Aircraft landing gear is one of the most critical structural systems of an aircraft, responsible for safely absorbing the enormous impact loads generated during landing while maintaining stability during taxiing and take-off. The design of an effective landing gear requires the integration of mechanical design principles, structural analysis, dynamic simulation and mathematical modelling to ensure both safety and performance.

In this project, I developed and validated a retractable nose landing gear for a Cessna 210-class aircraft using a multidisciplinary engineering workflow. Instead of relying on a single software package, the design was progressively developed and validated using analytical calculations, CAD modelling, multibody dynamics simulation, finite element analysis and MATLAB-based mathematical modelling.

The objective was not only to create a functional CAD model, but also to understand how the landing gear behaves under realistic landing conditions, verify its structural integrity, and compare different modelling approaches to gain deeper engineering insight.

Why I Chose This Project

Aircraft landing gear design combines several core areas of mechanical engineering into a single engineering problem. It involves analytical calculations, machine design, CAD modelling, multibody dynamics, structural mechanics, vibration analysis and numerical simulation.

I selected this project because it offered an opportunity to integrate multiple engineering software tools within a single workflow instead of treating each tool as an isolated exercise. The project also provided practical exposure to engineering validation by comparing analytical calculations with dynamic simulations and finite element analysis.

Rather than focusing solely on obtaining simulation results, the project emphasised understanding the engineering decisions behind spring selection, damping characteristics, suspension response and structural safety.

Engineering Objectives

The primary objectives of this project were:

• Design a retractable nose landing gear for a Cessna 210-class aircraft.

• Perform analytical calculations to determine landing loads, spring stiffness and damping characteristics.

• Develop a fully parametric CAD assembly in CATIA V5.

• Simulate landing impact using MSC Adams to evaluate suspension behaviour.

• Validate the structural integrity using static, modal and eigenvalue buckling analyses in ANSYS Mechanical.

• Develop a simplified spring–mass–damper model in MATLAB/Simulink for independent validation.

• Compare results obtained from different engineering approaches and understand the limitations of each modelling technique.

2. Project Background and Design Requirements

Project Background

Before developing the landing gear model, it was essential to establish a realistic engineering problem and define the operating conditions under which the landing gear would function. Instead of designing a generic landing gear without constraints, a Cessna 210-class aircraft was selected as the reference platform. This provided realistic aircraft parameters and enabled the design calculations and simulations to be based on representative operating conditions.

Since the nose landing gear supports only a portion of the aircraft weight during ground operations and landing, the design focused exclusively on the nose gear assembly. The project aimed to design a retractable landing gear capable of absorbing landing impact energy while maintaining structural integrity and ensuring stable suspension performance.

Design Requirements

The following engineering requirements were established before beginning the design process:

• Design a retractable nose landing gear for a Cessna 210-class aircraft.

• Safely absorb landing impact energy during touchdown.

• Maintain structural integrity under landing loads.

• Minimise excessive shock transmission to the aircraft structure.

• Provide sufficient suspension travel without bottoming out.

• Verify structural safety through finite element analysis.

• Validate dynamic behaviour using both multibody dynamics and mathematical modelling.

These requirements formed the basis for all subsequent calculations, CAD modelling and simulations.

Design Assumptions

To simplify the engineering analysis while maintaining realistic behaviour, several assumptions were made during the project.

These assumptions were selected to provide a practical engineering framework suitable for preliminary landing gear design and validation. While simplified compared to a complete aircraft certification process, they allow the behaviour of the landing gear to be analysed using widely accepted mechanical engineering techniques.

Engineering Workflow

The project followed a sequential engineering workflow in which the output of one stage became the input for the next. Preliminary calculations were first performed to determine the required spring stiffness, damping coefficient and suspension stroke. These parameters were then incorporated into the CAD model developed in CATIA V5. The completed assembly was subsequently analysed using MSC Adams to evaluate landing dynamics, followed by structural verification in ANSYS Mechanical. Finally, a simplified spring–mass–damper model was developed in MATLAB/Simulink to independently validate the dynamic response obtained from the multibody simulation.

