Including Air Conditioning Research Articles

Aerodynamic performance enhancement of centrifugal compressor using numerical techniques

Background Centrifugal compressors are dynamic machines utilizing a rotating impeller, efficiently accelerate incoming gases, transforming kinetic energy into pressure energy for compression. They serve a wide range of industries, including air conditioning, refrigeration, gas turbines, industrial processes, and applications such as air compression, gas transportation, and petrochemicals, demonstrating their versatility. Designing a centrifugal compressor poses challenges related to achieving high aerodynamic efficiency, surge and choke control, material selection, rotor dynamics, cavitation, erosion, and addressing environmental considerations while balancing costs. Optimizing maintenance, reliability, and energy efficiency are essential aspects of the design process. Methods The primary objective of this research is to comprehensively investigate and improve the aerodynamic performance of centrifugal compressors. To accomplish this, a comprehensive investigation of variables such as blade number and hub diameter, along with various turbulence models will be conducted. This approach will leverage numerical techniques to fill the significant gaps in the current literature regarding centrifugal compressor design and optimization. The study encompasses the evaluation of two turbulence models, namely Shear Stress Transport and K-epsilon. Furthermore, it delves into the fine-tuning of blade geometry, including variations in blade number and hub diameter, aiming to refine the design for optimal performance. Extensive analyses using Ansys CFX encompass key variables such as Pressure, Mach Number, Density, Velocity, Turbulence Kinetic Energy, and Temperature. Results Notably, the optimized pressure profile yielded remarkable results, achieving a substantial 36% improvement, demonstrating the tangible benefits of these design enhancements. Conclusion The outcomes of this research hold significant utility for engineers, manufacturers, and regulatory bodies, offering invaluable insights and guidance to enhance compressor performance and efficiency.

Capacitive sensing of frost growth dynamics on aluminum surfaces with different wettabilities

Frost formation is a critical issue in many industries including air conditioning and refrigeration, aviation, transportation, and cryogenics. Frost buildup on heat exchanger surfaces reduces their efficiency by acting as a thermal resistance and by reducing or blocking the airflow. Defrosting is therefore necessary to maintain system efficiency. However, defrost initiation and cycle duration are critical parameters that govern system performance. Excessively early or late defrosting can result in energy loss. Frost detection is thus essential to carry out defrosting efficiently. To know when to initiate defrost, both the density and thickness of the frost must be known as both affect the frost thermal resistance. However, state of the art approaches are not sensitive to both frost density and thickness. Here, a capacitive sensing method is proposed that is sensitive to both the density and thickness of the frost. The method can achieve precise frost detection, offering insights into the ideal time to initiate defrosting by being sensitive to factors that affect frost specific thermal resistance. Experiments are carried out on plain (hydrophilic), superhydrophobic, and superhydrophilic aluminum surfaces at three different surface temperatures (−6 °C, −10 °C, −16 °C) to investigate the sensing performance. The capacitance measured increases with time as frost forms and grows. This change in capacitance, along with mass and thickness data, was used as a tool to characterize frost growth. The results indicate that decreasing wettability (superhydrophilic to plain (hydrophilic) to superhydrophobic) results in a decreasing trend in mass accumulation, frost density, and change in capacitance and an increasing trend in frost thicknesses and thermal resistance. It was also demonstrated that increasing surface temperature results in decreasing changes in capacitance (measurement sensitivity), mass accumulation, frost thicknesses, and thermal resistance, with an increasing trend in frost density. A strong correlation between frost mass and change in capacitance was observed regardless of surface temperature or wettability. This rendered implications for the best time of defrosting initiation. The proposed approach offers high accuracy, sensitivity, and convenience as a non-invasive method to study dynamic frost growth with insights into more optimal defrosting cycles, contributing to the development of more efficient energy systems.

