The concept of circulation is presented, including the physical and mathematical concepts of circulation and lift. A description of how potential flow theory is used to model flow for airfoils, including the predictions of lift. Readers are presented with the concept of the Kutta condition, including how it impacts the development of airfoil theory. Thin-airfoil theory is developed for symmetric and cambered airfoils and methods for prediction lift and pitching moment are presented. The accuracy and limitations of thin-airfoil theory is also presented. Descriptions are presented for why laminar flow airfoils have different geometries than airfoils used at higher Reynolds numbers. Finally, high-lift systems are discussed, including why they are important for aircraft design.

The chapter will begin with the five characteristics that distinguish hypersonic flow from supersonic flow and then discuss each of the characteristics. Analysis methods will then be discussed, including Newtonian and Modified Newtonian methods, as well as tangent wedge and tangent cone methods. Analysis techniques are developed to determine the flow characteristics in the region of the stagnation point of a hypersonic vehicle, as well as the lift, drag, and pitch moment for simple geometries at hypersonic speeds. Information on the importance of heating at hypersonic speeds will be presented, followed by analysis approaches for estimating heating rates on blunt bodies. Finally, the complexities of hypersonic boundary-layer transition are introduced, including details about why transition is so challenging to predict.

Basic concepts are presented to show the difference between airfoils and wings, as well as the physical processes that cause those differences, such as wing-tip vortices. A physical description is presented for the impact of wing-tip vortices on the flow around the airfoil sections that make up a wing, and lift-line theory is developed to predict the effects of wing-tip vortices. A general description and calculation methods are presented for the basic approach and usefulness of panel methods and vortex lattice methods. A physical description for how delta wings produce lift and drag is also presented, including the importance of strakes and leading-edge extensions. High angle of attack aerodynamics is discussed, including the physical mechanisms that cause vortex asymmetry. Unmanned aerial vehicles and aerodynamic design issues are discussed. Finally, basic propeller theory and analysis approaches are introduced, including the use of propeller data to design low-speed propellers.

Readers will understand what is meant by inviscid flow, and why it is useful in aerodynamics, including how to use Bernoulli’s equation and how static and dynamic pressure relate to each other for incompressible flow. Concepts are presented to describe the basic process in measuring (and correcting) air speed in an airplane. A physical understanding of circulation is presented and how it relates to predicting lift and drag. Readers will be presented with potential flow concepts and be able to use potential flow functions to analyze the velocities and pressures for various flow fields, including how potential flow theory can be applied to an airplane.

In contrast to the previous two chapters, which detailed L-system topology optimization approaches that interpret gene-informed rules into a complex set of layout-building instructions, this chapter introduces a grammar-to-layout approach known as the Arrangement L-system (ALS). Here, developmental operations that mimic the processes of cellular division, growth, and movement are directly informed by the genes and then iteratively applied to an iteratively changing topological layout that, once complete, represents an individual. The differences between formulations of the L-system, parameterized L-system, and ALS are discussed; examples of how the cellular division processes are used to develop a topological layout are provided; and extensions to the ALS such as directed search cellular dynamics and cellular division via the two-point topological derivative are detailed. The applicability of the ALS to a variety of structural design problems will be demonstrated, and it will be shown that this approach compares favorably with both conventional topology optimization methods discussed throughout this work as well as the graph-based SPIDRS approach introduced in the previous chapter.

Topology optimization is a powerful tool that, when employed at the preliminary stage of the design process, can determine potential structural configurations that best satisfy specified performance objectives. This chapter explores both the different classifications of topology optimization methodologies and their implementation within the design process, specifically highlighting potential areas where such techniques may fall short. This motivates a discussion on the relevance of a bioinspired approach to topology optimization known as EvoDevo, where topologies developed by interpreting instructions from a Lindenmayer system (L-system) encoding are evolved using a genetic algorithm. Such an approach can lend itself well to multiobjective design problems with a vast design space and for which users have little/no experience or intuition.

To this point, the proposed L-system topology optimization methods have been considered in the context of benchmark structural topology optimization problems, as such problems afford an opportunity for comparison to both other topology optimization methodologies and mathematically proven optimal or ideal solutions. However, the motivation behind the development of these approaches stems from the need for preliminary design method capable of considering complex multiobjective problems involving multiple physics for which the user may not have an intuition. This chapter briefly summarizes several multiphysical problems that have been approached using L-system topology optimization, including fluid transport, heat transfer, electrical, and aeroelastic applications. By no means an exhaustive survey, these examples are intended to provide an overview of potential applications and hopefully provoke opportunities for future efforts.

To address the need for an inherently multiobjective preliminary design tool, this chapter introduces a heuristic alternative to the conventional topology optimization approaches discussed in the previous chapter. Specifically, a parallel rewriting system known as a Lindenmayer system (L-system) is used to encode a limited number of design variables into a string of characters which, when interpreted using a deterministic algorithm, governs the development of a topology. The general formulation of L-systems is provided before discussing how L-system encodings can be interpreted using a graphical method known as turtle graphics. Turtle graphics constructs continuous, straight line segments by tracking the spatial position and orientation of a line-constructing agent, leading to the creation of branched structures that mimic those found in numerous natural systems. The performance of the proposed method is then assessed using simple, well-known topology optimization problems and comparisons to mathematically known optimal or ideal solutions as well as those generated using conventional topology optimization methodologies.

While the L-system approach introduced in the previous chapter exhibits potential for topology optimization applications, the modeling power of the turtle graphics interpretation is severely limited due to its reliance on limited parameters and its inability to guarantee the deliberate formation of load paths. Based on these characteristics, this chapter introduces a graph-based interpretation approach known as Spatial Interpretation for the Development of Reconfigurable Structures (SPIDRS) that uses principles of graph theory to allow an edge-constructing agent to introduce deliberate topological modifications. Furthermore, SPIDRS operates using instructions generated by a parametric L-system, which enhances modeling power and affords greater design freedom. This approach can also be extended to consider a three-dimensional structural design domain. It will be demonstrated that this interpretation approach results in configurations comparable to known optimal/ideal solutions as well as those found using conventional topology optimization methods, especially when coupled with a sizing optimization scheme to determine optimal structural member thicknesses.