3. Preliminary Engineering Design and Analytical Calculations

Introduction

Every engineering design begins with analytical calculations before moving to computer-aided modelling and simulation. These preliminary calculations establish the fundamental design parameters that govern the behaviour of the landing gear throughout the entire development process.

In this project, Microsoft Excel was used to perform the initial engineering calculations for the nose landing gear. These calculations determined the suspension characteristics required to safely absorb the landing impact while maintaining acceptable structural loads and passenger comfort.

Rather than relying on trial-and-error during simulation, the analytical design provided the initial spring stiffness, damping coefficient and suspension stroke that were later validated using MSC Adams, ANSYS Mechanical and MATLAB/Simulink.

Landing Load Estimation

The first step in the design process was estimating the load acting on the nose landing gear during touchdown.

Since only the nose landing gear was being designed, the supported mass was assumed to be:

These values served as the primary inputs for determining the suspension characteristics required to absorb the landing energy.

Spring Design

The landing gear suspension was modelled as a linear spring system during the preliminary design stage.

The spring stiffness was selected to satisfy two important requirements:

• absorb the landing energy without excessive impact,

• provide adequate suspension travel without bottoming out.

The analytical calculations resulted in the following design parameter:

This value was subsequently implemented in both the MSC Adams and MATLAB/Simulink models.

Damper Design

A spring alone cannot adequately dissipate landing energy. Without damping, the landing gear would experience excessive oscillations after touchdown.

Therefore, a viscous damper was incorporated into the suspension model.

The analytical calculations produced the following damping coefficient:

This value was later refined through dynamic simulation to achieve a stable suspension response.

Suspension Stroke

The required suspension stroke was estimated analytically to ensure that the landing gear could absorb the landing impact without reaching its mechanical limits.

The calculated suspension compression was:

This analytical prediction later served as an important benchmark for comparison with the multibody dynamics simulation.

Summary of Preliminary Design

The analytical calculations established the baseline design parameters for the complete engineering workflow.

These values were subsequently used to develop the CAD model in CATIA V5 and served as the initial inputs for dynamic simulation and structural validation

Engineering Insight

One of the most important lessons from this phase was that analytical calculations are not intended to replace simulation. Instead, they provide a physically meaningful starting point that reduces design iterations and helps validate simulation results. By establishing realistic spring and damping characteristics before creating the numerical models, the subsequent analyses became more efficient and easier to interpret.

4. CAD Development in CATIA V5

The analytical design parameters were translated into a fully parametric 3D CAD model using CATIA V5. The model was developed for engineering analysis rather than visualization, enabling seamless integration with MSC Adams and ANSYS Mechanical.

The landing gear assembly consists of:

• Nose wheel

• Axle

• Shock strut

• Torque links

• Support frame

• Retraction mechanism

• Mounting brackets

Each component was modelled individually and assembled into the complete landing gear system.

After assembly, the model was exported to MSC Adams for multibody dynamic simulation.

5. Dynamic Simulation Using MSC Adams

The CATIA assembly was imported into MSC Adams to simulate the landing event and evaluate the suspension behaviour under dynamic loading.

The multibody model included:

• Revolute and translational joints

• Gravity

• Wheel-ground contact

• Spring-damper suspension

• Rigid runway

  

The simulation confirmed stable suspension behaviour and realistic landing response. These results were subsequently used for structural validation in ANSYS Mechanical.

6. Structural Validation Using ANSYS Mechanical

The landing gear was structurally validated in ANSYS Mechanical using static structural, modal and eigenvalue buckling analyses.

The analyses confirmed that the landing gear possessed adequate strength, stiffness and stability for the simulated landing loads.

7. MATLAB/Simulink Validation

A simplified spring-mass-damper model was developed in MATLAB/Simulink to independently validate the landing gear dynamics.

Although simplified, the model showed trends consistent with the MSC Adams simulation, providing additional confidence in the design.

