More Like this

1. Thermal Comfort: Radiant Systems - A Review of Experimentalbased thermal comfort research in Radiation systems

   JAWADWALA, HUZEFA; TIAN, ROBERT; YIN, HONGXI; QU, MING; LIU, XIAOLI; HOLMES, NIALL; KOHANOFF, JORGE; JANI, RUCHITA (March 2021, 108th ACSA Annual Meeting Proceeding)

   null (Ed.)

   Buildings use 40% of the global energy consumption and emit 30% of the CO2 emissions [1]. Of the total building energy, 30-40% are for building heating and cooling systems, which regulate the indoor thermal environment and provide thermal comfort to occupants. In the United States, most buildings use forced air technology to deliver heating/cooling to the targeted thermal zones. However, this system may cause complaints about thermal comfort from inhabitants due to excessive draft movement, inhomogeneous conditioning, and difficulty in accurately controlling the temperature for a system serving multiple rooms. Therefore, researchers have suggested using a radiant heating and cooling system as a better alternative to all-air systems to address these issues. Radiant systems supply heating or cooling directly to the building space using radiation released by the heated or cooled building enclosure via the embedded heating or cooling tubes. In the cooling season, the radiant system often works with a separated dehumidifier to meet space latent and sensible cooling load (called separate sensible and latent cooling system SSLC). The SSLC has shown higher efficiency than forced air systems. However, it is unsure whether the radiant heating and cooling system can provide better thermal comfort to occupants. Moreover, the evaluation method for thermal comfort in the current standard is suitable for forced air systems. Therefore, a new method shall be developed to evaluate the radiation system’s thermal comfort. In this paper, we review the experiment-based studies on the thermal comfort of radiant systems. According to the experimental studies regarding thermal comfort and radiant systems, the key findings are concluded to help guide the evaluation of thermal comfort for radiant systems. 

   more » « less
2. Thermal Comfort: Radiant Systems - A Review of Experimental-based thermal comfort research in Radiation systems

   Jawadwala, H.; Tian, R.; Yin, H.; Qu, M.; Liu, X.; Holmes, N.; Kohanoff, J.; Jani, R (March 2021, 109th ACSA Annual Meeting)

   Buildings use 40% of the global energy consumption and emit 30% of CO2 emissions. Of the total building energy, 30-40% is for building heating and cooling systems, which regulate the indoor thermal environment and provide thermal comfort to occupants. Most buildings use forced air technology in the United States to deliver heating/cooling to the targeted thermal zones. Researchers have suggested using radiant heating and cooling systems as a better alternative to all-air systems. Radiant systems supply heating or cooling directly to the building space using radiation released by the heated or cooled building enclosure via the embedded heating or cooling tubes. It is unsure whether the radiant heating and cooling system can provide better thermal comfort to occupants. Moreover, the evaluation method for thermal comfort in the current standard is only suitable for forced air systems. A new plan shall be developed to evaluate the radiation system’s thermal comfort. This paper reviews the experiment-based studies on the thermal comfort of radiant systems. According to the experimental studies regarding thermal comfort and radiant systems, the key findings are concluded to help guide the evaluation of thermal comfort for radiant systems. 

   more » « less
3. A Novel Hybrid Modeling Method for Predicting Energy Use of Hydronic Radiant Slab Systems

   Wu, Lichen; Wang, Liping; Braun, James (July 2022, International High Performance Buildings Conference)

