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Modelling and Product design featured in data cluster !
"design engineering using featured data in clusters, modelling and product design using design patterns, configured on Artificial Neural Network by machine learning algorithms"
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engineering free from constraints, and restrictions imposed by first principles of mechanical systems"
data driven engineering
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data driven engineering methodology do not follow first principles, instead it establishes governing rules from design patterns, featured in design data, vast amount of data processed, distributed in cluster, using machine learning algorithm, on ANN architecture.his situation is challenging than traditional design methods. Owing to the realization of multi-mission adaptability requires more complex mechanical structure, the candidates of future design models are confronted with more overall reliability problems than that of the conventional models. In order to overcome this challenge, we work to propose reliability based Methodology , an unconventional approach of engaging the reliability prediction by artificial neural network (ANN) surrogate models rather than the time-consuming Monte Carlo (MC) simulation .
multi point design methology
This situation is challenging than traditional design methods. Owing to the realization of multi-mission adaptability requires more complex mechanical structure, the candidates of future design
design by design patterns featured in cluster
Data driven engineering using machine learning/ deep learning algorithms on artificial neural network architecture starts with the setting up of design goals and then explores innumerable possible permutations of solutions, while simultaneously considering all constraints and requirements for finding the best design configuration. The algorithm aims to execute the design cycle multiple times and only applicable and near optimal solutions are preserved for each iteration. ML algorithm comprises data-driven optimization and applied regression techniques that are well suited for high-dimensional, nonlinear problems, such as those encountered in fluid dynamics, and structural dynamics. I have put my years of engineering experience and knowledge on engineering modelling and simulations into product design using inverse methodology of data driven engineering , which empowers design engineer to design by big data and design features, generated in cloud cluster.
hybrid modelling on ANN architecture
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.
learning through life cycles
The learning surrogate LCA method is appropriate to inform system design decision making in early conceptual stages of product development.
The approach facilitates an integrated system design process, allowing the approximate and rapid assessment of environmental impact based on high-level information typically known in the conceptual phase.
surrogate modelling
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
new propulsion system design
engineer-i, is design engineering suite of algorithms, featured using machine learning and deep learning technologies on artificial neural network architecture , to build surrogate quantum models, from classical mechanical models, enabled on cloud/edge.
engineer-i design engineering suite
engineer-i, is design engineering suite of algorithms, featured using machine learning and deep learning technologies on artificial neural network architecture , to build surrogate quantum models, from classical mechanical models, enabled on cloud/edge.
performance and fuel management on ANN
engineer-i, is design engineering suite of algorithms, featured using machine learning and deep learning technologies on artificial neural network architecture , to build surrogate quantum models, from classical mechanical models, enabled on cloud/edge.
engineer-i design engineering suite
engineer-i, engineer-i API’s enabled by (i) development of quantum-hybrid approach code, based on development of solver using open-source free software to analyse and design propulsion plant, to assess performance through life cycles of design as classical systems extension to quantum states or using (ii) unconventional Artificial Neural Network surrogate model in order avoid the problem of heavy computational burdens that inherently exist in Monte Carlo simulation.
electric propulsion design on ANN architure
engineer-i, is design engineering suite of algorithms, featured using machine learning and deep learning technologies on artificial neural network architecture , to build surrogate quantum models, from classical mechanical models, enabled on cloud/edge.
aLL-i @MOveO, founded to serve innovative minds, to empower design engineers, by design intelligence enabled to be operating in cloud cluster. aLL-i @MOveO has developed engineer-i API , Design engineering suite of AI tool kit series, on design features scripted simply in python, to design, to develop, and produce innovative design model beyond imagination and knowledge of designers, in cloud platform, using design analytics and computing power of quantum and neural chips, in distributed cluster.
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aLL-i @MOveO, founded to serve innovative minds, to empower design engineers, by design intelligence enabled to be operating in cloud cluster. aLL-i @MOveO has developed engineer-i API , Design engineering suite of AI tool kit series, on design features scripted simply in python, to design, to develop, and produce innovative design model beyond imagination and knowledge of designers, in cloud platform, using design analytics and computing power of quantum and neural chips, in distributed cluster.