8. Results Comparison

The combined workflow demonstrated how analytical calculations, CAD, multibody dynamics, finite element analysis and mathematical modelling can be integrated to design and validate an aircraft landing gear.

9.Conclusion

This project presented a complete engineering workflow for designing and validating a retractable aircraft nose landing gear using CATIA V5, MSC Adams, ANSYS Mechanical and MATLAB/Simulink.

Starting from analytical calculations, the design was progressively validated through multibody dynamics, structural analysis and mathematical modelling. The close agreement between these approaches increased confidence in the final design while demonstrating the importance of integrating multiple engineering tools within a single workflow.

The complete project files, CAD models and simulation results are available on GitHub.

GitHub Repository: https://github.com/Shawnpal18/aircraft-nose-landing-gear-design

LinkedIn: www.linkedin.com/in/shawn-pal-96b61a377

A complete engineering case study on autonomous drone navigation, warehouse simulation, real-time object detection, GPS-tagged mission logging, and MATLAB-based post-mission analytics

Introduction

Autonomous UAVs are rapidly transforming industries such as warehouse inspection, infrastructure monitoring, search and rescue, and precision agriculture. While many projects focus on either autonomous flight or computer vision, integrating both into a complete reconnaissance workflow presents a much more realistic engineering challenge.

To explore this problem, I developed an Autonomous UAV Reconnaissance System that combines:

• PX4 SITL

• Gazebo Harmonic

• MAVSDK

• YOLOv8

• MATLAB

The result is a complete autonomous pipeline capable of executing reconnaissance missions, detecting objects during flight, logging GPS-tagged detections, and generating post-mission analytics.

⭐ GitHub Repository:

https://github.com/Shawnpal18/Autonomous-Reconnaissance-UAV

🚀 Key Highlights

• 🚁 Autonomous waypoint-based UAV reconnaissance

• 🛰️ PX4 SITL + Gazebo Harmonic simulation

• 🧭 MAVSDK mission planning and execution

• 👁️ Real-time YOLOv8 object detection

• 📍 GPS-tagged detection logging

• 📊 MATLAB-based mission analytics

• 🎥 Automated mission video analysis

• 🧩 Modular software architecture for future extensions

Project Objectives

The objectives of this project were:

• Design an autonomous UAV reconnaissance workflow.

• Build a realistic warehouse simulation.

• Execute autonomous coverage path planning.

• Detect objects using YOLOv8.

• Store GPS-tagged detections.

• Analyse mission performance using MATLAB.

• Develop a modular and scalable software architecture.

System Architecture

The project follows a modular architecture consisting of independent subsystems responsible for flight control, mission planning, perception, and analytics.

The primary modules include:

• PX4 SITL Flight Controller

• Gazebo Harmonic Simulation

• MAVSDK Mission Controller

• YOLOv8 Perception Module

• MATLAB Analytics Engine

This modular design allows each component to evolve independently while maintaining a clean communication workflow.

Mission Pipeline

The complete mission follows a sequential execution pipeline.

Mission execution consists of:

1. Launch PX4 SITL

2. Start Gazebo Harmonic

3. Spawn the F450 UAV

4. Connect through MAVSDK

5. Upload waypoint mission

6. Autonomous Takeoff

7. Lawnmower Coverage Search

8. Real-time Object Detection

9. GPS Detection Logging

10. MATLAB Analytics

11. Return To Launch (RTL)

Simulation Environment

A custom warehouse environment was developed inside Gazebo Harmonic to simulate an industrial reconnaissance scenario.

The UAV platform is based on an F450 quadrotor running PX4 SITL.

Instead of manually controlling the vehicle, an autonomous lawnmower coverage mission systematically scans the warehouse while collecting perception data.

Autonomous Mission

The UAV autonomously performs:

• Vehicle arming

• Autonomous takeoff

• Coverage path execution

• Warehouse inspection

• Return To Launch

• Autonomous landing

The mission is executed entirely without manual intervention.