Implementation of Bluetooth Enabled Home Automation System

Home automation is becoming more and more popular as a concept because it increases productivity by lowering human oversight and labor. Home automation systems allow us to operate a variety of gadgets, including air conditioners, TVs, fans, lights, and more. Furthermore, additional functions like emergency systems, security, alarms, etc. may be incorporated into home automation systems. There are numerous varieties of home automation technologies, including those that are controlled via Bluetooth, the Internet, RF, infrared, and other remotes. Each type comes with its own benefits and drawbacks. For this research, we have programmed and constructed a Bluetooth-controlled home automation device using a Bluetooth-enabled Android phone. The designed device is able to switch enabled home appliances within a 100-meter radius of the phone.

Importance of Activation Energy on Magnetized Dissipative Casson-Maxwell Fluid through Porous Medium Incorporating Chemical Reaction, Joule Heating, and Soret Effects: Numerical Study

In recent decades, the study of non-Newtonian fluids has attracted the interest of numerous researchers. Their study is encouraged by the significance of these fluids in fields including industrial implementations. Furthermore, the importance of heat and mass transfer is greatly increased by a variety of scientific and engineering processes, including air conditioning, crop damage, refrigeration, equipment power collectors, and heat exchangers. The key objective of this work is to use the mathematical representation of a chemically reactive Casson-Maxwell fluid over a stretched sheet circumstance. Arrhenius activation energy and aspects of the magnetic field also have a role. In addition, the consequences of both viscous dissipation, Joule heating, and nonlinear thermal radiation are considered. The method transforms partial differential equations originating in fluidic systems into nonlinear differential equation systems with the proper degree of similarity which is subsequently resolved utilizing the Lobatto IIIA technique’s powerful computing capabilities. It is important to recall that the velocity profile drops as the Maxwell fluid parameter increases. Additionally, the increase in the temperature ratio parameter raises both the fluid’s temperature and the corresponding thickness of the boundary layer.

Assessment of the energy consumption of indoor farming for different climates and lighting system intensity

Nowadays, due to the energy, water, and arable land crises, as well as the constant global population growth and urbanization, water, energy, and food sources are strongly linked to each other, and a comprehensive analysis including all the influencing factors has to be carried out. In this context, the need to improve the energy performance of traditional cultivation systems, as well as to develop alternative and innovative ones, is causing a shift in interest from greenhouses to indoor farming methods (such as vertical farms) in the agricultural sector. In fact, they allow for completely controlled crop production without the use of chemical pesticides. Moreover, their productivity is independent of external conditions and improved compared to traditional systems. However, their energy consumption is larger than that of greenhouses. To evaluate the techno-economic feasibility of such systems, a comprehensive model of a vertical farm, including air conditioning, lighting systems, and plant evapotranspiration, has been developed to quantify the energy share for the different elements of the system (air conditioning and lighting) and to relate the energy consumption to the kilogram of produced crop, analysing exclusively the growth phase. The analysis is carried out for different cities and considers different scenarios in terms of lighting and electricity costs.

Energy saving and carbon reduction schemes for hospital with photovoltaic power generation and system upgrading technology

In order to help China achieve the double carbon target of total carbon peak and high-quality sustainable economic development, and to enrich the work and content of energy conservation and emission reduction in the building sector, the most complex and energy-consuming hospitals are taken as the key projects for energy conservation and emission reduction to carry out feasibility studies. The reasons for the high energy consumption of the existing hospital buildings were analysed, and it was proposed to upgrade the existing systems (including air conditioning, hot water system and intelligent control system) and to generate photovoltaic power for the existing buildings, taking into account the characteristics of the local climate. The results of the study showed that the energy saving and emission reduction effect of the hospital was obvious after the programme was adopted.

Comparative study of ternary hybrid nanofluids with role of thermal radiation and Cattaneo-Christov heat flux between double rotating disks

