A semi-analytical mass estimation method is proposed for composite, oval fuselages, but is also applicable to conventional fuselages and to metallic materials. Loads applied to the fuselage include pressurization, steady-state maneuver loads and inertial loads. The primary structure around the passenger cabin is sized, based on first-ply failure using the Tsai-Wu failure criterion, global and local buckling. Moreover, maximum deflection due to transverse pressure is constrained for skin panels and sandwich panels. Sandwich panels are also sized for crippling and wrinkling. Empirical factors are used to calculate secondary structure and non-structural mass. In order to reduce in-the-loop calculation time, surrogate models of the sizing procedures are used, by means of neural networks. Validation of the failure calculations was done by finite-element analysis. It was found that the proposed method is capable of predicting metal, conventional fuselage mass satisfactorily, with acceptable breakdown of weights and estimated thicknesses. Additionally, the method can be used for unconventional aircraft configurations and composite material. Using composite material, a weight saving of around 19% is observed for a single-aisle aircraft as compared to aluminum.
actuator instead of hydraulic), to behaviors (e.g. a structural element doubling as electric conductor), and to entire systems (e.g. a fixed-wing aircraft as opposed to a helicopter). Previously, a review on technology selection [1] identified the following challenges with technology evaluation and selection: 1) The application of design tools is limited to a specific vehicle type, because the sizing method is fixed [2] 2) Flexibility and scalability of disciplinary analysis tools is lacking [2] 3) Parameterization of geometry proves challenging in a generalized sense (i.e. problem specific parameterizations are possible, but geometry parameterization that holds for any problem is challenging, at the least) 4) Modeling of technologies is usually avoided and replaced by impact factors 5) Extensive use of expert judgment raises challenges due to subjectivity, conservatism, overconfidence and lack of experts Additionally, from discussions with practitioners at Saab, the following challenges with technology selection in the conceptual design phase were identified: (i) non-performance metrics (e.g. the effect of improved human-machine interaction) need to be included, although prove difficult to define and quantify, (ii) technology descriptions are not yet meaningful, and cannot traverse from detailed to high-level descriptions, nor capture quantifiable effects and enabling fair comparison between technologies, (iii) finding the best technology portfolio without enumerating all possible combinations cannot yet be done objectively, (iv) the assessment of dependencies between technologies is too subjective, and (v) when uncertainty is quantified, decision making is impaired in case of wide uncertainty bands. Several conclusions can be drawn from these challenges. Metrics that are not quantifiable cannot be addressed, and hence, technologies affecting those have no meaning. Alternatively, additional analysis methods should be developed. Uncertainty should be associated with technology (and system) readiness level (both on impact and development time). Additionally, the amount of uncertainty should be limited or decision-making in the event of large uncertainty should be supported. Insight is more important at this stage than arriving at a most optimal result, hence preference is given to evaluating current possibilities, rather than performing optimization. Therefore, a structured and traceable solution to technology definition, evaluation and selection is sought. Conventionally, technology selection is performed by collecting technology TRL and IRL levels, describing their effects in an impact matrix and their compatibility in a Technology Compatibility Matrix (TCM). Such an approach was taken in the works by Amadori et al. [3, 4], who depict the process as in Figure 1. The data collection phase is essentially carried out completely using expert judgment, with limited traceability and objectivity.
The need to reduce the pollutant impact of aircraft emission drives the research on aircraft design progress through off-design performance improvement. This report proposes to investigate the effect of maneuver load alleviation technology via wing control surfaces for this purpose. A methodology is presented to model the MLA technology in aircraft conceptual design and to evaluate its impact on both existing and clean-sheet design. In addition, the possibility to consider flexible wings when under the influence of 2.5-g maneuver loads is addressed, to assess the impact of aeroelasticity in on wing weight in the conceptual design phase. The aeroelastic analysis method is validated against a higher-order analysis method with excellent correlation between the results from the two methods. Subsequently, the method is applied to the redesign of medium-range, single-aisle aircraft. It is shown that applying MLA using both the flaps and the ailerons can result in a fuel burn reduction and maximum takeoff mass reduction of 2.1% and 2.2%, respectively.
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