Interview Questions for Engine Prototyping

Engine prototyping is a crucial phase in the development lifecycle, bridging the gap between design concepts and the final product. It involves creating physical or virtual representations of the engine to test and validate design choices before committing to full-scale production. The process typically flows as follows:

1. Conceptual Design: This stage involves defining engine specifications, performance targets, and initial design layouts using CAD software. We consider factors like displacement, power output, fuel efficiency, and emission standards.
2. Preliminary Design: This refines the initial concept, addressing critical aspects like component sizing, material selection, and manufacturing feasibility. Detailed 3D models are created, and preliminary simulations are run.
3. Prototyping: This is where physical or virtual prototypes are built. This might involve rapid prototyping techniques like 3D printing for smaller components or more elaborate methods for larger assemblies. The choice of prototyping method depends heavily on the stage and budget.
4. Testing and Validation: The prototype is rigorously tested under various operating conditions. Data acquisition systems gather performance metrics, which are analyzed to identify areas for improvement. This often involves dynamometer testing for power and torque measurements, along with emissions testing.
5. Iteration and Refinement: Based on testing results, design modifications are implemented, and the process repeats until the prototype meets performance and reliability targets. This iterative approach is key to optimizing the design.
6. Final Validation: Once the prototype performs satisfactorily, it undergoes final validation tests, including endurance testing and potentially field testing in a relevant application, to ensure robustness and reliability before mass production.

For example, in developing a new hybrid engine, we might start with a 3D-printed prototype of the crucial components of the electric motor housing to validate its thermal management system before moving to a fully functional prototype.

Several engine prototyping methods exist, each suited to different stages and needs:

• Rapid Prototyping (Additive Manufacturing): Techniques like 3D printing are ideal for creating complex shapes and iterating designs quickly. This is excellent for initial concept validation and producing small components for testing. For example, we might 3D print a combustion chamber to test various designs before casting a metal version.
• CNC Machining: Provides higher accuracy and surface finish compared to 3D printing, suitable for producing functional prototypes of individual parts or sub-assemblies. We might use CNC machining to create prototype cylinder heads for rigorous testing.
• Casting: Allows for the creation of complex metal parts in a cost-effective manner, suitable for creating near-production quality prototypes. This is particularly valuable when evaluating material properties and manufacturing processes.
• Simulation (Virtual Prototyping): Using software like ANSYS or GT-Power, we can create virtual representations of the engine and simulate its performance under various conditions. This significantly reduces costs and accelerates the design process, and is crucial in assessing performance even before creating physical prototypes.

The choice of method depends on factors such as budget, time constraints, required accuracy, and the specific component or system being prototyped.

Material selection for engine prototypes is critical; it needs to balance cost, performance, and manufacturability. The choice depends heavily on the prototype’s purpose and the component’s function.

For example, for a low-stress, low-temperature prototype component, we might use ABS plastic for 3D printing, allowing quick iteration. For high-temperature components or those subjected to high stress, we might opt for aluminum alloys or even specific steels. The manufacturing process is equally crucial. For high-precision parts, CNC machining is preferred, while casting might be used for complex geometries and mass production simulation.

Material selection also considers factors like thermal conductivity, strength, durability, and corrosion resistance. We’ll perform material analysis, using simulations and experimental tests to ensure material compatibility with anticipated operating conditions and manufacturing techniques. For instance, a piston prototype might require high-strength materials resistant to high temperatures and pressures, while the choice of material for the cylinder block would prioritize thermal management capabilities.

Key Performance Indicators (KPIs) for evaluating engine prototypes are crucial for ensuring designs meet specifications and identifying areas for improvement. These KPIs vary depending on the specific engine type and application, but some commonly used metrics include:

• Power Output (kW/hp): Measured using a dynamometer, this indicates the engine’s ability to generate power at different speeds.
• Torque (Nm): Another critical dynamometer measurement, reflecting the engine’s rotational force.
• Fuel Efficiency (mpg or L/100km): Measures the engine’s ability to convert fuel into usable power.
• Emissions (g/kWh): Assesses the environmental impact, including CO2, NOx, and particulate matter.
• Thermal Efficiency (%): Indicates how effectively the engine converts heat energy into mechanical work.
• Durability and Reliability: Evaluated through endurance testing, this examines the engine’s ability to withstand long-term operation under various conditions.
• Specific Fuel Consumption (SFC): Measures fuel consumption per unit of power produced, a crucial metric for efficiency.

