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A pneumatic radial piston motor is studied in this paper in order to establish a dynamic modelling and simulation method. As a result of using geometric parameters, the piston cylinder volume change was calculated, and the heat transfer equation, thermodynamic energy balance equation, and motion equation were combined in order to create a complete model of the piston cylinder. With the aid of compressed air, several experimental tests were conducted, and the results of rotational speed with varying inlet pressure were fed into the simulation to determine one of the critical unknown parameters, such as the overall friction coefficient of the system. For the studied piston motor, this coefficient was 0.0625 Nm. Computer simulations can be used to adjust design parameters in order to reach a higher rotation speed by using an accurate model. As a result, better efficiency and performance present several opportunities that would not be possible when running experimental tests in a lab. The mathematical model yielded higher rotational speeds of 50 RPM on average, with an increased piston diameter of 1.775 mm; by increasing the diameter of the cylinder to 25.8 mm, it was possible to achieve faster rotational speeds. The performed precise simulation could be used for further motor design and optimisation, and performance estimates under a broader range of operational conditions. Simulations should be conducted on multiple sets of experimental test results to determine the correct foverall value for each motor. In addition to guiding the design and optimisation of the motor, simulations could also predict its performance under a broader range of operating conditions by utilising effective parameters such as geometrical characteristics, flow conditions, and motion equations.

The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is due to the mitigation measures that include lockdown and Work from Home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-COVID-19 business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result to a net benefit for flexible stakeholders, such as consumer on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.

The accurate prediction of the performance output of photovoltaic (PV) installations is becoming ever more prominent. Its success can provide a considerable economic benefit, which can be adopted in maintenance, installation, and when calculating levelized cost. However, modelling the long-term performance output of PV modules is quite complex, particularly because multiple factors are involved. This article investigates the available literature relevant to the modelling of PV module performance drop and failure. A particular focus is placed on cracks and hotspots, as these are deemed to be the most influential. Thus, the key aspects affecting the accuracy of performance simulations were identified and the perceived relevant gaps in the literature were outlined. One of the findings demonstrates that microcrack position, orientation, and the severity of a microcrack determines its impact on the PV cell’s performance. Therefore, this aspect needs to be categorized and considered accordingly, for achieving accurate predictions. Additionally, it has been identified that physical modelling of microcracks is currently a considerable challenge that can provide beneficial results if executed appropriately. As a result, suggestions have been made towards achieving this, through the use of methods and software such as XFEM and Griddler.

To date, only a few studies have examined the execution of the actuator disc approximation for a full-size turbine. Small-scale models have fewer constraints than large-scale models because the range of time-scale and length-scale is narrower. Hence, this article presents the methodology in implementing the actuator disc approach via the Reynolds-Averaged Navier-Stokes (RANS) momentum source term for a 20-m diameter turbine in an idealised channel. A structured grid, which varied from 0.5 m to 4 m across rotor diameter and width was used at the turbine location to allow for better representation of the disc. The model was tuned to match known coefficient of thrust and operational profiles for a set of validation cases based on published experimental data. Predictions of velocity deficit and turbulent intensity became almost independent of the grid density beyond 11 diameters downstream of the disc. However, in several instances the finer meshes showed larger errors than coarser meshes when compared to the measurements data. This observation was attributed to the way nodes were distributed across the disc swept area. The results demonstrate that the accuracy of the actuator disc was highly influenced by the vertical resolutions, as well as the grid density of the disc enclosure.

Efficient vehicle routing is a major concern for any supply chain, especially when dealing with last-mile deliveries in highly urbanized areas. In this paper problems considering last-mile delivery in areas with the restrictions of motorized traffic are described and different types of cargo bikes are reviewed. The paper describes methods developed in order to solve a combination of problems for cargo bicycle logistics, including efficient packing, routing and load-dependent speed constraints. Proposed models apply mathematical descriptions of problems, including the Knapsack Problem, Traveling Salesman Problem and Traveling Thief Problem. Based on synthetically generated data, we study the efficiency of the proposed algorithms. Models described in this paper are implemented in Python programming language and will be further developed and used for solving the problems of electric cargo bikes’ routing under real-world conditions.

In this paper, a novel concept to integrate High Voltage Direct Current (HVDC)-connected offshore wind power plants with the onshore grid is presented. The concept makes use of a holistic wind farm controller along with a fully instrumented conventional synchronous generator at the point of common coupling. In our approach, the wind farm is able to replicate the natural response of the generator to a system, even enabling the wind farm to reproduce, in a scaled up manner, a range of ancillary services without having to rely on indirect frequency measurements which are prone to noise and delays. Simulation results are presented to validate the proposed solution.

This paper presents a numerical method to address function estimation problems in inverse heat transfer problems using parameter estimation approach without prior information on the functional form of the variable to be estimated. Using an inverse analysis, the functional form of a time-dependent heat transfer coefficient is estimated efficiently and accurately. The functional form of the heat transfer coefficient is assumed unknown and the inverse heat transfer problem should be treated using a function estimation approach by solving sensitivity and adjoint problems during the minimization process. Based on proposing a new sensitivity matrix, however, the functional form can be estimated in an accurate and very efficient manner using a parameter estimation approach without the need for solving the sensitivity and adjoint problems and imposing extra computational cost, mathematical complexity, and implementation efforts. In the proposed sensitivity analysis scheme, all sensitivity coefficients can be computed in only one direct problem solution at each iteration. In this inverse heat transfer problem, the body shape is irregular and meshed using a body-fitted grid generation method. The direct heat conduction problem is solved using the finite-difference method. The steepest-descent method is used as a minimization algorithm to minimize the defined objective function and the termination of the minimization process is carried out based on the discrepancy principle. A test case with three different functional forms and two different measurement errors is considered to show the accuracy and efficiency of the used inverse analysis.