Climb-Cruise Engine Matching
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Consider a use case in the context of a 24-hour operation aircraft; a key implication is that aircraft can operate within noise curfews. Then, using a Set-Based Design approach, explore a multitude aircraft configurations with respect to their climb out Noise Levels, the Cruise Performances and Gaseous Emissions, under uncertainty (Figure 1). The basic concept is to generate data for a set of representative single aisle aircraft configurations by combining a multitude of airframes with a multitude of engines. Specify the noise measurement criteria in terms of the location of the measurement system and the type of noise level to be deduced. Then, using coupled analyses “plug-ins”, derive the performance models that enable an architect to explore the sensitivities amongst three exemplar measures of aptness. For this use case these are:
- Noise Levels (lower is better); conversely, Noise Level Margin (higher is better)
- Cruise Fuel Consumptions (lower is better)
- Gaseous Emissions (lower is better); conversely Gaseous Emissions Margin (higher is better)
- robust design-making to narrow a set of possible aircraft configurations
- discovering the parameters that strongly contribute to the variations in the measures of aptness
- managing key parameters to drive reliably towards the desired properties and behaviours
In the case where historical data or test data is not available (for example: novel airframe or engine configurations) then the uncertainty problem definition may require inputs from domain specialists. These inputs are envisaged to be elicited through the application of Structured Expert Judgement (SEJ) . The results of the analyses would require special treatment and Probabilistic Design Ranking is proposed because it would:
- account for lack of detailed design knowledge when comparing alternative aircraft configurations
- account for alternative stakeholder biases and preferences for design metrics
- provide a measure of confidence in the relative ranking of the aircraft configurations Specifically, the process inputs are clustered into two groups: Design Variables (Table 1) and Uncertainty Variables (Table 2).
Whilst the process is multi-organizational and multi-disciplinary, for the purposes of this “use case”, it has been simplified to a chain of coupled analyses. Therefore, it is necessary to propagate the uncertainties stochastically across this chain of coupled analyses. The UQ&M treatment must be a non-intrusive approach around the process because the models are used for other purposes; the application of the UQ&M analyses in this use- case is to reuse these models. These models may be domain-specific models and are at various degrees of representations; from the abstract to the physical (including experimental data). The architect must be able to choose heterogeneous degrees of representation depending on the decisions that need to be made. The intention is that, using the architect’s selection of the models, a series of “design of experiments” and monte-carlo simulations approaches are used to generate domain- specific data. Then, this data is used to create appropriate surrogate models to represent the functional relationships amongst the variables. The surrogate models enable the rapid analyses of the uncertainty propagation and identification of those parameters that significantly contribute to the uncertainties. No prerequisite constrains are applied to the underlying design space; they may be linear or highly non-linear. However, further research is needed for cases where the design space may be discontinuous and where it may contain folds that are important to capture. The surrogates models are created semi-automatically, using multi-layer perception type feed-forward artificial neural networks. In this case, the analysis steps in the process are functionally interdependent; the process consist of tightly coupled analyses models, as shown in Figure 3. Inverse mapping is an important requirement because we want to be able to identify the uncertainties in the inputs that contribute the most to uncertainties in the output, thereby forming a strategy for robust design decision-making.
Probabilistic Design Ranking provides a way of accounting for the lack of detailed design knowledge for comparing alternative conceptual designs; balances the biases and preferences of alternative stakeholders; and provides a measure of confidence in the relative ranking of conceptual design options. Some examples of the types of information to be visualised is given in the following figures:
The proof of concept was executed during the period of performance of the of the Innovate UK funded project CONGA (Configuration Optimisation of the Next Generation Aircraft). The methods used were experimental with a view of understanding how to
- Propagate uncertainty through tightly-coupled analyses models
- Discover the significant parameters that give rise to uncertainty
- Quantify uncertainty when data is not available
- Visualise and present uncertainty to enable inclusive decision-making (i.e. by stakeholders who do not have a background in UQ&M)
- Use visual analytics as an interactive knowledge acquisitions approach for uncertainty propagation
- Engineering analyses (Environmental Design Space: An engine/airframe modelling and simulation framework )
- Mathwork’s Matlab® for creating multi-dimensional surrogate models
- SAS’s JMP® for conducting both batch and interactive statistical analyses
Cooke, R. & Goossens, L. (2000), “Procedures Guide for Structural Expert Judgement in Accident Consequence Modelling”, Radiation Protection Dosimetry 90(3), 303-309.
Kirby, M.R. and Mavris, D.N., “The Environmental Design Space”, 26th International Congress of the Aeronautical Sciences, Anchorage, AK, ICAS-2008-4.7.3, 2008.