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At least 19 records

A supersensitive accelerometer for spacecraft gradiometry

An exact knowledge of the earth's gravity field is needed for advanced studies related to solid earth and ocean physics. A study (GRAVSAT-A) is currently considered by NASA to determine the gravity field with an accuracy of 2-3 mgal change and a resolution of 100 km using satellite to satellite range rate tracking techniques. The present investigation has the objective to prepare the foundation of an advanced GRAVSAT-B mission in the 1990s with an accuracy of about 1 mgal and a resolution of about 50 km. It is possible that satellite to satellite tracking will not be able to satisfy these requirements. However, it appears that a new technology, called "high sensitivity gradient gradiometry", could be employed to achieve the aims of the GRAVSAT-B mission. Attention is given to a new type of gravity gradiometer using superconducting microwave cavity oscillator accelerometers.

Reinhardt, V. S.↗

On determining important aspects of mathematical models: Application to problems in physics and chemistry

The use of parametric and functional gradient sensitivity analysis techniques is considered for models described by partial differential equations. By interchanging appropriate dependent and independent variables, questions of inverse sensitivity may be addressed to gain insight into the inversion of observational data for parameter and function identification in mathematical models. It may be argued that the presence of a subset of dominantly strong coupled dependent variables will result in the overall system sensitivity behavior collapsing into a simple set of scaling and self similarity relations amongst elements of the entire matrix of sensitivity coefficients. These general tools are generic in nature, but herein their application to problems arising in selected areas of physics and chemistry is presented.

Rabitz, Herschel↗

Multigrid one shot methods for optimal control problems: Infinite dimensional control

The multigrid one shot method for optimal control problems, governed by elliptic systems, is introduced for the infinite dimensional control space. ln this case, the control variable is a function whose discrete representation involves_an increasing number of variables with grid refinement. The minimization algorithm uses Lagrange multipliers to calculate sensitivity gradients. A preconditioned gradient descent algorithm is accelerated by a set of coarse grids. It optimizes for different scales in the representation of the control variable on different discretization levels. An analysis which reduces the problem to the boundary is introduced. It is used to approximate the two level asymptotic convergence rate, to determine the amplitude of the minimization steps, and the choice of a high pass filter to be used when necessary. The effectiveness of the method is demonstrated on a series of test problems. The new method enables the solutions of optimal control problems at the same cost of solving the corresponding analysis problems just a few times.

Arian, Eyal↗

Motion-Contrast Sensitivity: Visibility of Motion Gradients of Various Spatial Frequencies

The purpose of our experiments was to estimate basic sensitivity to motion gradients and to evaluate the evidence for second-order integration and differentiation of motion signals. We measured sensitivity to spatially sinusoidal contrast modulation between two oppositely moving bandpass-filtered noise images. The motion-contrast sensitivity function, defined as the inverse of threshold modulation amplitude as a function of modulation spatial frequency, was bandpass in shape with declines at both highest and lowest frequencies. The functions for three noise spatial frequencies had approximately the same shape when modulation frequency was expressed as a fraction of noise frequency. We compared the data with a model in which linear motion filters, whose outputs are squared or rectified, are followed by a second stage of excitatory or inhibitory pooling. The data are consistent with a model in which (1) all excitatory pooling occurs at the linear stage and (2) the second stage contains a large inhibitory pooling area, with a radius approximately eight times that of the linear receptive field.

Watson, Andrew B.↗

Methods for Sensitivity Analysis in Gradient-Based Shape Optimization: A Review

CFD is now being widely used in the design of mechanical and aerospace systems and their components. Improving the accuracy and efficiency of this process can reduce its cost and increase its effectiveness. Other motivating factors that push and pull CFD into design optimization are; (1) the need for design methodologies (single- and multi-disciplinary) to reduce cost and time in developing new fluid and aero systems for fast response to market changes; (2) new design requirements outside existing database; (3) new CFD algorithms and growth in computing power; (4) need for increased product quality and reliability; (5) lack of test facilities; (6) limitations and cost of testing techniques; (7) limitations of existing design practices; and (8) prohibitive cost of and conflicting designs derived from isolated component analyses and design, which are repetitive in nature and omit mutual interference.

