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aLL-i engineering application framework for all electric propulsion design 

aLL-i is featured to bring computing solutions to a problem involving airframe loads, mass modelling and structural analysis targeted to be preserved structural integrity while optimising weight which is crucial to low operating costs and reduced environmental impact

Modelling on aLL-i aims to facilitate the reliability-based cycle design optimisation, which replaces the time-consuming probabilistic analysis based on Monte Carlo simulation. Structural integrity can be demonstrated by simulating key flight occurrences through life cycles of design, required by air worthiness regulations.

aLL-i uses methodology to generate a certain number of training scenarios and train the neural network for establishing reliability prediction hybrid models of each concerned operating condition. aLL=i methedology increases the aero engine performance redundancy to precisely reach the expected reliability of all concerned operating conditions.

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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.

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aLL-i   featured emission free product design  
application framework

aLL-i   design features and updates

aLL-i   featured using subsets  which does not require  neither datasets nor mesh, to train.

A key feature of this algorithm is that no dataset neither mesh is required for training the neural network. Instead, only a subset of points randomly sampled from the time and space domains are needed to assess whether the neural network output fits the PDE, its initial condition and its boundary conditions.
 

Multidisciplinary design optimization is an ongoing challenge in the aerospace industry, resulting in long design lead times and untapped optimisation potential. Quantum computing may offer a viable path towards efficient multi-parameter optimization covering the entire design space. Here we ask for the application of quantum computing solutions to a problem involving airframe loads, mass modelling and structural analysis.
 

Multidisciplinary design optimization is an ongoing challenge in the aerospace industry, resulting in long design lead times and untapped optimisation potential. Quantum computing may offer a viable path towards efficient multi-parameter optimization covering the entire design space. Here we ask for the application of quantum computing solutions to a problem involving airframe loads, mass modelling and structural analysis.

The target is to preserve structural integrity while optimizing propulsion performance. Weight optimisation is key to low operating costs and reduced environmental impact.

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The challenge arises when computing a broad range of aircraft design configurations simultaneously which is currently not possible with classical computing.

 

These methods are usually implemented in standard machine learning libraries such as TensorFlow Quantum. In particular, this library also provides tools to automatically differentiate the neural network, making the computation of the network spatial and time derivatives straightforward.

aLL-i   featured  to  run on  opensource deeplearning framework at internet.

aLL-i  featured with multidisciplinary design optimization