On geometry parameterization for simulation-driven design closure of antenna structures

Full-wave electromagnetic (EM) simulation tools have become ubiquitous in antenna design, especially final tuning of geometry parameters. From the reliability standpoint, the recommended realization of EM-driven design is through rigorous numerical optimization. It is a challenging endeavor with the major issues related to the high computational cost of the process, but also the necessity of handling several objectives and constraints over often highly-dimensional parameter spaces. From the numerical perspective, making decisions about the formulation of the optimization problem, the approach to handling the design constraints, but also parameterization of the antenna geometry, are all non-trivial. At the same time, these issues are interleaved, and may play an important role in the performance and reliability of the simulation-based design closure process. This paper demonstrates that the approach to arranging the structure parameterization (e.g., the use of absolute or relative parameters) may have a major effect of the optimization outcome. Our investigations are carried out using three broadband monopole antennas optimized under different scenarios and using different parameterizations. In particular, the results indicate that relative parameterization is preferred for optimization of input characteristics, whereas absolute parameterization is more suitable for size reduction.

On geometry parameterization for simulation-driven design closure of antenna structures

Full-wave electromagnetic (EM) simulation tools have become ubiquitous in antenna design, especially final tuning of geometry parameters. From the reliability standpoint, the recommended realization of EM-driven design is through rigorous numerical optimization. It is a challenging endeavor with the major issues related to the high computational cost of the process, but also the necessity of handling several objectives and constraints over often highly-dimensional parameter spaces. From the numerical perspective, making decisions about the formulation of the optimization problem, the approach to handling the design constraints, but also parameterization of the antenna geometry, are all non-trivial. At the same time, these issues are interleaved, and may play an important role in the performance and reliability of the simulation-based design closure process. This paper demonstrates that the approach to arranging the structure parameterization (e.g., the use of absolute or relative parameters) may have a major effect of the optimization outcome. Our investigations are carried out using three broadband monopole antennas optimized under different scenarios and using different parameterizations. In particular, the results indicate that relative parameterization is preferred for optimization of input characteristics, whereas absolute parameterization is more suitable for size reduction.

Centrifugal Compressor Aero-Mechanical Design: A Machine Learning Approach

The present paper presents an enhanced method for multi-disciplinary design and optimization of centrifugal compressors based on Machine Learning (ML) algorithms. The typical approach involves the preliminary design, the geometry parameterization, the generation of aero-mechanical databases and a surrogate-model based optimization. This procedure is able to provide excellent results, but it is time consuming and has to be repeated for each new design. The aim of the proposed procedure is to actively exploit the simulations performed in the past for subsequent designs thanks to the predictive capabilities of the ML surrogate model. A commercial 3D (three dimensional) computational fluid dynamics (CFD) solver for the aerodynamic computations and a commercial finite element code for the mechanical integrity calculations, coupled with scripting modules, have been adopted. Two different compressors, with different geometry and operating conditions, have been designed and two aero-mechanical databases have been developed. Then, these two databases have been joined and have been used for the training and validation of the surrogate model. To assess the performance of this approach, two new compressors have been designed, case 1 with operating conditions between those of the databases used for training and validation and case 2 with operating conditions far above. The use of an optimizer coupled to the prediction of the surrogate model has enabled to define the “best set” of model parameters, in compliance with aero-mechanical objectives and constraints. The accuracy of the ML algorithm forecast has been evaluated through CFD and FEM simulations carried out iteratively on the optimal samples, with new simulations added to the database for further training of the surrogate model. The results have been presented with reference to cases 1 and 2 and highlight all the benefits of the proposed approach.

Shape Optimization of a Two-Fluid Mixing Device Using Continuous Adjoint

In this paper, the continuous adjoint method is used for the optimization of a static mixing device. The CFD model used is suitable for the flow simulation of the two miscible fluids that enter the device. The formulation of the adjoint equations, which allow the computation of the sensitivity derivatives is briefly demonstrated. A detailed analysis of the geometry parameterization is presented and a set of different parameterization scenarios are investigated. In detail, two different parameterizations are combined into a two-stage optimization algorithm which targets maximum mixture uniformity at the exit of the mixer and minimum total pressure losses. All parameterizations are in conformity with specific manufacturability constraints of the final shape. The non-dominated front of optimal solutions is obtained by using the weighted sum of the two objective functions and executing a set of optimization runs. The effectiveness of the proposed synthetic parameterization schemes is assessed and discussed in detail. Finally, a reduced length mixer is optimized to study the impact of the length of the tube on the device’s performance.

Blackbox Optimization for Aircraft Engine Blades With Contact Interfaces

In modern aircraft engines, reduced operating clearances between rotating blade tips and the surrounding casing increase the risk of blade/casing structural contacts, which may lead to high blade vibration levels. Therefore, structural contacts must now be accounted for as early as in the engine design stage. As the vibrations resulting from contact are intrinsically nonlinear, direct optimization of blade shapes based on vibration simulation is not realistic in an industrial context. A recent study on a blade featuring significantly lower vibration levels following contact event identified a potential criterion to estimate a blade sensitivity to contact interactions. This criterion is based on the notion of dynamic clearance, a quantity describing the evolution of the blade/casing clearance as the blade vibrates along one of its free-vibration modes. This paper presents an optimization procedure, which minimizes the dynamic clearance as a first step toward the integration of structural criteria in blade design. A dedicated blade geometry parameterization is introduced to allow for an efficient optimization of the blade shape. The optimization procedure is applied to the three-dimensional (3D) properties of two different blades. In both cases, initial and optimized blades are compared by means of an in-house numerical tool dedicated to the simulation of structural contact events with a surrounding casing. The simulations focus on rubbing phenomena, involving the vibration of a single blade. Simulation results show a significant reduction of vibration levels following contact interactions for the optimized blades. Critical speeds related to the mode on which the dynamic clearance is computed are successfully eliminated by the blade shape optimization. For the investigated blade geometries, backward sweep and backward lean angles are associated with reduced contact interactions compared to forward sweep and forward lean angles.

Analytical Assessment of the Remaining Strength of Corroded Pipelines and Comparison With Experimental Criteria

Recently published analytical solutions for the remaining strength of a pipeline with narrow axial and axisymmetric volumetric flaws are described in this paper, and their experimental and numerical validation are reviewed. Next, the domains of applicability of each solution are studied, some simplifications suitable to steel pipelines are introduced, and an analytical model for the remaining strength of corroded steel pipelines is presented. This analytical solution is compared with the standards most widely used in the industry for assessment of corroded pipelines: ASME B31G, modified ASME, and DNV RP-F101. The empirical and analytical solutions are compared with respect to their most relevant parameters: critical (or flow) stress, flaw geometry parameterization, and Folias or bulging factor formulation. Finally, two common pipeline steels, API 5L grades X42 and X100, are selected to compare the different corrosion assessment methodologies. Corrosion defects of 75%, 50%, and 25% thickness reduction are evaluated. None of the experimental equations take into account the strain-hardening behavior of the pipe material, and therefore, they cannot properly model materials with very dissimilar plastic behavior. The comparison indicates that the empirical methods underestimate the remaining strength of shallow defects, which might lead to unnecessary repair recommendations. Furthermore, it was found that the use of a parameter employed by some of the empirical equations to model the assumed flaw shape leads to excessively optimistic and nonconservative results of remaining strength for long and deep flaws. Finally, the flaw width is not considered in the experimental criteria, and the comparative results suggest that the empirical solutions are somewhat imprecise to model the burst of wide flaws.