polymers are influenced by their micro- and macrostructural orders, which can be tailored by substituent modification. However, while the effect of substituents on conducting polymers is extensively investigated, chiral substituents are far less studied. Furthermore, many chiral conducting polymers have regioirregular structures, which result in polymer films with inferior properties. In this work, we apply electronic circular dichroism (ECD) spectroscopy to study the morphological changes to the chiral polymers under different polymerization conditions. For this purpose, we investigated 3,4-ethylenedioxythiophene (EDOT) derivatives having two stereogenic centers on each monomer and bearing methyl or phenyl side groups (dimethyl-EDOT and diphenyl-EDOT, respectively). Polymerizing the enantiomerically pure monomers produces regioregular and stereoregular dimethyl-PEDOT and diphenyl-PEDOT, respectively. The effect of the electrolyte and solvent on polymer film morphology was studied using scanning electron microscopy (SEM) and ECD, showing a correlation between the polymer's morphology and the chiroptical properties of its films. We found that, for diphenyl-PEDOT, the combination of perchlorate anion electrolyte and acetonitrile solvent resulted in a unique morphology characterized by significant intermolecular interactions. These interactions were clearly observable in the ECD spectra in the form of exciton couplings, whose presence was supported by TD-DFT calculations. A small enantiomeric excess was sufficient to induce very intense ECD signals, demonstrating chiral amplification in electropolymerized films
A computationally efficient reduced order model to generate multi-parameter fluid-thermal databases
A reduced order model (ROM) is proposed to generate multi-parameter databases of some fluid-thermal problems, using a combination of proper orthogonal decomposition, a gradient-like method, and a continuation method. The resulting ROM greatly reduces the CPU time required by slower methods based on genetic algorithm formulations. As a byproduct, the number of required snapshots is also reduced, which yields an additional improvement of the computational efficiency. The work presented in this article aims to facilitate the use of ROMs in industrial environments, in which time is a very important asset. The methodology is illustrated with the non-isothermal flow past a backward-facing step in the laminar regime, which is a representative problem, related to the engineering design of micro-heat sinks
Spin-Dependent Enantioselective Electropolymerization
The electro-oxidative polymerization of an enantiopure chiral 3,4-ethylenedioxythiophene monomer, performed using spin-polarized currents, is shown to depend on the electron spin orientation. The spin-polarized current is shown to influence the initial nucleation rate of the polymerization reaction. This observation is rationalized in the framework of the chiral-induced spin selectivity effect
Reduced-order semi-implicit schemes for fluid-structure interaction problems
POD-Galerkin reduced-order models (ROMs) for fluid-structure interaction problems (incompressible fluid and thin structure) are proposed in this paper. Both the high-fidelity and reduced-order methods are based on a Chorin-Temam operator-splitting approach. Two different reduced-order methods are proposed, which differ on velocity continuity condition, imposed weakly or strongly, respectively. The resulting ROMs are tested and compared on a representative haemodynamics test case characterized by wave propagation, in order to assess the capabilities of the proposed strategies
Low rank approximation of multidimensional data
In the last decades, numerical simulation has experienced tremendous improvements driven by massive growth of computing power. Exascale computing has been achieved this year and will allow solving ever more complex problems. But such large systems produce colossal amounts of data which leads to its own difficulties. Moreover, many engineering problems such as multiphysics or optimisation and control, require far more power that any computer architecture could achieve within the current scientific computing paradigm. In this chapter, we propose to shift the paradigm in order to break the curse of dimensionality by introducing decomposition to reduced data. We present an extended review of data reduction techniques and intends to bridge between applied mathematics
community and the computational mechanics one. The chapter is organized into two parts. In the first one bivariate separation is studied, including discussions on the equivalence of proper orthogonal decomposition (POD, continuous framework) and singular value decomposition (SVD, discrete matrices). Then, in the second part, a wide review of tensor formats and their approximation is proposed. Such work has already been provided in
the literature but either on separate papers or into a pure applied
mathematics framework. Here, we offer to the data enthusiast scientist a description of Canonical, Tucker, Hierarchical and Tensor train formats including their approximation algorithms. When it is possible, a careful analysis of the link between continuous and discrete methods will be performed.IV Research and Transfer Plan of the University of SevillaInstitut CarnotJunta de AndalucÃaIDEX program of the University of Bordeau
Reduced basis approximation and a posteriori error estimation for the time-dependent viscous Burgers’ equation
In this paper we present rigorous a posteriori L 2 error bounds for reduced basis approximations of the unsteady viscous Burgers’ equation in one space dimension. The a posteriori error estimator, derived from standard analysis of the error-residual equation, comprises two key ingredients—both of which admit efficient Offline-Online treatment: the first is a sum over timesteps of the square of the dual norm of the residual; the second is an accurate upper bound (computed by the Successive Constraint Method) for the exponential-in-time stability factor. These error bounds serve both Offline for construction of the reduced basis space by a new POD-Greedy procedure and Online for verification of fidelity. The a posteriori error bounds are practicable for final times (measured in convective units) T≈O(1) and Reynolds numbers ν[superscript −1]≫1; we present numerical results for a (stationary) steepening front for T=2 and 1≤ν[superscript −1]≤200.United States. Air Force Office of Scientific Research (AFOSR Grant FA9550-05-1-0114)United States. Air Force Office of Scientific Research (AFOSR Grant FA-9550-07-1-0425)Singapore-MIT Alliance for Research and Technolog
General treatment of essential boundary conditions in reduced order models for non-linear problems
Model Order Reduction in Fluid Dynamics: Challenges and Perspectives
This chapter reviews techniques of model reduction of fluid dynamics systems. Fluid systems are known to be difficult to reduce efficiently due to several reasons. First of all, they exhibit strong nonlinearities — which are mainly related either to nonlinear convection terms and/or some geometric variability — that often cannot be treated by simple linearization. Additional difficulties arise when attempting model reduction of unsteady flows, especially when long-term transient behavior needs to be accurately predicted using reduced order models and more complex features, such as turbulence or multiphysics phenomena, have to be taken into consideration. We first discuss some general principles that apply to many parametric model order reduction problems, then we apply them on steady and unsteady viscous flows modelled by the incompressible Navier-Stokes equations. We address questions of inf-sup stability, certification through error estimation, computational issues and — in the unsteady case — long-time stability of the reduced model. Moreover, we provide an extensive list of literature references
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