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aLL-i@MOveO Featured Engineering Frameworks
aLL-i@MOveO is reliable technology partner, on data driven engineering and featured product design using design patterns setup @data cluster. We partner with businesses to develop technology and featured engineering application frameworks, enabled @data cluster on artificial neural network architecture using machine learning algorithms, deep learning methods. Our application frameworks use quantum-hybrid approach, based on development of solver for building a quantum-hybrid approach code usingopen-source, free software written in C++ and Python for solving partial differential equations such as the dynamic equations in unstructured mesh topologies.
Deep learning of dynamics on data driven engineering models aim to be solved using data patterns, rather than first principles of mechanical engineering, without excluding non-linearity on models is aimed to deal with minimal set of eigenfunctions an alternative formalism for study of dynamical systems which offers great utility in data-driven analysis and control of nonlinear and high-dimensional systems.
saVVy featured application framework
saVVy featured emission free product design application framework
Open sourve ML Application Software using Learning surrogate product Life Cycle Assessment on artificial neural network architecture.
SaVVy is open source application software empowers emission free featured product design enabled at cloud using using learning surrogate Life cycle assessments on Artificial Neural Network, 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 design. Life-Cycle Assessment (LCA) is a "cradle-to-grave" approach for assessing the environmental performance of a product, process or service system from raw material acquisition through production, use and disposal.
An artificial neural network (ANN) is trained to generalize on characteristics of product concepts typically known in the conceptual design phase, and environmental data from pre-existing LCA. This is done without the overhead of defining new LCA models on a product-by-product basis.
The model possesses only a high-level input interface - product concept descriptors - allowing it to operate with the limited data available in early conceptual design stages.
aLL-i featured application framework
aLL-i is open source featured application framework developed by aLL-i @MOveO , enabled to design all electric propulsion plant using data driven engineering modellng and design features classified @data cluster on ANN architecture, linked to deep learning open source softwares at internet, using machine learning algorithm. aLL 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.
This project targets to increase reliability and accuracy of current simulations done by limited single point design approach, by integrating simulation models into models on ANN architecture, achieving multi point design not governed by first principles but physical occurrences through life cycles of product design.
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.
i-power featured application framework
i-poWEr is featured application software using Surrogate quantum ML modelling on ANN. 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 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.
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. Standard machine learning library in TensorFlow Quantum implemented, provided tools to automatically differentiate the neural network, making the computation of the network spatial and time derivatives straightforward.
aLL-i@MOveO Customised Engineering Frameworks
aLL-i application framewroks aim 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.
Methodology we use to generate a certain number of training scenarios and train the neural network for establishing reliability prediction hybrid models of each concerned operating condition.
Our aim is to validate and verify design by comprising 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, requires multidisciplinary engineering effort to deal with non-linearity exists in nature.
It is concerned with the aero-thermodynamic, aeromechanical, systems, operational and control aspects of the installation, an important feature of which is design and performance of overall power plant or propulsion system, requires a mapping of non-linear dynamics to linear dynamics provides a powerful approach to understanding and controlling fluid flows and fluid structure interaction problems on complex engineering of propulsion plant system.
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Customised product design
We have skilled to combine engineering experience and business sensibility to keep up our expertise on featured product design using data driven engineering modelling and simulation . We have applied multi point design methodologies governed by design patterns in data cluster. We apply machine learning algorithms on ANN, to increase reliability and safety on design, enhanced by immense processing power in cloud. We are open to remote contracts to provide services, support in agreed terms
Collaborative product design
Our 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. Surrogate models with ANN have been shown a good alternative to conventional solution with regard to the prediction of aerodynamic. Coefficients of airplanes of high accuracy. ANN has been used for space mapping for transonic air foil aerodynamic shape optimization
Joint research
aLL-i open skilled to combine engineering experience and business sensibility to keep up aLL-i expertise on featured product design using data driven engineering modelling and simulation. aLL-i has applied multi point design methodologies governed by design patterns in data cluster. aLL-i applies machine learning algorithms on ANN, to increase reliability and safety on design, enhanced by immense processing power in cloud. aLL-i open offer remote contract services and technical support in agreed terms.@all-i@moveo-moveo.co.uk
Research consortium
We target to increase reliability and accuracy of current simulations done by limited single point design approach, by integrating simulation models into models on ANN architecture, achieving multi point design not governed by first principles but physical occurrences through life cycles of product design. We are open and passionate to join rearch platforms sharing our passion and aims, and funded and resourced by reaarch institutitions in research consortiums and joint ventures.