Heat and mass transfer are crucial to numerous technical and commercial operations, including air conditioning, machinery power collectors, crop damage, processing food, heat transfer mechanisms, and cooling, among numerous others. The fundamental purpose of this research is to use the Cattaneo-Christov heat flux model to disclose an MHD flow of ternary hybrid nanofluid through double discs. The results of a heat source and a magnetic field are therefore included in a system of PDEs that model the occurrences. These are transformed into an ODE system using similarity replacements. The first-order differential equations that emerge are then handled using the computational technique Bvp4c shooting scheme. The Bvp4c function in MATLAB is used to numerically solve the governing equations. The influence of the key important factors on velocity, temperature, nanoparticles concentration, and is illustrated visually. Furthermore, increasing the volume fraction of nanoparticles improves thermal conduction, increasing the heat transfer rate at the top disc. The graph indicates that a slight increase in melting parameter rapidly declines the velocity distribution profile of nanofluid. The temperature profile was boosted due to the growing outcomes of the Prandtl number. The increasing variations of the thermal relaxation parameter decline the thermal distribution profile. Furthermore, for some exceptional instances, the obtained numerical answers were compared to previously disclosed data, yielding a satisfactory compromise. We believe that this discovery will have far-reaching ramifications in engineering, medicine, and the field of biomedical technology. Additionally, this model can be used to examine biological mechanisms, surgical techniques, nano-pharmacological drug delivery systems, and the therapy of diseases like cholesterol using nanotechnology.

Parametric study on thermal management system for the range of full (Tesla Model S)/ compact-size (Tesla Model 3) electric vehicles

Electric vehicle is slowly taking over the market share of internal combustion engine vehicle around the globe and range extension study turns into a one key research area. Electric vehicle thermal management system plays a large role in driving range, whereas existing literature mainly focus on component level and lack the vehicle level range analysis. This study conducts a parametric study on the vehicle level thermal management and its impacts on the range of two vehicles (full size - Tesla Model S/ compact size – Tesla Model 3). Vehicle level thermal system model is built considering air conditioning refrigerant loop, indirect battery liquid cooling loop, and cabin cooling air loop. In addition, vehicle propulsion system model is built and utilized in the range simulation. Five feedback controllers are designed for the thermal management system to control the temperature of battery pack, cabin climate, condenser subcool, evaporator superheat, and chiller superheat. Various factors are considered in the parametric study including air conditioning system on/off switch, vehicle size, driving cycle, ambient temperature, cabin target temperature, and battery pack target temperature. In the results analysis, the performance of five feedback controllers is evaluated in step response and a driving cycle. Energy consumption of aerodynamic drag, rolling resistance, thermal management, regenerative braking, and auxiliary load are discussed in the parametric study. The results show that turning air conditioning system on could potentially reduce the EV range by 38–45 % at city driving condition. In addition, Ambient temperature shows significant nonlinear impact on EV range and the impact gets worse as temperature turns higher. For Tesla Model 3, when the ambient temperature rises from 30 °C to 35 °C and then to 40 °C, the range drops by 3.3 % and 13.4 %, respectively. Compared with ambient temperature, cabin temperature and battery pack temperature settings have less impact on the range. The code utilized in this study is made open source via Github (https://github.com/binxuAI/EV_TMS), which can be utilized as the guidance in the thermal management range impact analysis and concept design of electric vehicle.

A novel energy management model among interdependent sections in the smart grids

AbstractTechnically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes.

Rural residents' climate change perceptions, personal experiences, and purchase intention–behavior gap in energy-saving refrigeration appliances in Southwest China

In the context of climate change, energy-saving refrigeration appliances play an essential role in lowering carbon emissions. Based on survey data from interviews with rural residents in Chongqing, China, this study investigated residents' purchase intention, behavior, and intention–behavior gap with respect to energy-saving refrigeration appliances, including air conditioners and refrigerators. Econometric models were employed to identify the gap and explore its influencing factors from the aspects of rural residents' climate change perceptions and personal experiences. The results demonstrate that there is an intention–behavior gap. Among the rural residents interviewed, approximately 84.19% had intention, but only 20.99% exhibited consistent behavior. The findings indicate that the influencing factors on rural residents' purchase intention and actual behavior are different. One's perception of the possibility of climate change and the self-efficacy and family efficacy of climate change adaptation positively promote his or her intention, behavior, and intention–behavior conversion. However, one's perception of inefficacy and affective impression hinder intention, behavior, and intention–behavior conversion. Cost perception impedes behavior and intention–behavior consistency, while a broad and direct perception of relationships between climate change and energy use behavior can facilitate them, although this perception fails to influence intention. Climate hazards suffered and experiences of climate change adaptation can promote intention but hinder actual behavior, resulting in negative intention–behavior consistency. Based on these findings, some effective policy implications were put forward to enhance intention–behavior transformation and sustainable household carbon reduction in relation to energy-saving refrigeration appliances.