Data acquisition systems are essential for capturing these metrics accurately and efficiently during testing. These data inform iterative design improvements.

Design changes and iterations are inherent to the prototyping process. We employ a structured approach to handle them effectively:

1. Problem Identification: Thoroughly analyze test data to pinpoint areas requiring improvement. For example, if emissions testing reveals high NOx levels, we’ll focus on combustion chamber design or fuel injection strategy.
2. Design Modification: Use CAD software to implement design changes, considering their impact on other components and the overall engine system.
3. Finite Element Analysis (FEA): For significant changes, we’ll use FEA to assess the impact on structural integrity and stress distribution.
4. Prototype Revision: Create a revised prototype incorporating the modifications. This might involve 3D printing, machining, or even casting a new part.
5. Retesting and Validation: Repeat the testing and validation process to verify the effectiveness of the changes and ensure they haven’t introduced new problems.
6. Documentation: Maintain detailed records of all design changes, test results, and analysis, providing traceability and facilitating future iterations.

This iterative process continues until the prototype meets the predetermined performance targets and design requirements. A robust version control system is crucial to manage and track these design iterations.

My experience with engine testing and data acquisition is extensive. I’m proficient in using dynamometers to measure power, torque, and fuel consumption. I have hands-on experience with various data acquisition systems (DAQ), including those using strain gauges, thermocouples, and pressure transducers. I can set up and configure DAQ systems, collecting data from multiple sensors simultaneously.

I have expertise in analyzing the collected data to identify trends, anomalies, and areas for improvement. Software such as LabVIEW or similar platforms are used for data logging, processing, and visualization. This allows for comprehensive analysis of engine performance characteristics under various operational conditions. For example, we’d use DAQ to monitor temperature profiles in a prototype engine during a thermal endurance test, identifying potential hot spots or thermal stress areas.

I possess significant experience with various engine simulation software and tools. My expertise includes:

• GT-Power: A leading 1D engine simulation software used for predicting engine performance, emissions, and fuel consumption. I’ve utilized GT-Power to optimize engine designs, predict performance under different operating conditions, and troubleshoot potential issues.
• AVL BOOST: Used for advanced combustion system modeling and analysis, enabling detailed studies of in-cylinder processes.
• ANSYS: A comprehensive FEA software used for structural analysis, thermal analysis, and fluid dynamics simulations. I use ANSYS to evaluate stress distribution in engine components and optimize their design for durability and reliability.
• MATLAB/Simulink: These tools are used for control system design and development. I have experience using them to simulate and refine engine control strategies, such as fuel injection and ignition timing control.

Proficiency in these tools allows for a comprehensive virtual prototyping approach, reducing the reliance on physical prototypes, saving time and resources, and improving the efficiency of the overall design process.

Ensuring accuracy and reliability in engine prototypes is paramount. It’s a multi-faceted process involving rigorous testing and validation at every stage. We begin with meticulous design reviews, employing techniques like Finite Element Analysis (FEA) to predict component behavior under stress. This helps identify potential weaknesses early on.

Next, we use high-fidelity simulations – employing Computational Fluid Dynamics (CFD) for combustion analysis and other relevant simulations – to virtually test the engine before physical prototyping. This reduces the number of iterations required and helps us to identify design flaws which can be costly and time-consuming to fix later.

Physical prototypes are subjected to a series of tests, starting with simple component tests (e.g., verifying individual sensor performance), then moving to increasingly complex system-level tests under controlled conditions. These tests cover performance parameters like power output, efficiency, emissions, and durability. We use sophisticated data acquisition systems to monitor numerous variables simultaneously, providing a comprehensive dataset for analysis. We also regularly compare the results against our simulation predictions. Finally, we employ statistical process control methods to identify trends and ensure consistent performance across multiple prototypes. This continuous monitoring and validation process is crucial for achieving high accuracy and reliability.

One major challenge I’ve encountered is integrating advanced engine control systems into a prototype. The complexity of modern engine control units (ECUs) and their interaction with various sensors and actuators makes calibration and testing a lengthy and intricate process. For example, I once worked on a prototype incorporating a new fuel injection system. The initial calibration was far from optimal, resulting in unstable combustion and poor performance. We addressed this by combining simulation with iterative testing, using a combination of software-based simulation tools and the physical prototype. We gradually refined the control algorithms, systematically analyzing data from each test run and adjusting parameters accordingly. We also developed customized software tools to streamline the data analysis process, drastically reducing the time taken for each calibration iteration.

