Digital computer analysis and design of a subreflector of complex shape
Digital computer technique for computing scattered pattern of complex hyperboloid subreflector in Cassegrain antenna feed systems
Ludwig, A.↗
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An analysis of the processing requirements of a complex perceptual-motor task
Current concerns in the assessment of mental workload are discussed, and the event-related brain potential (ERP) is introduced as a promising mental-workload index. Subjects participated in a series of studies in which they were required to perform a target acquisition task while also covertly counting either auditory or visual probes. The effects of several task-difficulty manipulations on the P300 component of the ERP elicited by the counted stimulus probes were investigated. With sufficiently practiced subjects the amplitude of the P300 was found to decrease with increases in task difficulty. The second experiment also provided evidence that the P300 is selectively sensitive to task-relevant attributes. A third experiment demonstrated a convergence in the amplitude of the P300s elicited in the simple and difficult versions of the tracking task. The amplitude of the P300 was also found to covary with the measures of tracking performance. The results of the series of three experiments illustrate the sensitivity of the P300 to the processing requirements of a complex target acquisition task. The findings are discussed in terms of the multidimensional nature of processing resources.
Kramer, A. F.↗
The integrated manual and automatic control of complex flight systems
Pilot/vehicle analysis techniques for optimizing aircraft handling qualities are presented. The analysis approach considered is based on the optimal control frequency domain techniques. These techniques stem from an optimal control approach of a Neal-Smith like analysis on aircraft attitude dynamics extended to analyze the flared landing task. Some modifications to the technique are suggested and discussed. An in depth analysis of the effect of the experimental variables, such as prefilter, is conducted to gain further insight into the flared land task for this class of vehicle dynamics.
Schmidt, D. K.↗
The extent of the unstable Monin-Obukhov layer for temperature and humidity above complex hilly grassland
An analysis is given of radiosonde measurements of the potential temperature, specific humidity, and wind profiles under unstable windy conditions during the First International Satellite Land Surface Climatology Project Field Experiment in Kansas. It is found that about 86 percent of the profiles have a height range over which the Monin-Obukhov (1954) similarity is valid. It is shown that the Businger-Dyer version of the similarity functions (Businger, 1988) is satisfactory for analyzing radiosonde profiles.
Brutsaert, Wilfried↗
Microgravity isolation system design: A case study
Many acceleration-sensitive, microgravity science experiments will require active vibration isolation from manned orbiters on which they will be mounted. The isolation problem, especially in the case of a tethered payload, is a complex three-dimensional one that is best suited to modern-control design methods. In this paper, extended H(sub 2) synthesis is used to design an active isolator (i.e., controller) for a realistic single-input-multiple-output (SIMO) microgravity vibration isolation problem. Complex mu-analysis methods are used to analyze the isolation system with respect to sensor, actuator, and umbilical uncertainties. The paper fully discusses the design process employed and the insights gained. This design case study provides a practical approach for isolation problems of greater complexity. Issues addressed include a physically intuitive state-space description of the system, disturbance and noise filters, filters for frequency weighting, and uncertainty models. The controlled system satisfies all the performance specifications and is robust with respect to model uncertainties.
Hampton, R. D.↗
Structural Analysis
NASTRAN basically performs complex analyses of a structural design and predicts how various elements of the design will react to many different conditions of stress and strain. NASTRAN applications include almost every kind of structure and construction, and the program's substructuring capability allows different segments of a structure to be modeled jointly after having been modeled separately. NASTRAN permits the effects of control systems, aerodynamic transfer functions and other nonstructural problems, such as static response to thermal expansion, to be incorporated in the solution of the structural problem. Examples of industrial users of NASTRAN include DeVlieg-Sundstrand, Belvidere, Illinois, Texas Instruments, Temple, Texas and Texas Instrument's Central Research Laboratories. NASTRAN is available to private industry through NASA's Computer Software Management and Information Center.
