Search

The ever-increasing complexity and environmental constraints associated with aerospace and defense projects are putting pressure on traditional organizational structures for design decision making. No longer can the design problem be assimilated and steered by a single chief engineer. Rather, to find consensus, design involves a complex iterative process between the different specialist committees (weights, aerodynamics, structures, cost, etc.). Furthermore, there is a growing need to take into account the life-cycle implications of low-level design decisions in addition to their more direct implications on performance. The Decision Environment for Complex Designs project at the University of Southampton aims to explore how the design process may be streamlined under these circumstances. The case study presented here centers around a maritime surveillance unmanned aerial vehicle for search and rescue tasks, developed in association with the U.K. Coastguard. The focus is on "value-driven" design and how advances in software engineering, rationale capture, operational simulation, and rapid prototyping can be leveraged to create an integrated design suite that allows the rapid design and manufacture of low-cost civilian unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

Behavioral models such as macromodels, surrogate models, metamodels, and response surface models have many applications in diverse research domains such as aerodynamics [1], hydrology [2], mechanical engineering [3], and many more. When considering the design flow of electronic devices, these models are often used to characterize the time- or frequency- dependent behavior of an electronic component while taking all electromagnetic (EM) phenomena into account: crosstalk, attenuation, dispersion, and coupling effects for example [4]. Such models are of crucial importance for efficient design space exploration, design optimization, and sensitivity analysis [5], [6]. A key advantage is that they are calculated independently of the device's physics and that they are valid for over a wide range of design variables, taking into account multiple geometrical layout or substrate features. Additionally, the models can easily be linked together in a model cascade. [ABSTRACT FROM PUBLISHER]

Many complex real-world systems can be accurately modeled by simulations. However, high-fidelity simulations may take hours or even days to compute. Because this can be impractical, a surrogate model is often used to approximate the dynamic behavior of the original simulator. This model can then be used as a cheap, drop-in replacement for the simulator. Because simulations can be very expensive, the data points, which are required to build the model, must be chosen as optimally as possible. Sequential design strategies offer a huge advantage over one-shot experimental designs because they can use information gathered from previous data points in order to determine I lie location of new data points. Each sequential design strategy must perform a trade-off between exploration and exploitation, where the former involves selecting data points in unexplored regions of the design space, while the latter suggests adding data points in regions which were previously identified to be interesting (for example, highly nonlinear regions). In this paper, a novel hybrid sequential design strategy is proposed which uses a Monte Carlo-based approximation of a Voronoi tessellation for exploration and local linear approximations of the simulator for exploitation. The advantage of this method over other sequential design methods is that it is independent of the model type, and can therefore be used in heterogeneous modeling environments, where multiple model types are used at the same time. The new method is demonstrated on a number of test problems, showing that it is a robust, competitive, and efficient sequential design strategy. [ABSTRACT FROM AUTHOR]

This paper presents a new advance in Neuro-space mapping (Neuro-SM) techniques for modeling nonlinear microwave devices. Suppose that existing device models (namely, coarse models) cannot match the behavior of a new device (referred to as the fine model). By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. However, the efficiency of mapping depends on both the mapping structure and the coarse model. In this paper, a structural optimization technique is presented to achieve optimal combinations of mapping structure and coarse model. An aggressive optimization formulation exploring detailed structural variations in both the mapping and the coarse model is proposed, where the internal branches of coarse models and separate mappings for the voltage and current at gate and drain are used as basic topology variables. The formulation of such a structural optimization by an evolutionary optimization algorithm is proposed. Numerical examples of metal–semiconductor field-effect transistor and high electron-mobility transistor modeling demonstrate that, by using the proposed algorithm, optimal combinations of space mapping and coarse model structures can be achieved leading to the best modeling accuracy with the simplest mapping function. [ABSTRACT FROM AUTHOR]

Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist (Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm (heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type. [ABSTRACT FROM AUTHOR]