The Effect of Systematic Error in Forced Oscillation Wind Tunnel Test Apparatuses on Determining Nonlinear Unsteady Aerodynamic Stability Derivatives

One of the basic problems of flight dynamics is the formulation of aerodynamic forces and moments acting on an aircraft in arbitrary motion. Classically conventional stability derivatives are used for the representation of aerodynamic loads in the aircraft equations of motion. However, for modern aircraft with highly nonlinear and unsteady aerodynamic characteristics undergoing maneuvers at high angle of attack and/or angular rates the conventional stability derivative model is no longer valid. Attempts to formulate aerodynamic model equations with unsteady terms are based on several different wind tunnel techniques: for example, captive, wind tunnel single degree-of-freedom, and wind tunnel free-flying techniques. One of the most common techniques is forced oscillation testing. However, the forced oscillation testing method does not address the systematic and systematic correlation errors from the test apparatus that cause inconsistencies in the measured oscillatory stability derivatives. The primary objective of this study is to identify the possible sources and magnitude of systematic error in representative dynamic test apparatuses. Using a high fidelity simulation of a forced oscillation test rig modeled after the NASA LaRC 12-ft tunnel machine, Design of Experiments and Monte Carlo methods, the sensitivities of the longitudinal stability derivatives to systematic errors are computed. Finally, recommendations are made for improving the fidelity of wind tunnel test techniques for nonlinear unsteady aerodynamic modeling for longitudinal motion

Propagation of Computational Uncertainty Using the Modern Design of Experiments

This paper describes the use of formally designed experiments to aid in the error analysis of a computational experiment. A method is described by which the underlying code is approximated with relatively low-order polynomial graduating functions represented by truncated Taylor series approximations to the true underlying response function. A resource-minimal approach is outlined by which such graduating functions can be estimated from a minimum number of case runs of the underlying computational code. Certain practical considerations are discussed, including ways and means of coping with high-order response functions. The distributional properties of prediction residuals are presented and discussed. A practical method is presented for quantifying that component of the prediction uncertainty of a computational code that can be attributed to imperfect knowledge of independent variable levels. This method is illustrated with a recent assessment of uncertainty in computational estimates of Space Shuttle thermal and structural reentry loads attributable to ice and foam debris impact on ascent

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

Applying Monte Carlo Simulation to Launch Vehicle Design and Requirements Analysis

This Technical Publication (TP) is meant to address a number of topics related to the application of Monte Carlo simulation to launch vehicle design and requirements analysis. Although the focus is on a launch vehicle application, the methods may be applied to other complex systems as well. The TP is organized so that all the important topics are covered in the main text, and detailed derivations are in the appendices. The TP first introduces Monte Carlo simulation and the major topics to be discussed, including discussion of the input distributions for Monte Carlo runs, testing the simulation, how many runs are necessary for verification of requirements, what to do if results are desired for events that happen only rarely, and postprocessing, including analyzing any failed runs, examples of useful output products, and statistical information for generating desired results from the output data. Topics in the appendices include some tables for requirements verification, derivation of the number of runs required and generation of output probabilistic data with consumer risk included, derivation of launch vehicle models to include possible variations of assembled vehicles, minimization of a consumable to achieve a two-dimensional statistical result, recontact probability during staging, ensuring duplicated Monte Carlo random variations, and importance sampling

