Hasil untuk "Heating and ventilation. Air conditioning"

Li Zhengkun, Ding Xupeng, Wang Xiaotao et al.

Low-temperature superconductivity and space exploration urgently require compact， highly reliable， and long-lifespan cooling technologies that operate in the liquid-helium temperature range. Multistage Stirling-type pulse tube cryocoolers are a promising solution. In this study， a thermally coupled three-stage Stirling-type pulse tube cryocooler was designed and constructed. The system employs a two high-frequency （70 Hz） Stirling cryocooler （model TC3130， Lihan） to precool the third stage， thus providing cooling capacities of 5 W and 2 W at 70 K and 32 K， respectively. For the third stage， simplified models were first established using Sage to determine the key operating parameters， including the operating frequency， average pressure， and precooling temperature. The third stage was fully simulated， followed by the final design and experimental set up. Experimental results show that under an average pressure of 1.4 MPa， a frequency of 21 Hz， and a total input power of approximately 370 W， the lowest no-load temperature reached 5.16 K， with typical cooling capacities of 50 mW and 102 mW at 6 K and 7 K， respectively.

Alexandre Narcisse, Olivier Vauquelin, Éric Casalé et al.

Smoke curtains are typically used in public-access buildings in connexion with ventilation effects without considering a crossed design. This paper aims to understand the fire smoke behaviour in the context of the interaction between mechanical ventilation and smoke curtains. This interaction is analysed here in the configuration of a tunnel with using numerical simulations. Initially, without the presence of fire smoke, the length of the vortex induced downstream of the curtain is determined as a function of the longitudinal velocity and the size of the curtain. It can be observed that for a sufficiently high velocity (i.e. a sufficiently high Reynolds number), the size of this vortex depends only on the height of the smoke curtain. Then, in the presence of a moderate fire heat release rate, we determined the air velocity required to prevent smoke from rising beyond a curtain measuring 1/5 the height of the tunnel. A significant reduction in this longitudinal velocity was observed in comparison to the velocity required to achieve the same level of containment without the presence of a curtain. The vortex generated by the curtain nevertheless interacts with the smoke layer, locally increasing its thickness. Lastly, this configuration is tested in the case of a medium and high fire heat release rate in a road tunnel with transverse ventilation. The work carried out suggests that the installation of smoke curtains of an appropriate size, combined with control of ventilation effects, is likely to reduce the need for them.

Saeed Asadi, Mohsen Mohammadagha, Hajar Kazemi Naeini

In recent decades, Earth-to-Air Heat Exchangers (EAHEs), also known as underground air ducts, have garnered significant attention for their ability to provide energy-efficient cooling and heating solutions while maintaining a minimal environmental footprint. These systems leverage the relatively stable underground temperature to regulate indoor climates, reducing reliance on conventional heating, ventilation, and air conditioning (HVAC) systems. This review systematically categorizes and synthesizes research on EAHEs into three primary areas: analytical, numerical, and exergoeconomic studies. Analytical approaches focus on developing theoretical models to predict thermal performance, while numerical simulations provide insights into system optimization and real-world applications. Exergoeconomic analyses, integrating thermodynamic efficiency with economic considerations, offer valuable perspectives on cost-effectiveness and long-term viability. By consolidating existing contributions across these domains, this study serves as a comprehensive reference for researchers, engineers, and policymakers seeking to enhance the design, implementation, and performance of EAHE systems. The findings emphasize the pivotal role of EAHEs in reducing energy consumption, lowering greenhouse gas emissions, and improving economic sustainability. Additionally, this review identifies key challenges, including soil thermal conductivity variations, moisture effects, and system integration with renewable energy sources, which require further investigation. By addressing these challenges, EAHEs can be further optimized to serve as a cornerstone in sustainable energy management, contributing to global efforts toward energy-efficient building solutions and climate change mitigation.

Andrej Orsula

In the context of addressing the Robot Air Hockey Challenge 2023, we investigate the applicability of model-based deep reinforcement learning to acquire a policy capable of autonomously playing air hockey. Our agents learn solely from sparse rewards while incorporating self-play to iteratively refine their behaviour over time. The robotic manipulator is interfaced using continuous high-level actions for position-based control in the Cartesian plane while having partial observability of the environment with stochastic transitions. We demonstrate that agents are prone to overfitting when trained solely against a single playstyle, highlighting the importance of self-play for generalization to novel strategies of unseen opponents. Furthermore, the impact of the imagination horizon is explored in the competitive setting of the highly dynamic game of air hockey, with longer horizons resulting in more stable learning and better overall performance.

