Search results

This study aims to establish a multi-physics-coupled model for an electrostatic particulate matter (PM) sensor. The focus lies on investigating the deposition patterns of particles within the sensor and the variation in the regeneration temperature field.

Computational simulations were initially conducted to analyse the distribution of particles under different temperature and airflow conditions. The study investigates how particles deposit within the sensor and explores methods to expedite the combustion of deposited particles for subsequent measurements.

The results indicate that a significant portion of the particles, approximately 61.8% of the total deposited particles, accumulates on the inside of the protective cover. To facilitate rapid combustion of these deposited particles, a ceramic heater was embedded within the metal shielding layer and tightly integrated with the high-voltage electrode. Silicon nitride ceramic, selected for its high strength, elevated temperature stability and excellent thermal conductivity, enables a relatively fast heating rate, ensuring a uniform temperature field distribution. Applying 27 W power to the silicon nitride heater rapidly raises the gas flow region's temperature within the sensor head to achieve a high-temperature regeneration state. Computational results demonstrate that within 200 s of heater operation, the sensor's internal temperature can exceed 600 °C, effectively ensuring thorough combustion of the deposited particles.

This study presents a novel approach to address the challenges associated with particle deposition in electrostatic PM sensors. By integrating a ceramic heater with specific material properties, the study proposes an effective method to expedite particle combustion for enhanced sensor performance.

At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).

The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.

The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO₂) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.

Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO₂ based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

This study aims to conduct environmental comfort analyses of vernacular architecture to establish design principles for a more sustainable design domain. In the scope of this research, 47 individual Harran earthen houses, comprising 32 different types and six typologies are examined.

Environmental comfort is selected as an umbrella term for the analysis of thermal comfort, visual comfort, and natural ventilation performance criteria. The performance simulations are conducted utilizing ClimateStudio and SolidWorks software. These simulations yield values for thermal comfort, glare, daylight, solar radiation, airflow, and pressure, which are used to compare different Harran earthen house typologies.

The study’s results indicate that various environmental comfort standards are met by Harran earthen houses through passive systems, without the need for technology or mechanical equipment. In terms of thermal comfort, visual comfort, and natural ventilation performance criteria, a typology that has advantages in one criterion may have disadvantages in the others. Factors such as orientation, material selection, opening arrangement, and architectural form are found to have an impact on environmental comfort.

This study differs from previous Harran earthen house and environmental comfort studies by focusing on multiple performance criteria and conducting a typology-based comparison based on performance analysis. The results of the study are expected to provide valuable insights into the environmental comfort studies of Harran earthen houses, emphasizing their relevance and applicability in contemporary architectural and urban design.

Nanotechnology (NT) advancements in personal protective textiles (PPT) or personal protective equipment (PPE) have alleviated spread and transmission of this highly contagious viral disease, and enabled enhancement of PPE, thereby fortifying antiviral behavior.

Review of a series of state of the art research papers on the subject matter.

This paper expounds on novel nanotechnological advancements in polymeric textile composites, emerging applications and fight against COVID-19 pandemic.

As a panacea to “public droplet prevention,” textiles have proven to be potentially effective as environmental droplet barriers (EDBs).

PPT in form of healthcare materials including surgical face masks (SFMs), gloves, goggles, respirators, gowns, uniforms, scrub-suits and other apparels play critical role in hindering the spreading of COVID-19 and other “oral-respiratory droplet contamination” both within and outside hospitals.

When used as double-layers, textiles display effectiveness as SFMs or surgical-fabrics, which reduces droplet transmission to <10 cm, within circumference of ∼0.3%.

NT advancements in textiles through nanoparticles, and sensor integration within textile materials have enhanced versatile sensory capabilities, robotics, flame retardancy, self-cleaning, electrical conductivity, flexibility and comfort, thereby availing it for health, medical, sporting, advanced engineering, pharmaceuticals, aerospace, military, automobile, food and agricultural applications, and more. Therefore, this paper expounds on recently emerging trends in nanotechnological influence in textiles for engineering and fight against COVID-19 pandemic.

The purpose of this study is to investigate and analyse the potential of existing buildings in the UK to contribute to the net-zero emissions target. Specifically, it aims to address the significant emissions from building fabrics which pose a threat to achieving these targets if not properly addressed.

The study, based on a literature review and ten (10) case studies, explored five investigative approaches for evaluating building fabric: thermal imaging, in situ U-value testing, airtightness testing, energy assessment and condensation risk analysis. Cross-case analysis was used to evaluate both case studies using each approach. These methodologies were pivotal in assessing buildings’ existing condition and energy consumption and contributing to the UK’s net-zero ambitions.

Findings reveal that incorporating the earlier approaches into the building fabric showed great benefits. Significant temperature regulation issues were identified, energy consumption decreased by 15% after improvements, poor insulation and artistry quality affected the U-values of buildings. Implementing retrofits such as solar panels, air vents, insulation, heat recovery and air-sourced heat pumps significantly improved thermal performance while reducing energy consumption. Pulse technology proved effective in measuring airtightness, even in extremely airtight houses, and high airflow and moisture management were essential in preserving historic building fabric.

