Search results

Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

This paper aims to present two Anand’s model parameter sets for the multilayer silver–tin (AgSn) transient liquid phase (TLP) foils.

The AgSn TLP test samples are manufactured using pre-defined optimized TLP bonding process parameters. Consequently, tensile and creep tests are conducted at various loading temperatures to generate stress–strain and creep data to accurately determine the elastic properties and two sets of Anand model creep coefficients. The resultant tensile- and creep-based constitutive models are subsequently used in extensive finite element simulations to precisely survey the mechanical response of the AgSn TLP bonds in power electronics due to different thermal loads.

The response of both models is thoroughly addressed in terms of stress–strain relationships, inelastic strain energy densities and equivalent plastic strains. The simulation results revealed that the testing conditions and parameters can significantly influence the values of the fitted Anand coefficients and consequently affect the resultant FEA-computed mechanical response of the TLP bonds. Therefore, this paper suggests that extreme care has to be taken when planning experiments for the estimation of creep parameters of the AgSn TLP joints.

In literature, there is no constitutive modeling data on the AgSn TLP bonds.

This work aims to concentrate on the mixed convection of the stagnation point flow of ternary hybrid nanofluids towards vertical Riga plate. Aluminum trioxide (Al₂O₃), silicon dioxide (SiO₂) and titanium dioxide (TiO₂) are regarded as nanoparticles, with water serving as the base fluid. The mathematical model incorporates momentum boundary layer and energy equations. The Grinberg term for the viscous dissipation and the wall parallel Lorentz force coming from the Riga plate are taken into consideration in the context of the energy equation.

Through the use of appropriate nonsimilar transformations, the governing system is transformed into nonlinear nondimensional partial differential equations (PDEs). The numerical method bvp4c (built-in package for MATLAB) is used in this study to simulate governing equations using the local non-similarity (LNS) approach up to the second truncation level.

Numerous graphs and numerical tables expound on the physical properties of the nanofluid temperature and velocity profiles. The local Nusselt correlations and the drag coefficient for pertinent parameters have been computed in tabular form. Additionally, the temperature profile drops while the velocity profile increases when the mixed convection parameter is included to oppose the flow.

The fundamental goal of this work is to comprehend how ternary nanofluids move towards a vertical Riga plate in a mixed convective domain with stagnation point flow.

This paper aims to propose and provide an overview of a model analysis that considers the main spatial design attributes that influence and produce the most relevant salutogenic outcomes. These results are essential for a healthy work experience, especially in shared workspaces.

This study departs from the theoretical contributions of the salutogenic approach, principles from supportive design theory, psychosocial supportive design and the environmental demands and resources model. After a scoping literature review covering different fields of workspace design, environmental psychology and evidence-based design of health-care facilities, a conceptual analysis is done on a proposed understanding of work, health and environmental relations to overview spatial attributes that enhance specific salutogenic and well-being-promoting outcomes needed for a healthy work experience.

The model of analysis, as a theoretical element that helps create methodological tools, combined with the application of a post occupancy evaluation, is thought to assist architects, designers, workspace owners and stakeholders in their new designs or to evaluate existing ones.

Studies on defining spatial attributes and their intended salutogenic outcomes have been formally done in health-care facilities. However, applying this idea to shared workspaces is something new and is expected to contribute to their design and evaluation, especially if the notion of environmental demands and resources is complemented.

The consolidation process and morphology evolution in ceramics-based additive manufacturing (AM) are still not well-understood. As a way to better understand the ceramic selective laser sintering (SLS), a dynamic three-dimensional computational model was developed to forecast thermal behavior of hydroxyapatite (HA) bioceramic.

AM has revolutionized automotive, biomedical and aerospace industries, among many others. AM provides design and geometric freedom, rapid product customization and manufacturing flexibility through its layer-by-layer technique. However, a very limited number of materials are printable because of rapid melting and solidification hysteresis. Melting-solidification dynamics in powder bed fusion are usually correlated with welding, often ignoring the intrinsic properties of the laser irradiation; unsurprisingly, the printable materials are mostly the well-known weldable materials.

The consolidation mechanism of HA was identified during its processing in a ceramic SLS device, then the effect of the laser energy density was studied to see how it affects the processing window. Premature sintering and sintering regimes were revealed and elaborated in detail. The full consolidation beyond sintering was also revealed along with its interaction to baseplate.

These findings provide important insight into the consolidation mechanism of HA ceramics, which will be the cornerstone for extending the range of materials in laser powder bed fusion of ceramics.

