Ebook: Mechanical and Aerospace Engineering

On behalf of the Organizing Committee, we are very pleased to welcome you all to the 15th International Conference on Mechanical and Aerospace Engineering (ICMAE 2024). ICMAE is a global conference that focuses on Aerospace and Mechanical Engineering. It has been held in various locations over the years. ICMAE 2024 will be held in Zagreb, Croatia from 17-20 July 2024.

This conference is sponsored by Science and Engineering Institute, co-hosted by University of Zagreb, Faculty of Electrical Engineering and Computing; Technical University of Kosice; School of Engineering (ISEP) of the Polytechnic of Porto (P. Porto). Patrons include Orleans University; Washington University in St. Louis; Capitol Technology University; University of Huddersfield; Istanbul Technical University, among others.

ICMAE 2024 has invited 4 distinguished plenary speakers. They are:

Ramesh K. Agarwal, Washington University in St. Louis, USA, “Analysis and Design of a Short to Mid-Range Hydrogen Fuel Cell Powered Commercial Aircraft”

Kambiz Ebrahimi, Loughborough University, UK, “Thermal Management System for Electric Vehicles”,

Nam Seo Goo, Konkuk University, South Korea, “Experimental and Numerical Study on of 3D Printed Carbon Fiber Composite Cylindrical Shell under Axial Loading”

Jorge Pomares, University of Alicante, Spain, “New Approaches for the Guidance, Navigation, and Control of Orbital Robotics in On-Orbit Servicing Applications”

We are honored to have them as plenary speakers and thank them in advance for coming to our conference to share their knowledge and experiences with us.

We like to thank everyone who contributed to this process with opinions, comments, and suggestions to choose the best papers and even improve their quality. Their support is invaluable in ensuring quality is achieved.

ICMAE organization was a challenging task due to the large and increasing interest of our research and application areas. Efforts from many people were required to shape the technical program, arrange meeting venues, manage the administrative aspects, and set up the social functions. We like to take this opportunity to thank all of them. We like also to thank the public and private organizations that supported the meeting in different ways. In closing, we thank you for participating and we hope you enjoy this conference! It is therefore with great honor and pride that we welcome you to Zagreb, and to the 15th International Conference on Mechanical and Aerospace Engineering (ICMAE 2024). We hope that you enjoy both your participation in the conference and your stay in beautiful Zagreb.

Conference Chairs

Prof. Pasquale Daponte, University of Sannio, Italy

Prof. Ian McAndrew, Capitol Technology University, USA

July 2024

In this work we demonstrate the possibility of estimating the wind environment of a UAV without specialised sensors, using only the UAV’s trajectory, applying a causal machine learning approach. We implement the causal curiosity method which combines machine learning times series classification and clustering with a causal framework. We analyse three distinct wind environments: constant wind, shear wind, and turbulence, and explore different optimisation strategies for optimal UAV manoeuvres to estimate the wind conditions. The proposed approach can be used to design optimal trajectories in challenging weather conditions, and to avoid specialised sensors that add to the UAV’s weight and compromise its functionality.

A 30-kg-class ducted fan UAV has been developed in this study, which has two ducted fans and one tail rotor. The transmission system, including several gearboxes, was designed for driving ducted fans by a combustion engine. For the attitude control of this UAV, a control concept has been proposed. The aerodynamic performances of ducted fan in hover condition have been investigated with the unsteady CFD method and ground test. A full-scale prototype was fabricated and hover flight tests have been carried out.

Automated plant diagnosis has a lot of promise to increase agricultural productivity, but the adoption of drone-based solutions is hampered by issues including the trade-off between processing speed and image resolution and the scarcity of labeled training data. To address these challenges, this research presents a novel two-step machine learning approach that uses Convolutional Neural Networks (CNN). Our approach guarantees the production of representative data from UAV photos while efficiently addressing class imbalance in datasets. Our method, which focuses on a dataset of apple trees with class imbalance, entails preprocessing the images, building a CNN architecture with dropout layers strewn in between convolutional and pooling layers to mitigate overfitting, and then training the model to distinguish between images that are diseased and those that are not. Our model then performs a two-step approach to identify possibly unhealthy plants and offer actual diagnosis. The experimental results provide a remarkable 80.90% accuracy rate on training data and 74.79% on test data, demonstrating the efficacy of our CNN-based drone technology for automated crop disease diagnosis and providing a viable substitute for Labor-intensive diagnostic techniques.

