Strain‐Visualization with Ultrasensitive Nanoscale Crack‐Based Sensor Assembled with Hierarchical Thermochromic Membrane

As eidetic signal recognition has become important, displaying mechanical signals visually has imposed huge demands for simple readability and without complex signal processing. Such visualization of mechanical signals is used in delicate urgent medical or safety‐related industries. Accordingly, chromic materials are considered to facilitate visualization with multiple colors and simple process. However, the response and recovery time is very long, such that rapid regular signals are unable to be detected, i.e., physiological signals, such as respiration. Here, the simple visualization of low strain ≈2%, with ultrasensitive crack‐based strain sensors with a hierarchical thermochromic layer is suggested. The sensor shows a gradient color change from red to white color in each strain, which is attributed to the hierarchical property, and the thermal response (recovery) time is dramatically minimized within 0.6 s from 45 to 37 °C, as the hierarchical membrane is inspired by termite mounds for efficient thermal management. The fast recovery property can be taken advantage of in medical fields, such as monitoring regular respiration, and the color changes can be delicately monitored with high accuracy by software on a mobile phone.     

Coevolutionary augmented Lagrangian methods for constrainedoptimization

This paper introduces a coevolutionary method developed for solving constrained optimization problems. This algorithm is based on the evolution of two populations with opposite objectives to solve saddle-point problems. The augmented Lagrangian approach is taken to transform a constrained optimization problem to a zero-sum game with the saddle point solution. The populations of the parameter vector and the multiplier vector approximate the zero-sum game by a static matrix game, in which the fitness of individuals is determined according to the security strategy of each population group. Selection, recombination, and mutation are done by using the evolutionary mechanism of conventional evolutionary algorithms such as evolution strategies, evolutionary programming, and genetic algorithms. Four benchmark problems are solved to demonstrate that the proposed coevolutionary method provides consistent solutions with better numerical accuracy than other evolutionary methods     

Design and analysis of optimal controller for fuzzy systems with input constraint

In this paper, we present a design method of the optimal and robust controller subject to the constraint on control inputs for continuous-time Takagi-Sugeno (TS) fuzzy systems. In order to establish this design method, we consider an optimal and robust control problem for nonlinear dynamic systems. For this problem, we present an analytic way which can provide the optimal controller for nonlinear dynamic systems by the dynamic programming approach and the inverse optimal approach. Moreover, we analyze the robustness property of the proposed optimal controller with respect to a class of input uncertainties by the passivity approach. Then, based on the theoretical results presented in this paper, we formulate the design problem of the optimal and robust controller with input constraint for continuous-time TS fuzzy systems as the semidefinite programming problem, and find the controller by solving it. The usefulness of the proposed approach is illustrated by considering the three-axis attitude stabilization problem of rigid spacecraft.     

Target tracking problems subject to kinematic constraints

Filtering problems with kinematic constraints which may arise in target tracking problems are considered. A novel approach which treats kinematic constraints as additional fictitious or pseudomeasurements is proposed. A numerical example is provided to show the technical feasibility of the proposed idea for target tracking problems. This example shows that the proposed method can improve estimation accuracy significantly for velocity and acceleration states in the tracking problem. However, it is noted that the tracking performance may be deteriorated if the constraints do not properly represent the target characteristics and a small R_c is chosen     

Hessian approximation algorithms for hybrid optimization methods

This article introduces Hessian approximation algorithms to estimate the search direction of the quasi-Newton methods for solving optimization problems of continuous parameters. The proposed algorithms are quite different from other well-known quasi-Newton methods, such as symmetric rank-one, Davidon–Fletcher–Powell, and Broyden–Fletcher–Goldfarb–Shanno, in that the Hessian matrix is not calculated from the gradient information, rather directly from the function values. The proposed algorithms are designed for a class of hybrid algorithms that combine evolutionary search with the gradient-based methods of quasi-Newton type. The function values calculated for the evolutionary search are used for estimation of the Hessian matrix (or its inverse) as well as the gradient vector. Since the estimation process of the Hessian matrix is independent of that of the gradient vector, more reliable Hessian estimation with a small population is possible compared with the previous methods based upon the classical quasi-Newton methods. Numerical experiments show that the proposed algorithms are very competitive with state-of-the-art evolutionary algorithms for continuous optimization problems.     

