Boost converter fed high performance BLDC drive for solar PV array powered air cooling system

This paper proposes the utilization of a DC-DC boost converter as a mediator between a Solar Photovoltaic (SPV) array and the Voltage Source Inverters (VSI) in an SPV array powered air cooling system to attain maximum efficiency. The boost converter, over the various common DC-DC converters, offers many advantages in SPV based applications. Further, two Brushless DC (BLDC) motors are employed in the proposed air cooling system: one to run the centrifugal water pump and the other to run a fan-blower. Employing a BLDC motor is found to be the best option because of its top efficiency, supreme reliability and better performance over a wide range of speeds. The air cooling system is developed and simulated using the MATLAB/Simulink environment considering the steady state variation in the solar irradiance. Further, the efficiency of BLDC drive system is compared with a conventional Permanent Magnet DC (PMDC) motor drive system and from the simulated results it is found that the proposed system performs better

Application-oriented modelling of domestic energy demand

Detailed residential energy consumption data can be used to offer advanced services and provide new business opportunities to all participants in the energy supply chain, including utilities, distributors and customers. The increasing interest in the residential consumption data is behind the roll-out of smart meters in large areas and led to intensified research efforts in new data acquisition technologies for the energy sector. This paper introduces a novel model for generation of residential energy consumption profiles based on the energy demand contribution of each household appliance and calculated by using a probabilistic approach. The model takes into consideration a wide range of household appliances and its modular structure provides a high degree of flexibility. Residential consumption data generated by the proposed model are suitable for development of new services and applications such as residential real-time pricing schemes or tools for energy demand prediction. To demonstrate the main features of the model, an individual household consumption was created and the effects of a possible change in the user behaviour and the appliance configuration presented. In order to show the flexibility offered in creation of the aggregated demand, the detailed simulation results of an energy demand management algorithm applied to an aggregated user group are used

Transfer Learning for HVAC System Fault Detection

Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity. Yet the lack of labeled data, such as normal versus faulty operational status, has slowed the application of machine learning to HVAC systems. In addition, for any particular building, there may be an insufficient number of observed faults over a reasonable amount of time for training. To overcome these challenges, we present a transfer methodology for a novel Bayesian classifier designed to distinguish between normal operations and faulty operations. The key is to train this classifier on a building with a large amount of sensor and fault data (for example, via simulation or standard test data) then transfer the classifier to a new building using a small amount of normal operations data from the new building. We demonstrate a proof-of-concept for transferring a classifier between architecturally similar buildings in different climates and show few samples are required to maintain classification precision and recall.Comment: 7 pages, 4 figures, accepted to American Control Conference 202

Jet-noise-prediction model for chevrons and microjets

This study develops a jet noise prediction model for chevrons and microjets. A novel equation is proposed to express the amplitude of the fourth– order space–time velocity cross–correlations, which represent the sources of noise emanated from unheated jets, in terms of mean flow parameters and turbulence statistics such as streamwise circulation, axial velocity and turbulent kinetic energy. The cross–correlations based on a Reynolds Averaged Navier–Stokes (RANS) flowfield showed a good agreement with those based on a Large Eddy Simulation (LES) flowfield. With the novel acoustic source description, there is a good agreement between the model’s jet noise predictions and the experimental data for unheated jets for a wide range of frequencies and observer angles for both chevrons and microjets. As the model provides quick and accurate jet noise predictions, a parametric study is performed to understand the impact of chevrons and microjets on jet noise. Chevron penetration is the underpinning factor for jet noise reduction and its optimum is found to be around one–seventh of the nozzle diameter. The number of chevrons has a considerable effect on jet noise and six is found to be an optimum number of chevrons. The injected mass flow rate of a system of microjets has a noticeable impact on jet noise and for 18 microjets its optimum is found to be around 0.0072 of the main jet mass flow rate. There is a good agreement between predicted and measured optimum values. This establishes that the model is indeed capable of assessing and optimising jet noise reduction concepts and could contribute towards the development of quieter nozzles for future aircraft.Dr Depuru Mohan expresses his gratitude to St John’s College, University of Cambridge, for the award of a Dr Manmohan Singh Scholarship and Cambridge Commonwealth, European and International Trust for the award of a Cambridge International Scholarship.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the American Institute of Aeronautics and Astronautics

Detection of location-specific intra-cranial brain tumors

Mutations or abnormalities in genes can occasionally cause cells to grow uncontrolled, resulting in a tumor, which is very dangerous. These are the most prevalent cancer causes. They are caused by significant damage to genes in a specific cell during a person's existence. Brain tumors are increasing rapidly, majorly brain tumor cases in the US are projected to rise from 27,000 in 2020 to 31,000 in 2023 at an annual growth rate of 1.5%, all the cases are rising because of the detection of the tumors in the late phase. Thus, it needs the hour to create something which can solve this anomaly and help us detect the tumor rapidly and efficiently. While major research papers on brain tumor detection mainly focus on the detection and classification of the tumors, the presented research aims to first detect the tumor using pre-recognized photos using machine learning object detection models. Then after successful detection of the tumor, the study team plans to determine its precise coordinates and display the tumor and its location in the picture

On the Challenges and Opportunities of Smart Meters in Smart Homes and Smart Grids

