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Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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Energy Line and Hydraulic Gradient Line01:27

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Open channel flow, where a fluid flows with a free surface exposed to the atmosphere, is primarily governed by gravitational and surface effects, distinguishing it from closed conduit or pipe flow. In open channels such as rivers, canals, and artificial channels, energy analysis provides valuable insights into flow behavior and the relationship between depth, velocity, and slope.Specific Energy and Flow DepthIn open channel flow, the specific energy, E, combines the gravitational potential...
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

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Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
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Energy and Power Signals01:17

Energy and Power Signals

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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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Related Experiment Video

Updated: Nov 7, 2025

The Floating Lab: Standard Operational Procedure for Collecting and Filtering Seawater Samples from Operating Ferries for Environmental DNA Analysis
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Predicting Seagoing Ship Energy Efficiency from the Operational Data.

Aleksandar Vorkapić1, Radoslav Radonja1, Sanda Martinčić-Ipšić2

  • 1Faculty of Maritime Studies, University of Rijeka, 51000 Rijeka, Croatia.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

Machine learning accurately predicts seagoing ship energy efficiency using vessel data. Random Forest models offer the best predictions, aiding maritime energy savings.

Keywords:
data miningenergy-efficient shippinglinear regressionmachine learningmultilayer perceptronrandom forestship operational performancesupport vector machine

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Area of Science:

  • Marine Engineering
  • Computational Science
  • Data Science

Background:

  • Optimizing energy efficiency in maritime transport is crucial for reducing operational costs and environmental impact.
  • Accurate prediction of ship energy consumption is essential for effective energy management strategies.
  • Liquefied petroleum gas (LPG) carriers present unique operational characteristics influencing energy efficiency.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting the energy efficiency of LPG carrier vessels.
  • To identify the most effective machine learning algorithm for energy efficiency prediction in maritime operations.
  • To provide insights into data selection and parameter analysis for building robust energy prediction models.

Main Methods:

  • Utilized shipboard automation data, logbook reports, and environmental data to train machine learning algorithms.
  • Compared Generalized Linear Model (GLM), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF) regression models.
  • Evaluated model performance using Root Mean Square Error (RMSE) and Relative Absolute Error (RAE) metrics.

Main Results:

  • The Random Forest (RF) model achieved the lowest RMSE of 17.2632 and RAE of 2.304%, indicating superior prediction accuracy.
  • Analysis identified key measurement data and input parameters significantly influencing energy efficiency predictions.
  • Discretization of prediction results was implemented for user-friendly interpretation within operational contexts.

Conclusions:

  • Machine learning, particularly Random Forest, provides a powerful tool for predicting seagoing ship energy efficiency.
  • The study highlights the importance of data quality and feature selection in developing accurate energy prediction models.
  • The developed prediction framework can support decision-making for energy consumption savings in marine transport.