ABSTRACT High outdoor‐air (OA) ventilation improves infection control but can sharply increase HVAC energy use in hot–dry climates. This study designs and optimizes a sealed, U‐shaped internal‐condenser heat‐pipe heat exchanger (HPHE) that thermally couples exhaust and supply ducts without air mixing. A hybrid porous/ ε –NTU screen is used to identify a practical operating band of 2.0–2.5 m·s⁻¹ (Δ T ≈ 20 K) for penalty‐aware operation. Transient two‐phase VOF simulations map loop stability versus condenser length and working‐fluid fill ratio, rejecting 300 mm legs due to flooding and selecting a 150 mm U‐leg at FR ≈ 50% in a laminar, capillary‐dominated regime. Conjugate CFD at the selected operating conditions predicts a ≈ 15.6 K reduction in hot‐duct outlet temperature and an ≈8.4 K increase in supply‐duct outlet temperature, with only watts–tens of watts of added fan power; the loop pumping requirement is O (10⁻² W). Verification and validation follow AHRI Guideline V conventions, including three‐level mesh/time refinement (GCI₉₅% < 5%), two‐phase energy‐balance checks, and cross‐study comparisons ( ε–v and ) against recent HPHE literature (2021–2024). At the nominal point, the recovered sensible heat is = 1.621 kW with ±1 σ = 0.064 kW (4.0%) and a 95% confidence interval of [1.497, 1.748] kW (±7.8%). The results establish a compact, stable HPHE module that delivers strong, net positive heat recovery for high‐ventilation, infection‐control HVAC operation in extreme hot–dry climates.
There has been limited quantitative research on the industrial application of direct cooling ice makers, resulting in lack of clarity in control mechanisms, and inadequate heat transfer capability and uniformity in ice making. A mathematical model focusing on the refrigerant side of the ice mold evaporator was established, and MATLAB simulation was used to analyze the changes of heat transfer and flow parameters with the flow process and ice making time, with comparisons drawn between experimental data and simulated outcomes. The results showed that the heat transfer rate before water icing is about 30% higher than that after water icing, and the refrigerant flow rate is obviously different. The unit heat transfer after overheating decreases by 40.9% compared to before overheating, reducing the overheating section can significantly enhance the heat transfer and improve the uniformity. The thermal resistance of water side and ice side accounted for 93.4% and 91.7% of the total respectively, and the heat transfer of water side or ice side should be improved first in the optimization of heat transfer. The simulation program can predict the change of flow rate and simulate the overheating section, which provides theoretical basis and practical guidance for the design and operation control of ice making machine, and helps to improve the product performance and accelerate the ice making process.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Bashir Eskander Kareem, Ahmed Mohammed Adham, Banipal Nanno Yaqob
The building sector is the largest energy consumer, responsible for 40% of the global energy consumption and one-third of CO2 emissions. Heating, ventilation, and air-conditioning (HVAC) systems constitute approximately half of this energy demand. Thermal energy storage (TES) systems offer a promising solution for reducing HVAC energy consumption by enabling free cooling, heating, and ventilation. A free-cooling system (FCS) stores cooling energy in TES at night, which is then utilized throughout the day. However, the effectiveness of FCSs is limited to regions where daily temperature fluctuations fall within thermal comfort ranges, with greater effectiveness in regions experiencing larger temperature variations. Latent heat TES utilizing phase-change materials (PCMs) is particularly advantageous because of its high energy-storage capacity with minimal changes in temperature and volume. This review examines various studies on PCM-to-air heat exchangers (PAHXs) within FCSs, highlighting key challenges such as the thermophysical properties of PCMs and the dependence of free cooling on climate. The factors influencing the PCM melting and solidification times, including the heat-transfer fluid (HTF) flow rate and the temperature difference between the HTF intake and PCM melting temperature, are also discussed. Additionally, improvements in PCM properties, encapsulation techniques, and heat exchanger designs are reviewed. This comprehensive analysis confirms the predominance of slab PAHX designs and paraffin-based PCMs. Methods such as fins, nanoparticles, and multi-PCM approaches are identified as effective strategies for enhancing the heat transfer in PCM systems.