Another challenge involves managing thermal stresses. In one project, we were working with a high-performance engine where thermal management was particularly critical. The initial prototype suffered from excessive heat build-up, leading to component failure. We mitigated this through improved cooling designs, incorporating advanced materials with better thermal conductivity, and redesigning the layout of cooling channels.

My experience spans various types of engine prototypes. Rapid prototyping, often using 3D printing, allows for quick iterations of components for initial design verification. This is invaluable in early stages where we need to visualize and assess the physical form and basic functionality quickly. For example, we might use rapid prototyping to create a scaled-down version of the engine block to verify assembly fit and clearances. This is efficient, cost-effective, and allows for rapid design adjustments.

Functional prototypes, on the other hand, represent a more advanced stage. These prototypes incorporate most of the core components and systems of the final design, allowing for extensive performance testing. In one project, we developed a functional prototype that included a fully operational combustion system, enabling us to evaluate performance parameters and emissions in a realistic environment. This helped us identify and resolve critical performance issues before progressing to more costly fully integrated prototypes. The key difference is the level of integration and functionality; rapid prototypes focus on form and basic functionality, while functional prototypes simulate the actual performance characteristics.

Managing budget and timeline effectively requires a well-defined plan from the outset. We begin with a detailed Work Breakdown Structure (WBS) to break down the project into manageable tasks with associated costs and timelines. This structure is crucial for tracking progress and identifying potential issues early on. We then use project management software and regular status meetings to track our progress against this plan. Contingency planning is also critical. We allocate a portion of the budget to cover unforeseen challenges, accounting for potential delays or material cost fluctuations. Regular reviews of the budget and timeline, along with adjustments based on actual progress and any unforeseen challenges, are crucial. This helps to proactively prevent budget overruns and ensure timely completion.

Engine control systems and calibration are integral parts of my work. I’m proficient in using various ECU calibration tools and software, such as INCA and ATI Vision. My experience encompasses working with different control strategies, including closed-loop feedback control, air-fuel ratio control, and ignition timing control. Calibration involves adjusting numerous parameters to optimize engine performance, emissions, and fuel economy. A recent project required me to calibrate an engine control system to meet stringent emissions regulations. This involved using advanced calibration techniques and optimization algorithms to minimize pollutants while maintaining optimal power output. The process involved numerous iterative test cycles, collecting data, analysing results, and refining the control algorithms. Each iteration led to incremental improvements until we achieved the desired emissions targets while adhering to other performance objectives.

Understanding engine emissions and regulations is crucial for developing compliant and environmentally friendly engines. I’m familiar with various emission standards, including Euro standards, EPA standards, and others depending on the target market. My work involves designing and testing engines to meet these stringent regulations. This includes designing efficient after-treatment systems such as catalytic converters and diesel particulate filters, as well as optimizing combustion strategies to minimize emissions. We use various analytical techniques, including gas chromatography and mass spectrometry, to precisely measure emissions levels. Staying current with the ever-evolving emission regulations is essential for ensuring the compliance of our designs and maintaining a competitive edge. For example, I recently worked on a project where we had to integrate a new selective catalytic reduction (SCR) system to meet the latest NOx emission standards. The project involved comprehensive testing to demonstrate the efficacy of the system and compliance with all the relevant regulations.

Collaboration is key in engine prototyping. We regularly interact with various engineering teams, including design, manufacturing, and testing teams. Effective communication is crucial. We use collaborative tools such as project management software, shared databases, and regular meetings to keep everyone informed and aligned. For example, in a recent project involving the development of a new turbocharger, we worked closely with the design team to ensure the turbocharger’s integration with the engine was seamless, and with the manufacturing team to ensure its manufacturability. We also collaborated with the testing team to define the test plan and interpret the results. This integrated approach minimizes potential conflicts and ensures that the prototype is developed efficiently and meets all the required specifications.