Due to land resource scarcity, sustainable urban development in high-density cities has long been challenging. As such, many cities are formulating plans to “dig deep”, resulting in more citizens working and/or staying underground for longer periods of time. However, owing to the particularities of underground space, the factors involved in the creation of a healthy environment are different from those involved in aboveground developments. This study thus aims to investigate the influences of various underground environment factors on users' health through a holistic approach.

To achieve this aim, 12 underground sites and 12 corresponding aboveground sites are selected for a large-scale questionnaire survey, resulting in 651 survey samples. The survey covers post-occupancy evaluation of health (physical and psychosocial), underground environmental quality (visual, thermal, acoustic comfort, indoor air quality and ventilation), space design and greenery. Independent-sample T-test, Pearson correlation, multiple regression modelling and structural equation modelling are used to investigate whether significant differences exist between health of underground and aboveground users, and to develop an underground environment-health model for unveiling the significant associations between underground environment factors and users' health. To cross validate the results, an objective field measurement study is further conducted on six underground sites. The objective measurement results are used to cross validate the survey results.

The questionnaire results provide the following evidence: (1) health of underground users is significantly poorer than that of their aboveground counterparts; (2) underground development users' health is significantly affected by space design, greenery and environmental quality in terms of thermal comfort, indoor air quality, ventilation and acoustic comfort but not visual comfort; and (3) amongst the various identified factors, space design has the strongest predicting effects on human health. The field study echoes the survey findings and further unveils the relationships between different environmental factors and human health.

The results shed light on the importance of distinguishing between underground developments and aboveground ones in various guidelines and standards, especially those related to space management.

Millets are ancient grains, following wheat, that have been a fundamental source of human sustenance. These are nutrient-rich small-seeded grains that have gained prominence and admiration globally due to their super resilience in diverse climates and significant nutritional benefits. As millets are renowned for their nutritional richness, the demand for millet-based products increases. Hence, this paper aims in identifying the growing need for innovative processing techniques that not only preserve their nutritional content but also extend their shelf life.

In traditional times, heat was the only means of cooking and processing of the foods, but the amount of damage they used to cause to the sensorial and nutritional properties was huge. Millets’ sensitivity toward heat poses a challenge, as their composition is susceptible to disruption during various heat treatments and manufacturing processes. To cater to this drawback while ensuring the prolonged shelf life and nutrient preservation, various innovative approaches such as cold plasma, infrared technology and high hydrostatic pressure (HPP) processing are being widely used. These new methodologies aim on inactivating the microorganisms that have been developed within the food, providing the unprocessed, raw and natural form of nutrients in food products.

Among these approaches, nonthermal technology has emerged as a key player that prioritizes brief treatment periods and avoids the use of high temperatures. Nonthermal techniques (cold plasma, infrared radiation, HPP processing, ultra-sonication and pulsed electric field) facilitate the conservation of millet’s nutritional integrity by minimizing the degradation of heat-sensitive nutrients like vitamins and antioxidants. Acknowledging the potential applications and processing efficiency of nonthermal techniques, the food industry has embarked on substantial investments in this technology. The present study provides an in-depth exploration of the array of nonthermal technologies used in the food industry and their effects on the physical and chemical composition of diverse millet varieties.

Nonthermal techniques, compared to conventional thermal methods, are environmentally sound processes that contribute to energy conservation. However, these conveniences are accompanied by challenges, and this review not only elucidates these challenges but also focuses on the future implications of nonthermal techniques.

This paper aims to derive a reduced-order model for the heat transfer across the interface between a millimetric thermocapillary liquid bridge from silicone oil and the surrounding ambient gas.

Numerical solutions for the two-fluid model are computed covering a wide parametric space, making a total of 2,800 numerical flow simulations. Based on the computed data, a reduced single-fluid model for the liquid phase is devised, in which the heat transfer between the liquid and the gas is modeled by Newton’s heat transfer law, albeit with a space-dependent Biot function Bi(z), instead of a constant Biot number Bi.

An explicit robust fit of Bi(z) is obtained covering the whole range of parameters considered. The single-fluid model together with the Biot function derived yields very accurate results at much lesser computational cost than the corresponding two-phase fully-coupled simulation required for the two-fluid model.

Using this novel Biot function approach instead of a constant Biot number, the critical Reynolds number can be predicted much more accurately within single-phase linear stability solvers.

The Biot function for thermocapillary liquid bridges is derived from the full multiphase problem by a robust multi-stage fit procedure. The derived Biot function reproduces very well the theoretical boundary layer scalings.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.