The Automated Guided Vehicles (AGVs) on the production lines mainly utilize positioning and navigation technology based on Light Detection and Ranging (LiDAR). There are problems with inaccurate positioning data due to data drift, environmental interference, and error accumulation. Based on Ultra-Wideband (UWB) sensors and data processing technology, the paper proposes a UWB sensor-assisted indoor localization method for AGVs in intelligent manufacturing factories. Three-dimensional spatial localization of the indoor production line is achieved by LiDAR and UWB sensors, which are used to acquire AGV coordinate information. During the operation of the AGV, the AGV communicates with the service end of the production line using the TCP communication protocol under the LAN to collect and store the coordinate data of the AGV in real time. Furthermore, this paper is based on statistical measurement principles and the “3σ principle”, preprocessing the data collected by the UWB sensor. The coordinate data collected by the two positioning methods are fitted and optimized using Kalman filtering. The experimental data are analyzed by trajectory analysis and calculating the average absolute error. The results show that the method in this paper controls the error range of AGV coordinates within ±0.2 meters, which meets the standard of indoor high-precision positioning. This paper combines LiDAR and UWB sensors with data processing techniques to provide a solution to the challenges associated with the high-precision indoor positioning of AGVs.

This research derives the ground target tracking algorithm of a fixed-wing UAV equipped with a gimballed camera. The gimballed camera is controlled to track a moving target independent of the UAV motion, and this is achieved by employing a UKF to estimate the position and velocity of the target. The input of the UKF is the bounding box of the target, which is generated by YOLO, a deep neural network algorithm used to detect the target. The estimated state is essential for image tracking control, and it is achieved by the IBVS algorithm and a gimbal controller to lock the target when the UAV is in flight. Simulations are conducted through to verify the efficacy of the developed controller.

This study investigates ignition enhancement strategies for a dual strut/cavity scramjet combustor under simulated Mach 2 flight conditions. To address the low-Mach-number combustion instability challenge, we employ a methane/oxygen rocket’s tail flame as a pilot flame combined with plasma jet activation. Experimental results reveal two critical findings: (1) Engine ignition failed without rocket assistance, confirming the necessity of external ignition support at low Mach numbers; (2) Flame flashback occurred in the secondary cavity when local equivalence ratios reached 0.5, correlating with amplified 100Hz pressure oscillations. While plasma injection enhanced kerosene combustion efficiency by intensifying the pilot flame, it simultaneously exacerbated flame flashback and pressure oscillation amplitudes, demonstrating the inherent trade-off between combustion enhancement and stability control in cavity-stabilized systems.

The pressure oscillation causing combustion instability has been a persistent challenge for researchers and designers to design a stable solid rocket propulsion system. The acoustic vortex coupling in the combustion chamber caused by vortex shedding is one of several mechanisms that drive pressure oscillation in motors.Taking the Clx motor with a typical backward-facing step configuration, in which acoustic vortex coupling occurs, as a reference, the plasma introduced into the Clx motor is expected to control the vortex movement and hence disrupt the frequency coupling between the vortex and the acoustic. A comprehensive three-dimensional large eddy simulation is conducted to analyze the motion patterns of vortices and the pressure oscillation characteristics within the combustion chamber with and without plasma. The primary objective is to clear the influence of plasma on the acoustic vortex coupling and pressure oscillation of the Clx motor. The numerical computation results reveal that: compared with the experimental data, the maximum error of the calculation on the pressure oscillation frequency is 6.52%. This means that the large eddy numerical methodology established in this paper can effectively work; the incorporation of plasma causes a pronounced advancement in the turbulent flow occurence position behind the step. Meanwhile, the energy of vortex structures progressing towards to the nozzle inlet is reduced; both the vortex intensity and the acoustic feedback at the nozzle inlet are reduced by the plasma actuation. All of those phenomena effectively mitigate the pressure oscillation caused by acoustic vortex coupling.

