Raymond M. Kolonay

Efficient Supersonic Air Vehicle Structural Modeling for Conceptual Design

The next generation efficient supersonic air vehicle (ESAV) will be designed to meet a very challenging set of performance objectives. These objectives will cover, among others, supercruise, supersonic dash, survivability, maneuverability and extended range. Meeting these objectives will require the use of advanced technologies such as active aeroelastic wing, flutter suppression, maneuver load control, tailless supersonic configurations, etc. These technologies require a flexible representation of a given concept to evaluate. Because the generation of a FEM model is a very labor intensive these technologies are not evaluated on a configuration until that configuration is set based on design methods that do not include flexibility. Therefore the results have very little feedback into the configuration itself, minimizing the potential effectiveness of these advanced technologies. This paper discusses the development of rapid conceptual FEM, aero, and control and sizing design models for ESAV concepts. The specifics of the method are given and Matlab code (MstcGeom) is used to demonstrate the rapid model generation methodology.

Quantification of Modeling Uncertainty in Aeroelastic Analyses

Traditional uncertainty quantification techniques in engineering analysis concentrate on the quantification of parametric uncertainties: inherent natural variations of the input variables. In problems with complex or newer modeling methodologies, the variabilities induced by the modeling process itself (known as model-form and predictive uncertainties) can become a significant source of uncertainty to the problem. This work demonstrates two model-form uncertainty quantificationmethods on an unsteady aeroelastic problem: Bayesianmodel averaging and the adjustment factors approach.While the Bayesianmodel averaging approach ismore robust and has been shown to more completely quantify the total uncertainty, it also requires the presence of experimental data, which are not always readily available in preliminary design. As such, this work introduces an uncertainty quantification methodology for use in aeroelastic analysis that uses the modeling uncertainty to drive the necessity of further experimental data points.Within thismethodology, themodified adjustment factors approach has been developed to calculate the sensitivity of the adjusted models to the model probability assumptions being input into the work, facilitating the flow of the design methodology.

Efficient Supersonic Air Vehicle Preliminary Conceptual Multi-Disciplinary Design Optimization Results

The Air Force Research Lab (AFRL) is investigating concepts for the next generation Efficient Supersonic Air Vehicle (ESAV). Research efforts are currently on going in the form of contracted efforts with the major airframers and internal research that is collaborative with academic partners. One of the goals of the research efforts is to investigate new design methods that can be applied to this class of vehicle. Accurate performance and especially weight estimation early in the design cycle are a priority. Simultaneous design of major sub-systems is another. With advance technologies coming from adaptive versatile engine technology for the propulsion system, active aero elastic wing, flutter suppression, gust load alleviation and maneuver load control, tailless supersonic for the airframe / control system, and directed energy weapon systems stressing the power and thermal sub-systems, the system design problem for this platform is complex beyond any existing aircraft flying today. This places pressure on the design methods to accurately predict the performance and weight of concepts accurately early in the design cycle. This paper presents initial efforts that are focused on airframe and stability control technology impacts on weight and performance in the conceptual design phase.

TURBINE BLADE PROBABILISTIC ANALYSIS USING SEMI-ANALYTICAL SENSITIVITIES

Over the years engineering design practices have matured to a point where significant performance gains are not likely obtainable with further enhancement of existing deterministic analyses. Future design improvements will require that engineers address the underlying uncertainty present in the parameters of engineering models. This paper investigates the application and implementation of first-order reliability and finite element semi-analytical sensitivities to the probabilistic analysis of an aircraft engine turbine blade. State-of-theart computer aided design (CAD) techniques are employed to automate and coordinate finite element meshing for shape sensitivity calculations. Random variables are used to model uncertainty in turbine blade shape parameters. Two reliability techniques are examined: the mean-value first-order (MVFO) and the Hasofer-Lind Rackwitz-Fiessler first-order (HLRF) reliability methods. The HLRF requires accurate gradient information which is accomplished via a semi-analytical finite element technique. Computational costs of the probabilistic analyses and a discussion of implementation issues are presented.

Continuum Sensitivity Analysis for Structural Shape Design Variables Using Finite-Volume Method

Two of the most important requirements when using gradient-based optimization for fluid–structure interaction problems are efficiency and accuracy in calculating the sensitivities. As a result, analytical continuum sensitivity formulations are finding their place due to their lower cost and accuracy. However, the necessary computation of mesh sensitivities in shape optimization is becoming a bottleneck, especially when handling complex geometries. In this research, an algorithm based on continuum sensitivity analysis for structural shape design variables is developed. In the proposed method, regularized Heaviside functions are used to modify the properties of mesh cells. Therefore, mesh dependency is removed from the sensitivity equations. This method does not require moving or modifying the mesh to handle the shape changes. This method is applied to two different validation cases where continuum sensitivity equations are formulated and solved. Validation cases are selected as a structural and thermal pro...

