Surrogate modeling of impedance matching transformers by means of variable‐fidelity electromagnetic simulations and nested cokriging

Accurate performance evaluation of microwave components can be carried out using full‐wave electromagnetic (EM) simulation tools, routinely employed for circuit verification but also in the design process itself. Unfortunately, the computational cost of EM‐driven design may be high. This is especially pertinent to tasks entailing considerable number of simulations (eg, parametric optimization, statistical analysis). A possible way of alleviating these difficulties is utilization of fast replacement models, also referred to as surrogates. Notwithstanding, conventional modeling methods exhibit serious limitations when it comes to handling microwave components. The principal challenges include large number of geometry and material parameters, highly nonlinear characteristics, as well as the necessity of covering wide ranges of operating conditions. The latter is mandatory from the point of view of the surrogate model utility. This article presents a novel modeling approach that incorporates variable‐fidelity EM simulations into the recently reported nested kriging framework. A combination of domain confinement due to nested kriging, and low‐/high‐fidelity EM data blending through cokriging, enables the construction of reliable surrogates at a fraction of cost required by single‐fidelity nested kriging. Our technique is validated using a three‐section miniaturized impedance matching transformer with its surrogate model rendered over wide range of operating frequencies. Comprehensive benchmarking demonstrates superiority of the proposed method over both conventional models and nested kriging.     

An adaptive algorithm for anomaly and novelty detection in evolving data streams

In the era of big data, considerable research focus is being put on designing efficient algorithms capable of learning and extracting high-level knowledge from ubiquitous data streams in an online fashion. While, most existing algorithms assume that data samples are drawn from a stationary distribution, several complex environments deal with data streams that are subject to change over time. Taking this aspect into consideration is an important step towards building truly aware and intelligent systems. In this paper, we propose GNG-A, an adaptive method for incremental unsupervised learning from evolving data streams experiencing various types of change. The proposed method maintains a continuously updated network (graph) of neurons by extending the Growing Neural Gas algorithm with three complementary mechanisms, allowing it to closely track both gradual and sudden changes in the data distribution. First, an adaptation mechanism handles local changes where the distribution is only non-stationary in some regions of the feature space. Second, an adaptive forgetting mechanism identifies and removes neurons that become irrelevant due to the evolving nature of the stream. Finally, a probabilistic evolution mechanism creates new neurons when there is a need to represent data in new regions of the feature space. The proposed method is demonstrated for anomaly and novelty detection in non-stationary environments. Results show that the method handles different data distributions and efficiently reacts to various types of change.     

Optimization‐Driven Antenna Design Framework With Multiple Performance Constraints

This work proposes an optimization‐driven framework that allows for antenna design with multiple constraints and tradeoff investigation between various objectives. In particular, it allows for antenna size reduction while maintaining other figures of merit (in terms of both electrical and field properties). We demonstrate our framework with UWB antennas size reduction taking into account matching, gain, efficiency, and radiation pattern stability constraints. Furthermore, we compare design tradeoffs between the minimum attainable antenna size and its electrical and field properties using seven design scenarios. The best possible tradeoffs are obtained using rigorous numerical optimization of all geometry parameters of the structure at hand. Two examples of UWB monopole antennas are provided to illustrate that the qualitative performance tradeoffs are very much dependent on a particular antenna topology and material properties of the substrate. Numerical results are supported by experimental validation.     

Generalized Pareto ranking bisection for computationally feasible multiobjective antenna optimization

Multiobjective optimization (MO) allows for obtaining comprehensive information about possible design trade‐offs of a given antenna structure. Yet, executing MO using the most popular class of techniques, population‐based metaheuristics, may be computationally prohibitive when full‐wave EM analysis is utilized for antenna evaluation. In this work, a low‐cost and fully deterministic MO methodology is introduced. The proposed generalized Pareto ranking bisection algorithm permits identifying a set of Pareto optimal sets of parameters representing the best trade‐offs between considered objectives. The subsequent designs are found by iterative partitioning of the intervals connecting previously obtained designs and executing Pareto‐ranking‐based poll search. The initial approximation of the Pareto front found using the bisection procedure is subsequently refined to the level of the high‐fidelity EM model of the antenna at hand using local optimization. The proposed framework overcomes a serious limitation of the original, recently reported, bisection algorithm, which was only capable of considering two objectives. The generalized version proposed here allows for handling any number of design goals. An improved poll search procedure has also been developed and incorporated. Our algorithm has been demonstrated using two examples of UWB monopole antennas with four figures of interest taken into account: structure size, reflection response, total efficiency, and gain variability.     