Source record↗
A Monte Carlo/response surface strategy for sensitivity analysis: application to a dynamic model of vegetative plant growth
We describe the application of a strategy for conducting a sensitivity analysis for a complex dynamic model. The procedure involves preliminary screening of parameter sensitivities by numerical estimation of linear sensitivity coefficients, followed by generation of a response surface based on Monte Carlo simulation. Application is to a physiological model of the vegetative growth of soybean plants. The analysis provides insights as to the relative importance of certain physiological processes in controlling plant growth. Advantages and disadvantages of the strategy are discussed.
Non-NASA Center↗
Applicability of Complexity Theory to Martian Fluvial Systems: A Preliminary Analysis
In the last 15 years, terrestrial geomorphology has been revolutionized by the theories of chaotic systems, fractals, self-organization, and selforganized criticality. Except for the application of fractal theory to the analysis of lava flows and rampart craters on Mars, these theories have not yet been applied to problems of Martian landscape evolution. These complexity theories are elucidated below, along with the methods used to relate these theories to the realities of Martian fluvial systems.
Rosenshein, E. B.↗
End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty
Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.
Ben D Phillips↗
End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty
Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.
Ben Phillips↗
Form Factors, Grey Bodies and Radiation Conductances (Radks)
With today's analysis tools, large, complex thermal radiation problems are easily solved; But, as with any analytical tool, lack of an understanding of the fundamental equations and technique limitations may leave you with the wrong answer; Whether you are a new engineer or a seasoned veteran, an understanding of the techniques employed by these powerful analysis tools is crucial.
Thermal↗
Determining a Method of Enabling and Disabling the Integral Torque in the SDO Science and Inertial Mode Controllers
During design of the SDO Science and Inertial mode PID controllers, the decision was made to disable the integral torque whenever system stability was in question. Three different schemes were developed to determine when to disable or enable the integral torque, and a trade study was performed to determine which scheme to implement. The trade study compared complexity of the control logic, risk of not reenabling the integral gain in time to reject steady-state error, and the amount of integral torque space used. The first scheme calculated a simplified Routh criterion to determine when to disable the integral torque. The second scheme calculates the PD part of the torque and looked to see if that torque would cause actuator saturation. If so, only the PD torque is used. If not, the integral torque is added. Finally, the third scheme compares the attitude and rate errors to limits and disables the integral torque if either of the errors is greater than the limit. Based on the trade study results, the third scheme was selected. Once it was decided when to disable the integral torque, analysis was performed to determine how to disable the integral torque and whether or not to reset the integrator once the integral torque was reenabled. Three ways to disable the integral torque were investigated: zero the input into the integrator, which causes the integral part of the PID control torque to be held constant; zero the integral torque directly but allow the integrator to continue integrating; or zero the integral torque directly and reset the integrator on integral torque reactivation. The analysis looked at complexity of the control logic, slew time plus settling time between each calibration maneuver step, and ability to reject steady-state error. Based on the results of the analysis, the decision was made to zero the input into the integrator without resetting it. Throughout the analysis, a high fidelity simulation was used to test the various implementation methods.
Vess, Melissa F.↗
Forward Bay Cover Separation Modeling and Testing for the Orion Multi-Purpose Crew Vehicle
Spacecraft multi‐body separation events during atmospheric descent require complex testing and analysis to validate the flight separation dynamics model and to verify no recontact. NASA Orion Multi‐Purpose Crew Vehicle (MPCV) teams examined key model parameters and risk areas to develop a robust but affordable test campaign in order to validate and verify the Forward Bay Cover (FBC) separation event for Exploration Flight Test‐1 (EFT‐1). The FBC jettison simulation model is highly complex, consisting of dozens of parameters varied simultaneously, with numerous multi‐parameter interactions (coupling and feedback) among the various model elements, and encompassing distinct near‐field, mid‐field, and far‐field regimes. The test campaign was composed of component‐level testing (for example gas‐piston thrusters and parachute mortars), ground FBC jettison tests, and FBC jettison air‐drop tests that were accomplished by a highly multi‐disciplinary team. Three ground jettison tests isolated the testing of mechanisms and structures to anchor the simulation models excluding aerodynamic effects. Subsequently, two air‐drop tests added aerodynamic and parachute parameters, and served as integrated system demonstrations, which had been preliminarily explored during the Orion Pad Abort‐1 (PA‐1) flight test in May 2010. Both ground and drop tests provided extensive data to validate analytical models and to verify the FBC jettison event for EFT‐1, but more testing is required to support human certification, for which NASA and Lockheed Martin are applying knowledge from Apollo and EFT‐1 testing and modeling to develop a robust but affordable human spacecraft capability.
