Robust topology optimization of optical cloaks under uncertainties in wave number and angle of incident wave

This article presents a robust topology optimization method for optical cloaks under uncertainties in the wave number and angle in the incident wave. We first discuss the governing equation derived from Maxwell's equation, and extend it to the entire domain including the dielectric material and air, based on the level set-based topology optimization method. Next, a robust optimization problem is formulated as a minimization problem of the weighted sum of the scattered wave norm and its standard deviation with respect to the wave number and angle of the incident wave. The standard deviation is mathematically expressed by the Taylor series approximation and the use of the adjoint variable method. The design sensitivity of the objective functional is also derived by the adjoint variable method. An optimization algorithm is then constructed, based on the proposed formulation for robust designs of optical cloaks. Several numerical examples are finally provided to demonstrate the validity and utility of the proposed method.     

Topology optimization for thermal conductors considering design-dependent effects,including heat conduction and convection

In structural designs considering thermal loading, in addition to heat conduction within the structure, the heat convection upon the structure’s surface can significantly influence optimal design configurations. In this paper, we focus on the influence of design-dependent effects upon heat convection and internal heat generation for optimal designs developed using a topology optimization scheme. The method for extracting the structural boundaries for heat convection loads is constructed using a Hat function, and heat convection shape dependencies are taken into account in the heat transfer coefficient using a surrogate model. Several numerical examples are presented to confirm the usefulness of the proposed method.     

Bubbles in SiC crystals formed by helium ion irradiation at high temperatures

Helium bubbles were found to be formed in SiC crystals by irradiation with He⁺ ions at 1000 to 1200° C. The size of bubbles increased with increasing irradiation temperatures.

The density of helium atoms in the bubbles was measured to be about 10²⁸ atoms/m³ by EELS measurement in combination with electron microscopic observation in the same selected areas, and the internal pressure of the bubbles was estimated therefrom to be on the order of 10⁸ Pa at room temperature.     

Plant calcium-dependent protein kinase-related kinases (CRKs) do not require calcium for their activities

In plants, calcium-dependent protein kinases (CDPKs) make up a large family that is characterized by a C-terminal calmodulin(CaM)-like domain. Recently, a novel carrot cDNA clone encoding an atypical CDPK, which has a significantly degenerate sequence in the CaM-like domain, was found and named CDPK-related protein kinase (CRK) [Lindzen, E. and Choi, J.H. (1995) Plant Mol. Biol. 28, 785-797]. We obtained two different cDNA clones from maize which encode CRKs. For the first enzymatic characterization of CRK, a maize cDNA clone was expressed in E. coli. The recombinant protein efficiently phosphorylated casein, a conventional protein substrate. Notably, in this in vitro phosphorylation assay, the kinase activity did not require calcium as an activator. Thus, CRKs were suggested to be novel calcium-independent protein kinases having a degenerate CaM domain, the function of which remains to be elucidated.     

Voltage and reactive power control using recurrent neural networks

Many controllers using neural networks have been recently developed. The hybrid and direct types are two major categories of such controllers. While the first type tunes the parameters of the conventional controller by means of neural networks, the latter constructs the controller by learning the inverse dynamics of the control target. Electric power systems require voltage and reactive power (VQ) control to avoid voltage collapse. The conventional VQ control, however, meets this requirement unsatisfactorily because the control is only approximate. In this paper, we will propose a new algorithm for VQ control using recurrent neural networks (RNNs), which have the ability to deal with the system's controlled target by means of RNNs. Second, we will apply this algorithm to VQ control. We will call this controller “neuroVQC.” Finally, the usefulness of the neuroVQC will be shown by comparison with the conventional VQ controller.     

Orthotropic material orientation optimization method in composite laminates

This paper proposes a optimization method that is capable of simultaneous design of multiple layers in a composite laminate with respect to multiple objective functions. The optimization process obtains a continuous orientation of an orthotropic material for each layer of the laminate. Each layer by itself is a single design domain, which allows multiple domains to be stacked in various orientations. Multiple optimization objectives are considered resulting in layers that perform different functions. The layers are modeled within a three-dimensional structure and by discretizing the structure using three-dimensional elements, the interaction between individual layers can be modeled. This also allows the optimization method to obtain a three-dimensional orientation vector. In this study, the individual layers are assumed to be thin, limiting the orientation vector to the mid-plane of the layer. The optimization model is tested on a two-layer laminate in which one layer is optimized for thermal control by directing heat toward specified sections while shielding other sections and the second layer is optimized to reduce the total deformation of the laminate structure that results from the thermal load. The results of simultaneous optimization for both layers are shown for several different configurations of boundary conditions.     

Reliability-based topology optimization under shape uncertainty modeled in Eulerian description

Structural and Multidisciplinary Optimization - This paper presents a reliability-based topology optimization method under geometrical uncertainties. First, we briefly introduce the concept of...     

Shape and topology optimization based on the convected level set method

The aim of this research is to construct a shape optimization method based on the convected level set method, in which the level set function is defined as a truncated smooth function obtained by using a sinus filter based on a hyperbolic tangent function. The local property of the hyperbolic tangent function dramatically reduces the generation of red the error between the specified profile of the hyperbolic tangent function and the level set function that is updated using a time evolution equation. In addition, the small size of the error facilitates the use of convective reinitialization, whose basic idea is that the reinitialization is embedded in the time evolution equation, whereas such treatment is typically conducted in a separate calculation in conventional level set methods. The convected level set method can completely avoid the need for additional calculations when performing reinitialization. The validity and effectiveness of our presented method are tested with a mean compliance minimization problem and a problem for the design of a compliant mechanism.