501 research outputs found
A Knowledge-Based Optimization Method for Aerodynamic Design
A new aerodynamic design method, CODISC, has been developed that combines a legacy knowledge-based design method, CDISC, with a simple optimization module known as SOUP. The primary goal of this new design system is to improve the performance gains obtained using CDISC without adding significant computational time. An additional objective of this approach is to reduce the need for a priori knowledge of good initial input variable values, as well as for subsequent manual revisions of those values as the design progresses. Several test cases illustrate the development of the process to date and some of the options available at transonic and supersonic speeds for turbulent flow designs. The test cases generally start from good baseline configurations and, in all cases, were able to improve the performance. Several new guidelines for good initial values for the design variables, as well as new design rules within CDISC itself, were developed from these cases
Aerodynamic shape optimization by multi-fidelity modeling and manifold mapping
Aerodynamic shape optimization (ASO) is important in contemporary engineering design of complex systems such as aircraft and wind turbines. The use of high-fidelity partial differential equation (PDE) simulations within the design process is becoming the standard. However, the overall computation cost of the ASO problem can be very high when considering the following key challenges: (1) time-consuming PDE simulations, (2) large number of design variables, and (3) conventional optimization require many system evaluations. Combined these form an optimization problem which may be prohibitive to solve, even when using high performance computing (HPC) systems. In this work, a computationally efficient optimization algorithm for aerodynamic design is presented. In our approach, direct optimization of a computationally expensive model is replaced by an iterative updating and re-optimization of a fast physics-based replacement model, following the surrogate-based optimization paradigm. The surrogate is constructed using a low-fidelity model which is corrected using manifold mapping (MM) to become a reliable representation of the high-fidelity one during the optimization process. Only one high-fidelity PDE simulation is required per design iteration. The version of MM utilized here does not require gradient information. The proposed method is validated and characterized by applying it to several benchmark ASO problems, including lift-constrained airfoil drag minimization in inviscid and viscous transonic flows, and comparing the results with state of the art techniques. MM yielded optimized shapes, with 8 B-spline design variables, that are comparable to the shapes obtained by direct optimization algorithms equipped with adjoint sensitivities and trust regions. In the inviscid benchmark case, MM needed less than 150 equivalent high-fidelity model evaluations (only flow solutions), or approximately 460 minutes on a HPC with 32 processors, whereas the direct algorithm needed 391 high-fidelity model evaluations (flow and adjoint), or approximately 4,494 minutes on the same HPC. In other words, the MM algorithm was an order of magnitude faster than the gradient-based search with adjoint sensitivities in this case. For the viscous case, MM yields an optimized shape using less than 300 equivalent high-fidelity evaluations, taking approximately 80 hours on the HPC. In this case, the direct algorithm was not able to reach a comparably efficient shape. MM is able to handle vector-valued design problems efficiently. This was demonstrated on a multipoint design problem as well as on an inverse design problem. The multipoint design showed that the optimized airfoil outperformed that original airfoil in terms of flight conditions and robustness in multiple cruise conditions. In the inverse design case, MM needed an order of magnitude less equivalent high-fidelity model evaluations than a derivative-free algorithm
High-Fidelity Wing Design Exploration with Gradient-Based Optimization
Numerical optimization has been applied to wing design problems for over 40 years. Over the decades, the scope and detail of optimization problems have advanced considerably. At the present time, the state-of-the-art in wing design optimization incorporates high-fidelity modeling of the steady-state aeroelastic response of the wing at both on-design and off-design operating conditions. Reynolds-averaged solutions of the Navier–Stokes equations coupled with linear finite element anal- ysis offer the highest fidelity modeling currently tenable in an optimization con- text. However, the complexity of implementing and cost of executing high-fidelity aerostructural optimization have limited the extent of research on the topic. The goal of this dissertation is to examine the general application of these tools to wing design problems and highlight several factors pertaining to their usefulness and versatility.
Two types of wing design problems are considered in this dissertation: refin- ing and exploratory. Refining problems are more common in practice, especially for high-fidelity optimization, because they start from a good design and make small changes to improve it. Exploratory problems are intended to have liberal parametrizations predisposed to have significant differences between the original and final designs. The investigation of exploratory problems yields novel findings regarding multimodality in the design space and robustness of the framework.
Multimodality in the design space can impact the usefulness and versatility of gradient-based optimization in wing design. Both aerodynamic and aerostructural wing design problems are shown to be amenable to gradient-based optimization despite the existence of multimodality in some cases. For example, a rectangular wing with constant cross-section is successfully converted, through gradient-based optimization, into a swept-back wing with transonic airfoils and a minimum-mass structure. These studies introduce new insights into the tradeoff between skin- friction and induced drag and its impact on multimodality and optimization. The results of these studies indicate that multimodality is dependent on model fidelity and geometric parametrization. It is shown that artificial multimodality can be eliminated by improving model fidelity and numerical accuracy of functions and derivatives, whereas physically significant multimodality can be controlled with the application of geometric constraints.
