scholarly journals Flow-relative control of an underwater robot

2013 ◽  
Vol 469 (2153) ◽  
pp. 20120671 ◽  
Author(s):  
Taavi Salumäe ◽  
Maarja Kruusmaa
Keyword(s):  
Energy Consumption ◽  
Lateral Line ◽  
Robot Navigation ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Underwater Robot ◽  
Flow Zone ◽  
Short Term ◽  
Aquatic Animals ◽  
Flow Sensing

This paper describes flow-relative and flow-aided navigation of a biomimetic underwater vehicle using an artificial lateral line for flow sensing. Most of the aquatic animals have flow sensing organs, but there are no man-made analogues to those sensors currently in use on underwater vehicles. Here, we show that artificial lateral line sensing can be used for detecting hydrodynamic regimens and for controlling the robot’s motion with respect to the flow. We implement station holding of an underwater vehicle in a steady stream and in the wake of a bluff object. We show that lateral line sensing can provide a speed estimate of an underwater robot thus functioning as a short-term odometry for robot navigation. We also demonstrate navigation with respect to the flow in periodic turbulence and show that controlling the position of the robot in the reduced flow zone in the wake of an object reduces a vehicle’s energy consumption.

From aquatic animals to robot swimmers

2018 ◽  
Author(s):  
Maarja Kruusmaa
Keyword(s):  
Fish Species ◽  
Lateral Line ◽  
Robot Control ◽  
Underwater Vehicles ◽  
Aquatic Animals ◽  
Trade Off ◽  
Aquatic Locomotion ◽  
Flow Sensing ◽  
Biomimetic Robots ◽  
Lateral Lines

Fish and other aquatic animals have developed a diverse repertoire of locomotion and sensing strategies in an environment that is 800 times denser than air. This chapter explains the underlying principles of aquatic locomotion and describes some landmark biomimetic robots based on those principles. Biological underwater swimmers face the trade-off between speed and manoeuvrability and it is argued that the same trade-off exists also with biomimetic vehicles. Biomimetic underwater vehicles mostly mimic carangiform and subcarangiform swimmers which are fast swimmers. The highly manoeuvrable fish species (lampreys, rays, etc.) are a less popular choice of bioinspiration arguably because of their higher complexity and limitations posed by current technology of electromechanical devices. A unique sensing organ, the lateral line, is utilized by all fish species. Artifical lateral lines for sensing flow are briefly discussed as well as the potential of robot control with the help of flow sensing.

scholarly journals Optimal design of nose and tail of an autonomous underwater vehicle hull to reduce drag force using numerical simulation

2019 ◽  
Vol 234 (1) ◽  
pp. 76-88 ◽  
Author(s):  
Mohammad Saghafi ◽  
Roham Lavimi
Keyword(s):  
Energy Consumption ◽  
Drag Force ◽  
Autonomous Underwater Vehicle ◽  
Turbulence Modeling ◽  
Stokes Equations ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Navier Stokes

In this research, the flow around the autonomous underwater vehicles with symmetrical bodies is numerically investigated. Increasing the drag force in autonomous underwater vehicles increases the energy consumption and decreases the duration of underwater exploration and operations. Therefore, the main objective of this research is to decrease drag force with the change in geometry to reduce energy consumption. In this study, the decreasing or increasing trends of the drag force of axisymmetric bare hulls have been studied by making alterations in the curve equations and creating the optimal geometric shapes in terms of hydrodynamics for the noses and tails of autonomous underwater vehicles. The incompressible, three-dimensional, and steady Navier–Stokes equations have been used to simulate the flow. Also, k-ε Realizable with enhanced wall treatment was used for turbulence modeling. Validation results were acceptable with respect to the 3.6% and 1.4% difference with numerical and experimental results. The results showed that all the autonomous underwater vehicle hulls designed in this study, at an attack angle of 0°, had a lower drag force than the autonomous underwater vehicle hull used for validation except geometry no. 1. In addition, nose no. 3 has been selected as the best nose according to the lowest value of stagnation pressure, and also tail no. 3 has been chosen as the best tail due to the production of the lowest vortex. Therefore, geometry no. 5 has been designed using nose and tail no. 3. The comparison made here showed that the maximum drag reduction in geometry no. 5 was equal to 26%, and therefore, it has been selected as the best bare hull in terms of hydrodynamics.