Energy efficiency of residential buildings in Tehran climate by saving energy

This paper examines and compares energy consumption in a sample building to reduce energy consumption and obtain the best performance in reducing energy consumption. Energy Plus software has done the modeling and simulation of energy consumption for a hypothetical five-story building in the climate of Tehran. The hypotheses used in this study include the use of four types of phase-change materials (PCMs) with different melting temperatures and specific latent temperatures in the Interior and exterior walls of the building, use of four different types of insulation, including glass wool (fiberglass), rock wool, elastomer and polyurethane in the outer walls of the building, the effect of the ratio of window area to exterior walls of the building on energy consumption, the use of various HVAC terminal systems including air conditioners, radiators and fan coils in the building. This study shows that, in general, the highest energy consumption with an increase of 71% compared to the reference building was related to the use of 90% of the area of the window to the external walls of the building, and the lowest energy consumption with a decrease of 20.81% compared to the reference building was related to the use of polyurethane thermal insulation.

Electricity Consumption Forecasting System Using Deep Learning

Energy modeling in Smart Buildings (SB) and planning and operating the generation of power based on the information extracted are key components of the Smart Grid’s (SG’s) energy management system. In the world, buildings use a significant amount of energy that contributes to energy efficiency programs. Additionally, excessive utilization power generation appliance also including air conditioners and heater, breathing, and climate control (HVAC) units, improper microclimate control, and inappropriate start-up and ordering of power equipment waste a lot of heat pumps. The utility can mitigate energy generation costs when it anticipates electrical loads and schedules generation resources in accordance with the demand. To estimate electricity usage at varying tiers of utility grid systems, a range of techniques have already been used. The goal of this study will be to create a hybrid deep learning model can predict resource utilization in infrastructures. Model building and data cleaning are the two stages of the proposed framework. Data cleaning involves pre-processing techniques and adding additional lag values to raw data. This hybrid deep learning (DL) approach is made up of a series of completely connected layered and linear Long Short-Term Memory (LSTM) sections layered over bi-directional Long Short-Term Memory (LSTM) components and is based also on collected information. You could incorporate the dependence structure of electricity usage on regressors and increase computation efficiency, training time, as well as computational complexity by using the results obtained.

A Guideline for Developing Resilient Office Buildings using Nanotechnology Applications

Resilient buildings have become a widely researched topic in the engineering discipline. A building has enabled connectivity between building systems, including air conditioning, ventilation, safety and security system. They are even more important with growing pandemic risks and the spread of airborne viruses such as Covid-19. The literature indicates that nanotechnology applications can have a great role in the designing resilient office buildings. Nanotechnologies play a major role in architectural design; building materials combined with nanotechnology became smaller, lighter, and more efficient than conventional materials. Thus, this research aims to develop design guidelines for resilient office buildings in Egypt using nanotechnology applications. The methodology applied includes a review on nanotechnology and resilient office buildings key design features, following a simulation using design builder program where conducted to modify the nanomaterials in resilient office buildings recorded through quantified data. After applying the nanotechnology concept on walls, the roof and glazing types by inserting nanomaterials. The research concluded that the use of vacuum insulation panel, Nano-gel glass and Nano-painting had the highest impact on decreasing the total energy consumption by 14.5% and carbon dioxide emissions by 42%.

Encapsulation of dodecane in gasification biochar for its prolonged thermal/shape stability, reliability, and ambient enthalpy storage

