Development of a CFD model for propeller simulation.

The article presents a development of numerical model for a single propeller simulation and comparison of obtained results with experimental data available from a test campaign in scale 1:1. Described simulation is a steady state computation taking advantage of Multiple Reference Frame model implemented in Ansys CFX. The paper includes an analysis of rotating domain thickness influence on numerical values of thrust and power. The results indicate that this type of simulation may be sensitive to the sizing of rotating domain especially when disc solidity is low, or when the number of blades is 2, a frequent situation in all electric flight vehicles. The analysis shows that performing simulations, using one domain sizing, for a number of flight scenarios requiring analysis of a few rotational speeds can produce unintuitive results. Therefore, it is suggested to calibrate the model, preferably by experimental results

Investigation on Aerodynamics of Super–Effective Car for Drag Reduction.

This paper focuses on shape optimization of a car body to be used in Shell Eco Marathon race. The work consists of the review of aerodynamic performance for currently used shapes, definition of the design constraints for the vehicle and recommendations for the final shape to be used in the oncoming Shell Eco Marathon editions. The designs are inspired by winning models, but adjusted to Iron Warriors technology and scaled accordingly to the driver’s space requirements. A range of velocities from 20 km/h to 45 km/h with 5 km/h interval is tested, giving idea about the full model performance. Results are then compared and the best solutions, concerning the coefficient and parameter taking into account the frontal area influence are recommended

Comparison of Empirical Mode Decomposition and Singular Spectrum Analysis for Quick and Robust Detection of Aerodynamic Instabilities in Centrifugal Compressors

Aerodynamic instabilities in centrifugal compressors are dangerous phenomena affecting machine efficiency and in severe cases leading to failure of the compressing system. Quick and robust instability detection during compressor operation is a challenge of utmost importance from an economical and safety point of view. Rapid indication of instabilities can be obtained using a pressure signal from the compressor. Detection of aerodynamic instabilities using pressure signal results in specific challenges, as the signal is often highly contaminated with noise, which can influence the performance of detection methods. The aim of this study is to investigate and compare the performance of two non-linear signal processing methods—Empirical Mode Decomposition (EMD) and Singular Spectrum Analysis (SSA)—for aerodynamic instability detection. Two instabilities of different character, local—inlet recirculation and global—surge, are considered. The comparison focuses on the robustness, sensitivity and pace of detection—crucial parameters for a successful detection method. It is shown that both EMD and SSA perform similarly for the analysed machine, despite different underlying principles of the methods. Both EMD and SSA have great potential for instabilities detection, but tuning of their parameters is important for robust detection

Data-driven aerodynamic instabilities detection in centrifugal compressors

Centrifugal compressors are machines of utmost importance in numerous industrial and high-tech applications. They are known to be prone to the appearance of aerodynamic instabilities at low mass flow rates, when operating close to peak performance. Instabilities are a number of flow structures that negatively impact the compressor. Their effects range from efficiency loss for inlet recirculation, through increased level of vibrations and risk of fatigue damage for rotating stall up to an abrupt machine destruction for surge. Quick and accurate instabilities detection is a challenge. Detection of surge is often a top priority as it has the biggest consequences for the machine operation, however detecting other instabilities is also important for overall performance and long-time operability. A promising approach to detection is based on datadriven techniques, using high frequency signals sampled from the compressor to capture the dynamics of the system. Such approach could warn about the approaching onset of instability, providing ample of time for reaction. However, the signal is often composed of a number of overlapping sources and a considerable amount of noise, which makes it a challenge to extract the meaningful indication of instability. A valuable insight into the system state could be obtained if the sources and the noise were separated. The aim of this thesis is to build an instabilities-detection methodology leveraging data-driven signal decomposition techniques. The goal is to use a pressure signal collected inside of the compressor and obtain a real-time indication of the compressor stability. Two distinct decomposition methods, Empirical mode decomposition (EMD) and singular spectrum analysis (SSA) are investigated for this purpose. The goal of each of the method is to provide components sensitive to the presence of individual instabilities to build instabilities-sensitive features. The features are combined in the feature space, dimensionality of which can be adjusted depending on the system under analysis and expected unstable conditions. Using the decomposition techniques it is possible to increase the dimensionality of a signal, enabling differentiation of different types of instabilities present in the signal that would otherwise provide an overlapping signature in the original signal. The proposed methodology is validated with the data from a low-pressure industrial compressor, equipped with five high-frequency pressure transducers located along the flow path. The compressor was operated through a wide spectrum of conditions. In the post-processing, the data was divided into different general conditions, being stable, locally unstable and globally unstable. The results highlight the potential of defining robust features using both EMD and SSA for detecting general conditions, even with a relatively short input signal. The features are physically interpretable, and it is possible to provide meaningful thresholds for the detection of instabilities based solely on stable conditions. This is an important advantage, as operating the compressor in an unstable range brings risk of its damage. The overall accuracy of both methods is over 90%, with the majority of misclassifications coming from the region where the conditions transition from locally unstable to globally unstable. For certain machines, the extension of the operating range at the expense of safety might be beneficial. The globally unstable conditions reported in the case study can be furtherly divided into transient and deep surge. It is shown that decoupling those two instabilities for a robust indication with either EMD or SSA is not fully possible, which may come from the physical character of each instability. The features values for unstable conditions have to be known to differentiate transient and surge, hence the benefit of relying solely on stable data is lost. Obtaining features sensitive to each instability requires a longer input signal and extended processing, which negatively affects the responsiveness of the detection system. To avoid such issue, it is possible to use a general condition feature. It also requires prior mapping, but a robust indication can be obtained with a short input signal. The values of features obtained from the process show certain level of variability and tend to overlap due to noise present in the data. With a prior mapping needed for the detection of exact instabilities, a probabilistic approach to classification can be leveraged. Apart from classification, such approach provides an information about the probability of a given class, which can be used to define no-classification zones in the feature space, where the probability of each of the classes is low. It is shown that the application of probabilistic model provides comparable classification rate, but it can offer increased flexibility and limit the number of sensors to be used for detection. The approach demonstrated in this thesis can enable better understanding of the compressor operating conditions in the proximity of the surge line. Consequently, it could be useful for ensuring that the machine can safely reach its peak performance, possibly extending its operating range for different conditions

Efficient Multi-Objective CFD-Based Optimization Method for a Scroll Distributor

An efficient approach to the geometry optimization problem of a non-axisymmetric flow channel is discussed. The method combines geometrical transformation with a computational fluid dynamics solver, a multi-objective genetic algorithm, and a response surface. This approach, through geometrical modifications and simplifications allows transforming a non-axisymmetric problem into the axisymmetric one in some specific devices i.e., a scroll distributor or a volute. It results in a significant decrease in the problem size, as only the flow in a quasi-2D section of the channel is solved. A significantly broader design space is covered in a much shorter time than in the standard method, and the optimization of large flow problems is feasible with desktop-class computers. One computational point is obtained approximately eight times faster than in full geometry computations. The method was applied to a scroll distributor. For the case under analysis, it was possible to increase flow uniformity, eradicate separation zones, and increase the overall efficiency, which was followed by energy savings of 16% for the scroll. The results indicate that this method can be successfully applied for the optimization of similar problems