Je-Hyeong Bahk, Thiraj D. Mohankumar, Abhishek Saini et al.

Thermoelectric (TE) heat pumps are a promising solid-state technology for space cooling and heating owing to their unique advantages such as environmental friendliness with no harmful refrigerants, small form factors, low noise, robustness with no moving parts, and demand-flexible operation. However, their low coefficient of performance (COP) presents a challenge towards broader adaptation of the technology at the market level. Built on our previous theoretical framework for a modular system design aimed at improving both COP and cooling capacity, this study focuses on experimental characterization and optimization of a unit air-to-air TE heat pump system. We employ double-sided heating/cooling and counterflow configuration for uniform heat transfer with TE modules and heat exchangers. System-level evaluations validate the theoretical model and show the variation of air temperature change and system COP with different electric current inputs and air flowrates. A maximum cooling COP of 4.4 and a maximum heating COP of 6.0 were obtained at optimized current levels. Further analysis shows that parasitic thermal resistances in the system significantly reduced both COP and air temperature change by over 50%.

Julia Garagnon, Marc Luetscher, Éric Weber

Cave climatology and its impact on contemporary biogeochemical cycles are still poorly documented. Ventilation in karst environment plays a fundamental role in these two fields and its understanding could bring elements to study them. However, only a few cavers have tried to understand and describe it, very often in a qualitative way or by theoretical approaches. The aim of this study is to test physical concepts with empirical data. For this purpose, a ventilation model has been built and compared with field temperature and air velocity measurements in the Milandre Cave Laboratory (Switzerland). The model explains about 95% of the measured airflow thus confirming the major role of temperature on the air dynamics. However, these first results also reveal that the measured winter air flow is lower than predicted by the model and that the air flow reversal occurs at a lower temperature than anticipated. Combined with a forced ventilation experiment these results underline the influence of the atmospheric composition (particularly the water vapor and concentration in CO$_2$ and O$_2$), waterflow rates and network geometry on the air flow. This work paves the way for a better quantification of heat and mass fluxes in relation to underground ventilation.

Lucia Stein-Montalvo, Liuyang Ding, Marcus Hultmark et al.

Ensuring adequate ventilation of exterior and interior urban spaces is essential for the safety and comfort of inhabitants. Here, we examine how angled features can steer wind into areas with stagnant air, promoting natural ventilation. Using Large Eddy Simulations (LES) and wind tunnel experiments with particle image velocimetry (PIV) measurements, we first examine how louvers, located at the top of a box enclosed on four sides, can improve ventilation in the presence of incoming wind. By varying louver scale, geometry, and angle, we identify a geometric regime wherein louvers capture free-stream air to create sweeping interior flow structures, increasing the Air Exchange Rate (ACH) significantly above that for an equivalent box with an open top. We then show that non-homogeneous louver orientations enhance ventilation, accommodating winds from opposing directions, and address the generalization to taller structures. Finally, we demonstrate the feasibility of replacing louvers with lattice-cut kirigami ("cut paper"), which forms angled chutes when stretched in one direction, and could provide a mechanically preferable solution for adaptive ventilation. Our findings for this idealized system may inform the design of retrofits for urban structures -- e.g. canopies above street canyons, and "streeteries" or parklets -- capable of promoting ventilation, while simultaneously providing shade.

Antonio Rosato, Mohammad El Youssef, Francesco Guarino et al.

Automated Fault Detection and Diagnosis (AFDD) algorithms could represent one of the most effective solutions in order to reduce energy demand, greenhouse gas emissions and running costs of Heating, Ventilation and Air-Conditioning (HVAC) systems equipped with Air-Handling Units (AHUs). In particular, data-driven AFDD tools are recognized as easier to be developed and able to provide a higher accuracy with respect to other AFDD tools. However, they are still in the early stage of adoption stock-wide mainly due to the facts that data-driven AFDD models (i) require labeled and reliable experimental faulty data that are time-consuming and expensive to be obtained under different operating scenarios, and (ii) cannot operate beyond the training data. In this paper the most significant scientific papers focusing on experimental analyses of AHUs aiming at the development of data-driven AFDD algorithms have been systematically reviewed and categorized in order to highlight the most important research gaps to be still covered. In particular, the AHU operating schemes, fault types, faults severities and climatic conditions requiring further studies have been identified with the main aim of supporting and guide the future development of new and accurate data-driven AFDD systems.

