Sensitivity analysis to compute advanced stochastic problems in uncertain and complex electromagnetic environments

Lalléchère, Sébastien; Jannet, Basile; Bonnet, Pierre; Paladian, Françoise

doi:10.7716/aem.v1i3.43

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…From [5], these data are stochastically modeled following The Fig.4 shows the great agreement between SC results (including 729 measurements) and 20,000 MC data regarding the averaged value . Without any supplementary measurements, the SC and MC dispersions around including ± one standard deviation are in accordance and provide a precise assessment of margins.…”

Section: From Sensitivity Analysis To Multi-rv Model Reductionmentioning

confidence: 92%

“…The theoretical details and some chosen examples of SC weighted points sets may be found in [5] where the limitations of the method were presented. Due to the complexity inherent to EMC systems, many random parameters may be involved to precisely describe their global behavior.…”

Section: Stochastic Theory and Sensitivity Analysis Needsmentioning

confidence: 99%

“…That is the reason why Sensitivity Analysis (SA) (jointly with SC technique) should provide first qualitative results with a reduced cost comparatively to MC experiments. In this paper and from [5], we focused on Morris screening formalism ( Fig.1-B) where the relative effect from each considered RV is computed regarding their mean and standard deviation impacts. This allows to separate RV between stochastically important and negligible ones as described in Fig.1-B involving 4,000 measurements ( Fig.1-A).…”

Section: Stochastic Theory and Sensitivity Analysis Needsmentioning

confidence: 99%

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Paradigm of sensitivity analysis in EMC stochastic enclosed environments

Lalléchère

Girard

Slama

et al. 2014

2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)

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The aim of this contribution is to present a whole experimental methodology to assess the effects of uncertainties for a classical EMC issue. To this end, a cabinet was designed and achieved with controlled geometrical parameters including moving external slot and internal plate, an inner rotating unit embedded with stirrer and cable. From industrial expectations, the experimental setup and the theoretical basis will be described briefly. Then, the validation (convergence, accuracy and robustness) of the proposed stochastic methods will be obtained facing Monte Carlo (MC) measurements including three random parameters. Finally, the combination of stochastic techniques with sensitivity study will improve the global process. Physical and industrial contextsIn the ElectroMagnetic Compatibility (EMC) literature, designing electronic large systems is mostly based upon "worst" cases approaches. In accordance with standards, this mainly sets two problems: the need for precise and efficient tools to quantify more realistic EMC margins, jointly with trustworthy reliability levels. Non-exhaustive stateof-the-art EMC stochastic issues contain different philosophies to integrate this problem for instance involving Printed Circuit Boards (PCBs) [1], cable coupling [2-3] and effects of uncertain High Intensity Radiated Fields (HIRFs) [3]. Materials and methods Overview of stochastic measurements setupsThe study presented in [4] detailed the design and achievement of a box including different geometrical parameters acting as Random Variables (RV). The governing idea was to manufacture a device providing mechanically various and precise configurations of the equipment. This allows defining different EMC classical setups involving different devices and outputs. Indeed, as depicted respectively in Fig.2-A and Fig.3-A, slots and inner volume modifications are allowed, and a rotating unit enables modifications around the location of different equipment (cable for instance). First, a spectrum analyzer Anritsu MS 2663C (bandwidth 9kHz-3GHz) has been used for power measurement ( Figure 2). Then, S 12 parameter (Figures 1 and 2) between emission and reception antennas is measured by a network analyzer Rohde & Schwarz ZVB 8 (bandwidth 300kHz-8GHz). A common strategy has been defined since the measurements were achieved in one go (no break); this has required a whole automation of the process (especially for MC treatments). Due to the similarity of the MC and Stochastic Collocation (SC) approaches (details in section 2.2), the statistical treatments required only to use the chosen points from this database. Stochastic theory and sensitivity analysis needsAccording to the SC foundations [4], the technique is close to MC philosophy since it only requires a smart sampling of input data and to straightforward apply the deterministic setup (numerical and/or experimental) from this set of points. The theoretical details and some chosen examples of SC weighted points sets may be found in [5] where the limitations of the method were presented. Du...

