The multilevel interactive objects approach to model and mesh large planar antennas and arrays: A full-JAVA Tool

Gheri, G.; Mosig, J. R.

doi:10.1007/bfb0037247

Cited by 2 publications

(5 citation statements)

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“…While this choice was mandatory to analyze the structure with the standard SFX-MoM, the same symmetries have been exploited with the SFX-AIM only to compare the two methods. The more general case, where no symmetries are considered, will be addressed in Section V-G. [11]. CPU TIME IS RELEVAT TO A 3.0 GHZ QUAD-CORE PROCESSOR, 128 GBYTE RAM, 1 THREAD…”

Section: Fast-accelerated Dd: Resultsmentioning

confidence: 99%

“…Finally, as an example of application to an existing antenna, two very large planar arrays for SAR applications [11] have been analyzed; the structure of the sub-array is sketched in Fig. 4.…”

Section: Fast-accelerated Dd: Resultsmentioning

confidence: 99%

“…We take as a case study the largest case for which we have all necessary pieces of information, i.e., the large (microstrip) array for SAR application [11] already considered in Section IV.…”

Section: G Accelerated Dd Versus Iterative Fast-factorizationmentioning

confidence: 99%

“…The far-field contribution will be computed via AIM. Because of convolutional character of the Toeplitz kernel, the vector can be computed by using a fast Fourier transform as (11) For surface problems, the computation complexity to calculate each vector is , where is a constant that depends on the AIM grid. Note that for quasi-planar structure this complexity reduces to .…”

Section: On-the-fly Dd Compression With Fast Factorizationmentioning

confidence: 99%

“…Sub-array of the large SAR antenna[11]. system due to the presence of a larger number of unknowns (36456 RWG) with regard to the SFX scheme (1302 SFs).…”

mentioning

confidence: 99%

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Fast-Factorization Acceleration of MoM Compressive Domain-Decomposition

Freni

Vita

Pirinoli

et al. 2011

IEEE Trans. Antennas Propagat.

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Domain-decomposition (DD) for Integral Equation can be achieved by aggregating standard basis functions into specialized basis functions on each sub-domain; this results in a strong compression of the MoM matrix, which allows an iteration-free (e.g., LU decomposition) solution also for electrically large problems. Fast matrix-vector product algorithms can be used in the matrix filling and compression process of the employed aggregate-functions approach: this hybrid approach has received considerable attention in recent literature. In order to quantitatively assess the performance, advantages and limitations of this class of methods, we start by proposing and demonstrating the use of the Adaptive Integral Method (AIM) fast factorization to accelerate the Synthetic Function eXpansion (SFX) DD approach. The method remains iteration free, with a significant boost in memory and time performances, with analytical predictions of complexity scalings confirmed by numerical results. Then, we address the complexity scaling of both stand-alone DD and its combined use with fast MoM; this is done analytically and discussed with respect to known literature accounts of various implementations of the DD paradigm, with nonobvious results that highlight needs and limitations, and yielding practical indications.Index Terms-Adaptive integral method, aggregate functions, domain decomposition, fast methods, moment method, numerical methods.

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Section: Fast-accelerated Dd: Resultsmentioning

confidence: 99%

Section: Fast-accelerated Dd: Resultsmentioning

confidence: 99%

Section: G Accelerated Dd Versus Iterative Fast-factorizationmentioning

confidence: 99%

Section: On-the-fly Dd Compression With Fast Factorizationmentioning

confidence: 99%

“…Sub-array of the large SAR antenna[11]. system due to the presence of a larger number of unknowns (36456 RWG) with regard to the SFX scheme (1302 SFs).…”

mentioning

confidence: 99%

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