Custom_w4.png

diog 2

Untitled design (8).jpg

i-poWEr engineering application framework for
Fuel Management System design and design integration.

Artificial neural network (ANN) is utilized to establish surrogate models to predict the aero engine performance reliability under multiple flight conditions. 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.Artificial neural network (ANN) is utilized to establish surrogate models to predict the aero engine performance reliability under multiple flight conditions. 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.

i-power.jpg

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.

There are many types of combination of optimization algorithms with ANN. Lastly, deep learning is beneficial to the field of aerodynamic design surrogate model because it no longer needs the a priori treatment to the input data and thus human experience can be relied less.

i-power_w3.png

i-poWEr   featured using advanced surrogate modelling

i-poWEr  design features and updates

i-poWEr featured on ANN surrogate models facilitate the reliability-based cycle design optimization

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

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 propulsion plant system as a whole is multidisciplinary engineering in nature. It is concerned with the aero-thermodynamic, aeromechanical, systems, operational and control aspects of the installation, an important feature of which is the design and performance of the overall power plant or propulsion system. This comprises the engine, air intake, exhaust system, and the nacelle or housing surrounding the engine which is quite challenging and constrained effort by means of classical mechanical system.

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.

i-poWEr ANN surrogate modelling offers a feasible approach

i-poWEr Benefits of using advanced surrogate models