By Muhammad OsamaReviewed by Susha Cheriyedath, M.Sc.Dec 19 2024
In an article recently published in the journal Aerospace, researchers comprehensively explored the application of machine learning (ML) techniques to predict wall pressure fluctuations on aerospace launchers during atmospheric ascent.
Their work addresses a key challenge in aerospace engineering, where pressure variations can cause strong surface vibrations, potentially damaging the payload. The goal was to improve prediction accuracy and streamline design processes, thereby supporting safer and more efficient aerospace operations.
Implications of Pressure Fluctuations
The aerospace industry faces significant challenges, particularly due to the unpredictable nature of pressure fluctuations during launch. As vehicles ascend through the dense atmosphere, they experience turbulent flows that generate varying pressure loads on their surfaces. These fluctuations can speed up structural fatigue and cause expensive damage. Traditionally, engineers and industry practitioners have used wind tunnel testing and semi-empirical models to predict these loads. However, these methods are both time-consuming and costly, highlighting the need for more efficient solutions.
Accurately predicting pressure loads is essential because it directly affects the structural strength/integrity and overall performance of launch vehicles. The ability to forecast these fluctuations with precision can minimize design risks and enhance safety measures, making it a crucial area of research within aerospace engineering.
Research Methodologies
In this paper, the authors focused on developing an ML-based predictive model for acoustic loads experienced by the European Advanced Generation Vehicle (VEGA) launcher family during atmospheric flight. They used a comprehensive dataset from wind tunnel tests conducted on a 1:30-scaled model of the VEGA launcher in the T1500 transonic wind tunnel at the Swedish Defence Research Agency. Pressure fluctuations were measured using 23 miniature pressure sensors strategically placed along the launcher's surface.
The researchers trained various supervised ML algorithms, including linear regression, decision trees, Support Vector Machines (SVMs), logistic regression, Gaussian Process Regression (GPR), and artificial neural networks (ANNs). These models were designed to predict sound pressure levels (SPLs) based on input parameters such as Mach number, angle of incidence, and sensor position. The dataset comprised 36,708 observations recorded at a sampling rate of 105 samples per second over 2.2 seconds, covering Mach numbers from 0.83 to 0.98 and angles of incidence from 0° to 6°.
Additionally, model performance was assessed using statistical metrics, like mean square error (MSE), root mean square error (RMSE), and the correlation coefficient (R-squared). Furthermore, a sensitivity analysis was conducted to assess how training dataset size influenced model accuracy, offering insights into the robustness of the different algorithms.
Key Findings of Using Machine Learning Models
The study highlighted the effectiveness of ensemble methods, especially the bagged tree approach, in predicting pressure fluctuations. The bagged tree model consistently outperformed other algorithms by achieving the lowest RMSE values and demonstrated reliable performance across various test conditions. In comparison, ANNs, GPR, and SVMs showed higher error rates, likely due to their sensitivity to data distribution and noise.
The analysis further showed that tree-based algorithms remained accurate and stable across different dataset sizes. Even when the training dataset was reduced, the RMSE values changed only slightly, underscoring the robustness of the bagged tree method. Additionally, the authors demonstrated that the best-performing models could generalize predictions to unseen flow conditions, a key requirement for real-world aerospace applications where launch vehicles encounter varying aerodynamic environments.
These outcomes emphasized the importance of reliable predictive models in improving launcher structural integrity and minimizing dependence on costly wind tunnel testing. The study highlighted the potential of ML techniques to optimize design processes, enhance safety, and improve the overall efficiency of aerospace vehicles.
Practical Applications
This research has significant potential for developing efficient aerospace launchers. ML models can be applied to various aerospace vehicles, helping engineers better predict acoustic loads and structural responses during flight. Using these techniques in predictive modeling can reduce wind tunnel testing and lower costs. Accurate pressure load forecasts under specific conditions can improve launcher safety and efficiency, benefiting the industry.
Integrating ML techniques into the design and testing phases can enhance prediction accuracy and extend these models' application to different geometries and flow conditions. Furthermore, the developed methodologies can be adapted to other fields where pressure fluctuations and acoustic loads are crucial. Reliable predictions in these areas can lead to stronger structures and improved performance across multiple usages.
Conclusion and Future Directions
In summary, ML techniques proved effective for predicting wall pressure fluctuations on aerospace launchers. Specifically, the bagged tree algorithm emerged as the most reliable method, demonstrating robust predictive capabilities even under varying flow conditions. These findings highlight the importance of leveraging advanced computational methodologies to improve the safety and efficiency of aerospace vehicles.
Future work should focus on validating these models across different geometries and flow conditions, addressing the complexities of turbulent flows. Additionally, combining traditional modeling methods with ML techniques could further enhance prediction accuracy. Overall, this research sets the foundation for developing a robust predictive framework for aerospace applications, ultimately supporting safer and more efficient launch vehicle designs.
Journal Reference
de Paola, E. et al. Predicting Wall Pressure Fluctuations on Aerospace Launchers Through Machine Learning Approaches. Aerospace 2024, 11, 972. DOI: 10.3390/aerospace11120972, https://www.mdpi.com/2226-4310/11/12/972
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