Instability of flow boiling in parallel microchannels

Heat exchangers are key components of energy conversion systems, including HVAC&R equipment. The compact microchannel design of a heat exchanger (MCHX) allows for significant reduction of its volume, weight, and raw material, in comparison to conventional fin-and-tube heat exchangers in HVAC&R systems. The current generation of microchannel evaporators along with advantages, such as high heat transfer rate and reduced refrigerant charge, encounters important problems related to flow maldistribution and instabilities in parallel-channels flow. The thermal and hydrodynamic performance of a microchannel evaporator can drastically decrease, due to the presence of instabilities, and maintaining a set point for operational parameters can become challenging. Microchannels are characterized by a large ratio of surface area to fluid volume, and rapid growth of bubbles in a confined space causes flow parameter oscillations. Fluctuations in pressure, pressure drop, temperature and mass flux can be triggered in the individual channels (ports) and they can affect neighboring channels. For instance, flow oscillations in parallel channels can lead to premature initiation of dryout that reduces the overall heat transfer. This research is motivated by the challenge of predicting flow boiling instabilities in parallel channels, since understanding the nature of these instabilities and their relationship to the operational parameters can be advantageous for the engineering community. Analysis of the pressure drop behavior in parallel non-uniformly heated microchannels is chosen as the primary method to explore instabilities. The possible nonuniformity of heat flux from channel to channel was studied by solving the conjugate, three-dimensional, transient heat transfer problem of louvered fins bounded with multiport aluminum plates using commercial software (ANSYS FLUENT). While the fin geometry was kept constant in all simulations, two different multiport plate configurations (11 round ports, D=1.2 mm; and 22 square ports, 0.54x0.54 mm) were analyzed at two air face velocities, 1m/s (Re_Lp = 82) and 5m/s (Re_Lp = 410), and two temperature differences, 10K and 20K, between the incoming air and the inside walls of the channels that have constant temperature of 10oC. Air flow was louver directed in both cases, while the large scale vortex shedding from the plate, in addition to the unstable wake of the exit louver, was observed at Re_Lp = 410. The magnitude of the heat flux difference between ΔT=10K and ΔT=20K cases was two times. The results show that the first channel, facing the flow, has the highest heat flux in all cases. The variation of the channel-to-channel heat flux downstream from the leading edge was dependent on the incoming flow velocity and air flow morphology. The overall heat flux difference between the leading channel and the trailing one was 73% at the incoming air velocity of 5 m/s, while this difference was almost 96% at lower velocity of 1 m/s. It might be concluded that a higher air velocity (mass flow rate) corresponds to a lower temperature drop for the air stream, and less variation in the temperature driving potential (port to port) causes less heat flux variation. Overall, the results of numerical simulations prove the presence of heat flux variation between neighboring channels; therefore, the effects of channel-to-channel heat flux variations on flow maldistribution and flow boiling instabilities between neighboring microchannels were considered. The region of significant flow boiling instabilities in multiple, nonuniformly heated channels bounded by constant pressure drop is predicted by modeling the pressure drop behavior in each individual channel using the internal characteristic or ΔPi-Gi curve. Combination of parallel channels ΔPi-Gi curves and definition of possible flow rate solutions at a given constant pressure drop across all channels can be used to demarcate regions of possible instabilities. In order to accomplish this, theoretical modeling of a single channel ΔP-G curve is undertaken in this research. Two-phase pressure drop was modeled based on semi-empirical correlations of the frictional two-phase pressure drop by Kim & Mudawar (2014), and the void fraction model by Xu & Fang (2014). A single channel characteristic curve model was experimentally validated for two channel sizes 2 mm and 1 mm using refrigerant R245fa at T_sat=24.5oC. The theoretical model consistently predicted the trends in the data very well, and it predicted pressure drop within 19.3% for the 2 mm tube and within 32.5% for the 1 mm tube. Furthermore, the effect of fluid properties, operational parameters, and geometrical parameters of a channel on a single channel ΔP-G curve behavior is theoretically analyzed. The span of the negative slope region (where instability is manifested) depends on with saturation conditions, inlet subcooling, heat flux, channel size and length, and fluid type. The negative slope region decreases with decreasing heat flux, liquid and vapor densities ratio, and as channel becomes shorter and smaller due to the reduced vapor generation. The negative slope region also decreases with increasing saturation pressure, specific heat and degree of subcooling. Multiple channel instabilities are analyzed by combining individual ΔPi-Gi curves of 2-6 unevenly heated microchannels and seeking flow rate solutions at a given constant pressure drop across multiple channels. Theoretical results show that for a given total flow rate the flow may split among parallel pipes in various ways satisfying the equal pressure drop condition in all channels; there exist a range of the incoming flow rates where maldistribution is the only possible solution. Furthermore, linear stability analysis was performed to differentiate between stable and unstable solutions. The analysis enabled the demarcation of unstable regions on the total ΔP-G curve. Therefore, it is possible to anticipate unstable regions if the inlet flow rate, number of channels, and operational parameters are known. In conclusion, this research is focused on the study of flow boiling in parallel microchannels subjected to uneven heat flux. Understanding the single channel pressure drop versus flow rate (ΔP-G) characteristic curve, and understanding the interactions between channels leads to the development of a map that demarks unstable regions. This map can provide guidance to engineers in choosing operational conditions and developing compact evaporators. Therefore, the results of this work have significant impact on understanding flow boiling behavior in multiple microchannels that could lead to practical applications

