Energy Consumption Modeling for Underwater Gliders Considering Ocean Currents and Seawater Density Variation

Energy management is a critical and challenging factor required for efficient and safe operation of underwater gliders (UGs), and the energy consumption model (ECM) is indispensable. In this paper, a more complete ECM of UGs is established, which considers ocean currents, seawater density variation, deformation of the pressure hull, and asymmetry of gliding motion during descending and ascending. Sea trial data are used to make a comparison between ECMs with and without the consideration of ocean currents, and the results prove that the ECM that considers the currents has a significantly higher accuracy. Then, the relationship between energy consumption and multiple parameters, including gliding velocity relative to the current, absolute gliding angle, and diving depth, is revealed. Finally, a simple example is considered to illustrate the effects of the depth-averaged current on the energy consumption.

Refined Node Energy Consumption Modeling in a LoRaWAN Network

LPWAN technologies such as LoRa are widely used for the deployment of IoT applications, in particular for use cases requiring wide coverage and low energy consumption. To minimize the maintenance cost, which can become significant when the number of sensors deployed is large, it is essential to optimize the lifetime of nodes, which remains an important research topic. For this reason, it is necessary that it is based on a fine energy consumption model. Unfortunately, many existing consumption models do not take into account the specifications of the LoRaWAN protocol. In this paper, a refined energy consumption model based on in-situ measurements is provided for a LoRaWAN node. This improved model takes into account the number of nodes in the network, the collision probability that depends on the density of sensors, and the number of retransmissions. Results show the influence of the number of nodes in a LoRaWAN network on the energy consumption of a node and demonstrate that the number of sensors that can be integrated into a LoRaWAN network is limited due to the probability of collision.

Challenges in Raw Material Treatment at the Mechanical Processing Stage

This paper concerns problems related to the mechanical processing of mineral raw materials. The aspects explored were limited to the analysis of comminution technologies in terms of their effectiveness and energy consumption, modeling and simulation approaches, the assessment of crushing results, and environmental aspects. This article includes investigation of new technologies of comminution, comparing HPGR, high-voltage pulses, and electromagnetic mills. In the area of modeling and optimization, special attention was paid to the approximation of the particle size distribution of crushing products by means of Weibull, log-normal, and logistic functions. Crushing products with an increased content of fines were well characterized by Weibull’s distribution, while log-normal function adequately described HPGR products with a relatively low content of fines.

Toward IoT fog computing-enabled system energy consumption modeling and optimization by adaptive TCP/IP protocol

Nowadays, due to the fast-growing wireless technologies and delay-sensitive applications, Internet of things (IoT) and fog computing will assemble the paradigm Fog of IoT. Since the spread of fog computing, the optimum design of networking and computing resources over the wireless access network would play a vital role in the empower of computing-intensive and delay-sensitive applications under the extent of the energy-limited wireless Fog of IoT. Such applications consume considarable amount of energy when sending and receiving data. Although there many approaches to attain energy efficiency already exist, few of them address the TCP protocol or the MTU size. In this work, we present an effective model to reduce energy consumption. Initially, we measured the consumed energy based on the actual parameters and real traffic for different values of MTU. After that, the work is generalized to estimate the energy consumption for the whole network for different values of its parameters. The experiments were made on different devices and by using different techniques. The results show clearly an inverse proportional relationship between the MTU size and the amount of the consumed energy. The results are promising and can be merged with the existing work to get the optimal solution to reduce the energy consumption in IoT and wireless networks.

Comparison of minimum quantity lubrication and wet milling based on energy consumption modeling

Estimating the energy consumed by machining process is substantial because it has a large share of environmental effects in the manufacturing industry. In this paper, a generic energy consumption model was developed for milling processes that is able to be applied in all milling machine tools. Energy consumption of each segment was estimated according to power characteristics and parameters extracted from numerical control (NC) codes, then the total energy consumption was estimated by adding energy consumption of the machine components. Energy consumption of milling process was measured and compared in conventional (wet) and minimum quantity lubrication (MQL) conditions. The developed method was verified by comparing the estimated values of energy consumption with experimental results. Various studies have suggested different types of energy consumption modeling with machining, however; only a few studies have focused on the use of these modeling techniques. Thus, the MQL method has been rarely compared with the wet milling in terms of energy consumption. In the proposed model, energy consumption for workpiece adjustment, accounting for a major part of the costs in machining economics was considered for the first time. The results showed that the proposed method is efficient and practical for predicting energy consumption, with the possibility of occurring 5% error. Analysis of the results revealed that using the MQL method in milling process leads to 33% lower power consumption than wet milling and therefore, the MQL method can reduce the cost of production.