Sizing, Optimization, and Financial Analysis of a Green Hydrogen Refueling Station in Remote Regions

Hydrogen (H2) can be a promising energy carrier for decarbonizing the economy and especially the transport sector, which is considered as one of the sectors with high carbon emissions due to the extensive use of fossil fuels. H2 is a nontoxic energy carrier that could replace fossil fuels. Fuel Cell Electric Vehicles (FCEVs) can decrease air pollution and reduce greenhouse gases when H2 is produced from Renewable Energy Sources (RES) and at the same time being accessible through a widespread network of Hydrogen Refueling Stations (HRSs). In this study, both the sizing of the equipment and financial analysis were performed for an HRS supplied with H2 from the excess electrical energy of a 10 MW wind park. The aim was to determine the optimum configuration of an HRS under the investigation of six different scenarios with various numbers of FCEVs and monthly demands, as well as ascertaining the economic viability of each examined scenario. The effect of the number of vehicles that the installation can refuel to balance the initial cost of the investment and the fuel cost in remote regions was investigated. The results showed that a wind-powered HRS could be a viable solution when sized appropriately and H2 can be used as a storage mean for the rejected wind energy. It was concluded that scenarios with low FCEVs penetration have low economic performance since the payback period presented significantly high values.

Structural Lattice Topology and Material Optimization for Battery Protection in Electric Vehicles Subjected to Ground Impact Using Artificial Neural Networks and Genetic Algorithms

A critical external interference that often appears to pose a safety issue in rechargeable energy storage systems (RESS) for electric vehicles (EV) is ground impact due to stone impingement. This study aims to propose the new concept of the sandwich for structural battery protection using a lattice structure configuration for electric vehicle applications. The protective geometry consists of two layers of a twisted-octet lattice structure. The appropriate lattice structure was selected through topology and material optimization using an artificial neural network (ANN), genetic algorithms (GA), and multi-objective optimization with technique for order of preference by similarity to ideal solution (TOPSIS) methods. The optimization variables are the lattice structure relative density, ρ¯, angle, θ, and strength of the materials, σy. Numerical simulations were used to model the dynamic impact loading on the structures due to a conical stone mass of 0.77 kg traveling at 162 km/h. The two-layer lattice structure configuration appears to be suitable for the purposes of RESS protection. The optimum configuration for battery protection is a lattice structure with an angle of 66°, relative density of 0.8, and yield strength of 41 MPa. This optimum configuration can satisfy the safety threshold of battery-shortening deformation. Therefore, the proposed lattice structure configuration can potentially be implemented for electric vehicle applications to protect the battery from ground impact.

A Dynamic Optimization Tool to Size and Operate Solar Thermal District Heating Networks Production Plants

The aim of the ISORC/OPTIMISER project is to increase and improve the use of solar thermal energy in district heating networks. One of the main tasks of the project is to develop an optimization tool for the sizing and operation of a solar district heating network. This is the first optimization tool using an open-source interface (Julia, JuMP) and solver (Ipopt) to solve nonlinear problems. This paper presents the multi-period optimization problem which is implemented to consider the dynamic variations in a year, represented by four typical days, with an hourly resolution. The optimum is calculated for a total duration of 20 years. First, this paper presents the modeling of the different components of a solar district heating network production plant: district network demand, storage and three sources, i.e., a fossil (gas) and two renewable (solar and biomass) sources. In order to avoid prohibitive computational time, the modeling of sources and storage has to be fairly simple. The multi-period optimization problem was formulated. The chosen objective function is economic: The provided economic model is accurate and use nonlinear equations. Finally the formulated problem is a nonlinear Programming problem. Optimization of the studied case exhibits consistent operating profiles and design. A comparison is made of different types of storage connection at the production site, highlighting the relevance of placing the storage at the solar field outlet. The optimum configuration supplies 49% of demand using solar energy, achieving a renewable rate of 69% in combination with the biomass boiler.

