Cst Microwave Studio Price
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I have to buy a workstation for the University where I work and I am looking for the best trade-off between performance and price offered by HP. Basically, I have to use on this computer the EM computational programs: CST Microwave Studio and COMSOL Multiphysics. According to the specific requirements of these software I have identified the following model:
This is an example of a microwave radiation simulation from a circular horn antenna solved in the frequency domain. The circular horn antenna is modeled as a perfect electric conductor (PEC) in a vacuum farfield. The antenna is being operated at a frequency of 10 GHz.
Due to a growing complexity of contemporary high-frequency structures (antennas, microwave components, integrated photonic devices, etc.), carrying out a complete design cycle at the level of simplified representations such as analytical/semi-empirical formulas or equivalent networks is no longer feasible. Certain phenomena that exhibit non-negligible effects on the system performance, e.g. electromagnetic (EM) cross-coupling1, the presence of connectors2, housing3, mutual coupling of radiators4, can only be accounted for by means of a full-wave EM analysis. Appropriate capturing of these effects, along with the necessity of fulfilling stringent performance requirements (which includes realization of various functionalities such as tunability5, multi-band operation6, circular polarization7, but also maintaining small physical size of the devices8,9,10), leads to problems with obtaining reasonable initial designs or even locating the regions of the parameter space that contain such designs.
The most widely used global optimization methods are by far population-based metaheuristics14,15,16,17,18,19,20. Well-known techniques of this sort include genetic14 and evolutionary algorithms15, particle swarm optimizers (PSO)16, differential evolution17, as well as numerous variations such as firefly algorithm18, harmony search19, grey wolf optimization20, and many others (e.g.,21,22,23,24,25,26). The underlying search mechanism is the exchange of information between a set of individuals (or agents) realized using appropriate exploratory (recombination) and exploitative (mutation) operators. This generally allows for identifying and exploiting the promising regions of the parameter space, in particular, escaping from the local optima. Metaheuristics are easy to implement and handle, but their global search capabilities come at a price of significant computational expenses, which may reach many thousands of objective function evaluations for a single algorithm run20,23. This is acceptable if the system evaluation cost is not of primary concern (e.g., for pattern synthesis of antenna arrays using analytical array factor models20), but turns prohibitive when directly optimizing full-wave EM models.
The response feature approach, originally fostered for local optimization47, and later for surrogate modeling48, offers supplementary advantages. The optimization (or modeling) process is reformulated to be carried out at the level of appropriately defined characteristic points of the system outputs (e.g., frequency/level allocation of multi-band antenna resonances or local maxima of the in-band return loss response of a microwave filter), which is in opposition to handling the original responses, typically, frequency characteristics. The dependence of the feature point coordinates on the geometry parameters is normally much less nonlinear than for the standard outputs, which leads to flattening of the functional landscape to be handled. In the case of local optimization, it results in faster convergence47, whereas in the case of surrogate modeling it brings a considerable reduction of the number of training data samples required to render a reliable model48. This paper capitalizes on the response feature technology to realize globalized optimization of electromagnetic computational models. The major component is an inverse surrogate constructed at the level of feature points extracted from a set of random observables. The predictions generated by the surrogate allow for rapid identification of the promising regions of the parameter space and yielding a reasonable initial design, which only needs to be tuned in a local sense. At the same time, the computational complexity of the proposed procedure is comparable to that of local procedures. Our methodology is validated using several high-frequency structures (a dual-band antenna, a microstrip coupler, an impedance matching transformer), optimized under different scenarios. The global search capability and computational efficiency are demonstrated through comprehensive comparisons with multiple-start local search, as well as particle swarm optimizer (PSO), a representative nature-inspired population-based procedure.
Figure 1 shows example reflection responses of a dual band antenna at various points of the parameter space. It is clear that using these particular designs as starting points for local optimization may lead to a failure of the optimization process, e.g., because the initial allocation of antenna resonances is incompatible with the target ones, or the resonances are not clearly distinguished. On the other hand, following the underlying physical properties of passive components, the parameters of high-frequency structures corresponding to designs that are optimum with respect to particular performance figures are largely correlated49,50,51. For example, dimension scaling of antenna or microwave components with respect to the operating frequency (or frequencies in the case of multi-band structures), substrate parameters, power split ratio (in the case of couplers), etc., requires synchronized adjustment of the design variables. In other words, the optimum designs are normally allocated along low-dimensional manifolds (surfaces) of the dimensionality equal to the number of operating conditions or performance figures considered for a given design task52. This observation has led to the development of a paradigm of constrained modeling, in which the modeling process is restricted to this part of the design space, where designs of high-quality reside52. As the number of operating conditions is normally considerably lower than the number of parameters of the system at hand, constrained modelling effectively reduces the modelling task dimensionality52. These manifolds are usually regular as indicated in Fig. 2, using the example of a miniaturized rat-race coupler53.
It should be emphasized that a rigorous mathematical definition of response features has not been formulated. This is because they are very much problem dependent, as the examples of the previous paragraph indicate. Thus, there exist no definition, which would be sufficiently general to cover all possible cases that may emerge when solving practical design tasks. This is a limitation of the response feature technology. A generalized definition of the response features has been given in56 for antenna input characteristics. Despite covering merely one type of antenna response, the definition of56 is intricate, which confirms that providing a rigorous mathematical definition of response feature for all types of characteristics of antenna and microwave structures that may be of interest in high-frequency design is far from trivial. Therefore, as explained above, the response features need to be defined in relation to a specific design task.
At this point, one needs to reiterate that the excellent performance of the presented optimization framework comes with some limitations. In particular, the underlying assumption is that the response features are identifiable in the EM-simulated responses of the considered structure (e.g., resonances, bandwidths), at least for the designs that are of decent quality. This is generally the case for structures such as narrow- or multi-band antennas, microwave couplers, power dividers, and so on. Broadband and ultra-broadband components may be trickier, yet the definition and extraction of the response features is normally realized on case-to-case basis. On the other hand, the specific verification devices considered in this work feature distinct characteristics that are representative to a range of high-frequency passive components. This indicates a relative wide spectrum of possible applications of the proposed methodology.
The Vivaldi antenna is developed as an ultrawideband antenna with a directive pattern and high gain, which is attractive for radar application and microwave sensing [4]. When the antenna is operational, the electromagnetic wave guided by the exponential slot line realized on the front metal layer excited by a lower microstrip line is radiated into free-space [5]. Lin et al. [6] designed an asymmetric wideband dual-polarized bilateral tapered-slot antenna for wireless measurement of electromagnetic compatibility. This antenna has achieved good results. Nevertheless, compared to the planar structure previously described, its three-dimensional structure takes up even more space. Teni et al. [7] designed a novel miniaturized antipodal Vivaldi antenna with improved radiation characteristics. It performs relatively well in all aspects except for the gain which does not meet the requirements of the standard moisture sensor, making it unsuitable for this application.
MultilayerThe CST Multilayer Solver is a 3D full-wave solver, based on the method of moments (MOM) technique. The Multilayer Solver uses a surface integral technique and is optimized for simulating planar microwave structures. The Multilayer Solver includes a characteristic mode analysis (CMA) feature which calculates the modes supported by a structure. Applications like; MMIC, feeding networks, planar antennas. 153554b96e
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