Mapping and functional optimization of control parameters in W18V50DF dual-fuel engines during combustion

Essola, Dieudonné; Offole, Florence; Kennedy, Fohoue; Lionel, Njenji Tchoumi; Regis, Koussouke Koussouke

doi:10.1088/2631-8695/ac01f8

Cited by 2 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Engine Optimization will allow us to model its functional or dysfunctional behavior: it is a question of identifying which are the parameters and entries which will make it possible to have an optimization of the system to improve its performances. This involves simulating the mathematical system under different conditions and thus determining which parameter will produce the best result [12][13][14].…”

Section: Fault Diagnosis Techniques 131 Remaining Useful Life (Rul) P...mentioning

confidence: 99%

Modeling and fault detection of a turbofan engine by deep-learning approach

Offole¹,

Dieudonne²,

Kennedy³

et al. 2022

Technium

Self Cite

View full text Add to dashboard Cite

Throughout the world, thousands of passengers travel by air, their quality depends on that of the equipment used. Predictive maintenance is increasingly used to estimate. The remaining useful life of system components and in particular turbofan engines as an essential component. It is used to predict failure before it occurs, optimize component design, extend equipment life, and reduce maintenance costs. However, the algorithms proposed in the literature to date to determine the remaining useful life lack precision with a quadratic error around 20 while the physical models have errors of the order of 0.02. The problem here is how to increase the accuracy of predicting the remaining useful life of a turbofan engine. The objective of this study is to develop a more realistic and accurate algorithm for calculating the remaining useful life of a turbofan engine. To do this, we considered the degradation of the high pressure compressor and the fan as essential organs of the turbojet engine and we used deep learning, known for its high precision linked to a great capacity for extracting information. More specifically, it involved acquiring data on a turbojet engine in operation, pre-processing this data, developing the prediction model, training the model and finally validating the approach in comparison with other diagnostic methods. and to model these defects. We compared two deep learning architectures per application against the CMAPSS dataset to assess their performance. The LSTM architecture we developed prevailed with an RMSE of 13.76, well positioned compared to the literature architecture.

show abstract

Section: Fault Diagnosis Techniques 131 Remaining Useful Life (Rul) P...mentioning

confidence: 99%

Modeling and fault detection of a turbofan engine by deep-learning approach

Offole¹,

Dieudonne²,

Kennedy³

et al. 2022

Technium

Self Cite

View full text Add to dashboard Cite

show abstract

Solar Irradiation and Temperature Prediction Using LSTM Neural Network and Solar Energy Potential in Northern Cameroon for Solar Electric Vehicle Application

Douswekreo,

Ndoumbe,

Baba

et al. 2024

Journal of Engineering

View full text Add to dashboard Cite

The fight against global warming is becoming increasingly important because of the race to find new sources of energy. This work presents a method for estimating solar energy (SE) potential by applying LSTM neural networks to predict the parameters influencing photovoltaic production, namely, solar irradiation and temperature over 24 h. The database comes from MERRA‐2 and includes the meteorological parameters’ temperature, relative humidity, atmospheric pressure, wind speed, wind direction, solar irradiation and rainfall for 20 locations in northern Cameroon with hourly time steps over 23 years, from 2000 to 2022. The method consists of designing an LSTM neural model to obtain the lowest deviation between the real and predicted data. The performance criteria used to validate the model that predicts the two parameters are an accuracy of 92.45% and an RMSE of 20.9. The model was tested in two localities, Makary and Banyo, with the most important values out of the 20 localities. The database was used to estimate that the average SE potential is 2.193 MWh/m2/year in Makary, and the lowest potential is 1.949 MWh/m2/year in Banyo. This information can be used to select sites for solar power plant installations, solar photovoltaic energy management, solar electric vehicle (EV) fleet management and to select sites for the construction of EV recharging stations.

show abstract

Mapping and functional optimization of control parameters in W18V50DF dual-fuel engines during combustion

Cited by 2 publications

References 10 publications

Modeling and fault detection of a turbofan engine by deep-learning approach

Modeling and fault detection of a turbofan engine by deep-learning approach

Solar Irradiation and Temperature Prediction Using LSTM Neural Network and Solar Energy Potential in Northern Cameroon for Solar Electric Vehicle Application

Contact Info

Product

Resources

About