The aim of this study is to find the cost design of RC tension with varying conditions using the Artificial Neural Network. Design constraints were used to cover all reliable design parameters, such as limiting cross sectional dimensions and; their reinforcement ratio and even the beahviour of optimally designed sections. The design of the RC tension members were made using Indian and European standard specifications which were discussed. The designed tension members according to both codes satisfy the strength and serviceability criteria. While no literature is available on the optimal design of RC tension members, the cross-sectional dimensions of the tension membersfor different grades of concrete and steel, and area of formwork are considered as the variables in the present optimum design model. A design example is explained and the results are presented. It is concluded that the proposed optimum design model yields rational, reliable, and practical designs.
The box wing system is an unconventional way to connect the lifting surfaces that the designers willingly to use in prototypes of new aircrafts. The article present a way to quickly optimize the wing structure of box wing airplane that can be useful during conceptual design. At the beginning, there is presented theory and methods used to code optimization program. Structure analysis is based on FEM beam model, which is sufficient in conceptual design. Optimization is performed using hybrid method, connection of simple iteration and gradient descent methods. Finally, the program is validated by case study.
Zinc plant residue is a hazardous waste which contains high quantity of nickel and other valuable metals. Process parameters such as reaction time, acid concentration, solid-liquid ratio, particle size, stirring speed and temperature for nickel extraction from this waste were optimized using factorial design. Main effects and their interactions were obtained by the analysis of variance ANOVA. Empirical regression model was obtained and used to predict nickel extraction with satisfactory results and to describe the relationship between the predicted results and the experiment results. The important parameters for maximizing nickel extraction were identifi ed to be a leaching time solid-liquid ratio and acid concentration. It was found that above 90% of nickel could be extracted in optimum conditions.
In the paper an application of evolutionary algorithm to design and optimization of combinational digital circuits with respect to transistor count is presented. Multiple layer chromosomes increasing the algorithm efficiency are introduced. Four combinational circuits with truth tables chosen from literature are designed using proposed method. Obtained results are in many cases better than those obtained using other methods.
The article discusses the weldment to casting conversion process of rocker arm designed for operation in a special purpose vehicle to obtain a consistency of objective functions, which assume the reduced weight of component, the reduced maximum effort of material under the impact of service loads achieved through topology modification for optimum strength distribution in the sensitive areas, and the development of rocker arm manufacturing technology. As a result of conducted studies, the unit weight of the item was reduced by 25%, and the stress limit values were reduced to a level guaranteeing safe application.
This work examines the reduced-cost design optimization of dual- and multi-band antennas. The primary challenge is independent yet simultaneous control of the antenna responses at two or more frequency bands. In order to handle this task, a feature-based optimization approach is adopted where the design objectives are formulated on the basis of the coordinates of so-called characteristic points (or response features) of the antenna response. Due to only slightly nonlinear dependence of the feature points on antenna geometry parameters, optimization can be attained at a low computational cost. Our approach is demonstrated using two antenna structures with the optimum designs obtained in just a few dozen of EM simulations of the respective structure.
In this paper, a novel structure of a compact UWB slot antenna and its design optimization procedure has been presented. In order to achieve a sufficient number of degrees of freedom necessary to obtain a considerable size reduction rate, the slot is parameterized using spline curves. All antenna dimensions are simultaneously adjusted using numerical optimization procedures. The fundamental bottleneck here is a high cost of the electromagnetic (EM) simulation model of the structure that includes (for reliability) an SMA connector. Another problem is a large number of geometry parameters (nineteen). For the sake of computational efficiency, the optimization process is therefore performed using variable-fidelity EM simulations and surrogate-assisted algorithms. The optimization process is oriented towards explicit reduction of the antenna size and leads to a compact footprint of 199 mm2 as well as acceptable matching within the entire UWB band. The simulation results are validated using physical measurements of the fabricated antenna prototype.
The parameters of sigma-delta audio DAC depend mainly on digital sigma-delta modulator's features, especially on its noise transfer function (NTF). Many methods of design and optimization of the loop filter's coefficients in sigma-delta modulators have been proposed so far. These methods enable the designer to get suitable noise transfer functions for specific application. This paper reviews NTF design and optimization methods which are particularly useful in audio applications.
