Daniel P. Raymer

Bio: Daniel P. Raymer is an academic researcher. The author has contributed to research in topics: Propulsion & Wing loading. The author has an hindex of 1, co-authored 1 publications receiving 2285 citations.

Aircraft Design: A Conceptual Approach

Abstract: * Design - A Separate Discipline * Overview of the Design Process * Sizing from a Conceptual Sketch * Airfoil and Geometry Selection * Thrust-to-Weight Ratio and Wing Loading * Initial Sizing * Configuration Layout and Loft * Special Considerations in Configuration Layout * Crew Station, Passengers, and Payload * Propulsion and Fuel System Integration * Landing Gear and Subsystems * Intermission: Step-by-Step Development of a New Design * Aerodynamics * Propulsion * Structures and Loads * Weights * Stability, Control, and Handling Qualities * Performance and Flight Mechanics * Cost Analysis * Sizing and Trade Studies * Design of Unique Aircraft Concepts * Conceptual Design Examples * Appendix A: Unit Conversion * Appendix B: Standard Atmosphere.

Engineering Design via Surrogate Modelling: A Practical Guide

Abstract: Preface. About the Authors. Foreword. Prologue. Part I: Fundamentals. 1. Sampling Plans. 1.1 The 'Curse of Dimensionality' and How to Avoid It. 1.2 Physical versus Computational Experiments. 1.3 Designing Preliminary Experiments (Screening). 1.3.1 Estimating the Distribution of Elementary Effects. 1.4 Designing a Sampling Plan. 1.4.1 Stratification. 1.4.2 Latin Squares and Random Latin Hypercubes. 1.4.3 Space-filling Latin Hypercubes. 1.4.4 Space-filling Subsets. 1.5 A Note on Harmonic Responses. 1.6 Some Pointers for Further Reading. References. 2. Constructing a Surrogate. 2.1 The Modelling Process. 2.1.1 Stage One: Preparing the Data and Choosing a Modelling Approach. 2.1.2 Stage Two: Parameter Estimation and Training. 2.1.3 Stage Three: Model Testing. 2.2 Polynomial Models. 2.2.1 Example One: Aerofoil Drag. 2.2.2 Example Two: a Multimodal Testcase. 2.2.3 What About the k -variable Case? 2.3 Radial Basis Function Models. 2.3.1 Fitting Noise-Free Data. 2.3.2 Radial Basis Function Models of Noisy Data. 2.4 Kriging. 2.4.1 Building the Kriging Model. 2.4.2 Kriging Prediction. 2.5 Support Vector Regression. 2.5.1 The Support Vector Predictor. 2.5.2 The Kernel Trick. 2.5.3 Finding the Support Vectors. 2.5.4 Finding . 2.5.5 Choosing C and epsilon. 2.5.6 Computing epsilon : v -SVR 71. 2.6 The Big(ger) Picture. References. 3. Exploring and Exploiting a Surrogate. 3.1 Searching the Surrogate. 3.2 Infill Criteria. 3.2.1 Prediction Based Exploitation. 3.2.2 Error Based Exploration. 3.2.3 Balanced Exploitation and Exploration. 3.2.4 Conditional Likelihood Approaches. 3.2.5 Other Methods. 3.3 Managing a Surrogate Based Optimization Process. 3.3.1 Which Surrogate for What Use? 3.3.2 How Many Sample Plan and Infill Points? 3.3.3 Convergence Criteria. 3.3.4 Search of the Vibration Isolator Geometry Feasibility Using Kriging Goal Seeking. References. Part II: Advanced Concepts. 4. Visualization. 4.1 Matrices of Contour Plots. 4.2 Nested Dimensions. Reference. 5. Constraints. 5.1 Satisfaction of Constraints by Construction. 5.2 Penalty Functions. 5.3 Example Constrained Problem. 5.3.1 Using a Kriging Model of the Constraint Function. 5.3.2 Using a Kriging Model of the Objective Function. 5.4 Expected Improvement Based Approaches. 5.4.1 Expected Improvement With Simple Penalty Function. 5.4.2 Constrained Expected Improvement. 5.5 Missing Data. 5.5.1 Imputing Data for Infeasible Designs. 5.6 Design of a Helical Compression Spring Using Constrained Expected Improvement. 5.7 Summary. References. 6. Infill Criteria With Noisy Data. 6.1 Regressing Kriging. 6.2 Searching the Regression Model. 6.2.1 Re-Interpolation. 6.2.2 Re-Interpolation With Conditional Likelihood Approaches. 6.3 A Note on Matrix Ill-Conditioning. 6.4 Summary. References. 7. Exploiting Gradient Information. 7.1 Obtaining Gradients. 7.1.1 Finite Differencing. 7.1.2 Complex Step Approximation. 7.1.3 Adjoint Methods and Algorithmic Differentiation. 7.2 Gradient-enhanced Modelling. 7.3 Hessian-enhanced Modelling. 7.4 Summary. References. 8. Multi-fidelity Analysis. 8.1 Co-Kriging. 8.2 One-variable Demonstration. 8.3 Choosing X c and X e . 8.4 Summary. References. 9. Multiple Design Objectives. 9.1 Pareto Optimization. 9.2 Multi-objective Expected Improvement. 9.3 Design of the Nowacki Cantilever Beam Using Multi-objective, Constrained Expected Improvement. 9.4 Design of a Helical Compression Spring Using Multi-objective, Constrained Expected Improvement. 9.5 Summary. References. Appendix: Example Problems. A.1 One-Variable Test Function. A.2 Branin Test Function. A.3 Aerofoil Design. A.4 The Nowacki Beam. A.5 Multi-objective, Constrained Optimal Design of a Helical Compression Spring. A.6 Novel Passive Vibration Isolator Feasibility. References. Index.