Transient pressure drop correlation between parallel minichannels during flow boiling of R134a

There is significant interest in the boiling performance of refrigerants in mini- and microchannels, especially in flow geometries relevant to compact heat exchangers for air-conditioning and refrigeration applications. Pressure drop (?P) characteristics during flow boiling of refrigerant R134a have been studied extensively over the past decade; however, in most research ?P is measured over a single channel or multiple parallel channels (manifold to manifold). There has been no work examining the individual pressure drop in each channel in multiple channel design. Moreover, correlations or relationships between the instantaneous ?P in individual minichannels operating in parallel have not been reported. In this work, an investigation of the effect of heat flux, mass flux and inlet vapor qualities on the flow patterns and pressure drop for flow boiling of R134a in 0.54 mm square parallel minichannels is reported. In particular, flow boiling experiments are conducted at flow rates between 0.1 and 0.51 g/s and heat fluxes from 0 to 36 kW/sq.m.. The heat flux input among a set of four horizontal, parallel minichannels is individually varied and controlled in each test. The focus of the work is on the investigation of correlations between flow boiling of R134a in parallel minichannels based on flow visualization and pressure drop measurements in each channel independently

Modeling local thermal responses of individuals : validation of advanced human thermo-physiology models

Human thermo-physiology models (HTPM) are useful tools to assess dynamic and non-uniform human thermal states. However, they are developed based on the physiological data of an average person. In this paper, we present a detailed evaluation of two sophisticated and well-known models, JOS3 and ThermoSEM, with the objective to evaluate their capabilities in predicting the local skin temperature of individual people as both models use individual parameters such as sex, height, weight, and fat percentage as input. For the purpose of validation, controlled experiments were conducted with six human subjects (3 males, 3 females) at different environmental conditions (22–28°C). The measured core temperature and the local skin temperature at 14 locations were used to evaluate the predicted values. Outputs from both HTPMs followed the dynamic trend of the experiments, with a root mean squared error (RMSE) of 0.9–0.3°C for core temperature and 1.3–0.9°C for mean skin temperature from both ThermoSEM and JOS3 correspondingly. However, the main errors came from the body extremities. The RMSE was different for each subject, and both models showed lower errors in the warmer environment. The average RMSE for the hands of all subjects was 2°C from ThermoSEM and 1.9°C from JOS3, while it was 0.8°C for the forehead in both models. The paper highlights the capabilities and limitations of the selected HTPMs and, furthermore, discusses the application of HTPMs in the field of personalized thermal comfort

Toward contactless human thermal monitoring : a framework for Machine Learning-based human thermo-physiology modeling augmented with computer vision