Real-Time Object Detection

YOLOv8 is integrated into the perception pipeline to detect vehicles during the reconnaissance mission.

Each detection records:

• Object Class

• Confidence Score

• GPS Coordinates

• Timestamp

This information is stored for post-flight analysis.

Detection Logging

Each successful detection is automatically stored inside a CSV log.

Example information recorded:

The detection log forms the foundation for mission analytics.

MATLAB Mission Analytics

After mission completion, MATLAB processes the detection logs to generate engineering insights.

Generated plots include:

• GPS Detection Map

• Altitude Profile

• Detection Confidence Profile

• Object Count Statistics

GPS Detection Map

Altitude Profile

Detection Confidence

Object Count

These visualisations provide a concise overview of mission performance and perception quality.

Technology Stack

Key Features

✔ Autonomous UAV Mission Execution

✔ PX4 SITL Integration

✔ Gazebo Harmonic Simulation

✔ MAVSDK Mission Planning

✔ Lawnmower Coverage Path Planning

✔ YOLOv8 Object Detection

✔ GPU Accelerated Inference

✔ GPS Detection Logging

✔ MATLAB Mission Analytics

✔ Modular Software Architecture

Challenges Faced

Developing this project involved overcoming several technical challenges:

• Configuring PX4 SITL with Gazebo Harmonic

• Establishing reliable MAVSDK communication

• Synchronizing autonomous flight with real-time object detection

• Managing GPU resources for inference

• Designing a structured detection logging system

• Building MATLAB analytics for mission evaluation

• Organizing the software into reusable modules

Each challenge strengthened my understanding of robotics software integration and system design.

Lessons Learned

Working on this project helped me gain practical experience in:

• Autonomous UAV Systems

• Robotics Simulation

• MAVSDK Mission Programming

• Computer Vision

• Engineering Analytics

• GPU Computing

• Software Architecture

• Robotics Debugging

The biggest takeaway was learning how independent robotics frameworks can be integrated into a single autonomous workflow.

Future Improvements

There are many exciting directions for extending this project:

• Dynamic Obstacle Avoidance

• ROS 2 Integration

• Multi-UAV Cooperative Missions

• Semantic Mapping

• SLAM-Based Navigation

• Advanced Path Planning Algorithms

• Deployment on Real UAV Hardware

• Live Telemetry Dashboard

Conclusion

This project demonstrates how multiple robotics technologies can be integrated into a complete autonomous reconnaissance system.

Rather than focusing on individual components, the emphasis was placed on building an end-to-end engineering workflow that combines autonomous flight, computer vision, GPS logging, and mission analytics.

The experience provided valuable insights into robotics software development, simulation, autonomous navigation, and engineering data analysis while creating a strong foundation for future work in intelligent aerial systems.

Source Code

The complete project, documentation, and implementation details are available on GitHub.

⭐ GitHub Repository:

https://github.com/Shawnpal18/Autonomous-Reconnaissance-UAV

If you found this project interesting, feel free to ⭐ the repository and share your feedback!

Thank You for Reading!

If you're interested in robotics, autonomous systems, UAVs, or computer vision, I'd love to connect and discuss ideas or collaborate on future projects.

Digital Twins are transforming the way engineers design, monitor, and maintain complex mechanical systems. Instead of relying only on simulations or physical testing, a Digital Twin combines engineering models with computational analysis to provide a virtual representation of a real system.

As a Mechanical Engineering student at NSUT, I wanted to build a project that combined multiple engineering disciplines rather than focusing on a single software tool. My goal was to integrate CAD modelling, finite element analysis, thermodynamics, and MATLAB programming into one complete engineering workflow.

The outcome was a Physics-Based High Pressure Compressor Digital Twin, developed using SolidWorks, ANSYS Mechanical, and MATLAB. The framework predicts compressor performance while evaluating structural integrity, monitoring system health, detecting resonance conditions, diagnosing faults, and recommending maintenance actions.

This article explains the motivation behind the project, the engineering workflow, the challenges I encountered, and the lessons I learned during development.