My experience with CAD software spans over eight years, encompassing a wide range of applications, from conceptual design to detailed engineering drawings. I’m proficient in industry-standard software such as SolidWorks, AutoCAD, and Creo Parametric. My expertise goes beyond simply creating models; I leverage these tools to perform simulations, stress analyses, and kinematic studies, ensuring the designs are not only aesthetically pleasing but also robust and functional. For example, during the prototyping of a novel internal combustion engine, I used SolidWorks to model the complex geometry of the combustion chamber, optimizing its shape for improved fuel efficiency and reduced emissions. Then, I utilized Creo Parametric to generate detailed manufacturing drawings, ensuring seamless production of the prototype components.

Beyond the standard features, I’m also experienced in utilizing advanced CAD functionalities such as surface modeling, parametric design, and tolerance analysis. This allows for efficient design iterations and detailed analysis, leading to better optimized engine prototypes.

Rapid prototyping techniques are integral to my workflow. I’ve extensively used 3D printing technologies, including Fused Deposition Modeling (FDM), Stereolithography (SLA), and Selective Laser Sintering (SLS), to create functional prototypes of engine components. My experience extends to material selection, considering factors such as strength, heat resistance, and cost-effectiveness, to ensure the prototype accurately represents the final product’s behavior. For instance, when prototyping a high-temperature exhaust manifold, I chose SLS printing with a high-temperature resistant nylon material to simulate real-world operating conditions. I’ve also utilized rapid prototyping for creating tooling for smaller components, significantly reducing lead times and speeding up development cycles. This approach allows for quick iteration and validation of designs before committing to more expensive manufacturing processes.

My understanding of engine thermodynamics and fluid dynamics is deeply rooted in both theoretical knowledge and practical application. I’m familiar with thermodynamic cycles such as the Otto and Diesel cycles, and I can utilize these principles to analyze engine performance and efficiency. I understand concepts such as heat transfer, combustion efficiency, and the impact of various parameters like compression ratio and air-fuel ratio on engine output. Furthermore, my understanding of fluid dynamics allows me to model and analyze the flow of air and fuel within the engine, optimizing intake and exhaust systems for improved performance and reduced losses. For example, I utilized Computational Fluid Dynamics (CFD) software to simulate airflow through a novel intake manifold design, identifying areas of high turbulence and making adjustments to enhance flow efficiency.

In addition, I’m well versed in applying these principles to different engine types including spark-ignition and compression-ignition engines. This allows for tailoring the design and optimization processes according to the specific requirements and characteristics of the engine being developed.

When encountering issues with engine prototypes, I employ a structured root cause analysis (RCA) approach, often using the ‘5 Whys’ technique combined with data analysis. I start by clearly defining the problem and then systematically ask “why” five times (or more, as needed) to delve deeper into the underlying cause. This is complemented by examining relevant data, including sensor readings, performance metrics, and visual inspection of the prototype. For instance, if an engine prototype exhibited unexpected high temperatures, I would start by asking: Why is the temperature high? (Poor cooling). Why is the cooling insufficient? (Insufficient coolant flow). Why is the coolant flow insufficient? (Clogged coolant passage). Why is the passage clogged? (Manufacturing defect). Why was there a manufacturing defect? (Inadequate quality control).

Beyond the ‘5 Whys,’ I often use Fishbone diagrams (Ishikawa diagrams) to systematically categorize potential causes and identify the most likely root cause. This structured approach ensures a thorough investigation and prevents overlooking potential contributing factors.

Data analysis and reporting are critical aspects of engine prototyping. I’m proficient in using various software tools, such as MATLAB and Python, to analyze large datasets from engine tests. I extract relevant parameters such as pressure, temperature, torque, and emissions, and then process and visualize this data to identify trends, correlations, and anomalies. I’m comfortable creating detailed reports that summarize the findings and present recommendations for design improvements. This often involves the use of charts and graphs to effectively communicate complex data to a non-technical audience. For example, during the testing phase of a prototype, I used Python to analyze thousands of data points, identifying a correlation between fuel injection timing and emission levels. This analysis allowed us to optimize the fuel injection strategy for better performance and cleaner emissions. My reports are designed to be clear, concise, and actionable, allowing for informed decision-making throughout the development process.

