Parametric Design & an Approach to Weight Optimization of a Metallic and Carbon Fiber Wing

Indiana University-Purdue University Indianapolis (IUPUI)In a multifidelity structural design process, depending on the required analysis, different levels of structural models are needed. Within the aerospace design, analysis and optimization community, there is an increasing demand for automatic generation of parametric feature tree (build recipe) attributed multidisciplinary models. Currently, this is mainly done by creating separate models for different disciplines such as mid-surface model for aeroelasticity, outer-mold line for aerodynamics and CFD, and built-up element model for structural analysis. Since all of these models are built independently, any changes in design parameters require updates on all the models which is inefficient, time-consuming and prone to deficiencies. In this research, Engineering Sketch Pad (ESP) is used to create attribution and maintain consistency between structural models with different fidelity levels. It provides the user with the ability to interact with a configuration by building and/or modifying the design parameters and feature tree that define the configuration. ESP is based an open-source constructive solid modeler, named OpenCSM, which is built upon the OpenCASCADE geometry kernel and the EGADS geometry generation system. The use of OpenCSM as part of the AFRL’s CAPS project on Computational Aircraft Prototype Syntheses for automatic commercial and fighter jet models is demonstrated. The rapid generation of parametric aircraft structural models proposed and developed in this work will benefit the aerospace industry with coming up with efficient, fast and robust multidisciplinary design standardization of aircraft structures. Metallic aircraft wings are usually not optimized to their fullest potential due to shortage of development time. With roughly \$1000 worth of potential fuel savings per pound of weight reduction over the operational life of an aircraft, airlines are trying to minimize the weight of aircraft structures. A stiffness based strategy is used to map the nodal data of the lower-order fidelity structural models onto the higher-order ones. A simple multi-fidelity analysis process for a parametric wing is used to demonstrate the advantage of the approach. The loads on the wing are applied from a stick model as is done in the industry. C program is created to connect the parametric design software ESP, analysis software Nastran, load file and design configuration file in CSV format. This problem gets compounded when it comes to optimization of composite wings. In this study, a multi-level optimization strategy to optimize the weight of a composite transport aircraft wing is proposed. The part is assumed to initially have some arbitrary number of composite super plies. Super plies are a concept consisting of a set of plies all arranged in the same direction. The thickness and orientation angles of the super plies are optimized. Then, each ply undergoes topometry optimization to obtain the areas of each super ply taking the least load so that it could be cut and removed. Each of the super plies are then optimized for the thickness and orientation angles of the sub plies. The work presented on this paper is part of a project done for Air Force Research Laboratory (AFRL) connecting the parametric geometry modeler (ESP) with the finite element solver (Nastran)

Multiscale Design for Solid Freeform Fabrication

One of the advantages of solid freeform fabrication is the ability to fabricate complex structures on multiple scales, from the macroscale features of an overall part to the mesoscale topology of its internal architecture and even the microstructure or composition of the constituent material. This manufacturing freedom poses the challenge of designing across these scales, especially when a part with designed mesostructure is part of a larger system with changing requirements that propagate across scales. A setbased multiscale design method is presented for coordinating design across scales and reducing iterative redesign of SFF parts and their mesostructures. The method is applied to design a miniature unmanned aerial vehicle system. The system is decomposed into disciplinary subsystems and constituent parts, including wings with honeycomb mesostructures that are topologically tailored for stiffness and strength and fabricated with selective laser sintering. The application illustrates how the design of freeform parts can be coordinated more efficiently with the design of parent systems.Mechanical Engineerin

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy

Improving machine dynamics via geometry optimization

The central thesis of this paper is that the dynamic performance of machinery can be improved dramatically in certain cases through a systematic and meticulous evolutionary algorithm search through the space of all structural geometries permitted by manufacturing, cost and functional constraints. This is a cheap and elegant approach in scenarios where employing active control elements is impractical for reasons of cost and complexity. From an optimization perspective the challenge lies in the efficient, yet thorough global exploration of the multi-dimensional and multi-modal design spaces often yielded by such problems. Morevoer, the designs are often defined by a mixture of continuous and discrete variables - a task that evolutionary algorithms appear to be ideally suited for. In this article we discuss the specific case of the optimization of crop spraying machinery for improved uniformity of spray deposition, subject to structural weight and manufacturing constraints. Using a mixed variable evolutionary algorithm allowed us to optimize both shape and topology. Through this process we have managed to reduce the maximum roll angle of the sprayer by an order of magnitude , whilst allowing only relatively inexpensive changes to the baseline design. Further (though less dramatic) improvements were shown to be possible when we relaxed the cost constraint. We applied the same approach to the inverse problem of reducing the mass while maintaining an acceptable roll angle - a 2% improvement proved possible in this cas

Reliability-based design optimization of shells with uncertain geometry using adaptive Kriging metamodels

Optimal design under uncertainty has gained much attention in the past ten years due to the ever increasing need for manufacturers to build robust systems at the lowest cost. Reliability-based design optimization (RBDO) allows the analyst to minimize some cost function while ensuring some minimal performances cast as admissible failure probabilities for a set of performance functions. In order to address real-world engineering problems in which the performance is assessed through computational models (e.g., finite element models in structural mechanics) metamodeling techniques have been developed in the past decade. This paper introduces adaptive Kriging surrogate models to solve the RBDO problem. The latter is cast in an augmented space that "sums up" the range of the design space and the aleatory uncertainty in the design parameters and the environmental conditions. The surrogate model is used (i) for evaluating robust estimates of the failure probabilities (and for enhancing the computational experimental design by adaptive sampling) in order to achieve the requested accuracy and (ii) for applying a gradient-based optimization algorithm to get optimal values of the design parameters. The approach is applied to the optimal design of ring-stiffened cylindrical shells used in submarine engineering under uncertain geometric imperfections. For this application the performance of the structure is related to buckling which is addressed here by means of a finite element solution based on the asymptotic numerical method