Baysal, Oktay↗

Least Squares Shadowing Sensitivity Analysis of Chaotic Flow Around a Two-Dimensional Airfoil

Gradient-based sensitivity analysis has proven to be an enabling technology for many applications, including design of aerospace vehicles. However, conventional sensitivity analysis methods break down when applied to long-time averages of chaotic systems. This breakdown is a serious limitation because many aerospace applications involve physical phenomena that exhibit chaotic dynamics, most notably high-resolution large-eddy and direct numerical simulations of turbulent aerodynamic flows. A recently proposed methodology, Least Squares Shadowing (LSS), avoids this breakdown and advances the state of the art in sensitivity analysis for chaotic flows. The first application of LSS to a chaotic flow simulated with a large-scale computational fluid dynamics solver is presented. The LSS sensitivity computed for this chaotic flow is verified and shown to be accurate, but the computational cost of the current LSS implementation is high.

Blonigan, Patrick J.↗

The role of large-scale eddies in the climate equilibrium. Part 2: Variable static stability

Lorenz's two-level model on a sphere is used to investigate how the results of Part 1 are modified when the interaction of the vertical eddy heat flux and static stability is included. In general, the climate state does not depend very much on whether or not this interaction is included, because the poleward eddy heat transport dominates the eddy forcing of mean temperature and wind fields. However, the climatic sensitivity is significantly affected. Compared to two-level model results with fixed static stability, the poleward eddy heat flux is less sensitive to the meridional temperature gradient and the gradient is more sensitive to the forcing. For example, the logarithmic derivative of the eddy flux with respect to the gradient has a slope that is reduced from approximately 15 on a beta-plane with fixed static stability and approximately 6 on a sphere with fixed static stability, to approximately 3 to 4 in the present model. This last result is more in line with analyses from observations. The present model also has a stronger baroclinic adjustment than that in Part 1, more like that in two-level beta-plane models with fixed static stability, that is, the midlatitude isentropic slope is very insensitive to the forcing, the diabatic heating, and the friction, unless the forcing is very weak.

Zhou, Shuntai↗

Computation of Sensitivity Derivatives of Navier-Stokes Equations using Complex Variables

Accurate computation of sensitivity derivatives is becoming an important item in Computational Fluid Dynamics (CFD) because of recent emphasis on using nonlinear CFD methods in aerodynamic design, optimization, stability and control related problems. Several techniques are available to compute gradients or sensitivity derivatives of desired flow quantities or cost functions with respect to selected independent (design) variables. Perhaps the most common and oldest method is to use straightforward finite-differences for the evaluation of sensitivity derivatives. Although very simple, this method is prone to errors associated with choice of step sizes and can be cumbersome for geometric variables. The cost per design variable for computing sensitivity derivatives with central differencing is at least equal to the cost of three full analyses, but is usually much larger in practice due to difficulty in choosing step sizes. Another approach gaining popularity is the use of Automatic Differentiation software (such as ADIFOR) to process the source code, which in turn can be used to evaluate the sensitivity derivatives of preselected functions with respect to chosen design variables. In principle, this approach is also very straightforward and quite promising. The main drawback is the large memory requirement because memory use increases linearly with the number of design variables. ADIFOR software can also be cumber-some for large CFD codes and has not yet reached a full maturity level for production codes, especially in parallel computing environments.

Vatsa, Veer N.↗

Grid sensitivity for aerodynamic optimization and flow analysis

After reviewing relevant literature, it is apparent that one aspect of aerodynamic sensitivity analysis, namely grid sensitivity, has not been investigated extensively. The grid sensitivity algorithms in most of these studies are based on structural design models. Such models, although sufficient for preliminary or conceptional design, are not acceptable for detailed design analysis. Careless grid sensitivity evaluations, would introduce gradient errors within the sensitivity module, therefore, infecting the overall optimization process. Development of an efficient and reliable grid sensitivity module with special emphasis on aerodynamic applications appear essential. The organization of this study is as follows. The physical and geometric representations of a typical model are derived in chapter 2. The grid generation algorithm and boundary grid distribution are developed in chapter 3. Chapter 4 discusses the theoretical formulation and aerodynamic sensitivity equation. The method of solution is provided in chapter 5. The results are presented and discussed in chapter 6. Finally, some concluding remarks are provided in chapter 7.