Why I Built This Project

Most engineering projects available online focus on either CAD modelling, finite element analysis, or programming independently. I wanted to challenge myself by combining all three into a single project that reflected how multidisciplinary engineering problems are solved in industry.

The idea was to create more than just a simulation. I wanted to build a Digital Twin that could integrate geometry, structural behaviour, thermodynamic calculations, and engineering decision support into one modular framework.

This project also gave me an opportunity to strengthen my skills in MATLAB programming, ANSYS Mechanical, and SolidWorks while learning how different engineering software packages communicate with one another.

Project Objectives

The main objectives of this project were:

• Develop a five-stage High Pressure Compressor model.

• Integrate SolidWorks, ANSYS Mechanical, and MATLAB.

• Build a modular Digital Twin architecture.

• Perform thermodynamic performance calculations.

• Analyze structural response under centrifugal loading.

• Detect resonance conditions using modal analysis.

• Monitor compressor health.

• Detect abnormal operating conditions.

• Recommend maintenance actions.

• Create a reusable engineering framework for future projects.

Engineering Software

The project integrates several engineering tools, each performing a specific task within the Digital Twin framework.

Using multiple software packages helped simulate a realistic engineering workflow where CAD, simulation, and computational modelling are closely connected.

System Architecture

The architecture below illustrates how geometry, finite element analysis, and MATLAB are integrated into a single Digital Twin framework.

The SolidWorks model provides the compressor geometry, ANSYS Mechanical generates structural and modal characteristics, and MATLAB combines these validated results with thermodynamic models to perform health monitoring, resonance detection, fault diagnosis, and maintenance recommendation.

The modular design allows each subsystem to be independently improved while maintaining compatibility with the complete Digital Twin framework.

Engineering Workflow

The Digital Twin follows a sequential engineering workflow beginning with user-defined operating conditions such as rotational speed, pressure ratio, ambient temperature, and mass flow rate.

Each computational module performs a dedicated engineering task before passing its results to the next stage. This modular workflow improves readability, simplifies debugging, and makes the framework suitable for future integration with real-time sensor data.

SolidWorks Model

The compressor assembly was modelled in SolidWorks using a modular design approach. The five-stage compressor geometry serves as the foundation for all subsequent structural and thermodynamic analyses.

Different visual configurations, including transparent and section views, were created to better understand the internal arrangement of compressor stages and support engineering visualization.

Material Selection

Selecting an appropriate material is critical for compressor components because they operate under high rotational speeds, significant centrifugal forces, and elevated temperatures.

For this project, the compressor blade was assigned Ti-6Al-4V Grade 5 Titanium Alloy, a material widely used in aerospace applications due to its excellent strength-to-weight ratio, corrosion resistance, and fatigue performance.

Material Properties

These properties were used during the structural, modal, and harmonic response analyses to obtain realistic engineering results.

Static Structural Analysis

Finite element analysis was performed using ANSYS Mechanical to evaluate the stress distribution produced by centrifugal loading.

The analysis identifies regions of maximum equivalent stress and confirms that the compressor operates within acceptable structural limits across the investigated operating speeds.

Total Deformation Analysis

Total deformation was evaluated to understand the structural response of the compressor during operation.

The deformation trends obtained from ANSYS were later integrated into the Digital Twin, enabling rapid structural assessment without repeating computationally expensive finite element simulations.

Prestressed Modal Analysis

Prestressed modal analysis was carried out to determine the natural frequencies of the rotating compressor under operating conditions.

These modal characteristics were used by the Digital Twin to evaluate resonance margins and identify potential resonance risks.

Harmonic Response Analysis

Harmonic response analysis was performed to investigate vibration amplitudes under periodic excitation.

The resulting frequency response supports dynamic assessment and complements the resonance detection capability implemented within the Digital Twin.

MATLAB Digital Twin

MATLAB serves as the computational core of the Digital Twin.

The framework integrates thermodynamic calculations with validated structural data obtained from ANSYS Mechanical to estimate compressor behaviour under different operating conditions.