Efficient Supersonic Air Vehicle Analysis and Optimization Implementation using SORCER

The Air Force Research Lab’s Multidisciplinary Science and Technology Center is currently investigating conceptual design processes and computing frameworks that could significantly impact the design of the next generation efficient supersonic air vehicle (ESAV). To make the technological advancements required of a new ESAV, the conceptual design process must accommodate both lowand high-fidelity multidisciplinary engineering analyses. These analyses may be coupled and computationally expensive, which poses a challenge since a large number of configurations must be analyzed. In light of these observations, a design process described herein uses the SORCER (Service-Oriented Computing Environment) software to combine propulsion, structures, aerodynamics, performance, and aeroelasticity in a multidisciplinary analysis (MDA) of an ESAV. The SORCER engineering software provides the MDA automation and tight integration to grid computing resources necessary to achieve the volume of analyses required for conceptual design. Details of the SORCER implementation are illustrated through ESAV design studies using a gradient-based optimization method. A discussion of preliminary optimization results and SORCER grid computing integration is provided.

Service-Oriented Programming for Design Space Exploration

Each computing system requires a platform that allows software to run. Each platform’s programming environment reflects a relevant abstraction, and usually the type and quality of the abstraction implies the complexity of problems we are able to solve. The Service ORiented Computing EnviRonment (SORCER) targets service abstractions for transdisciplinary complexity with support for distributed high performance computing. SORCER service commands are expressed in an Exertion-Oriented Language (EOL) in concert with two other languages. In this paper event-driven design space exploration is presented using these languages. A nonlinear optimization-programming example is described using the federation of services. The design service requestor collaborating with a space explorer, optimization model, and optimizer services in the network defines the service-oriented design space exploration.

Quantification of Modeling Uncertainty in Aeroelastic Design

Traditional uncertainty quantification techniques in engineering design concentrate on the quantification of parametric uncertainties, which are variations of the input variables. In problems with complex or newer modeling methodologies, the variability induced by the modeling process itself, known as model-form and predictive uncertainty, can become a significant source of uncertainty to the problem. This work demonstrates two model-form uncertainty quantification methods on an unsteady aeroelastic problem: Bayesian Model Averaging and the Adjustment Factors Approach. While the Bayesian Model Averaging approach is more robust and has been shown to more completely quantify the total uncertainty, it also requires the presence of experimental data, which is not always readily available in preliminary design. This work introduces a design methodology for use in preliminary design phases that instead of performing a blanket amount of experiments at various data points and configurations, utilizes the modeling uncertainty itself to drive the necessity of further experimental data points. Within this methodology, the Modified Adjustment Factors Approach has been developed to calculate the sensitivity of the adjusted models to the model probability assumptions being input into the work, facilitating the flow of the design methodology.

Vehicle Configuration Design Using Cellular-Division and Level-Set Based Topology Optimization

In this research, a combination of global-local optimization with the possibility of multi-objective trade-off solutions is considered for vehicle configuration design. At global-level, many potential configurations are created as initial designs using cellular-division method for design space exploration. At local-level, level-set method optimizes these initial shapes for strict constraint satisfaction. A combination of these two approaches with their individual strengths are synergistically integrated for evolving a configuration from an open-ended design space for better optima. This research demonstrates the framework using several benchmark problems of topology optimization.

TURBINE BLADE RELIABILITY-BASED OPTIMIZATION USING A VARIABLE- COMPLEXITY METHOD

This paper investigates the application and implementation of an aircraft engine turbine blade reliability-based optimization (RBO). The turbine blade is designed to be minimum volume while satisfying component reliability-based constraints on displacement and stress. Design variables consist of computer aided design (CAD) shape parameters. Uncertainty is introduced via random variable models of material and load parameters. A sequential qaudratic programming (SQP) technique is used in conjunction with first-order reliability theory to design the blade. State-of-the-art CAD techniques are employed to automate and coordinate the necessary finite element analyses required by the optimization and reliability algorithms. Three RBO constraint evaluation techniques are examined: the mean-value (MV) firstorder reliability method, the Hasofer-Lind RackwitzFiessler first-order reliability method (FORM), and a variable-complexity (VC) approach. Computational costs of the different RBO strategies and a discussion of implementation issues are presented.