Latent heat storage based on phase-changing materials (PCMs) is a promising technology for thermal energy storage because of its large heating enthalpy and isothermal phase-transition temperature. However, the practical applications of PCMs is highly restricted because of their intrinsically low thermal conductivity, which limits real-time energy storage/release, seepage during the phase-transition process, and expensive supporting materials. Therefore, the development of shape-stable composite PCMs by using renewable precursors, such as biomass, has received considerable research attention because of their low cost, environmental friendliness, and availability. In this study, waste biomass materials (food waste and wood waste) activated by KOH were pyrolyzed to produce biochar/biocarbon and subsequently used for organic PCM, dodecane, encapsulation. The obtained biochar exhibited a high specific surface area and pore size distribution. The biochar–dodecane composite (FWP) was designed by using a solvent-free facile vacuum impregnation method. The composite materials exhibited high leakage resistance, improved thermal stability, enhanced thermal conductivity, and improved PCM loading capacity (67.98%). FWP exhibited high latent heat retention of 87.7% after multiple thermal cycles and excellent chemical compatibility with composite constituents. Thus, biochar, derived from biowaste, with organic PCM can be effective in numerous thermal-regulation applications, including air conditioning, and further open avenue for converting waste-to-energy.

Investigation on household energy consumption of urban residential buildings in major cities of Indonesia during COVID-19 pandemic

The implementation of the movement control order (MCO) to curb the spread of the 2019 novel corona virus disease (COVID-19) have influenced household energy consumption patterns around the world. This study aims to investigate household energy consumption of urban residential buildings in major cities of Indonesia during COVID-19 pandemic. Three representative major cities of Indonesia were selected to investigate detailed information about household appliances and gas consumption through face-to-face interviews in 2021 (n = 311). The factors affecting household energy consumption were investigated by multiple regression analysis. The results showed that, overall, the average annual energy consumption of all samples during pandemic was approximately 23.5 GJ, 3.0 GJ larger than before pandemic. The difference was primarily attributed to the use of air conditioning and cooking. The statistical analysis clearly indicated that the increase in household income (low-to high-cost houses), which would increase household size and number of appliances including air conditioning, thus increased total household energy consumption. We recommended the following potential energy-saving strategies for urban houses in Indonesia: (a) control the number of family members, (b) use more energy efficiency standards for electrical appliances and (c) encourage energy-saving lifestyles, particularly to younger adults by adopting passive cooling techniques (window opening) whereever possible.

Exploring the Predictability of Temperatures in a Scaled Model of a Smarthome.

In modern smarthomes, temperature regulation is achieved through a mix of traditional and emergent technologies including air conditioning, heating, intelligent utilization of the effects of sun, wind, and shade as well as using stored heat and cold. To achieve the desired comfort for the inhabitants while minimizing environmental impact and cost, the home controller must predict how its actions will impact the temperature and other environmental factors in various parts of the home. The question we are investigating in this paper is whether the temperature values in different rooms in a home are predictable based on readings from sensors in the home. We are also interested in whether increased accuracy can be achieved by adding sensors to capture the state of doors and windows of the given room and/or the whole home, and what type of machine learning algorithms can take advantage of the additional information. As experimentation on real-world homes is highly expensive, we use ScaledHome, a 1:12 scale, IoT-enabled model of a smart home for data acquisition. Our experiments show that while additional data can improve the accuracy of the prediction, the type of machine learning models needs to be carefully adapted to the number of data features available.

Tapping on the Aggregate Flexibility of Heterogeneous Load Groups for EV Fast Charge Accommodation in Urban Power Distribution Networks

Smart sustainable grids are characterized with sophisticated demand response schemes. Flexible loads are the key in the concept of demand response and, if well-planned, can save significant costs and emissions through optimal use of energy resources. On the other hand, fast charge electric vehicles, despite their appeal, represent a special type of loads demanding an immediate attendance upon admission to charging stations. This may counterbalance the efforts toward flexible load management. Moreover, there is a rising demand for fast-charge stations with a more balanced spatial distribution. In this paper, a comprehensive multi-stage residential load planning scheme is proposed incorporating the most diverse load groups in a realistic market environment. Within this procedure, a unique concept is implemented which taps into the aggregated residential load flexibility to support fast charge of electric vehicles. Through cluster-wise scheduling for different load classes, the proposed approach aggregates the flexibility of a heterogeneous population of different multi-apartment loads including air conditioning and water heating devices as well as slow-charging EVs to also plan for fast-charge-powered EVs. The results demonstrate the feasibility of the successful implementation of the proposed framework within urban neighborhoods which proves efficient in reducing fast-charge load on the grid besides financial benefits for aggregators.