New Structure for an Aerodynamic Fin Control System for Tail Fin-Controlled STT Missiles

This paper presents a new design method for an aerodynamic fin-control system for tail-fin controlled skid to turn (STT) missiles. The performance of the actuation system plays a decisive role in determining the performance of the flight control system for a highly maneuverable missile. To control the missiles by aerodynamics, control surfaces, sometimes called fins, are used. Deflection angles of these fins are the control variables of aerodynamics, but aerodynamicists prefer to use analytic variables called aileron, elevator, and rudder instead of these physical variables because these three analytic variables dominantly influence the roll, pitch, and yaw motion of the missile, respectively; and each can be considered a linear combination of four fin deflection angles. On that basis, roll, pitch, and yaw autopilots for controlling the attitudes or lateral acceleration of the missile are designed, and aileron, elevator, and rudder commands, respectively, are generated as consequence outputs of each autopilot. In the existing fin-actuation control scheme for the typical tail-fin controlled cruciform missiles, these outputs are distributed to four fin defection commands. After that, the four fins are actuated by fin controllers so that their deflections follow the commands. This paper shows that such control schemes can cause a significant deterioration in flight control system performance when fin-actuators have certain physical constraints such as slew rate, voltage, or current limit, or have an uncertainty of actuator dynamics, and proposes a new control scheme that alleviates such problems. This scheme can be widely applied to various fin-actuation control systems. But in this paper, for convenience, a tail-fin controlled cruciform missile is used as the example, and the proposed control scheme is shown to give better performance than the existing one.     

Efficiency improvement of organic solar cells by tuning hole transport layer with germanium oxide

Improving optical property is critical for optimizing the power conversion efficiency of organic solar cells. In the present research, we show that modification of poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) layer with GeO2 leads to 15% improvement of power conversion efficiency in a polymer solar cells through enhancement of short circuit currents. Modified PEDOT:PSS layer with optimized concentration of GeO2 assists active layer absorbing much light by playing a role of optical spacer. Using AFM and grazing incidence X-ray diffraction (GIXD) data, we also present the evidence that an addition of GeO2 does not affect crystallinity of active layer.     

Artificial neural network modeling for fission gas release in LWR UO2 fuel under RIA conditions

A fission gas release (FGR) model was developed by using an artificial neural network method to predict fission gas release in UO₂ fuel under reactivity initiated accident (RIA) conditions. Based on the test data obtained in the CABRI test reactor and nuclear safety research reactor, the model takes into account the effect of the five parameters: pellet average burnup, peak fuel enthalpy, the ratio of peak fuel enthalpy to pulse width, fission gas release during base-irradiation, and grain size of a fuel pellet. The parametric study of the model, producing a physically reasonable trend of FGR for each parameter, shows that the pellet average burnup and the ratio of peak fuel enthalpy to pulse width are two of the most important parameters. Depending on the combination of input values for the five parameters, the application of the model to a fuel rod under typical RIA conditions of light water reactor produces 1.7-14.0% of FGR for the pellet average burnup ranging from 20 to 70 MW d/kg U.     

Evaluation of the effective thermal conductivity of UO2 fuel by combining Potts model and finite difference method

This paper evaluated the effects of porosity on the effective thermal conductivity of UO₂ fuel by combining the Potts model and the finite difference method (FDM). Two types of microstructures representing irradiated UO₂ microstructures were simulated by the Potts model in the three dimensional cubic system. One represented very small intragranular bubbles and a few intergranular bubbles under a low temperature condition. The other represented large intergranular bubbles under a high temperature or annealing condition. For the simulated microstructures, the effective thermal conductivities were determined by FDM calculation of the temperature distributions under steady state condition. They were compared with an experimental equation and the effect of bubble morphology was investigated by fitting a porosity shape factor in the Maxwell-Eucken equation. The simulation results showed a good agreement with an experimental equation and demonstrated the capability of the Potts model to provide information on microstructure for calculating the effective thermal conductivity of UO₂ fuel.