The usefulness of numerical optimization in wing design hinges on the ability of the optimizer to competently balance fundamental tradeoffs. With comprehensive access to the relevant design parameters and physics models of the aerostructural system, an optimizer can converge to a better multidisciplinary design than is pos- sible with a traditional, sequential design process. This dissertation features the high-fidelity aerostructural optimization of an Embraer regional jet, in which si- multaneous optimization of airfoil shape, planform, and structural sizing variables yields a significantly improved wing over the baseline design. For a regional jet, it is shown that the inclusion of climb and descent segments in the fuel burn com- putation has a significant impact on the tradeoff between structural weight and aspect ratio. Another study addresses the tradeoff between cruise performance and low-speed, high-lift flight characteristics. A separation constraint at a low-speed, high-lift condition is introduced as an effective method of preserving low-speed performance while still achieving significant fuel burn reduction in cruise.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163242/1/nbons_1.pd
Aerodynamic Shape Optimization Investigations of the Common Research Model Wing Benchmark
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140684/1/1.J053318.pd
Constrained Multipoint Aerodynamic Shape Optimization Using an Adjoint Formulation and Parallel Computers
An aerodynamic shape optimization method that treats the design of complex aircraft configurations subject to high fidelity computational fluid dynamics (CFD), geometric constraints and multiple design points is described. The design process will be greatly accelerated through the use of both control theory and distributed memory computer architectures. Control theory is employed to derive the adjoint differential equations whose solution allows for the evaluation of design gradient information at a fraction of the computational cost required by previous design methods. The resulting problem is implemented on parallel distributed memory architectures using a domain decomposition approach, an optimized communication schedule, and the MPI (Message Passing Interface) standard for portability and efficiency. The final result achieves very rapid aerodynamic design based on a higher order CFD method. In order to facilitate the integration of these high fidelity CFD approaches into future multi-disciplinary optimization (NW) applications, new methods must be developed which are capable of simultaneously addressing complex geometries, multiple objective functions, and geometric design constraints. In our earlier studies, we coupled the adjoint based design formulations with unconstrained optimization algorithms and showed that the approach was effective for the aerodynamic design of airfoils, wings, wing-bodies, and complex aircraft configurations. In many of the results presented in these earlier works, geometric constraints were satisfied either by a projection into feasible space or by posing the design space parameterization such that it automatically satisfied constraints. Furthermore, with the exception of reference 9 where the second author initially explored the use of multipoint design in conjunction with adjoint formulations, our earlier works have focused on single point design efforts. Here we demonstrate that the same methodology may be extended to treat complete configuration designs subject to multiple design points and geometric constraints. Examples are presented for both transonic and supersonic configurations ranging from wing alone designs to complex configuration designs involving wing, fuselage, nacelles and pylons
Nonlinear aerodynamic wing design
The applicability of new nonlinear theoretical techniques is demonstrated for supersonic wing design. The new technology was utilized to define outboard panels for an existing advanced tactical fighter model. Mach 1.6 maneuver point design and multi-operating point compromise surfaces were developed and tested. High aerodynamic efficiency was achieved at the design conditions. A corollary result was that only modest supersonic penalties were incurred to meet multiple aerodynamic requirements. The nonlinear potential analysis of a practical configuration arrangement correlated well with experimental data
The application of CFD for military aircraft design at transonic speeds
Numerous computational fluid dynamics (CFD) codes are available that solve any of several variations of the transonic flow equations from small disturbance to full Navier-Stokes. The design philosophy at General Dynamics Fort Worth Division involves use of all these levels of codes, depending on the stage of configuration development. Throughout this process, drag calculation is a central issue. An overview is provided for several transonic codes and representative test-to-theory comparisons for fighter-type configurations are presented. Correlations are shown for lift, drag, pitching moment, and pressure distributions. The future of applied CFD is also discussed, including the important task of code validation. With the progress being made in code development and the continued evolution in computer hardware, the routine application of these codes for increasingly more complex geometries and flow conditions seems apparent
Performance improvement of small Unmanned Aerial Vehicles through gust energy harvesting
Fixed-wing miniature aerial vehicles usually fly at low altitudes that are often exposed to turbulent environments. Gust soaring is a flight technique of energy harvesting in such a complex and stochastic domain. The presented work shows the feasibility and benefits of exploiting a nonstationary environment for a small unmanned aerial vehicle. A longitudinal dynamics trajectory is derived, showing significant benefits in extended flight with a sinusoidal wind profile. An optimization strategy for active control is performed, with the aim of obtaining the most effective set of gains for energy retrieval. Moreover, a three-dimensional multipoint model confirms the feasibility of energy harvesting in a more complex spatial wind field. The influence of unsteady aerodynamics is determined on the overall energy gain along the flight path with active proportional control. The aerodynamic derivatives describing the contribution to lift by a change in angle of attack and elevator deflection are identified as the most contributing aerodynamic parameters for energy harvesting in a gusty environment, and are therefore suggested as a basic objective function of an unmanned aerial vehicle design for such a flight strategy
Multipoint Aerodynamic Shape Optimization Investigations of the Common Research Model Wing
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140687/1/1.J054154.pd
Multimission Aircraft Fuel-Burn Minimization via Multipoint Aerostructural Optimization
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140677/1/1.J052940.pd
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