Ali, Yasmin↗
Nonlinear dynamics of global atmospheric and Earth-system processes
Researchers are continuing their studies of the nonlinear dynamics of global weather systems. Sensitivity analyses of large-scale dynamical models of the atmosphere (i.e., general circulation models i.e., GCM's) were performed to establish the role of satellite-signatures of soil moisture, sea surface temperature, snow cover, and sea ice as crucial boundary conditions determining global weather variability. To complete their study of the bimodality of the planetary wave states, they are using the dynamical systems approach to construct a low-order theoretical explanation of this phenomenon. This work should have important implications for extended range forecasting of low-frequency oscillations, elucidating the mechanisms for the transitions between the two wave modes. Researchers are using the methods of jump analysis and attractor dimension analysis to examine the long-term satellite records of significant variables (e.g., long wave radiation, and cloud amount), to explore the nature of mode transitions in the atmosphere, and to determine the minimum number of equations needed to describe the main weather variations with a low-order dynamical system. Where feasible they will continue to explore the applicability of the methods of complex dynamical systems analysis to the study of the global earth-system from an integrative viewpoint involving the roles of geochemical cycling and the interactive behavior of the atmosphere, hydrosphere, and biosphere.
Saltzman, Barry↗
SAR calibration and principal component analysis
Principal components analysis of ideal, complex quadpolarized synthetic aperture data tells us how the information is encoded in the eigenstructure of the data from homogeneous, distributed targets. Comparison with real data indicates distortion of the correlation matrices by system effects. The implications for measurement of the terms in the true correlation matrix are displayed. These are related to how accurately we can make inferences about the products of principle components analysis.
Quegan, S.↗
A New Approach to Parallel Interference Cancellation for CDMA
This paper introduces an improved nonlinear parallel interference cancellation scheme that significantly reduces the degrading effect of user interference with implementation complexity linear in the number of users. The scheme operates on the fact that parallel processing simultaneously removes from each user a part of the interference produced by the remaining users accessing the channel the amount being proportional to their reliability. The parallel processing can be done in multiple stages. Simulation results are given for a multitude of different situations, in particular those cases for which the analysis is too complex.
interference cancellation multiuser communication ↗
The Generalization of Mutual Information as the Information between a Set of Variables: The Information Correlation Function Hierarchy and the Information Structure of Multi-Agent Systems
The topic of this paper is a hierarchy of information-like functions, here named the information correlation functions, where each function of the hierarchy may be thought of as the information between the variables it depends upon. The information correlation functions are particularly suited to the description of the emergence of complex behaviors due to many- body or many-agent processes. They are particularly well suited to the quantification of the decomposition of the information carried among a set of variables or agents, and its subsets. In more graphical language, they provide the information theoretic basis for understanding the synergistic and non-synergistic components of a system, and as such should serve as a forceful toolkit for the analysis of the complexity structure of complex many agent systems. The information correlation functions are the natural generalization to an arbitrary number of sets of variables of the sequence starting with the entropy function (one set of variables) and the mutual information function (two sets). We start by describing the traditional measures of information (entropy) and mutual information.
Wolf, David R.↗