The adaptive cycle engine can maintain good performance in both supersonic and subsonic cruise with a wide adjustment range of bypass ratio. In this paper, the performance model of the adaptive cycle engine was established, using the XA100 as a baseline engine. Based on the performance model, the mass flow rate. The effect of mass flow distribution between the first and second splitters on the specific thrust and specific fuel consumption was analyzed. The results show that under the same bypass ratio, the first split ratio has a more effect on specific thrust than the second split ratio. The first split ratio reduced from 0.6 to 0.1, specific thrust increases by 24.3%. And the second split ratio has a greater effect on specific fuel consumption. The second split ratio increased from 0.1 to 0.5, saving 11.1% on specific fuel consumption. Then, the experimental model of the multistage splitter system (first and second splitters) and FVABI of the ACE was completed. And the experimental results show that the performance of the first and second splitters remain above 0.965 under the different mass-flow-ratio combinations. At the same time, the airflow of the CDFS duct has a stronger adjusting effect on the airflow of the second splitter than that of the first splitter.

The adaptive cycle engine can adapt to the requirements of aircraft subsonic flight and supersonic flight, and its acceleration process shows strong nonlinearity. The deep reinforcement learning method has strong adaptive learning ability for complex uncertain environments, and is suitable for the Markov decision-making process of adaptive cycle engine acceleration control. This paper proposes an adaptive cycle engine acceleration controller based on deep reinforcement learning method. The deep deterministic strategy gradient algorithm is applied to the continuous control process. Ornstein-uhlenbeck noise is used to explore the noise to match the inertial system of the engine acceleration process. A delay term is introduced to match the engine’s actuator performance. The simulation results show that the controller can fully tap the performance potential of the engine and shorten the acceleration time while ensuring safety.

Operating under harsh and complex environments, the high performance requirement of aircraft engines ask for stable and efficient controllers. Model predictive control (MPC) is a potential and recently emerging technique for aero-engine control system because of its excellent multivariable control ability and prediction ability of future information. However, MPC weight parameter tuning has significant influence on control effect and is usually selected based on human experience or simply trial-and-error. In this paper, based on the linear time-invariant (LTI) model of a typical aeroengine, deep deterministic policy gradient (DDPG) algorithm is combined with MPC, in which the MPC weight matrix is adjusted through DDPG to optimize the performance of MPC controller in real time to adapt to the changing flight conditions and engine states. This method of DDPG adjusting MPC weight matrix can not only improve the performance and efficiency of aero engine under various operating conditions, but also enhance the robustness and adaptability of the system. Simulation results verified the proposed approach with improved control performance compared with the typical MPC controller

The control system is the key to ensuring the stability and effectiveness of the aeroengine within the flight envelope. Due to the particular operating environment, prone sensor faults have a significant impact on the control performance. Considering the model uncertainty, a robust fault diagnosis method is proposed based on a linear parameter varying (LPV) robust filter. According to the multi-condition LPV engine model, the LPV robust filter is constructed. Then, the filter gain matrix is designed by combining with the H_∞/H_ theory so that the residuals have a minimum upper bound and a maximum upper bound on the uncertainties and faults, respectively, to balance the robustness and sensitivity. Finally, the filter solution is transformed into linear matrix inequalities. Simulation results show the effectiveness of the proposed method for sensor fault detection.

The aim of this present paper is that of developing a high performance intelligent controller for aero-engines based on deep reinforcement learning, in order to overcome the dependency of expertise and complex parameter tuning procedure for traditional aero-engine control design. In this paper, a deep reinforcement learning algorithm, i.e. twin delayed deep deterministic policy gradient algorithm(TD3), is combined with a traditional proportion integral differential (PID) controller for the shaft speed control of a typical dual-shaft turbofan engine. The combined controller performs self-tuning strategy to achieve performance optimization for the controlled system, without any human intuition and prior expert knowledge behind about the dynamics of the aero-engine. A nonlinear component level model of a dual-shaft turbofan engine is developed for the training and verification procedures of the proposed control algorithm. Simulation results demonstrate that compared with traditional PID controller with time-consuming manual parameter fine-tuning, the proposed intelligent control algorithm gives improved control performance for the aero-engine model with significant reduced tuning time, with no dependence of prior knowledge of the engine dynamics.

