A recent study has examined how machine learning (ML) techniques can be used to predict wall pressure fluctuations on aerospace launchers during atmospheric ascent. These predictions address a critical issue in aerospace engineering: pressure variations that can cause surface vibrations and potentially damage payloads. By improving accuracy and efficiency in these predictions, the research aims to support safer and more effective aerospace vehicle designs.
Study: Predicting Wall Pressure Fluctuations on Aerospace Launchers Through Machine Learning Approaches. Image Credit: Thierry Weber/Shutterstock.com
Understanding Pressure Fluctuations in Aerospace
During launch, aerospace vehicles experience intense pressure fluctuations as they ascend through the atmosphere. These turbulent flows generate varying pressure loads on the vehicle’s surfaces, which can lead to structural fatigue and damage over time. Traditionally, engineers have relied on wind tunnel testing and semi-empirical models to estimate these loads. While effective, these methods are time-consuming and expensive.
Improved predictive methods are essential for ensuring the structural integrity and performance of launch vehicles. Accurate forecasting can minimize risks during the design phase and enhance safety throughout the vehicle’s lifecycle.
Research Approach and Methodology
In this study, the researchers focused on predicting acoustic loads on the European Advanced Generation Vehicle (VEGA) launcher family during atmospheric flight. Their approach utilized data from wind tunnel tests performed on a scaled 1:30 model of the VEGA launcher, with pressure fluctuations recorded using 23 miniature sensors strategically placed on the vehicle’s surface. The experiments were conducted in the T1500 transonic wind tunnel at the Swedish Defence Research Agency.
The team applied a variety of supervised ML algorithms to predict sound pressure levels (SPLs). These algorithms included:
- Linear regression
- Decision trees
- Support Vector Machines (SVMs)
- Logistic regression
- Gaussian Process Regression (GPR)
- Artificial Neural Networks (ANNs)
The models relied on input parameters such as Mach number, angle of incidence, and sensor position. The dataset contained 36,708 observations recorded at 105 samples per second over 2.2 seconds. These observations spanned Mach numbers from 0.83 to 0.98 and angles of incidence ranging from 0° to 6°.
The performance of each model was evaluated using statistical metrics such as mean squared error (MSE), root mean squared error (RMSE), and correlation coefficient (R-squared). Sensitivity analyses were also conducted to determine how dataset size affected the accuracy of predictions.
Key Findings from the Study
The study highlighted the effectiveness of ensemble methods, particularly the bagged tree approach, in predicting pressure fluctuations. This model consistently achieved the lowest RMSE values, outperforming other algorithms across all test conditions. Models such as ANNs, GPR, and SVMs exhibited higher error rates, likely due to their sensitivity to data distribution and noise.
Tree-based algorithms proved especially robust. Even when the training dataset size was reduced, RMSE values showed minimal variation, underscoring the stability of the bagged tree method. Importantly, the models demonstrated strong generalization capabilities, successfully predicting outcomes for unseen aerodynamic conditions—a critical requirement for real-world aerospace applications.
The study emphasized that ML-driven models can significantly reduce dependence on costly wind tunnel testing by offering reliable and efficient predictive solutions. This shift can improve safety and optimize the design and testing processes for aerospace vehicles.
Applications and Broader Impact
The methodologies developed in this research have practical implications for aerospace engineering and beyond. Predictive ML models enable more precise forecasting of acoustic loads and structural responses during flight, reducing the need for extensive wind tunnel testing. These techniques can also extend to various aerospace vehicle designs, adapting to different geometries and flow conditions.
Beyond aerospace, these ML approaches hold promise for industries where pressure fluctuations and acoustic loads are critical. For example, automotive engineering and civil construction could benefit from similar predictive models, leading to stronger, more efficient designs.
Next Steps and Future Research
This research demonstrates the effectiveness of machine learning in predicting wall pressure fluctuations on aerospace launchers. The bagged tree algorithm emerged as the most reliable model, offering robust and accurate predictions across a range of test conditions.
Future work should focus on validating these models for diverse geometries and aerodynamic environments, addressing the complexities of turbulent flows. Integrating traditional methods with ML approaches may further enhance prediction accuracy and expand the applicability of these tools. Ultimately, this research provides a framework for safer and more efficient aerospace designs while reducing costs and improving reliability.
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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