Effects of disciplinary uncertainty on multi-objective optimization in aircraft conceptual design

Daskilewicz, Matthew J.; German, Brian; Takahashi, Timothy T.; Donovan, Shane; Shajanian, Arvin

doi:10.1007/s00158-011-0673-4

Cited by 28 publications

(16 citation statements)

References 23 publications

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“…269-274]. Such sensitivity analysis in the optimization of aircraft conceptual designs is similar to recent dissertation research [15] that had critical outputs of range, landing distance and specific range (nautical mile per pound). For the supersonic aircraft regression model in this study there are 32 combinations of sensitivity to consider, based on eight inputs and four outputs.…”

Section: Sensitivity Analysis For Robust Designmentioning

confidence: 63%

“…An EVA includes setting variation on each of the inputs and predicting through Monte Carlo analysis the variation in the output. Such uncertainty estimation work is reflected in recent dissertation research [8,14], including one involving early aircraft design [15]. The four probabilistic and historical transfer functions (declared earlier) were exported from DOE PRO XL TM to Quantum XL TM and the two additional output criteria added.…”

Section: Optimizing Initial Size Solutionmentioning

confidence: 99%

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Conceptual sizing of next supersonic passenger aircraft from regression of the limited existing designs

Joiner¹,

Zahra

Rehman

2018

MATEC Web Conf.

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Despite previous versions, there are no current supersonic passenger transport aircraft. Much aircraft research is focused on hypersonic flight and the new technologies therein and is therefore unlikely to add to commercial versions anytime soon. This study re-examines conceptual sizing of a supersonic transport aircraft based on extant supersonic designs in order to ignite research into whether a commercially-viable design might exist. Key metrics are developed using distances between likely airport network nodes, an assumed number of passengers, and a reduction in transport time to one-third of current journeys. The study uses multiple response regression of known designs to develop key performance formulae, which are then optimized to set performance values so as to estimate an initial aircraft size, including an expected value analysis to guide the next conceptual design iteration. Twenty years ago a NASA Langley Research optimization system was used to examine non-linear regression of supersonic aircraft designs and to optimize such a design around similar performance criteria. In contrast, this work is the first supersonic transport aircraft sizing to use commercially-available Excel add-on software and standard design-for-sixsigma analysis techniques; notably for the sensitivity analyses to guide the next design iteration.

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Section: Sensitivity Analysis For Robust Designmentioning

confidence: 63%

Section: Optimizing Initial Size Solutionmentioning

confidence: 99%

Conceptual sizing of next supersonic passenger aircraft from regression of the limited existing designs

Joiner¹,

Zahra

Rehman

2018

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…The differences in Conceptual Design estimates and the ultimate measured values can be attributed to at least three sources 9 :…”

Section: A Backgroundmentioning

confidence: 99%

Monte Carlo based Robust MDO applied to Aircraft Conceptual Design: a technical-financial coupling optimization strategy

Bianchi

Orra

Paglione

2015

15th AIAA Aviation Technology, Integration, and Operations Conference

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Aircraft Conceptual Design is a challenging task that requires not only the understanding of many different disciplines, but also how they interact with each other, leading to many trade-off analyses. The complexity of these interactions grows fast with the number of variables, disciplines and goals of the problem. Multidisciplinary Analysis and Optimization tools can be very helpful to explore the design space, but it is up to the engineering team to define the objective function: optimal with respect to what? For commercial aircraft segment there are two well defined objectives that mostly represent what costumers of this segment desire: minimization of block fuel or minimization of direct operating costs. The objective function is not so clear for the Executive Jets segment and it is usual to apply minimization of MTOW. This paper proposes an architecture that includes Costs, Market Share and Finances disciplines in the optimization loop, treating the market specifications provided by company's intelligence as constraints and maximizes the financial return to the shareholders. This way, the conceived solution complies with all customer needs and also provides the most attractive investment to the shareholders. A comparison is made with the traditional optimization strategy and the results shows that, although both strategies are not exactly conflicting objectives, to maximize the financial return can lead to a different design with significant improvement in financial return. Another key optimization issue treated in this work is the reliability and robustness of the design. Estimation methods have inherent model uncertainties that can not be mitigated even with in-house data calibration. Uncertainty quantification and Robust Design is held in this work by the use of Monte Carlo simulations using triangular distributions and superposition of effects. Results for the Robust Optimization showed that slight changes in the design can improve robustness in the outcomes of interest. Also, the proposed methodology reduces the computational cost of the Robust Design to almost the level of a deterministic design, presenting a significant improvement to this process. Engine Inoperative RC = Rate of Climb Re = Reynolds number Ri = Range for the i-th mission Sref, Sw = Wing reference area SRi = Specific Range for the i-th mission t/c = Thickness-to-Chord ratio TLR = Top Level Requirements TO = Take-off TOFL = Take-Off Field Length TSFC = Thrust Specific Fuel Consumption TSLS = Engine's Sea-Level Static Thrust V, TAS = True Airspeed Vfuel = Fuel Volume VH = Horizontal Tail Volume coefficient VT = Vertical Tail VV = Vertical Tail Volume coefficient Wresf = Fuel reserves weight α, AoA = Angle of Attack β = Sideslip angle Λ = Sweep angle λ = Taper-Ratio Financial acronyms and symbols DOC = Direct Operating Costs IRR = Internal Rate of Return NPV = Net Present Value Optimization acronyms and symbols MDO = Multidisciplinary Design Optimization VDD = Value-Driven Design MOGA = Multi-Objective Genetic Algorithm φ(.) = Vector of O...

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“…In this context Daskilewicz et al [14] highlight the impact of disciplinary uncertainties. They show that the impact of uncertainties on the optimal aircraft design depends on the amplitude of the uncertainties and on the location of the design parameters in the search space.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Multidisciplinary Design Optimization Under Both Model and Design Variables Uncertainty

Jaeger

Gogu

Segonds

et al. 2013

Journal of Aircraft

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Low-fidelity analytical models are often used at the conceptual aircraft design stage. Because of uncertainties on these models and their corresponding input variables, deterministic optimization may achieve under-design or overdesign. Therefore it is important to already consider these uncertainties at the conceptual design stage in order to avoid inefficient design and then costly time over runs due to re-design. This paper presents a procedure for reliable and robust optimization of an aircraft at the conceptual design phase. Uncertainties on model and design variables are taken into account in a probabilistic setting. More precisely, at each point of the optimization process uncertainties are modeled by an adaptive normal law strategy in order to fit the historical aircraft database. The statistical parameters are adjusted depending on the available information at the current point of the optimization process. To improve computational cost, response surface approximations are constructed to represent reliability constraints. The developed methodology is applied to the conceptual design of a short range aircraft. Compared to standard deterministic optimization without design margins, the result shows a modest increase on weight, which allows however to ensure a desired reliability and robustness of the design compared to the unreliable and sensitive deterministic optimum.

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Effects of disciplinary uncertainty on multi-objective optimization in aircraft conceptual design

Cited by 28 publications

References 23 publications

Conceptual sizing of next supersonic passenger aircraft from regression of the limited existing designs

Conceptual sizing of next supersonic passenger aircraft from regression of the limited existing designs

Monte Carlo based Robust MDO applied to Aircraft Conceptual Design: a technical-financial coupling optimization strategy

Aircraft Multidisciplinary Design Optimization Under Both Model and Design Variables Uncertainty

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