Search Results

In order to achieve the desired λ also in in-stationary use of the engine, it is necessary to use new-technology-based control systems. Artificial Intelligence provides methods to cope with difficulties like wide operation range, unknown nonlinearities and time delay.

This aim can be reached by using a specific feed-forward structure for the control of the paths of air and fuel based on identification abilities of Artificial Intelligence. As approximators for multidimensional nonlinear static functions we will use specific Neural Networks (NN) together with sophisticated stability-proven learning structures.

To this end, in the present work different Artificial Intelligence methodologies are compared, in order to verify the related performance as virtual sensor for the oxygen value at the intake. ...Several models are set-up and verified, of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and compared with Artificial Neural Networks Models (ANN). The analysis is carried out by using experimental data acquired on a compression ignition engine, either in steady-state tests, or in transient engine operational conditions, and the obtained results discussed.

So, the adaptive proportional integral derivative (APID) controller is utilized to enhance the results. An artificial neural network (ANN) controller is one of the recent control methods, which gives accurate and precise results and utilizes ANN to give more accurate results. ...But it lacks fuzzy logic, that is, human tendency, and finally, the artificial neuro-fuzzy inference system (ANFIS) controller is concluded as the best controller to limit the speed of the BLDC motor.

Considering the advancements in manufacturing industries, which are crucial for economic growth, there is a substantial demand for exploration and analysis of advanced materials, especially alloy materials, to enable efficient utilization of new technologies. Lightweight and high-strength materials, like aluminium alloys, are highly recommended for various applications that necessitate both strength and resistance to corrosion, such as automobile, marine and high-temperature applications. Therefore, there is a significant need to investigate and analyse these materials to facilitate their effective application in manufacturing sectors. This study investigates the machinability of drilling AA6061 using a micro-textured tool and proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) model for investigating the machinability of drilling AA6061 aluminium alloy with a micro-textured uncoated tool.

Vehicle systems may further be improved by the creation and implementation of artificial intelligence and machine learning (AI/ML) in the form of advanced predictive capabilities and more robust control policies.

This work proposes an Artificial Intelligence-based engine simulator for a Spark Ignition engine. The simulator relies on Neural Networks for the calculation of the main combustion metrics.

(c) Approach: One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence into plant modelling. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modelling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems.

Abstract Artificial intelligence-based computing systems like artificial neural networks (ANN) have recently found increasing applications in predicting complex chemical phenomena like combustion properties.

Establish a comprehensive taxonomy of Artificial Intelligence in aviation

This paper describes the applicable and capability of neural network as an artificial intelligence tool to determine the performance and emissions in a compressed natural gas direct injection (CNG-DI) engine. ...A feed-forward back-propagation artificial neural network (BPANN) approach is explored to predict the combustion performance in the term of indicated power and emissions in the appearance of CO and NO emissions level. ...The data for combustion process under various engine operating parameters at the fixed speed at 1000 rpm were obtained to train the developed artificial neural network (ANN). The operating conditions employed to represent the combustion parameters for controlling the injection and ignition event are start of injection (SOI), end of injection (EOI) and spark advance (SA) timing, which affects to the combustion processes, performance as well as emissions formation.

This paper describes the application and capability of neural network as an artificial intelligence tool to determine the performance and emissions in a compressed natural gas direct injection (CNG-DI) engine. ...A feed-forward back-propagation artificial neural network (BPANN) approach is explored to predict the combustion performance in terms of indicated power and emissions in the appearance of CO and NO emissions level. ...The data for combustion process under various engine operating parameters at the fixed speed at 1000 rpm were obtained to train the developed artificial neural network (ANN). The operating conditions employed to represent the combustion parameters for controlling the injection and ignition event are start of injection (SOI), end of injection (EOI) and spark advance (SA) timing, which affects to the combustion processes, performance as well as emissions formation.

When engine modifications are carried out in order to improve performance, the whole testing process needs to be repeated. Artificial intelligence-based prediction models can be utilized to reduce the repetitions in engine testing. ...The objective of this study is to predict the in-cylinder pressure of a diesel engine based on the crank angle and load using a model built using artificial neural networks (ANN) in machine learning with MATLAB. ANN prediction model is developed from the data gathered from testing a single-cylinder diesel engine.

Document possible AI technologies, architecture, validation approach, and safety concerns for each use case.

This paper reports on an Artificial Neural Network (ANN) based approach to the design of controllers. The system is developed from a truly parallel hardware implementation of a Self-Organizing Map (SOM) type network resulting in a controller suitable for embedded ‘real-time’ non-linear applications.