Transformer-Based Neural Network Metamodel for Nearly Orthogonal Sampling in Conceptual Design of Multistage Space Launch Vehicles

Dmytro Ivanenko; Ion Petrescu; Ahmet Yilmaz; Nikolaos Papadopoulos; Arben Joe; Milos Petrovic; James Wilson

doi:10.55524/ijircst.2025.13.1.10

Abstract

This study introduces an innovative methodology for the efficient conceptual design of complex, multidisciplinary systems that involve computationally intensive analyses and a vast array of design variables. A novel nearly-orthogonal sampling strategy with superior space-filling characteristics is employed to extract maximal insights into system behaviour using a significantly reduced number of trial designs. The sampled dataset serves as input for a metamodel constructed using advanced artificial neural networks, augmented by Transformer Networks to enhance the metamodel’s capacity for capturing intricate dependencies and complex interactions within the data. Furthermore, a stage-wise interconnection of discrete neural networks is proposed for trajectory metamodeling, effectively mitigating the dimensionality challenges inherent in traditional neural architectures. The optimization process integrates a hybrid approach, leveraging a Genetic Algorithm for global optimization in tandem with Sequential Quadratic Programming for localized refinement utilizing exact disciplinary analyses. The efficacy of the proposed methodology is demonstrated through its application to the conceptual design optimization of a multistage solid-fuelled space launch vehicle. The results reveal exceptional accuracy in approximating highly nonlinear functions, a substantial reduction in overall computational time, and significant minimization of the reliance on exhaustive disciplinary analyses, underscoring the transformative potential of this approach.

Keywords

Transformer Network, Space launch vehicle, Neural Network, Nearly-orthogonal sampling, Trajectory planning.

