Advisor: Dr. Dimitri Mavris

Milestone: PhD Thesis Final Examination (Defense)

Degree Program: Aerospace Engineering

Title: A Methodology for Quantifying the Trade-off between Fuel Efficiency and LTO Noise for a Commercial Supersonic Transport by Optimization of the Fan Design

Abstract: Commercial supersonic transports (SSTs) offer the promise of reduced travel times but have not existed since the retirement of the Concorde. For a new SST to be viable, it must improve on the fuel efficiency and landing and take-off (LTO) noise of its predecessors. However, simultaneously minimizing both is not possible, as design features that favor efficient high-speed flight, such as high-specific-thrust engines, also produce high jet velocities and noise. The inability to simultaneously minimize both fuel burn and LTO noise results in a trade-off. Lacking historical data, this trade-off is determined through simulations, typically as the Pareto front solution of a multi-objective optimization problem. That Pareto front, however, depends on the models used to predict the objectives and on the design variables that are optimized. Past work has quantified this trade-off by optimizing the engine cycle, engine size, and take-off procedure while making fixed assumptions about the fan design such as the number of blades and rotor–stator spacing. The LTO noise axis of this trade-off has been modeled with jet noise alone, without consideration for other sources such as the fan. Additionally, the fuel burn axis of this trade-off has modeled fan efficiency as a constant or a simple function of fan pressure ratio alone. Because the fan design affects fan efficiency, engine weight, and fan noise together, these simplifications risk misrepresenting the trade-off and the decisions made from it. Quantifying the consequences of these simplifications is the primary motivation of this research. This dissertation develops a parametric, physics-based Multidisciplinary Analysis (MDA) model for a Mach 1.7, 65-passenger SST that models the design block fuel and jet and fan effective perceived noise level (EPNL) at each certification observer. The MDA model also represents the fan efficiency, aerodynamic constraints, engine weight, and fan noise as parametric functions of the fan design variables. The MDA model is used to generate surrogate models that are utilized by a multi-objective optimization algorithm to find the Pareto front. The methodology is developed and evaluated through two research areas. The first evaluates the consequences of modeling LTO noise with jet noise alone. The second develops a streamline-curvature fan model, represented by surrogate models, and evaluates the consequences of fixing the fan design rather than optimizing it jointly with the engine cycle, sizing, and take-off procedure. The results demonstrate that fan noise contributes substantially to LTO noise, particularly at approach and for designs and take-off procedures that reduce jet noise. A Pareto front optimized with jet noise alone therefore underpredicts noise increasingly toward its minimum-noise end and can suggest that noise limits are achievable when they are not. Optimizing the fan design jointly with the traditional design variables produced a Pareto front that strongly outperformed every fixed-fan front considered. Moreover, different, equally plausible fixed-fan assumptions led to very different conclusions about the fuel required to meet a noise standard. The findings establish an improved methodology for quantifying the trade-off between fuel efficiency and LTO noise, showing that fan noise must be modeled and that the fan design should be optimized jointly with the traditional design variables or, at a minimum, its assumptions disclosed. 

Date and time: 2026-11-20, 11:00 AM

Location: Collaborative Visualization Environment (CoVE) Weber Space Science and Technology Building (SST III)

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Dr. Jerry Seitzman, School of Aerospace Engineering
Dr. Jechiel Jagoda, School of Aerospace Engineering
Dr. Jimmy Tai, School of Aerospace Engineering
Sriram Rallabhandi, Astro Mechanica
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