I have a strong understanding of various engine fuels and their impact on performance. My knowledge encompasses gasoline, diesel, ethanol, biodiesel, and alternative fuels like hydrogen and natural gas. I understand the combustion characteristics of each fuel, its impact on emissions, and its influence on engine efficiency and power output. For example, I’ve worked on projects comparing the performance of a spark-ignition engine using gasoline versus E85 (85% ethanol), analyzing the changes in power, efficiency, and emissions profiles. I also understand how fuel properties such as cetane number (for diesel) and octane number (for gasoline) affect engine operation and the potential for knock or pre-ignition. My experience includes analyzing the challenges and opportunities associated with using alternative fuels, considering aspects like fuel infrastructure, cost, and environmental impact. This knowledge is vital in selecting the appropriate fuel for a particular engine design and optimizing the engine for maximum performance and minimum environmental impact.

Virtual prototyping, using simulation software, allows for rapid design iterations and cost-effective analysis before any physical components are manufactured. It offers the ability to explore a wide range of design options, simulate operating conditions, and identify potential problems early in the development process, saving significant time and resources. However, it’s important to remember that virtual prototypes are limited by the accuracy of the underlying models and assumptions. Physical prototyping, on the other hand, provides a tangible representation of the design, allowing for real-world testing and validation. This approach captures unforeseen factors not accounted for in simulations and provides valuable data on the actual performance of the design. However, physical prototyping is typically more time-consuming and expensive.

Ideally, a combination of both approaches is used. Virtual prototyping is used extensively during the early design stages to explore design space and refine the design. Then, physical prototypes are created for validation and testing under real-world operating conditions. This iterative approach maximizes the benefits of both methods, resulting in a well-optimized and reliable engine design.

Durability and reliability testing for engine prototypes are crucial steps in ensuring the engine can withstand the stresses of real-world operation and last for its intended lifespan. It involves subjecting the prototype to various simulated and actual operating conditions to identify potential weaknesses and failures.

Durability testing focuses on the engine’s ability to endure extended periods under heavy load. This often includes endurance runs at various speeds, loads, and temperatures. Data logging is essential, meticulously recording parameters like oil pressure, temperature, vibration, and fuel consumption. We’d look for signs of wear, fatigue, or degradation in components like bearings, seals, and piston rings. We might even accelerate wear through techniques like high-speed testing or thermal cycling.

Reliability testing, on the other hand, aims to assess the probability of failure and the engine’s consistency in performance over its operational life. This can involve running the engine repeatedly under various stress conditions and recording the time to failure. Statistical methods are then applied to analyze the data and predict mean time between failures (MTBF). Techniques like fault injection can be used to simulate unexpected events and evaluate the engine’s response.

For example, in a project involving a new heavy-duty diesel engine, we conducted a 500-hour durability test at peak load and temperature, simulating a demanding off-road application. This was followed by a reliability test involving multiple start-stop cycles and load changes to evaluate potential failure modes in the fuel injection system.

Safety is paramount during engine prototype testing. We employ a multi-layered approach encompassing rigorous safety protocols, specialized testing facilities, and advanced monitoring systems. This starts with a detailed risk assessment that identifies potential hazards associated with each test. This includes explosions, fires, high-pressure leaks, and toxic emissions.

Our testing facilities are designed with safety features like explosion-proof enclosures, fire suppression systems, and robust containment structures. The prototypes are equipped with numerous sensors that continuously monitor critical parameters, providing real-time data and triggering automatic shutdowns if predefined safety limits are exceeded. This data is constantly reviewed by the engineering team. All personnel involved in testing undergo thorough safety training and follow strict procedures.

Furthermore, we use remote operation and control systems, minimizing human exposure to potential hazards. Emergency shutdown protocols are clearly defined and regularly practiced. Regular inspections and maintenance of the testing equipment are carried out to prevent accidents. Each test is carefully planned, including the development of detailed test procedures and contingency plans for unexpected events.

My experience with engine component testing is extensive, encompassing a wide range of techniques and methodologies. We commonly test individual components to ensure they meet stringent performance and durability requirements before integration into the complete engine. This allows for more focused problem-solving, reducing time and resources spent on the full engine assembly.

For example, cylinder head testing typically involves pressure testing to verify sealing integrity and verifying the flow dynamics within the cooling channels. Finite Element Analysis (FEA) is used to predict the stress and strain distribution under various load conditions. We evaluate the combustion chamber design, porting, and valve geometry to optimize performance.

Piston testing often involves simulating the high temperatures and pressures encountered during combustion. We might use dynamometers to measure the force applied to the piston and analyze piston ring wear. The piston’s thermal stress is evaluated through temperature mapping and analysis.