The research stresses the need to understand investigative approaches’ strengths, limitations and synergies for cost-effective energy performance strategies. It emphasizes the urgency of eliminating carbon dioxide (CO₂) and greenhouse gas emissions to combat global warming and meet the 1.5° C threshold.

Exposure to indoor air pollutants poses a significant health risk, contributing to various ailments such as respiratory and cardiovascular diseases. These unhealthy consequences are specifically alarming for athletes during exercise due to their higher respiratory rate. Therefore, studying, predicting and curtailing exposure to indoor air contaminants during athletic activities is essential for fitness facilities. The objective of this study is to develop a neural network model designed for predicting optimal (in terms of health) occupancy intervals using monitored indoor air quality (IAQ) data.

This research study presents an innovative approach employing a long short-term memory (LSTM) recurrent neural network (RNN) to determine optimal occupancy intervals for ensuring the safety and well-being of occupants. The dataset was collected over a 3-month monitoring campaign, encompassing 15 meteorological and indoor environmental parameters monitored. All the parameters were monitored in 5-min intervals, resulting in a total of 77,520 data points. The dataset collection parameters included the building’s ventilation methods as well as the level of occupancy. Initial preprocessing involved computing the correlation matrix and identifying highly correlated variables to serve as inputs for the LSTM network model.

The findings underscore the efficacy of the proposed artificial intelligence model in forecasting indoor conditions, yielding highly specific predicted time slots. Using the training dataset and established threshold values, the model effectively identifies benign periods for occupancy. Validation of the predicted time slots is conducted utilizing features chosen from the correlation matrix and their corresponding standard ranges. Essentially, this process determines the ratio of recommended to non-recommended timing intervals.

Humans do not have the capacity to process this data and make such a relevant decision, though the complexity of the parameters of IAQ imposes significant barriers to human decision-making, artificial intelligence and machine learning systems, which are different. Present research utilizing multilayer perceptron (MLP) and LSTM algorithms for evaluating indoor air pollution levels lacks the capability to predict specific time slots. This study aims to fill this gap in evaluation methodologies. Therefore, the utilized LSTM-RNN model can provide a day-ahead prediction of indoor air pollutants, making its competency far beyond the human being’s and regular sensors' capacities.

The investigation concentrated on studying a distinct category of tubular heat exchanger that uses swirling airflow over tube bundle maintained at constant heat flux. Swirl flow is achieved using a novel perforated baffle plate with rectangular openings and multiple adjustable opposite-oriented saw-tooth flow deflectors. These deflectors were strategically placed at the inlet of the heat exchanger to create a swirling flow downstream.

The custom-built axial flow heat exchanger consists of three baffle plates arranged longitudinally supporting tube bundle maintained at constant heat flux. The baffle plate equipped with saw-tooth flow deflector of various geometry represented by space height ratio(e/h). Next, ambient air was then directed over the tube bundle at varying Reynolds number and the effect of baffle spacing (PR), Space height ratio (e/h) and inclination angle(a) of deflectors on performance of heat exchanger was experimentally analyzed.

The heat transfer augmentation of heat exchanger for given operating condition is strongly dependent on geometry, inclination angle of deflector and baffle spacing.

An average improvement of 1.42 times in thermal enhancement factor was observed with inclination angle of 30°, space height ratio of 0.4 and a pitch ratio of 1.2 when compared to a heat exchanger without a baffle plate under similar operating conditions.

Waiting for a bus may represent a period of intense exposure to traffic particles in hot and noisy conditions in the street. To lessen the particle load and tackle heat in bus stops a shelter was equipped with an electrostatic precipitator and a three-step adiabatic cooling system capable of dynamically adjust its operation according to actual conditions. This study evaluates the effectiveness of the Airbitat Oasis Smart Bus Stop, as the shelter was called, to provide clean and cool air.

The particle exposure experienced in this innovative shelter was contrasted with that in a conventional shelter located right next to it. Mass concentrations of fine particles and black carbon, and particle number concentration (as a proxy of ultrafine particles) were simultaneously measured in both shelters. Air temperature, relative humidity and noise level were also measured.

The new shelter did not perform as expected. It only slightly reduced the abundance of fine particles (−6.5%), but not of ultrafine particles and black carbon. Similarly, it reduced air temperature (−1 °C), but increased relative humidity (3%). Its operation did not generate additional noise.

The shelter's poor performance was presumably due to design flaws induced by a lack of knowledge on traffic particles and fluid dynamics in urban environments. This is an example where harnessing technology without understanding the problem to solve does not work.

It is uncommon to come across case studies like this one in which the performance and effectiveness of urban infrastructure can be assessed under real-life service settings.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.