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Weighted least squares parameter estimation, Kalman filter, and random search problems for aerospace guidance control system design
The design, procurement, testing, and application of aerospace quality, hermetically sealed nickel-cadmium cells and batteries are presented. Cell technology, cell and battery development, and spacecraft applications are emphasized. Long term performance is discussed in terms of the effect of initial design, process, and application variables. Design guidelines and practices are given.
Problems encountered by aerospace designers in attempting to optimize whole aircraft are discussed, along with possible solutions. Large scale optimization, as opposed to component-by-component optimization, is hindered by computational costs, software inflexibility, concentration on a single, rather than trade-off, design methodology and the incompatibility of large-scale optimization with single program, single computer methods. The software problem can be approached by placing the full analysis outside of the optimization loop. Full analysis is then performed only periodically. Problem-dependent software can be removed from the generic code using a systems programming technique, and then embody the definitions of design variables, objective function and design constraints. Trade-off algorithms can be used at the design points to obtain quantitative answers. Finally, decomposing the large-scale problem into independent subproblems allows systematic optimization of the problems by an organization of people and machines.
A method is developed for the optimal design of composite links based on dynamic performance criteria directly related to structural modal damping and dynamic stiffness. An integrated mechanics theory correlates structural composite damping to the parameters of basic composite material systems, laminate parameters, link shape, and modal deformations. The inclusion of modal properties allows the selective minimization of vibrations associated with specific modes. Ply angles and fiber volumes are tailored to obtain optimal combinations of damping and stiffness. Applications to simple composite links indicate wide margins for trade-offs and illustrate the importance of various design variables to the optimal design.
A method is developed for the optimal design of composite links based on dynamic performance criteria directly related to structural modal damping and dynamic stiffness. An integrated mechanics theory correlates structural composite damping to the parameters of basic composite material systems, laminate parameters, link shape, and modal deformations. The inclusion of modal properties allows the selective minimization of vibrations associated with specific modes. Ply angles and fiber volumes are tailored to obtain optimal combinations of damping and stiffness. Applications to simple composite links indicate wide margins for trade-offs and illustrate the importance of various design variables to the optimal design.
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Four methods for preliminary aerospace vehicle design are reviewed. The first three methods (classical optimization, system decomposition, and system sensitivity analysis (SSA)) employ numerical optimization techniques and numerical gradients to feed back changes in the design variables. The optimum solution is determined by stepping through a series of designs toward a final solution. Of these three, SSA is argued to be the most applicable to a large-scale highly coupled vehicle design where an accurate minimum of an objective function is required. With SSA, several tasks can be performed in parallel. The techniques of classical optimization and decomposition can be included in SSA, resulting in a very powerful design method. The Taguchi method is more of a 'smart' parametric design method that analyzes variable trends and interactions over designer specified ranges with a minimum of experimental analysis runs. Its advantages are its relative ease of use, ability to handle discrete variables, and ability to characterize the entire design space with a minimum of analysis runs.
The optimization concepts are described in relation to an overall design process as opposed to a detailed, part-design process where the requirements are firmly stated, the optimization criteria are well established, and a design is known to be feasible. The overall design process starts with the stated requirements. Some of the design criteria are derived directly from the requirements, but others are affected by the design concept. It is these design criteria that define the performance index, or objective function, that is to be minimized within some constraints. In general, there will be multiple objectives, some mutually exclusive, with no clear statement of their relative importance. The optimization loop that is given adjusts the design variables and analyzes the resulting design, in an iterative fashion, until the objective function is minimized within the constraints. This provides a solution, but it is only the beginning. In effect, the problem definition evolves as information is derived from the results. It becomes a learning process as we determine what the physics of the system can deliver in relation to the desirable system characteristics. As with any learning process, an interactive capability is a real attriubute for investigating the many alternatives that will be suggested as learning progresses.
Numerical optimization algorithms require the knowledge of an initial set of design variables. Starting from an initial design x(sup 0), improved solutions are obtained by updating the design iteratively in a way prescribed by the particular algorithm used. If the algorithm is successful, convergence is achieved to a local optimal solution. Let A denote the iterative procedure that characterizes a typical optimization algorithm, applied to the problem: Find x belonging to R(sup n) that maximizes f(x) subject to x belonging to Omega contained in R(sup n). We are interested in problems with several local maxima (x(sub j))(sup *), j=1, ..., m, in the feasible design space Omega. In general, convergence of the algorithm A to a specific solution (x(sub j))(sup *) is determined by the choice of initial design x(sup 0). The domain of convergence D(sub j) of A associated with a local maximum (x(sub j))(sup *) is a subset of initial designs x(sup 0) in Omega such that the sequence (x(sup k)), k=0,1,2,... defined by x(sup k+1) = A(x(sup k)), k=0,1,... converges to (x(sub j))(sup *). The set D(sub j) is also called the basin of attraction of (x(sub j))(sup *). Cayley first proposed the problem of finding the basin of attraction for Newton's method in 1897. It has been shown that the basin of attraction for Newton's method exhibits chaotic behavior in problems with polynomial objective. This implies that there may be regions in the feasible design space where arbitrarily close starting points will converge to different local optimal solutions. Furthermore, the boundaries of the domains of convergence may have a very complex, even fractal structure. In this paper we show that even simple structural optimization problems solved using standard gradient based (first order) algorithms exhibit similar features.
