American Institute of Aeronautics and Astronautics 1 Probabilistic Assessment of Handling Qualities Characteristics in Preliminary Aircraft Design Dimitri N. Mavris * , Daniel A. DeLaurentis  , Danielle S. Soban  Aerospace System Design Laboratory (ASDL) School of Aerospace Engineering Georgia Institute of Technology Atlanta, GA 30332-0150 Abstrac t A method is introduced and demonstrated which uses parametric stability derivative data (in the form of regression equations) and probabilistic analysis techniques to evaluate the impact of uncertainty on the handling qualities characteristics of a family of aircraft alternatives. While the method is based on the use of elementary design parameters familiar to the configuration designer, it enables the computation of responses more familiar to the stability and control engineer. This connection is intended to bring about a more complete accounting of stability and handling quality characteristics in aircraft design, based on engineering analysis instead of historical data. Another key advantage of the method is that it allows for the quantification of analysis imprecision and information quantity/quality trades through fidelity uncertainty models. The metrics for these quantifications are the cumulative distribution function and probability sensitivity derivatives. The method is exemplified through the investigation of the longitudinal handling qualities trends for a defined High Speed Civil Transport design space, in the presence of fidelity uncertainty in the stability derivatives. Introduction This paper describes techniques developed for evaluating aircraft stability and handling qualities. These techniques are part of a larger, overall design methodology under development by the authors. The core focus of the overall method is evaluating aircraft system feasibility and viability in a multidisciplinary and probabilistic fashion. A simplified view of the new approach is shown inFigure 1, and Ref. [1] provides a comprehensive description of key elements of, and the rationale behind, the method. In this setting, the desire to reduce design cycle time and to improve the quality of information available during conceptual design motivate the need for multidisciplinary analysis. As a * Assistant Professor, ASDL Manager, AIAA Senior Member  Graduate Research Assistant, AIAA Student Member AIAA 98-0492, 36th Aerospace Sciences Meeting & Exhibit Copyright ' 1998 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. consequence, the method begins with the building of parametric relationships of disciplinary metrics as a function of elementary design variables (e.g. configuration geometry), based on engineering analysis. These relationships are called metamodels. For example, response surface equations (RSEs) representing drag polars may be formed by using a aerodynamic analysis code with geometric variables as inputs. 2 These RSEs, which capture the individual discipline physics for a class of aircraft, are then integrated into a sizing/synthesis code, which sizes the vehicle for a given mission. After uncertainty models are established for the operation of the vehicle (e.g. random variables for fuel cost, utilization rate, etc.), standard Monte Carlo simulation methods or Fast Probability Integration (FPI) 3 techniques may then be used to determine the system feasibility and viability via the construction of cumulative probability distributions (CDFs) for key system constraints and objectives. The ultimate objective of these probabilistic feasibility and viability investigations is to find robust solutions, which are solutions that maximize the probability of achieving success while satisfying imposed constraints. 4 Monte Carlo or FPI Monte Carlo or FPI Sizing and Synthesis Tool Propulsion Sized Vehicle Additional Vehicle Information (CG location, moments of inertia, etc) Stability & Control Meta Models Parametric Formulation as a function of elementary variables Subject of This Paper Uncertainty Models m(X) f(x) Constraint or Objective P Cumulative Dist. Function (CDF) HQ P Design Space Definition (Elementary Design Variables) Aero Structures Meta Models System Feasibility/Viability Investigation and Robust Design Figure 1 : Representation of Proposed Multidisciplinary Aircraft Design Method