This chapter presents the main jet engine components: inlet diffuser, compressor, combustor, turbine, and exit nozzle. Typical configurations are presented for each component, followed by a description of the main processes and parameters. The performance of each component is then related to the engine real cycle, which establishes a tight connection between this chapter and . The section describing the combustors is also connected toand .

Rocket propulsion is a form of jet propulsion where mass (or matter) is accelerated from storage to high exit velocities. Rockets differ from typical air-breathing jet propulsion in that the rocket vehicle itself supplies all the propellant for the rocket motor. The exception to this is the mixed-mode (or multi-mode) engine that will be discussed later in this chapter.

An important challenge of compressor design is flow separation. A significant challenge of turbine design is heat transfer from the hot gases to the metal blades. To understand the physics of these two challenges, this chapter will introduce the viscous boundary layer and thermal boundary layer concepts.

A surrogate model, also known as a response surface model or metamodel, is an approximate model of a functional output that represents a “curve fit” to some underlying data. The goal of a surrogate model is to build a model that is much faster to compute than the original function, but that still retains sufficient accuracy away from known data points.

Within a short run, a novel class of mechanisms and systems has been created with parametric (elastic-dissipative) elements of sign-changing stiffness controlled in a range from positive to negative or quasi-zero values. A great deal of natural and hand-made designs on different physical bases appeared that could reveal such a phenomenon. These mechanisms and systems can cut the stiffness and provide a perfect vibration protection in a frequency range required. However, only some of them either are ready for to substitute or could be used in advanced hybrids in parallel with conventional vibration protection mechanisms and systems in certain types of machines and equipment. The main reason is very small travel where the negative or quasi-zero stiffness can be realized. A small error in passive control or a soft fault in an active one is enough to move such mechanisms and systems to performance degradation. A generic model of the parametric elements with negative and quasi-zero stiffness in small and a transition model to provide these effects in large are formulated. The model analysis led to important predictions on how to obtain an optimal trade-off between the dimensions and performance of the mechanisms and systems of novel class.

General nonlinear optimization problems are difficult to solve. Depending on particular optimization algorithm, they may require tuning parameters, providing derivatives, adjusting scaling, and trying multiple starting points. Convex optimization problems do not have any of those issues and are thus easier to solve. The challenge is that these problems must meet strict requirements. Even for candidate problems with the potential to be convex, significant experience is usually needed to recognize and utilize techniques that reformulate the problems into an appropriate form.

Engineering design optimization problems are rarely unconstrained. In this chapter, we explain how to solve constrained problems. The methods in this chapter build on the gradient-based unconstrained methods fromand also assume smooth functions. We first introduce the optimality conditions for a constrained optimization problem and then focus on three main methods for handling constraints: penalty methods, sequential quadratic programming (SQP), and interior-point methods.

Simulation and instrumental measurement are the most reliable methods to examine, prove, and enhance adequacy of theoretical models and prototypes and predict their practical use. A trade-off complex of testing and measuring instruments is presented for designing vibration protection systems. This includes both the standard test equipment and add-on devices to recognize specifics in behavior of the systems with negative and quasi-zero stiffness. This covers the computer-aided tensile machines and special adapters for static and low-cycle testing, optical aids for holographic interferometry and structural testing, electro- and/or hydrodynamic exciters, sets of special infra-frequency accelerometers and external filters, AD/DA-boards, FFT- and/or wavelet analyzers, recording equipment, and standard and special software. The complex developed provides a full cycle of the system experiment, including (a) path-generation and optimization of elastic responses, (b) strain state analysis of parametric elements and mechanism units, (c) simulation and online analysis of dynamic behavior of scaled models of the systems with extremely small stiffness and damping under vibrations in a frequency range starting from near-zero values. The method of laboratory experiment is an integral part of the methodology to investigate the systems in the field.

Most algorithms in this book assume that the design variables are continuous. However, sometimes design variables must be discrete. Common examples of discrete optimization include scheduling, network problems, and resource allocation. This chapter introduces some techniques for solving discrete optimization problems.