Nowadays, electricity companies have started applying smart grid intheir systems rather than the conventional electrical grid (manualgrid). Smart grid produces an efficient and effective energy managementand control, reduces the cost of production, saves energy and it is morereliable compared to the conventional grid. As an advanced energy meter,smart meters can measure the power consumption as well as monitor andcontrol electrical devices. Smart meters have been adopted in manycountries since the 2000s as they provide economic, social andenvironmental benefits for multiple stakeholders. The design of smartmeter can be customized depending on the customer and the utilitycompany needs. There are different sensors and devices supported bydedicated communication infrastructure which can be utilized toimplement smart meters. This paper presents a study of the challengesassociated with smart meters, smart homes and smart grids as an effortto highlight opportunities for emerging research and industrialsolutions

Understanding Electricity-Theft Behavior via Multi-Source Data

Electricity theft, the behavior that involves users conducting illegal operations on electrical meters to avoid individual electricity bills, is a common phenomenon in the developing countries. Considering its harmfulness to both power grids and the public, several mechanized methods have been developed to automatically recognize electricity-theft behaviors. However, these methods, which mainly assess users' electricity usage records, can be insufficient due to the diversity of theft tactics and the irregularity of user behaviors. In this paper, we propose to recognize electricity-theft behavior via multi-source data. In addition to users' electricity usage records, we analyze user behaviors by means of regional factors (non-technical loss) and climatic factors (temperature) in the corresponding transformer area. By conducting analytical experiments, we unearth several interesting patterns: for instance, electricity thieves are likely to consume much more electrical power than normal users, especially under extremely high or low temperatures. Motivated by these empirical observations, we further design a novel hierarchical framework for identifying electricity thieves. Experimental results based on a real-world dataset demonstrate that our proposed model can achieve the best performance in electricity-theft detection (e.g., at least +3.0% in terms of F0.5) compared with several baselines. Last but not least, our work has been applied by the State Grid of China and used to successfully catch electricity thieves in Hangzhou with a precision of 15% (an improvement form 0% attained by several other models the company employed) during monthly on-site investigation.Comment: 11 pages, 8 figures, WWW'20 full pape

Supersonic flow field reconstruction using CNNs

The accurate prediction of a projectile’s aerodynamic coefficients is crucial in high-precision external ballistic calculations. The aerodynamic forces and moments exerted on a projectile in flight influence key performance parameters such as range, accuracy, time of flight and stability. A large body of work has therefore been dedicated to understanding the flow dynamics around projectile bodies and obtaining the critical force and moment coefficients. This has been traditionally achieved in aeroballistic range experiments, wind tunnel set-ups and through the use of numerical models. Nevertheless, a widespread still exists between different techniques, revealing the fluid physics is not yet fully understood. A better understanding of the aerodynamics at play is accessible through a combination of the three techniques. However, reliable wind tunnel results will require matching a series of similarity parameters imposed by the firing conditions, which will inevitably relate to the physical scale of the models used. The size of small calibre projectiles may prove challenging for measurement in wind tunnel set-ups, however upscaling the models inappropriately will result in unrepresentative flow fields due to wall interactions and blockage effects. On the other hand, sting supports for wind tunnel models disturb a smaller portion of the flow with increasing projectile scale, particularly in terms of wake perturbation - a key contributor to aerodynamic coefficients. Clearly, scale effects have important consequences, however they have not been explicitly treated in the supersonic projectile literature. This study aims to explore the effects and limits of projectile scaling in supersonic wind tunnels, through a series of experimental techniques (Schlieren visualization, pressure measurements, force balance measurements...) and numerical modelling. Additionally, we aim to develop the Background-Oriented-Schlieren technique a step further through the use of machine learning models to reconstruct complete flow fields from optical data.Royal Higher Institute for Defense, BelgiumDefence and Security Doctoral Symposia 2024 (DSDS24

Supersonic projectile flow field reconstruction using background oriented schlieren and physics informed convolutional neural networks

Session: Supersonics Design and AnalysisThis work explores the use of axisymmetric background-oriented schlieren (BOS) imaging for reconstructing supersonic flow fields over a scaled NATO 5.56 mm M855 projectile at Mach 1.50, 2.00, and 2.50, as well as a 15° cone at Mach 2.50. A method for recovering density fields from BOS displacement maps was implemented, with results compared to a Taylor–Maccoll solution for the cone and a RANS CFD wind tunnel model for the projectile. Density field reconstructions showed errors below 15% overall and under 10% across most of the field, with the largest deviations near shock boundaries and stagnation regions. Additionally, force balance measurements were conducted on the projectile at Mach 2.50, showing an agreement of 1.2% with firing data from the literature and 8% with the RANS model. A custom U-Net was subsequently trained to predict pressure, temperature, and velocity fields from grid-transformed numerical density inputs over the cone, using a physics-exclusive loss function derived from the Euler conservation laws and specified boundary conditions. However, large residuals near the shock and stagnation point due to grid interpolation were found to impede the network’s performance. A purely data-driven model demonstrated good accuracy for pressure and temperature, a moderate performance for radial velocity, and poor accuracy for axial velocity. The model failed to generalize when fed with experimental data, reinforcing the need for strong physical constraints.AIAA Aviation Forum and Ascend 202