Statistical design centering of RF cavity linear accelerator via non-derivative trust region optimization

Hassan, Ali H.; Abdel-Malek, H.L.; Mohamed, A.S.A.; Abuelfadl, Tamer M.; Elqenawy, Ahmed E.

doi:10.1109/nemo.2015.7415027

Cited by 24 publications

(13 citation statements)

References 11 publications

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“…In (6), Bk(x) is a symmetric part of the kth bandwidth, i.e., Bk(x) = 2min{f0k -f1k(x), f2k(x) -f0k} with f1k and f2k being the frequencies corresponding to -10 dB level of |S11| (left-and right-hand-side ends of the kth resonance). Note that both (6) and 7are formulated in a minimax sense, i.e., the improvement of the worst case, the bandwidth in (6) and the reflection levels at the operating frequencies in (7). It should be reiterated that particular formulations of the problems (6) and (7) are motivated by the need for identifying the directions corresponding to possibly large change of the antenna responses at and around its operating frequencies.…”

Section: Yield Optimization Using Performance-driven Surrogatesmentioning

confidence: 99%

“…Note that both (6) and 7are formulated in a minimax sense, i.e., the improvement of the worst case, the bandwidth in (6) and the reflection levels at the operating frequencies in (7). It should be reiterated that particular formulations of the problems (6) and (7) are motivated by the need for identifying the directions corresponding to possibly large change of the antenna responses at and around its operating frequencies. They do not need to coincide with the formulation of the original design problem used to generate the nominal design of the antenna of interest.…”

Section: Yield Optimization Using Performance-driven Surrogatesmentioning

confidence: 99%

“…These are the inherent (or aleatory) uncertainties, most often related to manufacturing tolerances [6] and typically characterized by probability distributions. Due to their stochastic nature, quantification requires performing statistical analysis [7]- [9]. Reducing the effects of tolerances normally entails stochastic design that aims at improving statistical performance measures, e.g., the yield [10], [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Expedited Yield Optimization of Narrow- and Multi-Band Antennas Using Performance-Driven Surrogates

2020

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Uncertainty quantification is an important aspect of engineering design, also pertaining to the development and performance evaluation of antenna systems. Manufacturing tolerances as well as other types of uncertainties, related to material parameters (e.g., substrate permittivity) or operating conditions (e.g., bending) may affect the antenna characteristics. In the case of narrow-or multi-band antennas, this usually leads to frequency shifts of the operating bands. Quantifying these effects is imperative to adequately assess the design quality, either in terms of the statistical moments of the performance parameters or the yield. Reducing the antenna sensitivity to parameter deviations is even more essential when increasing the probability of the system satisfying the prescribed requirements is of concern. The prerequisite of such procedures is statistical analysis, normally carried out at the level of full-wave electromagnetic (EM) analysis. While necessary to ensure reliability, it entails considerable computational expenses, often prohibitive. Following the recently fostered concept of constrained modeling, this paper proposes a simple technique for rapid surrogate-assisted yield optimization of narrow-and multi-band antennas. The keystone of the approach is an appropriate definition of the optimization domain. This is realized by considering a few pre-optimized designs that represent the directions of the major changes of the antenna resonant frequencies and operating bands. Due to a small volume of such a domain, an accurate replacement model can be established therein using a small number of training samples, and employed to improve the antenna yield. Verification results obtained for a ring-slot antenna, a dual-band and a triple-band uniplanar dipoles indicate that the optimization process can be accomplished at low cost of a few dozen of EM simulations: 62, 74 and 132 EM simulations, respectively. Result reliability is validated through comparisons with EM-based Monte Carlo simulations. INDEX TERMS Uncertainty quantification; tolerance-aware design; yield optimization; multi-band antennas; performance-driven modeling.

