Neural Network-Based Prediction of Liquid-Phase Diffusion Coefficient to Model Fuel-Oil Dilution on Engine Cylinder Walls

AbstractDeep eutectic solvents (DESs) have received much attention in modern green chemistry as inexpensive and easy to handle analogous ionic liquids. This work employed molecular dynamics techniques to investigate the structure and dynamics of a DES system composed of choline chloride and phenyl propionic acid as a hydrogen bond donor and acceptor, respectively. Dynamical parameters such as mean square displacement, liquid phase self-diffusion coefficient and viscosity are calculated at the pressure of 0.1 MPa and temperatures 293, 321 and 400 K. The system size effect on the self-diffusion coefficient of DES species was also examined. Structural parameters such as liquid phase densities, hydrogen bonds, molecular dipole moment of species, and radial and spatial distribution functions (RDF and SDF) were investigated. The viscosity of the studied system was compared with the experimental values recently reported in the literature. A good agreement was observed between simulated and experimental values. The electrostatic and van der Waals nonbonding interaction energies between species were also evaluated and interpreted in terms of temperature. These investigations could play a vital role in the future development of these designer solvents.

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An Engineering Calculation Method for Multi-Component Stagnant Droplet Evaporation With Finite Diffusivity

Volume 3: Coal, Biomass and Alternative Fuels; Combustion and Fuels; Oil and Gas Applications; Cycle Innovations ◽

10.1115/94-gt-440 ◽

1994 ◽

Cited By ~ 4

Author(s):

J. S. Chin

Keyword(s):

Heat Transfer ◽

Diffusion Coefficient ◽

Liquid Phase ◽

Vapor Pressure ◽

Molecular Mass ◽

Calculation Method ◽

Engineering Calculation ◽

Droplet Evaporation ◽

Diffusion Resistance ◽

Distillation Curve

A practical engineering calculation method has been formulated for commercial multicomponent fuel stagnant droplet evaporation with variable finite mass and thermal diffusivity. Instead of solving the transient liquid phase mass and heat transfer partial differential equation set, a totally different approach is used. With zero or infinite mass diffusion resistance in liquid phase, it is possible to obtain vapor pressure and vapor molecular mass based on the distillation curve of these turbine fuels. It is determined that Peclet number (Pef) is a suitable parameter to represent the mass diffusion resistance in liquid phase. The vapor pressure and vapor molecular mass at constant finite Pef is expressed as a function of finite Pef, vapor pressure, and molecular mass at zero Pef and infinite Pef. At any time step, with variable finite Pef, the above equation is still valid, and PFsPef=∞, PFsPef=0, MfvPef=∞, MfvPef=0 are calculated from PFsPef≡∞, PFsPef≡0, MfvPef≡∞, MfvPef≡0, thus PFs and Mfv can be determined in a global way which eventually is based on the distillation curve of fuel. The explicit solution of transient heat transfer equation is used to have droplet surface temperature and droplet average temperature as a function of surface Nusselt number and non-dimensional time. The effect of varying com position of multi-component fuel evaporation is taken into account by expressing the properties as a function of molecular mass, acentric factor, critical temperature, and critical pressure. A specific calculation method is developed for liquid fuel diffusion coefficient, also special care is taken to calculate the binary diffusion coefficient of fuel vapor-air in gaseous phase. The effect of Stefan flow and natural convection has been included. The predictions from the present evaporation model for different turbine fuels under very wide temperature ranges have been compared with experimental data with good agreement.

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Investigation of the gaseous phase diffusion and liquid phase self-diffusion of n-decane on NaCaA zeolites

Zeolites ◽

10.1016/0144-2449(85)90082-x ◽

1985 ◽

Vol 5 (2) ◽

pp. 113-117 ◽

Cited By ~ 24

Author(s):

M. Bülow ◽

P. Struve ◽

L.V.C. Rees

Keyword(s):

Liquid Phase ◽

Gaseous Phase ◽

Phase Diffusion ◽

Self Diffusion

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Neural Network Approach for Predicting Ship Speed and Fuel Consumption

Journal of Marine Science and Engineering ◽

10.3390/jmse9020119 ◽

2021 ◽

Vol 9 (2) ◽

pp. 119

Author(s):

Lúcia Moreira ◽

Roberto Vettor ◽

Carlos Guedes Soares

Keyword(s):

Neural Network ◽

Fuel Consumption ◽

Computing System ◽

Fuel Oil ◽

Network Computing ◽

Peak Period ◽

Neural Network Approach ◽

Data Set ◽

Navigation Data ◽

Ship Speed

In this paper, simulations of a ship travelling on a given oceanic route were performed by a weather routing system to provide a large realistic navigation data set, which could represent a collection of data obtained on board a ship in operation. This data set was employed to train a neural network computing system in order to predict ship speed and fuel consumption. The model was trained using the Levenberg–Marquardt backpropagation scheme to establish the relation between the ship speed and the respective propulsion configuration for the existing sea conditions, i.e., the output torque of the main engine, the revolutions per minute of the propulsion shaft, the significant wave height, and the peak period of the waves, together with the relative angle of wave encounter. Additional results were obtained by also using the model to train the relationship between the same inputs used to determine the speed of the ship and the fuel consumption. A sensitivity analysis was performed to analyze the artificial neural network capability to forecast the ship speed and fuel oil consumption without information on the status of the engine (the revolutions per minute and torque) using as inputs only the information of the sea state. The results obtained with the neural network model show very good accuracy both in the prediction of the speed of the vessel and the fuel consumption.

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