The transition towards a human-centered indoor climate is beneficial from occupants’ thermal comfort and from an energy reduction perspective. However, achieving this goal requires the knowledge of the thermal state of individuals at the level of body parts. Many current solutions rely on intrusive wearable technologies, which require physical access to individuals facing limitations in scalability. Personalizing the indoor environment demands increased sensing at individual levels presenting challenges in terms of data collection and ensuring privacy protection. To address this challenge, this paper introduces a novel approach to non-intrusive personalized humans thermal sensing that can acquire personal data while minimizing the amount of sensing required. The method investigates multi-modal sensing solutions based on IR and RGB images, and it includes the development of a Machine Learning-based Human Thermo-Physiology Model (ML-HTPM). With the help of computer vision, features important for thermal comfort such as activity level, clothing insulation, posture, age, and sex can be extracted from an RGB image sequence using models such as the SlowFast network, YOLOv 7, while limited skin temperatures can be extracted from an IR image using OpenPifPaf for body parts detection. The developed ML-HTPM is based on data generated from an open-source JOS3 model after applying a prediction model based on Long Short-Term Memory (LSTM). The results showed that a human thermo-physiology model using machine learning can be trained, showing an RMSE of less than 0.5°C in most of the local skin temperatures

Review of health and well-being aspects in Green Certification Protocols

Over the past decades, the world-leading Green Certification Protocols have paid increasing attention to health-related aspects of buildings. However, the way and the extent to which green certifications currently account for these aspects vary largely. This paper aims to review and compare four certification protocols, namely LEED v4, BREEAM 2018, WELL v2, and MINERGIE-ECO v1.4, and to provide insights on how aspects related to occupants’ health and well-being and their influencing factors are accounted for and assessed. To that scope, indicators used to assess the users' health and well-being are extracted from each certification and compared. Indicators traditionally used to evaluate IEQ in buildings (thermal, indoor air quality, visual and acoustic) based on international or national standards were found in all certifications. However, the analysis highlights that their assessment and verification stage (e.g., pre- vs. post- occupancy) significantly differs from one label to another. More “advanced” indicators, which are related to mind, promotion of physical activities, and community engagement, have come to light. While a comprehensive approach to the evaluation of well-being might include a combination of objective (e.g., measurement-based evaluations) and subjective components (e.g., people’s subjective evaluation), the review highlighted that only in one protocol (i.e., WELL), direct feedback from occupants is kept in the loop for further optimization of the building management during operation. Otherwise, indicators are mainly verified through quantitative measurements, reports, or implemented policies

Resolving indoor shortwave and longwave human body irradiance variations for mean radiant temperature and local thermal comfort

A spatially and directionally resolved longwave and shortwave radiant heat transfer model is presented via a series of experiments in a thermal lab to input surface temperatures and geometries, as well as skin temperature readings from a human subject, in order to test mean radiant temperature (MRT) and thermal comfort results for the person. Combining novel scanning and thermography methods together with ray-tracing simulation, high-resolution thermal models are derived fully characterizing the longwave and shortwave radiant heat fluxes in space and resolving the impact of these variations on MRT. The study demonstrates the significant amount of spatial variation of both shortwave and longwave radiant heat transfer on MRT through the room and also across body segments: the experimental results show variations of up to 14.5°C across the room, leading to PMV comfort variations from −0.27 to 2.45, clearly demonstrating the importance of mapping the entire radiant field rather than assuming one MRT value for a thermal zone. Furthermore, local radiant temperature, newly defined Body Segment Plane Radiant Temperature (BSPRT), variations across the body of more than 30°C are found. Finally, a detailed human thermo-physiology model was used to evaluate the possible variation in thermal sensation between the different body segments due to the large differences in local MRT

Thermal conditions in indoor environments: Exploring the reasoning behind standard-based recommendations

Professionals in the building design and operation fields typically look at standards and guidelines as a reliable source of information and guidance with regard to procedural, contractual, and legal scope and requirements that are relevant to accountability issues and compliance necessities. Specifically, indoor environmental quality (IEQ) standards support professionals to bring about comfortable thermal, air quality, acoustic, or visual conditions in buildings. In this context, it appears essential to regularly examine the IEQ standards’ applicability and scientific validity. The present contribution focuses on common thermal comfort standards in view of the reasoning and includes evidence behind their recommendations and requirements. Thereby, several international and national thermal comfort standards are examined via a structured matrix to assess basic parameters, design and performance variables targeted by the standards, suggested value ranges, and both general and specific evidence from the scientific literature. Finally, this paper discusses findings and points to the identified gaps in the chain of evidence from the results of scientific studies and the recommendations included in the thermal standards. As such, the present contribution has the potential to inform future developments regarding transparent and evidence-based thermal standards

The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?