Airbreathing launch vehicles continue to be a subject of great interest in the space access community. In particular, horizontal takeoff and horizontal landing vehicles are attractive with their airplane-like benefits and flexibility for future space launch requirements. The most promising of these concepts involve airframe integrated propulsion systems, in which the external undersurface of the vehicle forms part of the propulsion flowpath. Combining of airframe and engine functions in this manner involves all of the design disciplines interacting at once. Design and optimization of these configurations is a most difficult activity, requiring a multi-discipline process to analytically resolve the numerous interactions among the design variables. This paper describes the design and optimization of one configuration in this vehicle class, a lifting body with turbine-based low-speed propulsion. The integration of propulsion and airframe, both from an aero-propulsive and mechanical perspective are addressed. This paper primarily focuses on the design details of the preferred configuration and the analyses performed to assess its performance. The integration of both low-speed and high-speed propulsion is covered. Structural and mechanical designs are described along with materials and technologies used. Propellant and systems packaging are shown and the mission-sized vehicle weights are disclosed.
Our main project objectives are to develop computational techniques based on inverse problem theory that can be used to design directional solidification processes that lead to desired temperature gradient and growth conditions at the freezing front at various levels of gravity. It is known that control of these conditions plays a significant role in the selection of the form and scale of the obtained solidification microstructures. Emphasis is given on the control of the effects of various melt flow mechanisms on the local to the solidification front conditions. The thermal boundary conditions (furnace design) as well as the magnitude and direction of an externally applied magnetic field are the main design variables. We will highlight computational design models for sharp front solidification models and briefly discuss work in progress toward the development of design techniques for multi-phase volume-averaging based solidification models.
Airbreathing launch vehicles continue to be a subject of great interest in the space access community. In particular, horizontal takeoff and horizontal landing vehicles are attractive with their airplane-like benefits and flexibility for future space launch requirements. The most promising of these concepts involve airframe integrated propulsion systems, in which the external undersurface of the vehicle forms part of the propulsion flowpath. Combining of airframe and engine functions in this manner involves all of the design disciplines interacting at once. Design and optimization of these configurations is a most difficult activity, requiring a multi-discipline process to analytically resolve the numerous interactions among the design variables. This paper describes the design and optimization of one configuration in this vehicle class, a lifting body with turbine-based low-speed propulsion. The integration of propulsion and airframe, both from an aero-propulsive and mechanical perspective are addressed. This paper primarily focuses on the design details of the preferred configuration and the analyses performed to assess its performance. The integration of both low-speed and high-speed propulsion is covered. Structural and mechanical designs are described along with materials and technologies used. Propellant and systems packaging are shown and the mission-sized vehicle weights are disclosed.
An approach for conducting reliability-based design and optimization (RBDO) of a Boeing 767 raked wing tip (RWT) is presented. The goal is to evaluate the benefits of RBDO for design of an aircraft substructure. A finite-element (FE) model that includes eight critical static load cases is used to evaluate the response of the wing tip. Thirteen design variables that describe the thickness of the composite skins and stiffeners are selected to minimize the weight of the wing tip. A strain-based margin of safety is used to evaluate the performance of the structure. The randomness in the load scale factor and in the strain limits is considered. Of the 13 variables, the wing-tip design was controlled primarily by the thickness of the thickest plies in the upper skins. The report includes an analysis of the optimization results and recommendations for future reliability-based studies.
Uncertainty quantification (UQ) can provide a more robust understanding of a system, leading to better informed decisions earlier in the design process. The additional information that UQ provides can be leveraged during a design optimization process known as design under uncertainty that, when incorporated with multidisciplinary design and optimization, can become computationally infeasible due to the large number of responses required for meaningful results. Previous work addressed reducing the computational expense in design under uncertainty by incorporating analytic derivatives throughout polynomial chaos expansion. Although this allows design under uncertainty to be feasible for more systems, some multidisciplinary systems have design-dependent uncertain variables. This paper details an implementation of design dependent uncertain variables in a manner than preserves derivatives required for efficient gradient-based optimization throughout the process. Two analytic examples of design-dependent uncertain variables are given: the first transforms a uniform uncertain variable with one design variable and the second transforms a normal uncertain variable with two design variables. The polynomial chaos expansion (PCE) results are comparable to both the Monte Carlo (MC) results and the analytic results for the two examples. A case study that maximizes the lift-to-drag ratio with a design-dependence between the wing leading edge sweep angle and uncertain parameter percentage of laminar flow is compared to a MC and alternative optimization formulations. This paper demonstrates that design-dependent uncertain variables are valid and hold throughout PCE.