A systematic approach to design for lifelong aircraft evolution

Modern aerospace systems rely heavily on legacy platforms and their derivatives. Historical examples show that after a vehicle design is frozen and delivered to a customer, successive upgrades are often made to fulfill changing requirements. Current practices of adapting to emerging needs with derivative designs, retrofits, and upgrades are often reactive and ad-hoc, resulting in performance and cost penalties. Recent DoD acquisition policies have addressed this problem by establishing a general paradigm for design for lifelong evolution. However, there is a need for a unified, practical design approach that considers the lifetime evolution of an aircraft concept by incorporating future requirements and technologies. This research proposes a systematic approach with which the decision makers can evaluate the value and risk of a new aircraft development program, including potential derivative development opportunities. The proposed Evaluation of Lifelong Vehicle Evolution (EvoLVE) method is a two- or multi-stage representation of the aircraft design process that accommodates initial development phases as well as follow-on phases. One of the key elements of this method is the Stochastic Programming with Recourse (SPR) technique, which accounts for uncertainties associated with future requirements. The remedial approach of SPR in its two distinctive problem-solving steps is well suited to aircraft design problems where derivatives, retrofits, and upgrades have been used to fix designs that were once but no longer optimal. The solution approach of SPR is complemented by the Risk-Averse Strategy Selection (RASS) technique to gauge risk associated with vehicle evolution options. In the absence of a full description of the random space, a scenario-based approach captures the randomness with a few probable scenarios and reveals implications of different future events. Last, an interactive framework for decision-making support allows simultaneous navigation of the current and future design space with a greater degree of freedom. A cantilevered beam design problem was set up and solved using the SPR technique to showcase its application to an engineering design setting. The full EvoLVE method was conducted on a notional multi-role fighter based on the F/A-18 Hornet.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Bishop, Carlee; Committee Member: Costello, Mark; Committee Member: Nam, Taewoo; Committee Member: Schrage, Danie

A methodology for the validated design space exploration of fuel cell powered unmanned aerial vehicles

Unmanned Aerial Vehicles (UAVs) are the most dynamic growth sector of the aerospace industry today. The need to provide persistent intelligence, surveillance, and reconnaissance for military operations is driving the planned acquisition of over 5,000 UAVs over the next five years. The most pressing need is for quiet, small UAVs with endurance beyond what is capable with advanced batteries or small internal combustion propulsion systems. Fuel cell systems demonstrate high efficiency, high specific energy, low noise, low temperature operation, modularity, and rapid refuelability making them a promising enabler of the small, quiet, and persistent UAVs that military planners are seeking. Despite the perceived benefits, the actual near-term performance of fuel cell powered UAVs is unknown. Until the auto industry began spending billions of dollars in research, fuel cell systems were too heavy for useful flight applications. However, the last decade has seen rapid development with fuel cell gravimetric and volumetric power density nearly doubling every 2-3 years. As a result, a few design studies and demonstrator aircraft have appeared, but overall the design methodology and vehicles are still in their infancy. The design of fuel cell aircraft poses many challenges. Fuel cells differ fundamentally from combustion based propulsion in how they generate power and interact with other aircraft subsystems. As a result, traditional multidisciplinary analysis (MDA) codes are inappropriate. Building new MDAs is difficult since fuel cells are rapidly changing in design, and various competitive architectures exist for balance of plant, hydrogen storage, and all electric aircraft subsystems. In addition, fuel cell design and performance data is closely protected which makes validation difficult and uncertainty significant. Finally, low specific power and high volumes compared to traditional combustion based propulsion result in more highly constrained design spaces that are problematic for design space exploration. To begin addressing the current gaps in fuel cell aircraft development, a methodology has been developed to explore and characterize the near-term performance of fuel cell powered UAVs. The first step of the methodology is the development of a valid MDA. This is accomplished by using propagated uncertainty estimates to guide the decomposition of a MDA into key contributing analyses (CAs) that can be individually refined and validated to increase the overall accuracy of the MDA. To assist in MDA development, a flexible framework for simultaneously solving the CAs is specified. This enables the MDA to be easily adapted to changes in technology and the changes in data that occur throughout a design process. Various CAs that model a polymer electrolyte membrane fuel cell (PEMFC) UAV are developed, validated, and shown to be in agreement with hardware-in-the-loop simulations of a fully developed fuel cell propulsion system. After creating a valid MDA, the final step of the methodology is the synthesis of the MDA with an uncertainty propagation analysis, an optimization routine, and a chance constrained problem formulation. This synthesis allows an efficient calculation of the probabilistic constraint boundaries and Pareto frontiers that will govern the design space and influence design decisions relating to optimization and uncertainty mitigation. A key element of the methodology is uncertainty propagation. The methodology uses Systems Sensitivity Analysis (SSA) to estimate the uncertainty of key performance metrics due to uncertainties in design variables and uncertainties in the accuracy of the CAs. A summary of SSA is provided and key rules for properly decomposing a MDA for use with SSA are provided. Verification of SSA uncertainty estimates via Monte Carlo simulations is provided for both an example problem as well as a detailed MDA of a fuel cell UAV. Implementation of the methodology was performed on a small fuel cell UAV designed to carry a 2.2 kg payload with 24 hours of endurance. Uncertainty distributions for both design variables and the CAs were estimated based on experimental results and were found to dominate the design space. To reduce uncertainty and test the flexibility of the MDA framework, CAs were replaced with either empirical, or semi-empirical relationships during the optimization process. The final design was validated via a hardware-in-the loop simulation. Finally, the fuel cell UAV probabilistic design space was studied. A graphical representation of the design space was generated and the optima due to deterministic and probabilistic constraints were identified. The methodology was used to identify Pareto frontiers of the design space which were shown on contour plots of the design space. Unanticipated discontinuities of the Pareto fronts were observed as different constraints became active providing useful information on which to base design and development decisions.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Nam, Taewoo; Committee Member: Parekh, David; Committee Member: Soban, Danielle; Committee Member: Volovoi, Vital