Sadrehaghighi, I.↗

High-Fidelity Multidisciplinary Sensitivity Analysis Framework for Multipoint Rotorcraft Optimization

A multidisciplinary, gradient-based sensitivity-analysis methodology is evaluated for optimization of rotorcraft configurations. The tightly coupled discipline models include physics-based fluid dynamics and rotorcraft comprehensive analysis. A discretely-consistent adjoint method accounts for sensitivities of the unsteady flow and unstructured, dynamic, over set grids, while sensitivities of structural responses to aerodynamic loads are computed using a complex-variable method. The methodology is applied to optimize the shape of UH-60A Blackhawk helicopter blades for hover and forward flight conditions. The objective of the multipoint design is to simultaneously increase the rotorcraft figure of merit in a hover flight and reduce the rotor power in a forward flight. Trimmed loose-coupling solutions for the baseline configuration are used to initiate the tight-coupling multidisciplinary analysis. Target thrust and rolling and pitching moments are enforced as optimization constraints. The optimized configuration improves the optimization metrics at both design points. The improved performance and all constraints are maintained over many revolutions beyond the optimization interval, satisfying the required flight conditions. Computational cost of the optimization cycle is assessed in a high-performance computing environment and found affordable for design of rotorcraft in general level-flight conditions.

Wang, Li↗

Development of a Self-Aligned Compact Focusing Schlieren System for NASA Test Facilities

A novel compact, inherently self-aligned focusing schlieren system is presented for use in experiments that require flow visualization. Unlike existing focusing schlieren systems that require a separate, and precisely aligned, source grid and cutoff grid to achieve sensitivity to density gradients, this system projects an image of a physical grid element onto a background and then reimages this projection back onto the physical grid element thereby achieving self-alignment. Further, unlike existing focusing schlieren systems where the image of the source grid is physically translated relative to the cutoff grid to achieve sensitivity to density gradients, a polarization prism in the system acts on the polarization state of the light of the projected grid element to provide sensitivity adjustment. By adjusting the position of the prism along the optical axis of the instrument, changes to the sensitivity can be realized. Since the outgoing and returning light used to project an image of the grid element are orthogonally polarized to one another, reflections from windows that may be present between the lens and background of the system can be eliminated. The system can also be implemented in test facilities where optical access is limited and/or exists on only one side. A discussion on different aspects of the development of this technology is provided, as are sample images from the system at the different stages of development. Images that demonstrate the ability to make on-the-fly adjustments to the instrument's field-of-view and working distance are also presented.

Brett F Bathel↗

Annular-Cross-Section CFE Chamber

Proposed continuous-flow-electrophoresis (CFE) chamber of annular cross section offers advantages over conventional CFE chamber, and wedge-cross-section chamber described in "Increasing Sensitivity in Continuous-Flow Electrophoresis" (MFS-26176). In comparison with wedge-shaped chamber, chamber of annular cross section virtually eliminates such wall effects as electro-osmosis and transverse gradients of velocity. Sensitivity enhanced by incorporating gradient maker and radial (collateral) flow.

Sharnez, Rizwan↗

Structural optimization with approximate sensitivities

Computational efficiency in structural optimization can be enhanced if the intensive computations associated with the calculation of the sensitivities, that is, gradients of the behavior constraints, are reduced. Approximation to gradients of the behavior constraints that can be generated with small amount of numerical calculations is proposed. Structural optimization with these approximate sensitivities produced correct optimum solution. Approximate gradients performed well for different nonlinear programming methods, such as the sequence of unconstrained minimization technique, method of feasible directions, sequence of quadratic programming, and sequence of linear programming. Structural optimization with approximate gradients can reduce by one third the CPU time that would otherwise be required to solve the problem with explicit closed-form gradients. The proposed gradient approximation shows potential to reduce intensive computation that has been associated with traditional structural optimization.