The Digital Twin is organized into modular computational blocks responsible for:

• Thermodynamic performance prediction

• Stage-by-stage compressor analysis

• Structural health assessment

• Resonance detection

• Fault diagnosis

• Maintenance recommendation

• Automatic engineering plot generation

The modular architecture makes the framework easy to maintain and extend with additional models in future versions.

Digital Twin Intelligence

One of the primary goals of this project was to move beyond simulation and incorporate engineering decision support.

The Digital Twin continuously evaluates compressor behaviour by combining thermodynamic and structural information to generate meaningful engineering insights.

The framework currently performs:

• Health Index Calculation

• Resonance Detection

• Fault Detection

• Maintenance Recommendation

These capabilities demonstrate how simulation results can be transformed into actionable engineering information.

Results

The completed Digital Twin successfully integrates multiple engineering disciplines into a single software framework.

The project provides:

• Stage-by-stage compressor analysis

• Pressure and temperature prediction

• Compressor power estimation

• Structural stress assessment

• Deformation prediction

• Resonance margin evaluation

• Health monitoring

• Fault diagnosis

• Predictive maintenance recommendation

The framework automatically generates engineering plots and reports, enabling rapid assessment of compressor operating conditions.

Challenges Faced

Developing this project involved several practical engineering challenges.

One challenge was integrating outputs from different software packages into a consistent Digital Twin workflow while maintaining a modular code structure.

Another challenge involved validating thermodynamic calculations with structural simulation results and ensuring that engineering outputs remained physically meaningful.

I also encountered several implementation issues during MATLAB development, including graphics rendering, modular function integration, and automated report generation. Solving these problems improved both the robustness of the project and my understanding of engineering software development.

Lessons Learned

This project significantly improved my understanding of multidisciplinary engineering.

Some of the most valuable lessons include:

• Designing modular MATLAB applications

• Integrating CAD, FEA, and analytical models

• Understanding Digital Twin architecture

• Performing structural and modal analyses

• Organizing engineering software projects

• Presenting technical results effectively

More importantly, the project demonstrated that engineering decisions become much more powerful when information from multiple domains is combined into a unified framework.

Future Improvements

Although the current framework demonstrates the core concepts of a Digital Twin, several enhancements are planned for future versions.

Potential improvements include:

• MATLAB App Designer graphical interface

• Simulink integration

• CFD-based compressor flow analysis

• Real-time sensor data integration

• Machine Learning-based anomaly detection

• Remaining Useful Life (RUL) estimation

• Automatic synchronization with ANSYS simulation results

• Cloud-based Digital Twin deployment

  ---

Source Code

The complete source code, engineering workflow, documentation, and project architecture are available on GitHub.

GitHub Repository:

👉 https://github.com/Shawnpal18/High-Pressure-Compressor-Digital-Twin

Conclusion

Building this Physics-Based High Pressure Compressor Digital Twin has been an excellent learning experience that brought together mechanical engineering principles, numerical simulation, and software development.

The project demonstrates how CAD modelling, finite element analysis, and physics-based analytical models can be integrated into a modular engineering framework capable of supporting health monitoring, resonance assessment, fault diagnosis, and maintenance planning.

Beyond the technical outcomes, this project strengthened my skills in multidisciplinary engineering, problem-solving, and technical communication. It also laid a strong foundation for future work in Digital Twins, autonomous systems, and advanced simulation-driven design.

I hope this project encourages other engineering students to explore practical, hands-on projects that bridge theory with real engineering applications.

About the Author

Hi! I'm Shawn Pal, a Mechanical Engineering student at Netaji Subhas University of Technology (NSUT).

My interests include:

• CAD & Product Design

• Finite Element Analysis (FEA)

• Digital Twins

• MATLAB Programming

• Autonomous Systems

• Aerospace Engineering

I'm building a portfolio of multidisciplinary engineering projects that combine mechanical design, simulation, and software development.

If you have feedback or suggestions, I'd love to connect and discuss engineering ideas.