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Section: Yield Optimization Using Performance-driven Surrogatesmentioning

confidence: 99%

Section: Yield Optimization Using Performance-driven Surrogatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Expedited Yield Optimization of Narrow- and Multi-Band Antennas Using Performance-Driven Surrogates

2020

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show abstract

“…Consequently, quantification of such effects and, eventually, reducing them already at the design stage is essential to ensure the structure robustness. 3,4 The latter normally means diminishing statistical moments of the system outputs, especially their variance. 5 However, for microwave components, design specifications are often expressed in a minimax form, that is, using upper/lower bounds for the selected figures of interest (eg, maximum acceptable level of reflection, minimum acceptable bandwidth, maximum acceptable power split error, etc.)…”

Section: Introductionmentioning

confidence: 99%

Rapidtolerance‐awaredesign of miniaturized microwave passives by means ofconfined‐domainsurrogates

Pietrenko‐Dabrowska

2020

Int J Numerical Modelling

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The effects of uncertainties, primarily manufacturing tolerances but also incomplete information about operating conditions or material parameters, can be detrimental to the performance of microwave components. Quantification of such effects is essential to ensure a meaningful evaluation of the structure, in particular, its reliability under imperfect fabrication procedures. The improvement of the circuit robustness can be achieved by reducing sensitivity to geometry/material parameter deviations, which requires optimization of suitably chosen statistical performance metrics such as the yield. The prerequisite for the latter is statistical analysis. In the case of compact circuits, it is executed through full-wave electromagnetic (EM) analysis. The fundamental difficulty, that is, the high CPU cost, can be alleviated by the employment of fast surrogate models, which is the method of choice for the majority of contemporary approaches. Despite its advantages, a practical challenge of surrogate-assisted design is the initial computational overhead related to metamodel construction. As a workaround, this work proposes the employment of a recently introduced concept of constrained modelling, where the surrogate domain is confined only to contain the essential subsets of the parameter space. In the context of yield optimization, the domain needs to correspond to directions featuring maximum variability of the circuit responses (particularly the parts thereof that affect the yield value in the most significant way) with respect to its geometry parameters. The small volume of the domain spanned by such directions permits setting up an accurate model using a fraction of training data samples required by conventional methods. The proposed technique is demonstrated using a miniaturized microstrip rat-race coupler with its yield optimized at the cost of just a few dozen of EM simulations of the circuit. EM-based Monte Carlo simulations corroborate the reliability of the approach.

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“…leads to topologically complex structures that can be neither developed nor optimized using simpler (analytical or equivalent network) models. Yet, EM-driven parameter tuning but also other tasks that require a large number of antenna simulations (uncertainty quantification [11], robust design [12]) may incur considerable computational expenses. Reduction of these costs has been the focus of extensive research, resulting in various methods such as incorporation of adjoints sensitivities into gradient-based optimization procedures [13], expedited optimization using sparse sensitivity updates [14], [15], utilization of machine learning methods (typically in the context of global optimization) [16], or surrogate-assisted frameworks involving both data-driven [17], [18] and The associate editor coordinating the review of this manuscript and approving it for publication was Haiwen Liu .…”

Section: Introductionmentioning

confidence: 99%

Accelerated Re-Design of Antenna Structures Using Sensitivity-Based Inverse Surrogates

Koziel

Bekasiewicz

2020

IEEE Access

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The paper proposes a novel framework for accelerated redesign (dimension scaling) of antenna structures using inverse surrogates. The major contribution of the work is a sensitivity-based model identification procedure, which permits a significant reduction of the number of reference designs required to render the surrogate. Rigorous formulation of the approach is supplemented by its comprehensive numerical validation using a triple-band uniplanar dipole antenna and a dual-band monopole antenna redesigned with respect to operating frequencies as well as the substrate parameters (thickness and dielectric permittivity). It is demonstrated that-for the considered test cases-the reliable inverse model can be set up using a significantly smaller (by a factor of three) number of reference points as compared to the original version of the method, whereas the dimension scaling process itself requires up to four EM simulations of the antenna structure. INDEX TERMS Antenna design, dimension scaling, inverse modeling, simulation-driven design, response sensitivities.

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Statistical design centering of RF cavity linear accelerator via non-derivative trust region optimization

Cited by 24 publications

References 11 publications

Expedited Yield Optimization of Narrow- and Multi-Band Antennas Using Performance-Driven Surrogates

Expedited Yield Optimization of Narrow- and Multi-Band Antennas Using Performance-Driven Surrogates

Rapidtolerance‐awaredesign of miniaturized microwave passives by means ofconfined‐domainsurrogates

Accelerated Re-Design of Antenna Structures Using Sensitivity-Based Inverse Surrogates

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