Yunjae Hwang, Catherine Gorlé

Natural ventilation is gaining popularity in response to an increasing demand for a sustainable and healthy built environment, but the design of a naturally ventilated building can be challenging due to the inherent variability in the operating conditions that determine the natural ventilation flow. Large-eddy simulations (LES) have significant potential as an analysis method for natural ventilation flow, since they can provide an accurate prediction of turbulent flow at any location in the computational domain. However, the simulations can be computationally expensive, and few validation and sensitivity studies have been reported. The objectives of this study are to validate LES of wind-driven cross-ventilation and to quantify the sensitivity of the solution to the grid resolution and the inflow boundary conditions. We perform LES for an isolated building with two openings, using three different grid resolutions and two different inflow conditions with varying turbulence intensities. Predictions of the ventilation rate are compared to a reference wind-tunnel experiment available from literature, and we also quantify the age of air and ventilation efficiency. The results show that a sufficiently fine grid resolution is needed to provide accurate predictions of the detailed flow pattern and the age of air, while the inflow condition is found to affect the standard deviation of the instantaneous ventilation rate. However, for the cross-ventilation case modeled in this paper, the prediction of the mean ventilation flow rate is very robust, showing negligible sensitivity to the grid resolution or the inflow characteristics.

Bhawana Chhaglani, Camellia Zakaria, Adam Lechowicz et al.

Proper indoor ventilation through buildings' heating, ventilation, and air conditioning (HVAC) systems has become an increasing public health concern that significantly impacts individuals' health and safety at home, work, and school. While much work has progressed in providing energy-efficient and user comfort for HVAC systems through IoT devices and mobile-sensing approaches, ventilation is an aspect that has received lesser attention despite its importance. With a motivation to monitor airflow from building ventilation systems through commodity sensing devices, we present FlowSense, a machine learning-based algorithm to predict airflow rate from sensed audio data in indoor spaces. Our ML technique can predict the state of an air vent-whether it is on or off-as well as the rate of air flowing through active vents. By exploiting a low-pass filter to obtain low-frequency audio signals, we put together a privacy-preserving pipeline that leverages a silence detection algorithm to only sense for sounds of air from HVAC air vent when no human speech is detected. We also propose the Minimum Persistent Sensing (MPS) as a post-processing algorithm to reduce interference from ambient noise, including ongoing human conversation, office machines, and traffic noises. Together, these techniques ensure user privacy and improve the robustness of FlowSense. We validate our approach yielding over 90% accuracy in predicting vent status and 0.96 MSE in predicting airflow rate when the device is placed within 2.25 meters away from an air vent. Additionally, we demonstrate how our approach as a mobile audio-sensing platform is robust to smartphone models, distance, and orientation. Finally, we evaluate FlowSense privacy-preserving pipeline through a user study and a Google Speech Recognition service, confirming that the audio signals we used as input data are inaudible and inconstructible.

Ignacio Díaz-Arellano, Manuel Zarzo, Fernando-Juan García-Diego et al.

The monitoring and control of thermo-hygrometric indoor conditions is necessary for an adequate preservation of cultural heritage. The European standard EN 15757:2010 specifies a procedure for determining if seasonal patterns of relative humidity (RH) and temperature are adequate for the long-term preservation of hygroscopic materials on display at museums, archives, libraries or heritage buildings. This procedure is based on the characterization of the seasonal patterns and the calculation of certain control limits, so that it is possible to assess whether certain changes in the microclimate can be harmful for the preventive conservation of artworks, which would lead to the implementation of corrective actions. In order to discuss the application of this standard, 27 autonomous data-loggers were located in different points at the Archaeological Museum of l’Almoina (Valencia). The HVAC system (heating, ventilation and air conditioning) at the museum tries to reach certain homogeneous environment, which becomes a challenge because parts of the ruins are covered by a skylight that produces a greenhouse effect in summer, resulting in severe thermo-hygrometric gradients. Based on the analysis of temperatures recorded during 16 months, the air conditions in this museum are discussed according to the standard EN 15757:2010, and some corrective measures are proposed to improve the conservation conditions. Although this standard is basically intended for data recorded from a single sensor, an alternative approach proposed in this work is to find zones inside the museum with a homogeneous microclimate and to discuss next the average values collected in each area. A methodology is presented to optimize the application of this standard in places with a complex microclimate like this case, when multiple sensors are located at different positions.