Investigation into the flow physics of large experimental offshore wind farms in a turbulent boundary layer wind tunnel

Large offshore wind turbine array power plants will soon be appearing along the United States\u27 eastern seaboard. With recent advances in technology, turbines have been growing in diameter and arrays have been growing in scale, but not without technical challenges. There exist knowledge gaps in the fluid dynamics that govern the interaction of the incoming atmospheric boundary layer with the wakes of the turbines and the sequential wake-wake interactions that result in an unavoidable decreased bulk power production compared to predicted capacity from the array. Wind farm experiments were conducted with scale model wind turbines in a high Reynolds number boundary layer wind tunnel. The studies were conducted in the University of New Hampshire Flow Physics Facility which is the world\u27s largest flow physics quality turbulent boundary layer wind tunnel, with test section dimensions of 6 m wide, 2.7 m tall and 72 m long. The long fetch of the facility offers unique opportunities to study the downstream evolution of the wake of single wind turbines, and the flow through model wind turbine arrays over long distances. Two different types of model turbines were built for these studies at a 1:500 scale based on the National Renewable Energy Laboratory 5 MW offshore reference turbine. Nine 0.25 meter diameter rotating model turbines, and 95 drag matched porous disks of equal diameter were constructed. The two models were shown to have similar enough wake characteristics that they were then used to build up an experimental array. Several experimental campaigns were carried out and selected results are presented here. An experimental campaign using an array of porous disks placed in atmospheric boundary layer flow was carried with spacings of 8 diameters in the stream wise direction and 4 diameters in the span wise direction. Far downstream within a wind farm it is proposed that the flow through the farm reaches a fully developed state where the flow field becomes similar from one row to the next. It is suggested that the wind turbine array acts as a sparse displaced roughness, which creates an internal layer whose origin (in the wall-normal direction) remains fixed in space, while the turbulent boundary layer it was placed in continues to grow. To within experimental uncertainty, a fully developed wind turbine array boundary layer condition is observed in the mean velocity, for defined inlet conditions and spacings, from row 12 on. A careful consideration of experimental uncertainty is discussed due to the large physical scale of the wind tunnel and open-to-atmosphere nature. An expanded uncertainty analysis using the Taylor series method is executed to predict uncertainty for the system of interest in the mean flow. This expanded uncertainty prediction was confirmed by a Monte Carlo simulation. A workable compromise between data acquisition time and uncertainty was used in the experiments, mitigating changing initial conditions due to exposure to atmospheric conditions and temperature changes. This experimental array is sufficiently large to converge on a statistically stationary state in the mean to within 95% confidence level. Another campaign was carried out with the combination of porous disks and rotating model turbines to study the phenomenon of wake meandering, a dynamic non-periodic shift in the wake over time caused by the atmospheric wake interaction. High temporal resolution velocity time series were obtained at high enough frequencies to resolve oscillatory trends behind individual and coalescing wakes of turbines in boundary layer flow. For single turbine models, incipient wake meandering frequencies decay with downstream distance, along with peak spectral energies and eventually return to those of the incoming turbulent boundary layer flow. However, the meandering also presents itself in the large experimental array, and far downstream the peak meandering frequency is dominated by the turbine spacing, indicating a type of resonance of the array itself. Porous disk turbine models are the experimental equivalent of numerical actuator disks, therefore, in addition to insights gained into the flow physics of turbine arrays, the publicly available data set is expected to be useful for numerical model validation

Aeronautical engineering: A special bibliography with indexes, supplement 80

This bibliography lists 277 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1977