QuantumHybrid Solver to Integrate and Manage PropulsionPlant

Benefits of using quantum hybrid models

This approach is based on classical systems extension to quantum states. Quantum-hybrid approach, based on development of solver for building a quantum-hybrid approach code using open-source, free software written in C++ and Python for solving partial differential equations such as the dynamic equations in unstructured mesh topologies.

The utilization of the ANN surrogate models aims to facilitate the reliability-based cycle design optimization, which replaces the time-consuming probabilistic analysis based on Monte Carlo simulation.

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.

Challenges of using quantum hybrid models

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.

Implementation of quantum hybrid models

The reliability-based multi-design point methodology is proposed to acquire the appropriate key design parameters by comprehensive reliability analysis for multiple operating conditions of interest.

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.

Advantages of using quantum hybrid models

The proposed design method is applied on the cycle design of the propulsion plant performance model could empower propulsion plant designers, with

.Hybrid quantum algorithm which integrates the APSO (Accelerated Particle Swarm Optimization- ) algorithm, an optimization method that was based on the idea that we perform better by communicating with each other.) based pre-training technique into the network training procedure.

It respectively reduced the average error and maximum error of Artificial Neural Network.

Utilization of the ANN surrogate models facilitate the reliability-based cycle design optimization, which replaces the time-consuming probabilistic analysis based on Monte Carlo simulation

Optimization design solution of presented methodology reasonably increases the aero engine performance redundancy to precisely reach the expected reliability of all concerned operating conditions.

This methodology is universal and can be easily applied to other types of propulsion plant.

It facilitates the application of the reliability-based multi-design point method for candidates of future aviation propulsion, which are noted for excellent multi-mission adaptability. .

Further remarks using quantum hybrid models

◉ This methodology addresses the limitation of traditional deterministic design method that determines the key design parameters by subjectively setting the performance redundancy. Therefore, the overall performance redundancy could be set at a reasonable level so that contributes to the technical risk management and cost control of aero engine manufacturing.

◉ Advanced deep learning techniques (deep belief network, deep reinforcement learning, etc.) should also be considered to be further alternatives to accommodate development of high-precision surrogate model.

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