Due to the absence of an internal combustion engine and the corresponding waste heat, battery electric vehicles have a significantly reduced range in cold environments. Moreover, also at high ambient temperatures, the energy consumption of the air-conditioning system has a negative effect on the range. In this paper, a thermal management strategy for the passenger compartment of a battery electric vehicle is developed with the aim to reduce the power consumption of the heating, ventilation, and air-conditioning (HVAC) system. Simultaneously, the thermal comfort of the passengers has to be ensured. To address both objectives, a predictive, optimization-based approach for the thermal management system is developed. Models for the vehicle cabin and the HVAC system are derived and identified using measurement data. These models are used to formulate an optimal control problem, where also system limitations are considered explicitly. To avoid the computationally expensive solution of a nonlinear optimal control problem, it is approximated by a linear-quadratic model predictive control problem. This can be solved very efficiently and is suitable for real-time implementation on an automotive control unit. The proposed linear-quadratic strategy is compared with a baseline strategy and the nonlinear strategy to show the effectiveness of the proposed approach.
Antonio Rosato, Francesco Guarino, Mohammad El Youssef
et al.
Data-driven Automated Fault Detection and Diagnosis (AFDD) are recognized as one of the most promising options to improve the efficiency of Air-Handling Units (AHUs). In this study, the field operation of a typical single-duct dual-fan constant air volume AHU is investigated through a series of experiments carried out under Mediterranean (southern Italy) climatic conditions considering both fault-free and faulty scenarios. The AHU performances are analyzed while artificially introducing the following five different typical faults: (1) post-heating coil valve stuck at 100% (always open); (2) post-heating coil valve stuck at 0% (always closed); (3) cooling coil valve stuck at 100% (always open); (4) cooling coil valve stuck at 0% (always closed); (5) humidifier valve stuck at 0% (always closed). The measured faulty data are compared against the corresponding fault-free performance measured under the same boundary conditions with the aim of assessing the faults’ impact on both thermal/hygrometric indoor conditions, as well as patterns of 16 different key operating parameters. The results of this study can help building operators and facility engineers in identifying faults’ symptoms in typical AHUs and facilitate the related development of new AFDD tools.
Scientific and technological progress and innovation help the design industry, which plays an important role in sustainable development. It will improve the operation efficiency of enterprises and explore a blue sea for enterprise. In essence, design should be the process of deriving the optimal scheme from different schemes or imaginary scenes. Based on this, this paper proposes an overall optimization method for Heating, Ventilation and Air Conditioning (HVAC) design. Compared with the traditional design method, under certain constraints, it can obtain the optimal design scheme that maximizes the value of the designed product. This study provides new inverse problem ideas and methods for HVAC designers, and provides solutions for enhancing the value of HVAC design products.
Abstract Building energy simulation programs can be useful tools in evaluating building energy performance during a building's lifecycle, both at the design and operation stages. In addition, simulating building energy usage has become a key strategy in designing high performance buildings that can better meet the needs of society without consuming excess resources. Therefore, it is important to provide accurate predictions of building energy performance in building design and construction projects. Although many previous studies have addressed the accuracy of building energy simulations, very few studies of this subject have mentioned the importance of Heating, Ventilation, and Air-Conditioning (HVAC) thermal zoning strategies to sustainable building design. This research provides a systematic literature review of building thermal zoning for building energy simulation. This work also reviews previous definitions of HVAC thermal zoning and its application in building energy simulation programs, including those appearing in earlier studies of the development of new thermal zoning methods for simulation modeling. The results indicate that future research is needed to develop a well-documented and accurate thermal zoning method capable of assisting designers with their building energy simulation needs.
Individual thermal discomfort perception gives important feedback signals for energy efficient control of building heating, ventilation and air conditioning systems. However, there is few effective ...
Marcello Fiducioso, Sebastian Curi, B. Schumacher
et al.
We tune one of the most common heating, ventilation, and air conditioning (HVAC) control loops, namely the temperature control of a room. For economical and environmental reasons, it is of prime importance to optimize the performance of this system. Buildings account from 20 to 40 % of a country energy consumption, and almost 50 % of it comes from HVAC systems. Scenario projections predict a 30 % decrease in heating consumption by 2050 due to efficiency increase. Advanced control techniques can improve performance; however, the proportional-integral-derivative (PID) control is typically used due to its simplicity and overall performance. We use Safe Contextual Bayesian Optimization to optimize the PID parameters without human intervention. We reduce costs by 32 % compared to the current PID controller setting while assuring safety and comfort to people in the room. The results of this work have an immediate impact on the room control loop performances and its related commissioning costs. Furthermore, this successful attempt paves the way for further use at different levels of HVAC systems, with promising energy, operational, and commissioning costs savings, and it is a practical demonstration of the positive effects that Artificial Intelligence can have on environmental sustainability.