Compact radiators with circular polarization are important components of modern mobile communication systems. Their design is a challenging process which requires maintaining simultaneous control over several performance figures but also the structure size. In this work, a novel design framework for multi-stage constrained miniaturization of antennas with circular polarization is presented. The method involves se- quential optimization of the radiator in respect of selected performance figures and, eventually, the size. Optimizations are performed with iteratively increased number of design constraints. Numerical efficiency of the method is ensured using a fast local-search algorithm embedded in a trust-region framework. The proposed design framework is demonstrated using a compact planar radiator with circular polarization. The optimized antenna is characterized by a small size of 271 mm2 with 37% and 47% bandwidths in respect of 10 dB return loss and 3 dB axial ratio, respectively. The structure is benchmarked against the state-of-the-art circular polarization antennas. Numerical results are confirmed by measurements of the fabricated antenna prototype.
Internet of Things (IoT) will play an important role in modern communication systems. Thousands of devices will talk to each other at the same time. Clearly, smart and efficient hardware will play a vital role in the development of IoT. In this context, the importance of antennas increases due to them being essential parts of communication networks. For IoT applications, a small size with good matching and over a wide frequency range is preferred to ensure reduced size of communication devices. In this paper, we propose a structure and discuss design optimization of a wideband antenna for IoT applications. The antenna consists of a stepped-impedance feed line, a rectangular radiator and a ground plane. The objective is to minimize the antenna footprint by simultaneously adjusting all geometry parameters and to maintain the electrical characteristic of antenna at an acceptable level. The obtained design exhibits dimensions of only 3.7 mm × 11.8 mm and a footprint of 44 mm2, an omnidirectional radiation pattern, and an excellent pattern stability. The proposed antenna can be easily handled within compact communication devices. The simulation results are validated through measurements of the fabricated antenna prototype.
Re-design of a given antenna structure for various substrates is a practically important issue yet non trivial, particularly for wideband and ultra-wideband antennas. In this work, a technique for expedited redesign of ultra-wideband antennas for various substrates is presented. The proposed approach is based on inverse surrogate modeling with the scaling model constructed for several reference designs that are optimized for selected values of the substrate permittivity. The surrogate is set up at the level of coarse-discretization EM simulation model of the antenna and, subsequently, corrected to provide prediction at the high-fidelity EM model level. The dimensions of the antenna scaled to any substrate permittivity within the region of validity of the surrogate are obtained instantly, without any additional EM simulation necessary. The proposed approach is demonstrated using an ultra-wideband monopole with the permittivity scaling range from 2.2 to 4.5. Numerical validation is supported by physical measurements of the fabricated prototypes of the re-designed antennas.
There were two aims of the research. One was to enable more or less automatic confirmation of the known associations – either quantitative or qualitative – between technological data and selected properties of concrete materials. Even more important is the second aim – demonstration of expected possibility of automatic identification of new such relationships, not yet recognized by civil engineers. The relationships are to be obtained by methods of Artificial Intelligence, (AI), and are to be based on actual results from experiments on concrete materials. The reason of applying the AI tools is that in Civil Engineering the real data are typically non perfect, complex, fuzzy, often with missing details, which means that their analysis in a traditional way, by building empirical models, is hardly possible or at least can not be done quickly. The main idea of the proposed approach was to combine application of different AI methods in a one system, aimed at estimation, prediction, design and/or optimization of composite materials. The paradigm of the approach is that the unknown rules concerning the properties of concrete are hidden in experimental results and can be obtained from the analysis of examples. Different AI techniques like artificial neural networks, machine learning and certain techniques related to statistics were applied. The data for the analysis originated from direct observations and from reports and publications on concrete technology. Among others it has been demonstrated that by combining different AI methods it is possible to improve the quality of the data, (e.g. when encountering outliers and missing values or in clustering problems), so that the whole data processing system will be giving better prediction, (when applying ANNs), or the newly discovered rules will be more effective, (e.g. with descriptions more complete and – at the same time – possibly more consistent, in case of ML algorithms).