Buildings’ expected (projected, simulated) energy use frequently does not match actual observations. This is commonly referred to as the energy performance gap. As such, many factors can contribute to the disagreement between expectations and observations. These include, for instance, uncertainty about buildings’ geometry, construction, systems, and weather conditions. However, the role of occupants in the energy performance gap has recently attracted much attention. It has even been suggested that occupants are the main cause of the energy performance gap. This, in turn, has led to suggestions that better models of occupant behavior can reduce the energy performance gap. The present effort aims at the review and evaluation of the evidence for such claims. To this end, a systematic literature search was conducted and relevant publications were identified and reviewed in detail. The review entailed the categorization of the studies according to the scope and strength of the evidence for occupants’ role in the energy performance gap. Moreover, deployed calculation and monitoring methods, normalization procedures, and reported causes and magnitudes of the energy performance gap were documented and evaluated. The results suggest that the role of occupants as significant or exclusive contributors to the energy performance gap is not sufficiently substantiated by evidence.</jats:p

Thermal resistance of ventilated air-spaces behind external claddings; definitions and challenges (ASHRAE 1759-RP)

The presence of an air-space within a building envelope is known to have a varying contribution to the overall thermal performance of the wall assembly due to the combined effect of convection and radiation in the air cavity. In particular, the thermal resistance of a ventilated air-space can vary significantly depending on multiple environmental and thermo-physical parameters. Although the thermal resistance of enclosed air-spaces in the building structures has been thoroughly investigated in the literature, it has not been defined for a ventilated cavity. This paper aims to introduce the plausible definitions of the thermal resistance of a vertical ventilated air-space behind external cladding systems. Both theoretical and applied formulations are provided and compared. The energy balance method is used to model the steady-state heat transfer through two types of traditional external wall systems (i.e., brick and vinyl siding) in summer and winter conditions. A range of air exchange rates in the cavity is examined, and the effect of the presence of reflective insulation in the air-space on the thermal resistance of the air gap is analyzed. The results show that the presence of a ventilated cavity in the wall assembly can influence the thermal performance of the building envelope. In particular, the effective thermal resistance of a ventilated air-space behind a brick cladding wall could be between 0.17 and 1.85 times the thermal resistance of the cladding in the range of air change rate in the cavity from 0 to 100 1/h. The effective thermal resistance of the ventilated air gap behind vinyl siding could reach up to 9 times the thermal resistance of the cladding

Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition

With improved insulation of building envelopes and the use of low-temperature space heating systems, the share of energy use for domestic hot water (DHW) production in buildings has increased significantly, and nearly become the most energy-expensive service in modern buildings. Early prediction of the energy use for DHW is required for many advanced applications such as smart control, demand-side management, and optimal operation of electric or heat storage. However, predicting energy use of the solar-assisted water heating system is more challenging than typical DHW systems, as it is strongly affected by two stochastic phenomena, demand pattern and solar radiation. Given the increasing use of solar-assisted water heating systems, this paper aims to evaluate the potential to predict energy use in such systems using a novel machine learning approach. In this novel model, a Long-Short Term Memory (LSTM) neural network is enhanced by (1) implementing the attention mechanism, a recent development in deep learning inspired by human vision to pay selective attention to the input data, and (2) decomposition of input data into sub-layers. The performance of simple LSTM neural network, Attention-based LSTM neural network (ALSTM) and Attention-based LSTM using decomposed data (ALSTM-D) are compared to a Feed-Forward neural network as a baseline model. Results show that LSTM, ALSTM and ALSTM-D models have a Mean Absolute Error (MAE) of 25%, 28% and 41% lower than Feed-Forward model, respectively. These results indicate the superior performance of the proposed ALSTM-D model over conventional models for solar-assisted DHW systems