Simon Giebenhain, Bastian Goldlücke

This paper introduces Attentive Implicit Representation Networks (AIR-Nets), a simple, but highly effective architecture for 3D reconstruction from point clouds. Since representing 3D shapes in a local and modular fashion increases generalization and reconstruction quality, AIR-Nets encode an input point cloud into a set of local latent vectors anchored in 3D space, which locally describe the object's geometry, as well as a global latent description, enforcing global consistency. Our model is the first grid-free, encoder-based approach that locally describes an implicit function. The vector attention mechanism from [Zhao et al. 2020] serves as main point cloud processing module, and allows for permutation invariance and translation equivariance. When queried with a 3D coordinate, our decoder gathers information from the global and nearby local latent vectors in order to predict an occupancy value. Experiments on the ShapeNet dataset show that AIR-Nets significantly outperform previous state-of-the-art encoder-based, implicit shape learning methods and especially dominate in the sparse setting. Furthermore, our model generalizes well to the FAUST dataset in a zero-shot setting. Finally, since AIR-Nets use a sparse latent representation and follow a simple operating scheme, the model offers several exiting avenues for future work. Our code is available at https://github.com/SimonGiebenhain/AIR-Nets.

Daniel Suo, Naman Agarwal, Wenhan Xia et al.

Mechanical ventilation is one of the most widely used therapies in the ICU. However, despite broad application from anaesthesia to COVID-related life support, many injurious challenges remain. We frame these as a control problem: ventilators must let air in and out of the patient's lungs according to a prescribed trajectory of airway pressure. Industry-standard controllers, based on the PID method, are neither optimal nor robust. Our data-driven approach learns to control an invasive ventilator by training on a simulator itself trained on data collected from the ventilator. This method outperforms popular reinforcement learning algorithms and even controls the physical ventilator more accurately and robustly than PID. These results underscore how effective data-driven methodologies can be for invasive ventilation and suggest that more general forms of ventilation (e.g., non-invasive, adaptive) may also be amenable.

Bruce Hyndman

Sung-Kyung Kim, Won-Hwa Hong, Jung-Ha Hwang et al.

This study examined a method to reduce energy consumption in office buildings. Correspondingly, an optimal control method was proposed for heating, ventilation, and air conditioning (HVAC) systems via two control algorithms that considered the indoor thermal environment. The control algorithms were developed by considering temperature and humidity as the factors of the indoor thermal environment that influence the control of HVAC systems and the predicted mean vote comfort ranges. Furthermore, an experiment was performed using office equipment that incorporated the two control algorithms for HVAC systems, and the correlation between changes in the thermal environment within the office and the occupant’s comfort levels was estimated via an actual survey. The results demonstrated that the proposed control method for HVAC systems, which considered the comfort ranges of temperature and humidity and the thermal adaptation capability, can efficiently maintain the occupant’s comfort with lower energy usage compared with conventional HVAC systems. Thus, the use of the control method contributes to the reduction of total energy consumption in buildings with HVAC systems.

Yu Yunxia, Liu Zhiqiang, Tang Yifang et al.

In order to study the evolution of the size of ice crystal during its flow in horizontal straight pipes, both experiment research and numerical simulation were performed in this study. The size distribution and evolution of ice crystals for different sub-cooling degrees, flow velocities, and Ice Packing Fractions (IPF) were studied by numerical simulation using the CFD-PBM coupling model, and the numerical simulation was then verified by experiment. The results show that the distribution of ice number density could be approximately described by Gaussian distribution at the entrance of the ice slurry pipe with an IPF of 15%. With the increase in velocity and IPF, the average size of ice crystal increases along the central axis. Additionally, the size distribution of ice crystals gets more uneven along the vertical direction. With the decrease of flow velocity and the increase of IPF, the peak value of the length number density distribution of the ice crystal’s particle size increases, and the average value of the corresponding ice crystal’s particle size decreases.

Guangxu Zhu, Jie Xu, Kaibin Huang et al.