Crankshaft testing focuses on torsional stiffness, fatigue strength, and balancing. We commonly utilize specialized rigs to simulate the cyclical loading on the crankshaft and measure stress and strain. We’d apply FEA and modal analysis to predict the crankshaft’s dynamic behavior.

I’ve worked with various engine architectures, each presenting unique design challenges and performance characteristics. V-engines offer a good balance of power and compactness, making them suitable for high-performance applications, although they can be more complex and expensive to manufacture.

Inline engines generally offer superior balance and smoother operation compared to V-engines, particularly at higher speeds. They typically have a lower center of gravity. However, they can be longer, limiting their use in applications where space is constrained.

Rotary engines, while offering a compact design and high power-to-weight ratio, are known for their complexities in sealing and maintaining stable combustion. They’re particularly challenging in terms of emissions and fuel efficiency. My experience includes modeling and simulation of each architecture, allowing us to compare their performance characteristics and select the most suitable option based on the application requirements.

Each architecture presents trade-offs between power output, space constraints, manufacturing costs, and maintenance complexity. Selecting the right architecture is a critical decision based on a holistic evaluation of all these factors.

Balancing cost, performance, and time constraints in engine prototyping requires a carefully planned and iterative approach. We often employ design of experiments (DOE) methodologies to optimize the design parameters efficiently, minimizing the number of prototypes required.

Early in the design phase, we use sophisticated computer-aided engineering (CAE) tools for virtual prototyping and simulation, allowing us to evaluate different design options without the expense of building physical prototypes. This helps identify potential problems early on, significantly reducing costs and development time. This also allows for exploring several design variations virtually before choosing the most cost-effective one.

We often prioritize the most critical performance aspects first, focusing on the core functionality of the engine. Less critical aspects can be addressed later in the development process as resources allow. This may involve using cost-effective materials in early prototypes. We may also leverage modular design principles, enabling us to test individual components separately and then integrate them into the complete engine.

For example, we might use a simplified version of a component in the initial prototype to test the core functionality and then iterate towards the final, more complex design in subsequent iterations. This iterative approach allows us to manage the project’s scope and cost effectively, while maintaining the project timeline.

Optimizing engine performance and efficiency during prototyping involves a systematic approach that combines advanced simulation techniques, experimental validation, and data-driven decision-making. We leverage computational fluid dynamics (CFD) simulations to optimize combustion chamber design, intake and exhaust port geometry, and internal flow patterns to improve combustion efficiency and reduce emissions.

Engine performance and efficiency are intrinsically linked to several aspects. We’d use sophisticated instrumentation to measure parameters such as pressure, temperature, flow rates, and emissions. Data analysis tools are then employed to identify areas for improvement. Advanced techniques such as machine learning can help identify complex relationships between different parameters and provide insights for optimization.

For example, we might use a genetic algorithm to find the optimal parameters for the fuel injection system to maximize combustion efficiency while minimizing emissions. We’d then validate the findings experimentally. We also carefully consider design choices concerning friction reduction, employing low-friction materials and advanced lubrication techniques to minimize energy losses.

Continuous monitoring and analysis of data during testing are key. This allows for adjustments and iterations based on real-time observations, leading to incremental improvements in performance and efficiency.

During the prototyping of a high-performance gasoline engine, we encountered an unexpected issue during a high-speed endurance test. The engine experienced a significant loss of power and overheating after several hours of operation. Initial diagnostics revealed no obvious mechanical failures.

After careful review of the sensor data, we discovered a subtle anomaly in the fuel delivery system that was not initially apparent. The fuel pump was experiencing cavitation due to a combination of high fuel demand and a minor design flaw in the fuel delivery line. This resulted in a fluctuating fuel supply and ultimately caused the engine to overheat and lose power.

Faced with a tight deadline, I had to make a critical decision. We could either continue with extensive analysis to fully understand the root cause, delaying the project significantly, or implement a temporary fix to resolve the immediate problem. After assessing the risks and consulting with senior engineers, we opted for a temporary fix, addressing the immediate problem by modifying the fuel delivery line to mitigate cavitation. This allowed us to resume testing and deliver the prototype on time. Subsequently, we thoroughly analyzed the issue, leading to a permanent design change in the fuel delivery system.