References

T. Wekerle, S. T. Buhl, J. M. P. de Souza, and J. R. Wertz, "Status and trends of smallsats and their launch vehicles—An up-to-date review," J. Aerosp. Technol. Manag., vol. 9, no. 3, pp. 269–286, 2017 Available from: https://doi.org/10.5028/jatm.v9i3.853
R. Ullah, G. Ma, Z. Yan, Y. Qian, and M. Y. Javed, "An approach for space launch vehicle conceptual design and multi-attribute evaluation," Aerosp. Sci. Technol., vol. 25, no. 1, pp. 65–74, 2013. Available from: https://tinyurl.com/4mvpb3r6
D. J. Bayley, C. D. Brown, D. L. Hunley, and R. J. Cipolla, "Design optimization of a space launch vehicle using a genetic algorithm," J. Spacecr. Rockets, vol. 45, no. 4, pp. 733–740, 2008. Available from: https://doi.org/10.2514/1.35318
Y. Zhang et al., "Self-adaptive robust motion planning for high DOF robot manipulator using deep MPC," in Proc. 3rd Int. Conf. Robot., Artif. Intell. Intell. Control (RAIIC), 2024. Available from: https://doi.org/10.1109/RAIIC61787.2024.10671222
A. Jiang, "Building load analysis and forecasting—A case study of the building load of the North Carolina State University Centennial Campus," 2014. Available from: https://tinyurl.com/34xfsphp
Zou, Zhibin, et al. "Joint Interference Cancellation with Imperfect CSI." MILCOM 2024-2024 IEEE Military Communications Conference (MILCOM). IEEE, 2024. Available from: https://doi.org/10.1109/milcom61039.2024.10774048
L. Gao et al., "Autonomous multi-robot servicing for spacecraft operation extension," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), 2023. Available from: https://doi.org/10.1109/IROS55552.2023.10341875
R. Liu et al., "Enhanced detection classification via clustering SVM for various robot collaboration tasks," arXiv preprint arXiv:2405.03026, 2024. Available from: https://doi.org/10.48550/arXiv.2405.03026
L. Gao, C. Danielson, and R. Fierro, "Adaptive robot detumbling of a non-rigid satellite," arXiv preprint arXiv:2407.17617, 2024. Available from: https://doi.org/10.48550/arXiv.2407.17617
J. Andrews and D. Andrews, "Designing reusable launch vehicles for future space markets," in Proc. AIAA Space Conf. Expo., 2001. Available from: https://doi.org/10.2514/6.2001-4544
Y. Zhang et al., "Development and application of a Monte Carlo tree search algorithm for simulating Da Vinci Code game strategies," arXiv preprint arXiv:2403.10720, 2024. Available from: https://doi.org/10.48550/arXiv.2403.10720
Zou, Zhibin, and Aveek Dutta. "Multidimensional eigenwave multiplexing modulation for non-stationary channels." GLOBECOM 2023-2023 IEEE Global Communications Conference. IEEE, 2023. Available from: https://doi.org/10.1109/globecom54140.2023.10436899
J. S. Haberl et al., Methodology to Develop the Airport Terminal Building Energy Use Intensity (ATB-EUI) Benchmarking Tool, no. ACRP 09-10, 2015. Available from: https://trid.trb.org/view/1407333
Y. Zhang et al., "Manipulator control system based on machine vision," in Int. Conf. Appl. Tech. Cyber Intell. (ATCI 2019), Springer, 2020. Available from: https://tinyurl.com/yjzsch34
Y. Zhang et al., "Enhancing text authenticity: A novel hybrid approach for AI-generated text detection," arXiv preprint arXiv:2406.06558, 2024. Available from: https://doi.org/10.48550/arXiv.2406.06558
L. Gao et al., "Decentralized adaptive aerospace transportation of unknown loads using a team of robots," arXiv preprint arXiv:2407.08084, 2024. Available from: https://doi.org/10.48550/arXiv.2407.08084
Y. Zhang et al., "DeepGI: An automated approach for gastrointestinal tract segmentation in MRI scans," arXiv preprint arXiv:2401.15354, 2024. Available from: https://doi.org/10.48550/arXiv.2401.15354
D. Rasky, R. B. Pittman, and M. Newfield, "The reusable launch vehicle challenge," in Proc. Space 2006, 2006, p. 7208. Available from: https://doi.org/10.2514/6.2006-7208
L. Tan et al., "Enhanced self-checkout system for retail based on improved YOLOv10," J. Imaging, vol. 10, no. 10, p. 248, 2024. Available from: https://doi.org/10.3390/jimaging10100248
N. Crisp, K. Smith, and P. Hollingsworth, "Small satellite launch to LEO: A review of current and future launch systems," Trans. Jpn. Soc. Aeronaut. Space Sci., Aerosp. Technol. Jpn., vol. 12, no. ISTS29, pp. Tf_39–Tf_47, 2014. Available from: https://doi.org/10.2322/tastj.12.Tf_39
M. Zhu et al., "Ensemble methodology: Innovations in credit default prediction using LightGBM, XGBoost, and Local Ensemble," arXiv preprint arXiv:2402.17979, 2024. Available from: https://doi.org/10.48550/arXiv.2402.17979
X. Ke, A. Jiang, and N. Lu, "Load profile analysis and short-term building load forecast for a university campus," in Proc. IEEE Power Energy Soc. Gen. Meeting (PESGM), 2016. Available from: https://doi.org/10.1109/PESGM.2016.7742034
Zou, Zhibin, Iresha Amarasekara, and Aveek Dutta. "Learning to Decompose Asymmetric Channel Kernels for Generalized Eigenwave Multiplexing." IEEE Conference on Computer Communications workshops proceedings. IEEE, 2024. Available from: https://doi.org/10.1109/infocom52122.2024.10621411
A. Jiang, "A simplified dynamic model of DFIG-based wind generation for frequency support control studies." Available from: https://tinyurl.com/25pyn8et
J. Manning et al., "Machine-learning space applications on smallsats platforms with TensorFlow," 2018. Available from: https://tinyurl.com/2weafbtn
K. Mo et al., "DRAL: Deep reinforcement adaptive learning for multi-UAVs navigation in unknown indoor environment," arXiv preprint arXiv:2409.03930, 2024. Available from: https://doi.org/10.48550/arXiv.2409.03930
L. He, "Nearly-orthogonal sampling and neural network metamodel driven conceptual design of multistage space launch vehicle," Comput. -Aided Des., vol. 38, no. 6, pp. 595–607, 2006. Available from: https://doi.org/10.1016/j.cad.2006.02.001
K. Mo et al., "Fine-tuning Gemma-7B for enhanced sentiment analysis of financial news headlines," arXiv preprint arXiv:2406.13626, 2024. Available from: https://doi.org/10.48550/arXiv.2406.13626
B. Li et al., "A machine learning-based approach for improved orbit predictions of LEO space debris with sparse tracking data from a single station," IEEE Trans. Aerosp. Electron. Syst., vol. 56, no. 6, pp. 4253–4268, 2020. Available from: https://doi.org/10.1109/TAES.2020.2989067
A. Jiang et al., "Maximum solar energy tracking leverage high-DoF robotics system with deep reinforcement learning," arXiv preprint arXiv:2411.14568, 2024. Available from: https://doi.org/10.1145/3707402.3707414
K. Mo et al., "Precision kinematic path optimization for high-DoF robotic manipulators utilizing advanced natural language processing models," in Proc. 5th Int. Conf. Electron. Commun. Artif. Intell. (ICECAI), 2024. Available from: https://doi.org/10.1109/ICECAI62591.2024.10675146
Zou, Zhibin, and Aveek Dutta. "Capacity achieving by diagonal permutation for mu-mimo channels." GLOBECOM 2023-2023 IEEE Global Communications Conference. IEEE, 2023. Available from: https://doi.org/10.1109/globecom54140.2023.10437159
Y. Zhang et al., "Optimized coordination strategy for multi-aerospace systems in pick-and-place tasks by deep neural network," arXiv preprint arXiv:2412.09877, 2024. Available from: https://doi.org/10.48550/arXiv.2412.09877
A. Siemuri et al., "Machine learning utilization in GNSS—Use cases, challenges and future applications," in Proc. Int. Conf. Localization GNSS (ICL-GNSS), 2021. Available from: https://doi.org/10.1109/ICL-GNSS51451.2021.9452295

Cites this article as

D. Ivanenko, I. Petrescu, A. Yilmaz, N. Papadopoulos, A. Joe, M. Petrovic, J. Wilson, "Transformer-Based Neural Network Metamodel for Nearly Orthogonal Sampling in Conceptual Design of Multistage Space Launch Vehicles ", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-13, Issue-1, Page No-68-73, 2025. Available from: https://doi.org/10.55524/ijircst.2025.13.1.10

Corresponding Author

Dmytro Ivanenko

Aerospace and Rocket Systems Engineering, National Technical University of Ukraine, Ukraine

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