Wireless data aggregation (WDA), referring to aggregating data distributed at devices (e.g., sensors and smartphone), is a common operation in 5G-and-beyond machine-type communications to support Internet-of-Things (IoT), which lays the foundation for diversified applications such as distributed sensing, learning, and control. Conventional WDA techniques that are designed based on a separated-communication-and-computation principle encounter difficulty in accommodating the massive access under the limited radio resource and stringent latency constraints imposed by emerging applications (e.g, auto-driving). To address this issue, over-the-air computation (AirComp) is being developed as a new WDA solution by seamlessly integrating computation and communication. By exploiting the waveform superposition property of a multiple-access channel, AirComp turns the air into a computer for computing and communicating functions of distributed data at many devices, thereby allowing low-latency WDA over massive devices. In view of growing interests on AirComp, this article provides a timely overview of the technology by introducing basic principles, discussing advanced techniques and applications, and identifying promising research opportunities.

Eric G. Blackman, Gourab Ghoshal

To mitigate the SARS-CoV-2 pandemic, officials have employed social distancing and stay-at-home measures, with increased attention to room ventilation emerging only more recently. Effective distancing practices for open spaces can be ineffective for poorly ventilated spaces, both of which are commonly filled with turbulent air. This is typical for indoor spaces that use mixing ventilation. While turbulence initially reduces the risk of infection near a virion-source, it eventually increases the exposure risk for all occupants in a space without ventilation. To complement detailed models aimed at precision, minimalist frameworks are useful to facilitate order of magnitude estimates for how much ventilation provides safety, particularly when circumstances require practical decisions with limited options. Applying basic principles of transport and diffusion, we estimate the time-scale for virions injected into a room of turbulent air to infect an occupant, distinguishing cases of low vs. high initial virion mass loads and virion-destroying vs. virion-reflecting walls. We consider the effect of an open window as a proxy for ventilation. When the airflow is dominated by isotropic turbulence, the minimum area needed to ensure safety depends only on the ratio of total viral load to threshold load for infection. The minimalist estimates here convey simply that the equivalent of ventilation by modest sized open window in classrooms and workplaces significantly improves safety.

Huang Ronggeng, Long Jing, Pan Zhigang et al.

This paper proposes an energy consumption-prediction method for metro heating, ventilation and air-conditioning (HVAC) systems based on an auto-regressive moving average (ARMA) model using a time-series data analysis. Firstly, stationarity analysis and white-noise analysis (also known as pure stochastic analysis) were carried out on the collected energy-consumption data from actual metro HVAC systems. Secondly, optimal model parameters were determined using the autocorrelation function (ACF), and partial autocorrelation function (PACF) and Akaike information criterion (AIC). Finally, an effective energy consumption-prediction model was established. Four different methods were employed to test the effectiveness of the established ARMA model. Meanwhile, two performance indexes, namely, mean absolute error and root mean square error, were adopted to evaluate its performance in terms of fitting the observed energy consumption data. The results demonstrate that the proposed method based on the ARMA model could extract useful information from the energy data and is thus effective for energy consumption prediction of metro HVAC systems.

Srushti Rath, Joseph Y. J. Chow

Witnessing the rapid progress and accelerated commercialization made in recent years for the introduction of air taxi services in near future across metropolitan cities, our research focuses on one of the most important consideration for such services, i.e., infrastructure planning (also known as skyports). We consider design of skyport locations for air taxis accessing airports, where we present the skyport location problem as a modified single-allocation p-hub median location problem integrating choice-constrained user mode choice behavior into the decision process. Our approach focuses on two alternative objectives i.e., maximizing air taxi ridership and maximizing air taxi revenue. The proposed models in the study incorporate trade-offs between trip length and trip cost based on mode choice behavior of travelers to determine optimal choices of skyports in an urban city. We examine the sensitivity of skyport locations based on two objectives, three air taxi pricing strategies, and varying transfer times at skyports. A case study of New York City is conducted considering a network of 149 taxi zones and 3 airports with over 20 million for-hire-vehicles trip data to the airports to discuss insights around the choice of skyport locations in the city, and demand allocation to different skyports under various parameter settings. Results suggest that a minimum of 9 skyports located between Manhattan, Queens and Brooklyn can adequately accommodate the airport access travel needs and are sufficiently stable against transfer time increases. Findings from this study can help air taxi providers strategize infrastructure design options and investment decisions based on skyport location choices.