Brownian Motion and Thermophoretic Effects in Mini Channels with Various Heights

Flow-through mini channels have received tremendous interest from researchers over a long period. However, the study of flow between the channel and on top of the channel has received little to no attention. In the present paper, different parameters have been used to investigate this heat enhancement. The height of 10 mini channels has been varied, allowing the corresponding aspect ratio to vary from 3 to 6, 9, and 12. When the aspect ratio is 12, flow circulates through the mini channel only, and when the aspect ratio is less than 12, flow is distributed between the one circulating inside the channel and moving on top of the channel. Different flow rates are studied corresponding to a Reynolds number varying from 250 to 1250 if water is the working fluid. Brownian and thermophoresis effects are taken into consideration to investigate the nanoparticle sedimentation. Results revealed that the optimum configuration, if one needs to take into consideration the friction factor, is 12. If one ignores the pressure drops, then the optimum configuration is when the aspect ratio is equal to 6. This means that the flow interaction between the one circulating in the channel and above the channel plays a major effect in heat removal.

Numerical investigations on the effect of volute casing treatment for performance augmentation in a centrifugal fan

This article presents the effect of volute chamfering on the performance of a forward swept centrifugal fan. The numerical analysis is performed to obtain the performance parameters such as static pressure rise coefficient and total pressure coefficient for various flow coefficients. The chamfer ratio for the volute is optimized parametrically by providing a chamfer on either side of the volute. The influence of the chamfer ratio on the three dimensional flow domain was investigated numerically. The simulation is carried out using Re-Normalisation Group (RNG) k-[Formula: see text] turbulence model. The transient simulation of the fan system is done using standard sliding mesh method available in Fluent. It is found from the analysis that, configuration with chamfer ratio of 4.4 is found be the optimum configuration in terms of better performance characteristics. On an average, this optimum configuration provides improvement of about 6.3% in static pressure rise coefficient when compared to the base model. This optimized chamfer configuration also gives a higher total pressure coefficient of about 3% validating the augmentation in static pressure rise coefficient with respect to the base model. Hence, this numerical study establishes the effectiveness of optimally providing volute chamfer on the overall performance improvement of forward bladed centrifugal fan.

LTE network performance evaluation based on effects of various parameters on the cell range and MAPL

These days long term evaluation (LTE) is considered the common mobile technology around the world and there is a need to maximize the network performance to satisfy the increased demand in terms of the cell capacity and coverage. These are many parameters in the network configuration and in the surrounded environment, which have great effects on the network performance. Examples of parameters are the system overhead rations, the required capacity of the network, neighbor cell load, and link budget parameters. The determination of the optimum configuration parameters, which achieve the best network performance, is a main step in the planning process in addition to it is continuous step in network optimization phase. In this study, the effects of some parameters will be investigating to get the best parameters that achieve the best network performance in terms of capacity of the cells and coverage area. The study will start by discussing introduction about LTE network components and protocols, and then the main parameters of the protocols will be revising. The study will display the results of changing many parameters related to LTE protocols and surrounding environment parameters on the LTE network performance.

Mechatronic system parameter idenfitcation [i.e. identification] via genetic algorithms

This thesis develops a novel way to identify both the joint friction parameters and a built in torque sensor gain and offset. The identification method is based on a genetic algorithm (GA). A model based friction compensation method and a real coded GA are selected from a variety of methods available. A model of a single degree of freedom mechatronic joint with a link is presented. Numerical simulations are run to determine the optimum configuration of the GA with respect to the population size and maximum number of generations necessary to identify the parameters to within 5% of their actual value. The GA identification technique is then used on an experimental mechatronic joint with a harmonic drive and built-in torque sensor. The friction parameters as well as the sensor gain and offset are identified in the experimental system and the position tracking error is reduced. Based on the experimental results, the method is found to be an effective way of identifying system parameters in a mechatronic joint.

Mechatronic system parameter idenfitcation [i.e. identification] via genetic algorithms

This thesis develops a novel way to identify both the joint friction parameters and a built in torque sensor gain and offset. The identification method is based on a genetic algorithm (GA). A model based friction compensation method and a real coded GA are selected from a variety of methods available. A model of a single degree of freedom mechatronic joint with a link is presented. Numerical simulations are run to determine the optimum configuration of the GA with respect to the population size and maximum number of generations necessary to identify the parameters to within 5% of their actual value. The GA identification technique is then used on an experimental mechatronic joint with a harmonic drive and built-in torque sensor. The friction parameters as well as the sensor gain and offset are identified in the experimental system and the position tracking error is reduced. Based on the experimental results, the method is found to be an effective way of identifying system parameters in a mechatronic joint.