Large-scale multidisciplinary optimization under uncertainty for electric vertical takeoff and landing aircraft

Ha, Tae Hyon; Lee, Keunseok; Hwang, John T.

doi:10.2514/6.2020-0904

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…Figure 3 depicts the relation between the generator-modelled prototypes, the inverse of which is the motor prototype. The following are the equations (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) developed to determine the generator model steps and calculation for the range of voltage, resistance and load [15][16][17]: The following are the Equations (1)- (14) developed to determine the generator model steps and calculation for the range of voltage, resistance and load [15][16][17] (3)…”

Section: Generatormentioning

confidence: 99%

“…The need for sampling study in the turboelectric system presented to find the accuracy of the output approximation of the mathematical model which the study will result in error estimation for the numerical method used [4,5]. Moreover, the sampling method been used to optimise turboelectric propulsion system with the two objectives electrical power and efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Turboelectric Uncertainty Quantification and Error Estimation in Numerical Modelling

Alrashed¹,

Nikolaidis²,

Pilidis³

et al. 2020

Applied Sciences

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Turboelectric systems can be considered complex systems that may comprise errors and uncertainty. Uncertainty quantification and error estimation processes can, therefore, be useful in achieving accurate system parameters. Uncertainty quantification and error estimation processes, however, entail some stages that provide results that are more positive. Since accurate approximation and power optimisation are crucial processes, it is essential to focus on higher accuracy levels. Integrating computational models with reliable algorithms into the computation processes leads to a higher accuracy level. Some of the current models, like Monte Carlo and Latin hypercube sampling, are reliable. This paper focuses on uncertainty quantification and error estimation processes in turboelectric numerical modelling. The current study integrates the current evidence with scholarly sources to ensure the incorporation of the most reliable evidence into the conclusions. It is evident that studies on the current subject began a long time ago, and there is sufficient scholarly evidence for analysis. The case study used to obtain this evidence is NASA N3-X, with three aircraft conditions: rolling to take off, cruising and taking off. The results show that the electrical elements in turboelectric systems can have decent outcomes in statistical analysis. Moreover, the risk of having overload branches is up to 2% of the total aircraft operation lifecycle, and the enhancement of the turboelectric system through electrical power optimisation management could lead to higher performance.

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Section: Generatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Turboelectric Uncertainty Quantification and Error Estimation in Numerical Modelling

Alrashed¹,

Nikolaidis²,

Pilidis³

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In addition, it is important to implement multi-disciplinary design optimization (MDO) and leverage uncertainties and errors in the design process and operation for eVTOL aircraft. For system-level eVTOL designs, Ha et al [39] assembled a large-scale MDO to minimize the required power. By merging thrust margin as a model uncertainty, significant improvements in robustness were made with a relatively minimal cost in efficiency.…”

Section: Introductionmentioning

confidence: 99%

Transfer-Learning-Enhanced Regression Generative Adversarial Networks for Optimal eVTOL Takeoff Trajectory Prediction

Yeh,

2024

Electronics

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Electric vertical takeoff and landing (eVTOL) aircraft represent a crucial aviation technology to transform future transportation systems. The unique characteristics of eVTOL aircraft include reduced noise, low pollutant emission, efficient operating cost, and flexible maneuverability, which in the meantime pose critical challenges to advanced power retention techniques. Thus, optimal takeoff trajectory design is essential due to immense power demands during eVTOL takeoffs. Conventional design optimizations, however, adopt high-fidelity simulation models in an iterative manner resulting in a computationally intensive mechanism. In this work, we implement a surrogate-enabled inverse mapping optimization architecture, i.e., directly predicting optimal designs from design requirements (including flight conditions and design constraints). A trained inverse mapping surrogate performs real-time optimal eVTOL takeoff trajectory predictions with no need for running optimizations; however, one training sample requires one design optimization in this inverse mapping setup. The excessive training cost of inverse mapping and the characteristics of optimal eVTOL takeoff trajectories necessitate the development of the regression generative adversarial network (regGAN) surrogate. We propose to further enhance regGAN predictive performance through the transfer learning (TL) technique, creating a scheme termed regGAN-TL. In particular, the proposed regGAN-TL scheme leverages the generative adversarial network (GAN) architecture consisting of a generator network and a discriminator network, with a combined loss of the mean squared error (MSE) and binary cross-entropy (BC) losses, for regression tasks. In this work, the generator employs design requirements as input and produces optimal takeoff trajectory profiles, while the discriminator differentiates the generated profiles and real optimal profiles in the training set. The combined loss facilitates the generator training in the dual aspects: the MSE loss targets minimum differences between generated profiles and training counterparts, while the BC loss drives the generated profiles to share analogous patterns with the training set. We demonstrated the utility of regGAN-TL on optimal takeoff trajectory designs for the Airbus A3 Vahana and compared its performance against representative surrogates, including the multi-output Gaussian process, the conditional GAN, and the vanilla regGAN. Results showed that regGAN-TL reached the 99.5% generalization accuracy threshold with only 200 training samples while the best reference surrogate required 400 samples. The 50% reduction in training expense and reduced standard deviations of generalization accuracy achieved by regGAN-TL confirmed its outstanding predictive performance and broad engineering application potential.

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“…Electric propulsion can be used continuously throughout flight or for a part of a mission, e.g., boosting power for takeoff or landing [3]. Though more complex, hybrid-electric aircraft also have the potential to increase the mission range over all-electric designs [4][5][6]. Various aircraft designs and demonstrators have been proposed at various levels of technical readiness [7][8][9][10], with commercial flights expected between 2022 and 2025 [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Modelling Green VTOL Concept Designs for Reliability and Efficiency

Booker

Patel

Mellor

2021

Designs

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All-electric and hybrid-electric aircraft are a future transport goal and a possible ‘green’ solution to increasing climate-related pressures for aviation. Ensuring the safety of passengers is of high importance, informed through appropriate reliability predictions to satisfy emerging flight certification requirements. This paper introduces another important consideration related to redundancy offered by multiplex electric motors, a maturing technology which could help electric aircraft manufacturers meet the high reliability targets being set. A concept design methodology is overviewed involving a symbolic representation of aircraft and block modelling of two important figures of merit, reliability, and efficiency, supported by data. This leads to a comparative study of green aircraft configurations indicating which have the most potential now, and in the future. Two main case studies are then presented: an electric tail rotor retrofitted to an existing turbine powered helicopter (hybrid) and an eVTOL aircraft (all-electric), demonstrating the impact of multiplex level and number of propulsion channels on meeting target reliabilities. The paper closes with a summary of the important contribution to be made by multiplex electric machines, well as the advancements necessary for green VTOL aircraft sub-systems, e.g., power control unit and batteries, to improve reliability predictions and safety further.

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Large-scale multidisciplinary optimization under uncertainty for electric vertical takeoff and landing aircraft

Cited by 4 publications

References 26 publications

Turboelectric Uncertainty Quantification and Error Estimation in Numerical Modelling

Turboelectric Uncertainty Quantification and Error Estimation in Numerical Modelling

Transfer-Learning-Enhanced Regression Generative Adversarial Networks for Optimal eVTOL Takeoff Trajectory Prediction

Modelling Green VTOL Concept Designs for Reliability and Efficiency

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