Rapid design closure of microwave components by means of feature‐based optimization and adjoint sensitivities

In this article, fast design closure of microwave components using feature‐based optimization (FBO) and adjoint sensitivities is discussed. FBO is one of the most recent optimization techniques that exploits a particular structure of the system response to “flatten” the functional landscape handled during the optimization process, which leads to reducing its computational complexity. When combined with gradient‐based search involving adjoint sensitivities, the design cost becomes even lower, allowing us to find the optimum design using just a few electromagnetic (EM) simulations of the structure at hand. Here, operation and performance of the algorithm is demonstrated using a waveguide filter and a miniaturized microstrip rat‐race coupler (RRC). Comparative studies indicate considerable savings that can be achieved even compared with adjoint‐based gradient search. In case of RRC, numerical results are supported by experimental validation.     

Multi‐objective design optimization of antennas for reflection,size, and gain variability using kriging surrogates and generalized domain segmentation

Cost‐efficient multi‐objective design optimization of antennas is presented. The framework exploits auxiliary data‐driven surrogates, a multi‐objective evolutionary algorithm for initial Pareto front identification, response correction techniques for design refinement, as well as generalized domain segmentation. The purpose of this last mechanism is to reduce the volume of the design space region that needs to be sampled in order to construct the surrogate model, and, consequently, limit the number of training data points required. The recently introduced segmentation concept is generalized here to allow for handling an arbitrary number of design objectives. Its operation is illustrated using an ultra‐wideband monopole optimized for best in‐band reflection, minimum gain variability, and minimum size. When compared with conventional surrogate‐based approach, segmentation leads to reduction of the initial Pareto identification cost by over 20%. Numerical results are supported by experimental validation of the selected Pareto‐optimal antenna designs.     

Compact cell topology selection for size‐reduction‐oriented design of microstrip rat‐race couplers

In this work, we address the problem of compact cell topology selection for miniaturization of rat‐race couplers. The principal objective of the design process is to achieve the smallest possible footprint of the coupler, while maintaining the required levels of electrical parameters imposed on its components. Our approach permits identification of the minimum achievable coupler area, provided that the circuit is composed of a given compact cell and folded lines. This allows for the quantitative assessment of a set of considered cells with respect to the miniaturization capabilities they exhibit under certain design specifications. The proposed method is validated using 6 different cells with unified parameterization to identify the smallest rectangular‐like rat‐race coupler described by 2 design specifications. The obtained results attest that circuit topology and electrical parameters of the reference design are critical factors determining the final miniaturization rate. The proof‐of‐concept prototype devices occupy merely 8% of the conventional coupler area, while preserving fractional bandwidths (20% and 13.5%) of their conventional counterparts. The experimental results confirm the claims inferred from the numerical data.     

Influence of the HVOF Gas Composition on the Thermal Spraying of WC-Co Submicron Powders (−8 + 1 μm) to Produce Superfine Structured Cermet Coatings

Thermal spraying technology represents a novel and promising approach to protect forming tools with complex surfaces and highest shape accuracy against abrasive wear and galling. However, due to high or nonuniform layer thicknesses or inappropriate surface roughness conventional coarse-structured coatings are not suitable to achieve this aim. The application of novel submicron or nanoscaled feedstock materials in the thermal spray process can provide the deposition of cermet coatings with significantly improved characteristics and is recently of great interest in science and industry. In this collaborative study, the feeding and HVOF spraying of WC-Co submicron powders (?8 + 1 μm) have been investigated to manufacture superfine structured, wear resistant, near-net-shape coatings with improved macroscopic properties and smooth surfaces. The influences of varying HVOF gas compositions on the spray process and the coating properties have been analyzed.     

Oligomer and Polymer Formation in Hexamethylcyclotrisiloxane (D<Subscript>3</Subscript>) – Hydrosilane Systems Under Catalysis by tris(pentafluorophenyl)borane

Summary Reactions of hexamethylcyclotrisiloxane, D₃, with 1,1,3,3-tetramethyldisiloxane, ^HMM^H, 1,1,1,3,3-pentamethyldisiloxane, ^HMM, phenyldimethylsilane and phenylmethylsilane catalyzed by tris(pentafluorophenyl)borane were studied. These reactions lead to ring opening of D₃ by the SiH reactant producing open chain oligomers with hydrosilane functionality at one or both chain ends. The reactivity of the hydrosilanes toward D₃ decreases in the series: PhMeSiH₂ > ^HMM^H > PhMe₂SiH > ^HMM. Competitive self-oligomerization of ^HMM^H and ^HMM also occurs. Primary products of these processes are able to enter into reactions with the SiH and D₃ reactants; some also undergo cyclization. Thus, consecutive and competitive processes lead to a series of various oligohomologues. Gas chromatography in conjunction with chemical ionization mass spectroscopy permitted identification of structure and determination of the basic directions of these oligomerization processes. Polysiloxanes of higher molecular weight may be also formed in some of these systems. The reactions, which occur in the systems studied, are rationalized on the basis of the mechanism involving the hydride transfer from silicon to trivalent boron. This includes the transient formation of tertiary trisilyloxonium borate which decomposes by the hydride transfer to one of the silicon atoms of the trisilyloxonium center. Footnote: This paper is dedicated to Professor Ian Manners in recognition of his significant contributions to the field of organometallic polymers.