scholarly journals Underwater Robot Detection System Based on Fish’s Lateral Line

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 566 ◽  
Author(s):  
Zhijie Tang ◽  
Zhen Wang ◽  
Jiaqi Lu ◽  
Gaoqian Ma ◽  
Pengfei Zhang
Keyword(s):  
Lateral Line ◽  
Detection System ◽  
Near Field ◽  
Pressure Sensors ◽  
Underwater Vehicle ◽  
The Body ◽  
Underwater Robot ◽  
Pressure Amplitude ◽  
Pressure Source ◽  
Field Detection

This paper introduces the near-field detection system of an underwater robot based on the fish lateral line. Inspired by the perception mechanism of fish’s lateral line, the aim is to add near-field detection functionality to an underwater vehicle. To mimic the fish’s lateral line, an array of pressure sensors is developed and installed on the surface of the underwater vehicle. A vibrating sphere is simulated as an underwater pressure source, and the moving mechanism is built to drive the sphere to vibrate at a certain frequency near the lateral line. The calculation of the near-field pressure generated by the vibrating sphere is derived by linearizing the kinematics and dynamics conditions of the free surface wave equation. Structurally, the geometry shape of the detection system is printed by a 3D printer. The pressure data are sent to the computer and analyzed immediately to obtain information of the pressure source. Through the experiment, the variation law of the pressure is generated when the source vibrates near the body, and is consistent with the simulation results of the derived pressure calculation formula. It is found that the direction of the near-field pressure source can distinguished. The pressure amplitude of the sampled signals are extracted to be prepared for the next step to estimate the vertical distance between the center of the pressure source and the lateral line.

scholarly journals A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2742
Author(s):  
Fatih Ünal ◽  
Abdulaziz Almalaq ◽  
Sami Ekici
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Unified Framework ◽  
Short Term ◽  
Planning And Scheduling ◽  
Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

scholarly journals Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 405
Author(s):  
Anam Nawaz Khan ◽  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Statistical Model ◽  
Residential Buildings ◽  
Residential Building ◽  
Experimental Results ◽  
Consumption Patterns ◽  
Ensemble Prediction ◽  
Short Term ◽  
Term Energy ◽  
Prediction Approach

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

scholarly journals Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1149
Author(s):  
Pedro Oliveira ◽  
Bruno Fernandes ◽  
Cesar Analide ◽  
Paulo Novais
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Wastewater Treatment Plants ◽  
Learning Approach ◽  
Sustainable Cities ◽  
Short Term ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.

The relationship between energy consumption and economic growth: New evidence from Greece

2021 ◽  
pp. 131-153
Author(s):  
Antonia Gkergki
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Gdp Per Capita ◽  
Short Term ◽  
Vector Error Correction ◽  
Per Capita ◽  
Econometric Tests ◽  
The Government ◽  
New Research ◽  
The Relationship

This paper examines the relationship between the energy consumption and economic growth from 1968 to 2019 in Greece, by employing the vector error-correction model estimation. A series of econometric tests are employed concerning the stationary of the data, and the co-integration and the relationship among the variables during the long- and short-term. The em-pirical results suggest that there is no bidirectional relationship between economic growth and energy consumption. More specifically, GDP per capita does not affect the energy consump-tion of the three primary sources either in the long-term or the short-term. In other words, the economic crisis and its implications for GDP do not affect energy consumption, and they are not responsible for the considerable decrease in energy sources' consumption. On the other hand, the energy consumption of oil and coal negatively affect the GDP per capita. These re-sults are different from previous studies' conclusions for Greece; this is because the never been experienced before. These findings raise new research questions and also show the limi-tations of the Greek market, as it is regulated and controlled by the government.

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