Design Arduino-Based Smart Laboratory

Electronic devices including Air Conditioner is always used during lecture process in computer laboratory. However, based on the observation, the device is often still in turn on condition even when the lecture activities have been ended. Such less efficient use leads to electricity waste which causes electricity bills to swell. This issue can be overcome by applying automation to the electronic devices in the laboratory. It is expected that by using this automation, these electronic devices are not easily damaged because the device will only turn on when needed so that it works more efficiently and electricity waste can be avoided. Current research was conducted aiming to develop an Arduino-based smart laboratory. This research was conducted in a computer laboratory at the Department of Commerce Administration, State Polytechnic of Medan. Based on the data obtained from the implementation stage, it showed that the Arduino-based Smart Laboratory System was functioning properly and can be used in computer laboratories. Keywords: Smart laboratory, Arduino, Microcontroller.

A Qualitative Study on Concerns, Needs, and Expectations of Hospital Patients Related to Climate Change: Arguments for a Patient-Centered Adaptation.

This study explores the concerns, needs, and expectations of inpatients with the goal to develop a patient-centered climate change adaptation agenda for hospitals. Statements of patients from geriatrics, internal medicine, psychiatry, and surgery (N = 25) of a German tertiary care hospital were analyzed using semi-structured interviews and the framework method. Areas of future adaptation were elaborated in joint discussions with transdisciplinary experts. Concerns included the foresight of severe health problems. The requested adaptations comprised the change to a patient-centered care, infrastructural improvements including air conditioning, and adjustments of the workflows. Guidelines for the behavior of patients and medical services appropriate for the climatic conditions were demanded. The patient-centered agenda for adaptation includes the steps of partnering with patients, reinforcing heat mitigation, better education for patients and medical staff, and adjusting work processes. This is the first study demonstrating that hospital patients are gravely concerned and expect adjustments according to climate change. Since heat is seen as a major risk by interviewees, the fast implementation of published recommendations is crucial. By synthesizing inpatients’ expectations with scientific recommendations, we encourage patient-centered climate change adaptation. This can be the start for further collaboration with patients to create climate change resilient hospitals.

Investigation and disinfection of bacteria and fungi in sports fitness center.

This study investigated the air quality improvement in terms of bacterial and fungal contamination in an exercise room of a fitness center under normal operating conditions. Environmental conditions including air conditioning, ventilation, moisture, CO2, particulate matters, and total number of users were also recorded. In addition, fungal and bacterial load were assessed and disinfection on sports equipment surface was also examined. Background bacteria and fungi densities in bioaerosols were in the range of 249 ± 65 to 812 ± 111 CFU/m3 and 226 ± 39 to 837 ± 838 CFU/m3 in the exercise room of the fitness center and 370 ± 86 to 953 ± 136 CFU/m3 and 465 ± 108 to 1734 ± 580 CFU/m3 in the outdoor air, respectively. Chlorine dioxide and weak acid hypochlorous water aerosols could remove both bacteria and fungi much better than water scrubbing. Contact time of 15 min was sufficient to control both bacteria and fungi to comply with the official air quality standards. User density and carbon dioxide deteriorated both bacteria and fungi disinfection performance whereas temperature was only statistically significant on fungi disinfection. Other factors including relative humidity, airflow velocity, and particulate matters did not have any statistically significant effect on microbial inactivation. Apart from bioaerosol disinfection, inactivation of microorganisms on surfaces of sports equipment was also conducted using chlorine dioxide, zinc oxide, weak acid hypochlorous water, and commercial disinfectant. The surfaces of bicycle handle, dumbbell, and sit-up bench were found to be contaminated with bacteria. Overall bacterial load was 390 to 3720 CFU/cm2 with Escherichia coli specifically 550 to 1080 CFU/cm2. Chlorine dioxide and zinc oxide were noticeably better than weak acid hypochlorous water and commercial disinfectant in terms of bacteria inactivation whereas all tested disinfectants had comparable effectiveness on E. coli disinfection. Targeted microorganisms on the sports equipment surface were sufficiently inactivated within 2 min after the application of disinfectant.