Uncertainty quantification (UQ) can provide a more robust understanding of a system, leading to better informed decisions earlier in the design process. The additional information that UQ provides can be leveraged during a design optimization process known as design under uncertainty that, when incorporated with multidisciplinary design and optimization, can become computationally infeasible due to the large number of responses required for meaningful results. Previous work addressed reducing the computational expense in design under uncertainty by incorporating analytic derivatives throughout polynomial chaos expansion. Although this allows design under uncertainty to be feasible for more systems, some multidisciplinary systems have design-dependent uncertain variables. This paper details an implementation of design dependent uncertain variables in a manner than preserves derivatives required for efficient gradient-based optimization throughout the process. Two analytic examples of design-dependent uncertain variables are given: the first transforms a uniform uncertain variable with one design variable and the second transforms a normal uncertain variable with two design variables. The polynomial chaos expansion (PCE) results are comparable to both the Monte Carlo (MC) results and the analytic results for the two examples. A case study that maximizes the lift-to-drag ratio with a design-dependence between the wing leading edge sweep angle and uncertain parameter percentage of laminar flow is compared to a MC and alternative optimization formulations. This paper demonstrates that design-dependent uncertain variables are valid and hold throughout PCE.
A new approach for combining conceptual and preliminary design techniques for wing optimization is presented for the high-speed civil transport (HSCT). A wing-shape parametrization procedure is developed which allows the linking of planform and airfoil design variables. Variable-complexity design strategies are used to combine conceptual and preliminary-design approaches, both to preserve interdisciplinary design influences and to reduce computational expense. In the study, conceptual-design-level algebraic equations are used to estimate aircraft weight, supersonic wave drag, friction drag and drag due to lift. The drag due to lift and wave drag are also evaluated using more detailed, preliminary-design-level techniques. The methodology is applied to the minimization of the gross weight of an HSCT that flies at Mach 3.0 with a range of 6500 miles.
An axisymmetric version of the Dual Throat Nozzle concept with a variable expansion ratio has been studied to determine the impacts on thrust vectoring and nozzle performance. The nozzle design, applicable to a supersonic aircraft, was guided using the unsteady Reynolds-averaged Navier-Stokes computational fluid dynamics code, PAB3D. The axisymmetric Dual Throat Nozzle concept was tested statically in the Jet Exit Test Facility at the NASA Langley Research Center. The nozzle geometric design variables included circumferential span of injection, cavity length, cavity convergence angle, and nozzle expansion ratio for conditions corresponding to take-off and landing, mid climb and cruise. Internal nozzle performance and thrust vectoring performance was determined for nozzle pressure ratios up to 10 with secondary injection rates up to 10 percent of the primary flow rate. The 60 degree span of injection generally performed better than the 90 degree span of injection using an equivalent injection area and number of holes, in agreement with computational results. For injection rates less than 7 percent, thrust vector angle for the 60 degree span of injection was 1.5 to 2 degrees higher than the 90 degree span of injection. Decreasing cavity length improved thrust ratio and discharge coefficient, but decreased thrust vector angle and thrust vectoring efficiency. Increasing cavity convergence angle from 20 to 30 degrees increased thrust vector angle by 1 degree over the range of injection rates tested, but adversely affected system thrust ratio and discharge coefficient. The dual throat nozzle concept generated the best thrust vectoring performance with an expansion ratio of 1.0 (a cavity in between two equal minimum areas). The variable expansion ratio geometry did not provide the expected improvements in discharge coefficient and system thrust ratio throughout the flight envelope of typical a supersonic aircraft. At mid-climb and cruise conditions, the variable geometry design compromised thrust vector angle achieved, but some thrust vector control would be available, potentially for aircraft trim. The fixed area, expansion ratio of 1.0, Dual Throat Nozzle provided the best overall compromise for thrust vectoring and nozzle internal performance over the range of NPR tested compared to the variable geometry Dual Throat Nozzle.
Stirling power conversion is being considered for use in a Radioisotope Power System for deep-space science missions because it offers a multifold increase in the conversion efficiency of heat to electric power. Quantifying the reliability of a Radioisotope Power System that utilizes Stirling power conversion technology is important in developing and demonstrating the capability for long-term success. A description of the Stirling power convertor is provided, along with a discussion about some of the key components. Ongoing efforts to understand component life, design variables at the component and system levels, related sources, and the nature of uncertainties is discussed. The requirement for reliability also is discussed, and some of the critical areas of concern are identified. A section on the objectives of the performance model development and a computation of reliability is included to highlight the goals of this effort. Also, a viable physics-based reliability plan to model the design-level variable uncertainties at the component and system levels is outlined, and potential benefits are elucidated. The plan involves the interaction of different disciplines, maintaining the physical and probabilistic correlations at all the levels, and a verification process based on rational short-term tests. In addition, both top-down and bottom-up coherency were maintained to follow the physics-based design process and mission requirements. The outlined reliability assessment approach provides guidelines to improve the design and identifies governing variables to achieve high reliability in the Stirling Radioisotope Generator design.