According to the aero-engine Performance Seeking Control (PSC) of Adaptive Cycle Engine (ACE) in multiple operating modes, an engine performance optimization control strategy based on Particle Swarm Optimization (PSO) is proposed. PSO algorithm has a simple structure and requires fewer parameter settings, and is suitable for ACE with many control variables. This method was applied to the ACE model with two optimization modes: maximum thrust model and minimum specific fuel consumption model. The simulation results showed 11.7% increase in maximum thrust mode and 0.13% reduction in minimum fuel consumption mode for ACE with a core-driven fan respectively.

Model predictive control(MPC) is a model optimal control algorithm that is widely used in industrial automation, automotive systems and power systems. This article presents an MPC control algorithm designed for the output feedback control of variable cycle engine(VCE), introducing an upper limit on shaft speed and turbine inlet temperature as constraints. The control algorithm was implemented on a discretized linear model of a variable cycle engine for simulation. And the simulation results demonstrate a notable decrease in the overshoot of the state variables.

In order to study the start-up characteristics of liquid oxygen methane full flow staged combustion cycle engine(FFSC), a comprehensive simulation model of the engine system is constructed based on the general simulation software AMEsim model library. The start-up process is simulated to investigate the influencing factors. The simulation results show that the full flow staged combustion cycle engine exhibits a significant start-up overshoot phenomenon, with the pressure overshoot in the main combustion chamber significantly higher than that in the two preburners. Delaying the ignition time of oxygen-rich preburner can effectively reduce the pressure overshoot in the main combustion chamber, but it will increase the pressure adjustment time and decrease the starting speed. The maximum power of the forced start-up device should not be less than 40% of the steady-state power of the turbopump, as excessive power or prolonged operation of the device will increase the start-up overshoot.

FABRE (Fan Augmented Air-Breathing Ramjet Engine) represents a hypersonic combined cycle power system with extensive applications in the aerospace industry, particularly in TSTO scenarios, capable of achieving speeds up to Mach 7. However, a persistent challenge lies in achieving optimal flow matching at high Mach numbers within the FABRE dual-mode scramjet combustor, characterized by a low length-to-diameter ratio. To address this challenge, this study employs numerical simulations to scrutinize the flow dynamics within the FABRE ramjet combustor across varying geometric throat areas at Mach 7, culminating in the derivation of a matching law between the geometric throat and the isolator. The findings underscore that insufficient throat area causes inlet issues despite efficient combustion. At 89% throat area, combustion efficiency peaks with improved total temperature. Optimal efficiency is around 89% throat area. Throat area from 22% to 67% enables subsonic combustion; beyond 67%, it shifts towards supersonic combustion. Subsonic combustion sees reduced thrust and specific impulse with increased throat area, while supersonic combustion shows the opposite trend. Prioritize the 22% throat area for performance or the 100% for lower thermal protection needs. This investigation elucidates crucial insights into the geometric throat matching characteristics of hypersonic combustors, thus furnishing valuable support for future advancements in aerospace propulsion systems.

During lunar landings, the engine plume impinges the lunar surface, stirring up lunar dust. To analyze the impact of lunar dust throughout the entire landing process and provide researchers with reliable dust visualization, transient calculations for lunar dust are necessary. Direct simulation Monte Carlo (DSMC) simulations for the plume field demand significant computational resources, making efficient transient calculations impractical. In this study, the first step involved precalculating the flow field for different altitudes and angles within the specified range. Subsequently, by decoupling the motion of lunar dust particles from lunar soil erosion and utilizing interpolation and coordinate transformations based on flow field data, transient calculations of lunar dust were achieved. Analyzing particle results yielded distribution patterns at various altitudes, forming the foundation for assessing and mitigating the hazards posed by lunar dust. Additionally, this research establishes the groundwork for real-time simulation of lunar dust distribution during landing processes.

