aLL-i@MOveO Customised Engineering Frameworks
aLL-i is featured to design, test, analyse and assess future aviation propulsion, powered by combined hybrid clean energy of battery pack and hydrogen fuel cells, to enhance higher efficiency, higher range, maintaining availability while reducing cost, to provide emission free thrust power.
It is Quantum-hybrid approach, based on development of solver for building a quantum-hybrid approach code to facilitate the reliability-based cycle design optimization, New reliability based multi design point methodology, adopted using unconventional approaches of engaging reliability prediction on our trained GRAce-X net, Neural Network architecture. The reliability-based multi-design point methodology is proposed to acquire appropriate key design parameters by comprehensive reliability analysis for multiple operating conditions of interest.
Customised engineering application frameworks features and updates
all-i@MOveO customised application framework
ANN surrogate modelling offers a feasible approach to aerodynamic design in the field of rapid investigation of design space and optimal solution searching. The direct motive of a surrogate model is to establish a data-driven and bottom-up approach used when an outcome of interest cannot be easily obtained. The principle behind surrogate modeling is that data at input and output are related through the pattern of the trained neural network. It is noted that ANN needs a certain data set to be effectively trained. In particular, new forms of geometric representation method are proposed specifically to the network training. ANN not only performs well in surrogate modeling, but also produces potential advantages in optimization. The utilization of various levels of information, the multilevel surrogate modeling is proposed to obtain the optimal area in the design space quickly thus saving computational resource.
multi point design methedology
New reliability based multi design point methodologies, adopted using unconventional approaches of engaging reliability prediction by Artificial Neural Network, surrogate models, can also make use of new computational hardware such as neural chips and quantum annealers
The key to improving computational efficiency is to avoid performing the time-consuming probabilistic analysis by thermodynamic-based aero engine simulation model. Artificial neural network (ANN) is utilized to establish surrogate models to predict the aero engine performance reliability under multiple flight conditions.
This methodology establishes corresponding thermodynamics-based simulation model of aircraft engine with uncertainty component performance;
It divides the whole flight profile into multiple critical operating conditions and simultaneously regard them as the on-design points, and then respectively determine their overall performance requirements;
This methodology generates a certain number of training scenarios and train the neural network for establishing reliability prediction surrogate models of each concerned operating condition;
It constructs objective function by the attained surrogate models to facilitate the comprehensive performance reliability analysis in optimization design. Simultaneous consideration of mutually complementary coordinate and momentum portraits provides a deeper understanding of the propulsion plant dynamical system chaotic behavior.
Novel aero engine mechanical structure becomes more complex, the following overall reliability problems would undoubtedly be even worse than that of the conventional gas turbine engine.
Benefits of using quantum hybrid models aero engine conceptual design is already an extremely complicated problem, which involves the interaction of each component and the coupling of multiple disciplines. When the existence of uncertainty factors cannot be ignored, solving this problem becomes more difficult. Therefore, the traditional conceptual design method is facing challenges and it is worthwhile devoting much effort to this.
We have to bring computing solutions to a problem involving airframe loads, mass modelling and structural analysis. Our target should be preserving structural integrity while optimising weight. Weight optimisation is key to low operating costs and reduced environmental impact.
Structural integrity can be demonstrated by simulating key flight occurrences through life cycles of design, required by air worthiness regulations.