Morphing Aircraft Systems: Historical Perspectives and Future Challenges

Abstract: The term ‘morphing aircraft’ describes a broad range of air vehicles and vehicle components that adapt to planned and unplanned multipoint mission requirements. Adaptation or morphing requires changing system features including vehicle ‘states,’ such as vehicle shape, during in-flight operation. The term morphing can be applied to almost any activity in which in-flight vehicle features are changed. As such, morphing has become a buzzword loosely applied to a wide variety of activities, some of which are disconnected from air vehicle morphing development. This has led to three myths: 1) morphing shape change is too expensive, 2) morphing aircraft must weigh more than nonmorphing aircraft, and 3) morphing requires exotic materials and complex systems. This paper attempts to dispel these myths by reviewing early morphing aircraft history to identify inventions and innovations that led to both successes and failures. The review also discusses recent government-sponsored activities in the United States: in par...

Underwater gliders: dynamics, control and design

Abstract: This dissertation concerns modelling the dynamics of underwater gliders and application of the model to analysis of glider dynamics, control, navigation, and design. Underwater gliders are a novel type of autonomous underwater vehicle that glide by controlling their buoyancy and attitude using internal actuators. We develop a first principles based model of the dynamics of a general underwater glider, including hydrodynamic forces, buoyancy and added mass effects, and the nonlinear coupling between glider and moving internal masses. This model is applicable to a wide range of gliders, as opposed to being vehicle specific. Development of a model of the dynamics of a general underwater glider is necessary for systematic model based control and design of this class of vehicles. This work builds on existing aircraft and underwater vehicle theory and facilitates application of existing techniques in dynamics and controls to this new type of vehicle. The glider model is applied to an analysis of the dynamics of underwater gliders, identifying gliding equilibria and their stability in a longitudinal, vertical-plane model, in a simplified dynamic model based on Lanchesters phugoid assumptions, and in full three dimensional gliding. In addition to modelling a class of vehicles, our model can be tailored to a specific glider for the purpose of predicting performance, developing improved control and navigation algorithms, and design analysis. We adapt the glider model to model the Slocum electric glider. Experimental data from trials at sea using a Slocum glider and reference data are used to identify the buoyancy trim and hydrodynamic coefficients of the experimental glider. iii The general glider model is applied to study control of gliders using buoyancy control, internal mass actuators, and external surfaces. A controller and observer for steady gliding and inflections between glides is designed. Control systems on operational gliders are described and analyzed. Controller induced limit cycles are analyzed. An analysis of glider design begins with a comparison of underwater gliders and sailplanes in the air. The glider model is then applied to analysis of glider design and glide speed, glider and ballast sizing, and alternate glider designs, including flying wings.

Next Generation More-Electric Aircraft: A Potential Application for HTS Superconductors

Abstract: Sustainability in the aviation industry calls for aircraft that are significantly quieter and more fuel efficient than today's fleet. Achieving this will require revolutionary new concepts, in particular, electric propulsion. Superconducting machines offer the only viable path to achieve the power densities needed in airborne applications. This paper outlines the main issues involved in using superconductors for aeropropulsion. We review our investigation of the feasibility of superconducting electric propulsion, which integrate for the first time, the multiple disciplines and areas of expertise needed to design electric aircraft. It is shown that superconductivity is clearly the enabling technology for the more efficient turbo-electric aircraft of the future.