The distribution of heat release (HR) is crucial for the formation of the thermal throat and the control of operational modes in the dual-mode scramjet. In this study, OpenFOAM was utilized to simulate the combustion of a dual-mode scramjet operating at Mach 6 with a low dynamic pressure of 30 kPa. A combustion efficiency statistical method was defined based on the HR distribution, and it was validated using simulation data. Under different injection strategies, differences in HR distribution characteristics and combustion performance were obtained. The research results indicate that using the gas mass-averaged parameters for calculating HR distribution achieves good accuracy, with an error of less than 3.3% compared to HR obtained through three-dimensional direct integration. Therefore, this method can accurately obtain the one-dimensional HR distribution and combustion efficiency of dual-mode scramjet. When using two-stage injection, the peak HR is lower, the length of the HR interval is greater, and the HR tail extends to the nozzle. In contrast, with single-stage injection, the peak HR is higher, and the HR is mainly concentrated in the area of the strut and cavity. When the global equivalence ratio (ER) is greater than 0.6, two-stage injection has higher combustion efficiency, while as the ER decreases, adopting a single-stage concentrated injection strategy can achieve a maximum 4.7% increase in combustion efficiency. And the difference of combustion efficiency under different ER is more than 10%. The conclusions of this study are beneficial for characterizing the HR distribution and combustion efficiency of the dual-mode scramjet, providing more efficient solutions for combustion organization of dual-mode scramjet.

The electric pump-fed rocket engine is currently a highly promising liquid rocket engine, due to its flexible thrust regulation. This paper established the dynamic equilibrium model of the electric pump-fed rocket engine and studied the influence of motor torque scheme on the thrust regulation time. The thrust regulation characteristics of the electric pump-fed rocket engine under different braking torque and acceleration torque have been analyzed and the following conclusions are drawn: the application of braking torque and acceleration torque successfully reduces the time of thrust reduction process and thrust increase process, respectively. Both of them exist the optimal value to minimize the thrust regulation time. Due to the limitation of the range of motor torque, the electric pump-fed engine has a minimum value of the thrust regulation time and it is more suitable for the multi-stage depth thrust regulation scheme.

Accurate numerical simulation results of turbulent combustion are crucial for combustion organization and performance prediction studies of rocket-based combined cycle engine. However, there are few relevant lightweight chemical kinetic mechanisms available for kerosene. In this study, the two different kerosene detailed mechanisms were reduced, and six kinds of skeletal mechanisms were obtained, namely: 59 species/410 reactions (abbreviated as 59s/410r), 38s/180r, 27s/98r, and 64s/460r, 36s/191r, and 21s/70r. In order to validate the accuracy of the reduced mechanisms, these mechanisms were firstly validated in the ideal zero-dimensional constant-pressure reactor. The mechanism’s fidelity was validated under the following conditions: pressure of 0.1-0.3 MPa, ignition initial temperature of 1200-2500 K, and equivalence ratio of 0.6-1.5. The results for the ignition delay time, laminar flame speed, adiabatic flame temperature, and the evolution of main species are consistent with those obtained from the detailed mechanism. Additionally, the simulation of turbulent combustion in the combined engine was performed using the reduced 27s/98r skeletal mechanism and the 11s/10r global reactions with optimized kinetic parameters. The numerical simulation results are validated, analyzed, and compared with the ground experiment data.

The liquid rocket engine pipe is mostly welded, which makes it easy to fail under the residual stress and strain after welding. This paper establishes a numerical welding model for liquid rocket engine pipe in order to accurately predict the welding temperature field and structural field, so as to investigate the method of improving the welding quality and mitigating structural failure. The model uses a Gaussian moving hemispherical heat source, which is embedded in the 3-D finite element thermal elastic-plastic coupling analysis through a subprogram. The transient welding temperature, stress and strain distributions are calculated considering the phases of preheating, welding and cooling. Then, the temperatures and residual stresses calculated by the simulation are compared with those of the experiment. It is shown that the numerical model is accurate. Based on the verified welding model, this paper analyses the influence of welding parameters. The results indicate that increasing the heat source power is beneficial to reducing the residual stress on the premise of ensuring the welding quality.