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Section: From Sensitivity Analysis To Multi-rv Model Reductionmentioning

confidence: 92%

Section: Stochastic Theory and Sensitivity Analysis Needsmentioning

confidence: 99%

Section: Stochastic Theory and Sensitivity Analysis Needsmentioning

confidence: 99%

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Paradigm of sensitivity analysis in EMC stochastic enclosed environments

Lalléchère

Girard

Slama

et al. 2014

2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)

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“…It is to be noted that CV is the rate between standard deviation (std) and mean value of SE extracted from 10 time domain 3-D simulations (for each volumic fraction ν 1 and ν 2 ). Moreover, as expressed in [24], CV may be useful to detect the sensitivity of a given model: high sensitivity is often related to high (greater than 10% for instance) CV values. Fig.…”

Section: Materials Characterization At Microwave Frequency 31 Validimentioning

confidence: 99%

Optimized 3-D electromagnetic models of composite materials in microwave frequency range: application to EMC characterization of complex media by statistical means

Lalléchère¹

2017

AEM

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The aim of this proposal is to demonstrate the ability of tridimensional (3-D) electromagnetic modeling tool for the characterization of composite materials in microwave frequency band range. Indeed, an automated procedure is proposed to generate random materials, proceed to 3-D simulations, and compute shielding effectiveness (SE) statistics with finite integration technique (FIT, CST microwave studio solver). In this context, 3-D electromagnetic models rely on random locations of conductive inclusions; results are compared with classical electromagnetic mixing theory (EMT) approaches (e.g. Maxwell-Garnett formalism), and dynamic homogenization model (DHM).The article aims to demonstrate the interest of the proposed approach in various domains such as propagation and electromagnetic compatibility (EMC).

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“…Among the many recent approaches to account for uncertainty in electromagnetic problems (see, e.g., [11,12]), an alternative strategy was proposed to characterize cable responses from a statistical standpoint [13]. The approach is based on the so-called polynomial chaos (PC) framework [14] and was also applied to printed circuit board (PCB) lines [15].…”

Section: Introductionmentioning

confidence: 99%

Efficient Statistical Extraction of the Per-Unit-Length Capacitance and Inductance Matrices of Cables with Random Parameters

Manfredi

Canavero

2015

AEM

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Cable bundles often exhibit random parameter variations due to uncertain or uncontrollable physical properties and wire positioning. Efficient tools, based on the so-called polynomial chaos, exist to rapidly assess the impact of such variations on the per-unit-length capacitance and inductance matrices, and on the pertinent cable response. Nevertheless, the state-of-the-art method for the statistical extraction of the per-unit-length capacitance and inductance matrices of cables suffers from several inefficiencies that hinder its applicability to large problems, in terms of number of random parameters and/or conductors. This paper presents an improved methodology that overcomes the aforementioned limitations by exploiting a recently-published, alternative approach to generate the pertinent polynomial chaos system of equations. A sparse and decoupled system is obtained that provides remarkable benefits in terms of speed, memory consumption and problem size that can be dealt with. The technique is thoroughly validated through the statistical analysis of two canonical structures, i.e., a ribbon cable and a shielded cable with random geometry and position.

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Sensitivity analysis to compute advanced stochastic problems in uncertain and complex electromagnetic environments

Cited by 4 publications

References 12 publications

Paradigm of sensitivity analysis in EMC stochastic enclosed environments

Paradigm of sensitivity analysis in EMC stochastic enclosed environments

Optimized 3-D electromagnetic models of composite materials in microwave frequency range: application to EMC characterization of complex media by statistical means

Efficient Statistical Extraction of the Per-Unit-Length Capacitance and Inductance Matrices of Cables with Random Parameters

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