Large-scale wind and solar power integration are likely to cause a short-term mismatch between generation and load demand because of the intermittent nature of the renewables. System frequency is therefore challenged. In recent years, it has been proposed that a part of the residential load can be controlled for frequency regulation with little impact on customer comfort. This paper proposes a thermostatic load control strategy for primary and secondary frequency regulation, in particular, using heating, ventilation, and air-conditioning units and electric water heaters. First, daily demand profile modeling indicates that these two loads are complementary in the daytime and can provide a relatively stable frequency reserve. Second, the progressive load recovery is specifically considered in the control scheme. The random switching and cycle recovery method is proposed for mitigating power rebound after switching the air conditioners on again. The proposed control strategy can organize a large population of thermostatic loads for the provision of a frequency reserve. Consequently, the requirement of a spinning reserve is reduced. Finally, the proposed control strategy is verified by the dynamic simulation of IEEE RTS 24-bus system.
Jerson A. Pinzon, P. Vergara, L. D. da Silva
et al.
This paper presents a mixed integer non-linear programming model to optimize, in a centralized fashion, the operation of multiple buildings in a microgrid. The proposed model aims to minimize the total cost of the energy imported from the main grid at the interconnection point, managing the power demand and generation of buildings, while operational constraints of the electrical grid are guaranteed. This approach considers the management of heating, ventilation, and air conditioning units, lighting appliances, photovoltaic generation and energy storage system of each building. Comfortable indoor conditions for the occupants are kept by a set of mathematical constraints. Additionally, a strategy that simplifies the original model is presented, based on a set of linearization techniques and equivalent representations, obtained through a pre-processing stage executed in EnergyPlus software. This strategy allows approximating the proposed model into a mixed integer linear programming formulation that can be solved using commercial solvers. The proposed model was tested in a 13-bus microgrid for different deterministic cases of study with non-manageable loads and smart buildings. A large-size test case is also considered. Finally, a rolling horizon strategy is proposed with the aim of addressing the uncertainty of the data, as well as reducing the amount of forecasting data required.
Abstract A building heating, ventilation, and air-conditioning (HVAC) system consumes large amounts of energy. Energy consumption prediction is an effective strategy for operation optimization and energy management in a building. The energy consumption of an HVAC system in a building is influenced by many factors, such as weather conditions, building usage, and thermal performance. However, it is impractical to consider all factors for predicting energy consumption. In this paper, a simplified data-driven model is proposed for predicting the energy consumption of an HVAC system in a building. A novel feature transformation method is introduced to select the most relevant features. Three input features (i.e., degree-day, day type, and month type) are finally adopted in this model. Compared to models developed in previous studies, this simplified model largely reduces the computation time and is easier to operate. The cross-validated root mean square error of this method for cooling energy prediction is less than 20%, indicating its suitability for use in engineering applications.
This study aimed to optimize the heat production performance of an enhanced vapor injection air source heat pump system with R32. Combined with the structure and actual operation characteristics of a scroll compressor system, a complete mathematical model was established and programmed using MATLAB. After verification of the simulation results with experimental data, the influences of the vapor injection pressure of the system, and the specific volume ratio of the quasi-one-stage compression on the relative vapor injection volume of the system were explored under different ambient temperatures. It was found that the vapor injection heat pump system is more suitable for operation at ambient temperatures of below ?10 °C than an ordinary heat pump system. The optimal operating range of the heating mode of the air-source heat pump system with vapor injection was obtained. Because the specific volume ratio of the quasi-one-stage compression of the system is 1.1, the relative vapor injection volume is the maximum. When the ambient temperature is below -10 °C, within ?10– -5 °C or above -5 °C respectively, the optimal relative injection volume should be within 0.22–0.33, 0.20–0.22 and 0.18–0.20 respectively. The corresponding optimal relative inject pressure ranges 0.85–1.31. Within the range of the optimal vapor injection parameters, the advantages of the enhanced vapor injection air-source heat pump system with R32 are obvious.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards the over-represented class where windows remained closed. In addition, they require tuning for each occupant which can not be efficiently scaled to the increased number of occupants. This paper presents a window opening model for commercial buildings using deep learning methods. The model is trained using data from occupants from an office building in Germany. In total the model is evaluated using almost 20 mio. data points from 3 independent buildings, located in Aachen, Frankfurt and Philadelphia. Eventually, the results of 3100 core hours of model development are summarized, which makes this study the largest of its kind in window states modeling. Additionally, the practical potential of the proposed model was tested by incorporating it in the Modelica-based thermal building simulation. The resulting evaluation accuracy and F1 scores on the office buildings ranged between 86-89 % and 0.53-0.65 respectively. The performance dropped around 15 % points in case of sparse input data, while the F1 score remained high.
In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.