Patnaik, S. N.↗

Evolution of Geometric Sensitivity Derivatives from Computer Aided Design Models

The generation of design parameter sensitivity derivatives is required for gradient-based optimization. Such sensitivity derivatives are elusive at best when working with geometry defined within the solid modeling context of Computer-Aided Design (CAD) systems. Solid modeling CAD systems are often proprietary and always complex, thereby necessitating ad hoc procedures to infer parameter sensitivity. A new perspective is presented that makes direct use of the hierarchical associativity of CAD features to trace their evolution and thereby track design parameter sensitivity. In contrast to ad hoc methods, this method provides a more concise procedure following the model design intent and determining the sensitivity of CAD geometry directly to its respective defining parameters.

Jones, William T.↗

Concept of an Opto-electronic Accelerometer System (OAS)

An accelerometer based on a spring-suspended reference mass and its precise relative motion measurement by means of 3 two-dimensional position sensitive detectors (PSD) is described. A breadboard model achieves a resolution (longterm linearity) of 25 nm in one direction. Due to the physical principle of the PSD, the second layer is slightly less sensitive than the top layer; accordingly for gravity gradient detection the more sensitive layer is selected for the Z/X component detection. At 10 Hz sampling rate an acceleration gradient of under 10 to the minus 12th power g (i.e., 0.01 E or 0.001 Gal/km) is considered to be detectable.

Kunkel, B.↗

Post-optimality analysis in aerospace vehicle design

This analysis pertains to the applicability of optimal sensitivity information to aerospace vehicle design. The present analysis demonstrates that post-optimality information generated through first-order computations can be used to accurately predict file effect of constraint and parameter perturbations on the optimal solution. This assessment is based on the solution of an aircraft design problem in which the post-optimality estimates are shown to be within a few percent of the true solution over the practical range of constraint and parameter variations. Through solution of a reusable, single-stage-to-orbit, launch vehicle design problem, this optimal sensitivity information is also shown to improve the efficiency of the design process. For a hierarchically decomposed problem, this computational efficiency is realizable by estimating the main-problem objective gradient through optimal sensitivity calculations. By reducing the need for finite differentiation of a re-optimized subproblem, a significant decrease in the number of objective function evaluations required to reach the optimal solution is obtained.

Braun, Robert D.↗

Application of Adjoint Methodology in Various Aspects of Sonic Boom Design

One of the advances in computational design has been the development of adjoint methods allowing efficient calculation of sensitivities in gradient-based shape optimization. This paper discusses two new applications of adjoint methodology that have been developed to aid in sonic boom mitigation exercises. In the first, equivalent area targets are generated using adjoint sensitivities of selected boom metrics. These targets may then be used to drive the vehicle shape during optimization. The second application is the computation of adjoint sensitivities of boom metrics on the ground with respect to parameters such as flight conditions, propagation sampling rate, and selected inputs to the propagation algorithms. These sensitivities enable the designer to make more informed selections of flight conditions at which the chosen cost functionals are less sensitive.

Rallabhandi, Sriram K.↗

Aerodynamic design optimization using sensitivity analysis and computational fluid dynamics

A new and efficient method is presented for aerodynamic design optimization, which is based on a computational fluid dynamics (CFD)-sensitivity analysis algorithm. The method is applied to design a scramjet-afterbody configuration for an optimized axial thrust. The Euler equations are solved for the inviscid analysis of the flow, which in turn provides the objective function and the constraints. The CFD analysis is then coupled with the optimization procedure that uses a constrained minimization method. The sensitivity coefficients, i.e. gradients of the objective function and the constraints, needed for the optimization are obtained using a quasi-analytical method rather than the traditional brute force method of finite difference approximations. During the one-dimensional search of the optimization procedure, an approximate flow analysis (predicted flow) based on a first-order Taylor series expansion is used to reduce the computational cost. Finally, the sensitivity of the optimum objective function to various design parameters, which are kept constant during the optimization, is computed to predict new optimum solutions. The flow analysis of the demonstrative example are compared with the experimental data. It is shown that the method is more efficient than the traditional methods.

Baysal, Oktay↗