How do you assess the trade-offs between performance and reliability in propulsion system selection?

1 Performance metrics

Performance is a measure of how well a propulsion system delivers the required thrust and power for a given mission and flight profile. Performance metrics include specific fuel consumption, thrust-to-weight ratio, specific impulse, efficiency, and range. These metrics depend on the design features and operating conditions of the propulsion system, such as the type of cycle, the compressor and turbine stages, the combustion chamber, the nozzle, the inlet, and the bypass ratio. To evaluate the performance of different propulsion systems, you need to define the design point and the off-design performance, and use analytical models, numerical simulations, or experimental data to calculate the performance metrics.

2 Reliability metrics

Reliability is a measure of how well a propulsion system operates without failures or malfunctions that affect its functionality or safety. Reliability metrics include mean time between failures, failure rate, availability, maintainability, and life cycle cost. These metrics depend on the quality and durability of the components and materials, the design margins and redundancies, the maintenance and inspection schedules, and the operating environment and stresses. To evaluate the reliability of different propulsion systems, you need to define the failure modes and effects, and use statistical models, historical data, or reliability testing to estimate the reliability metrics.

3 Trade-off analysis

Trade-off analysis is a process of comparing and ranking different propulsion systems based on their performance and reliability metrics, as well as other criteria that are relevant for your aircraft design, such as cost, weight, noise, emissions, and safety. Trade-off analysis can help you identify the optimal or preferred propulsion system, or the feasible range of alternatives that meet your requirements and constraints. To conduct a trade-off analysis, you need to define the objectives and weights of the criteria, and use quantitative or qualitative methods, such as multi-criteria decision analysis, cost-benefit analysis, or pairwise comparison, to evaluate and rank the propulsion systems.

4 Sensitivity analysis

Sensitivity analysis is a process of assessing how the performance and reliability metrics of a propulsion system vary with changes in the design parameters, operating conditions, or uncertainty factors. Sensitivity analysis can help you understand the effects of different scenarios and assumptions on the propulsion system selection, and identify the critical or robust parameters that influence the trade-offs. To perform a sensitivity analysis, you need to define the range and distribution of the parameters, and use analytical models, numerical simulations, or experimental data to calculate the performance and reliability metrics for different cases.

5 Validation and verification

Validation and verification are processes of ensuring that the performance and reliability metrics of a propulsion system are accurate and consistent with the design specifications and expectations. Validation is the process of checking that the metrics meet the requirements and objectives of the aircraft design, while verification is the process of checking that the methods and data used to calculate the metrics are correct and reliable. To perform validation and verification, you need to compare the metrics with the design targets and standards, and use error analysis, uncertainty analysis, or quality assurance methods to evaluate the accuracy and reliability of the methods and data.

6 Optimization and improvement

Optimization and improvement are processes of enhancing the performance and reliability of a propulsion system by modifying or adjusting its design features or operating conditions. Optimization is the process of finding the optimal or best values of the design parameters that maximize or minimize a performance or reliability metric, while improvement is the process of finding feasible or practical ways of increasing or reducing a performance or reliability metric. To perform optimization and improvement, you need to define the optimization or improvement objective and constraints, and use analytical models, numerical simulations, or experimental data to find the optimal or improved values of the design parameters.