   Accurately predicting the performance of radiant slab systems can be challenging due to the large thermal capacitance of the radiant slab and room temperature stratification. Current methods for predicting heating and cooling energy consumption of hydronic radiant slabs include detailed first-principles (e.g., finite difference) and reduced-order (e.g., thermal Resistor-Capacitor (RC) network) models. Creating and calibrating detailed first-principles models, as well as detailed RC network models for predicting the performance of radiant slabs require substantial effort. To develop improved control, monitoring, and diagnostic methods, there is a need for simpler models that can be readily trained using in-situ measurements. In this study, we explored a novel hybrid modeling method that integrates a simple RC network model with an evolving learning-based algorithm termed the Growing Gaussian Mixture Regression (GGMR) modeling approach to predict the heating and cooling rates of a radiant slab system for a Living Laboratory office space. The RC network model predicts heating or cooling load of the radiant slab system that is provided as an input to the GGMR model. Three modeling approaches were considered in this study: 1) an RC network model; 2) a GGMR model, and 3) the proposed hybrid modeling between RC and GGMR. The three modeling methods have been compared for predicting the energy use of a radiant slab system of a Living Laboratory office space using measurement data from January 15th to March 7th, 2022. The first two weeks of data were used for training, while the remaining data was used for testing of all three modeling methods. The hybrid approach had a Normalized Root Mean Square Error (NRMSE) of 15.46 percent (8.62 percent less than the RC-Model 3 alone and 19.36 percent less than the GGMR alone), a Coefficient of Variation of RMSE (CVRMSE) of 6.43 percent (3.59 percent less than the RC-Model 3 and 8.05 percent less than the GGMR), a Mean Absolute Error (MAE) of 3.61 kW (2.13 kW and 3.87 kW less than the RC-Model 3 and GGMR, respectively), and a Mean Absolute Percentage Error (MAPE) of 5.28 percent (3.85 percent and 3.92 percent lower than the RC-Model 3 and GGMR, respectively). 

   more » « less
4. Performance Modeling and Analysis of a Thermoelectric Building Envelope for Space Heating.

   Liu, X.; Qu, M.; Yazawa, K.; Kohanoff, J.; Chudzinski, P.; Stella, L.; Norton, B.; Holmes, N.; Jani, R.; Yin, H. (July 2022, 2022 (7th) International High Performance Buildings Conference)

   To provide energy-efficient space heating and cooling, a thermoelectric building envelope (TBE) embeds thermoelectric devices in building walls. The thermoelectric device in the building envelope can provide active heating and cooling without requiring refrigerant use and energy transport among subsystems. Thus, the TBE system is energy and environmentally friendly. A few studies experimentally investigated the TBE under limited operating conditions, and only simplified models for the commercial thermoelectric module (TEM) were developed to quantify its performance. A holistic approach to optimum system performance is needed for the optimal system design and operation. The study developed a holistic TBE-building system model in Modelica for system simulation and performance analysis. A theoretical model for a single TEM was first established based on energy conversion and thermoelectric principles. Subsequently, a TBE prototype model combining the TEM model was constructed. The prototype model employing a feedback controller was used in a whole building system simulation for a residential house. The system model computed the overall building energy efficiency and dynamic indoor conditions under varying operating conditions. Simulation results indicate the studied TBE system can meet a heating demand to maintain the desired room temperature at 20 °C when the lowest outdoor temperature is at -26.3 °C, with a seasonal heating COP near 1.1, demonstrating a better heating performance than electric heaters. It suggests a potential energy-efficient alternative to the traditional natural gas furnaces and electric heaters for space heating. 

   more » « less
5. Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries

   https://doi.org/10.3390/pr7010038  

   Son, Jeongeun; Du, Yuncheng (January 2019, Processes)

   The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop a reliable thermal management system to accurately predict and monitor thermal behavior of a Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in Li-ion battery cells, using easily measured quantities such as temperatures. In addition, models used for FDD are typically derived from the underlying physical phenomena. To make a model tractable and useful, it is common to make simplifications during the development of the model, which may consequently introduce a mismatch between models and battery cells. Further, FDD algorithms can be affected by uncertainty, which may originate from either intrinsic time varying phenomena or model calibration with noisy data. A two-step FDD algorithm is developed in this work to correct a model of Li-ion battery cells and to identify faulty operations in a normal operating condition. An iterative optimization problem is proposed to correct the model by incorporating the errors between the measured quantities and model predictions, which is followed by an optimization-based FDD to provide a probabilistic description of the occurrence of possible faults, while taking the uncertainty into account. The two-step stochastic FDD algorithm is shown to be efficient in terms of the fault detection rate for both individual and simultaneous faults in Li-ion batteries, as compared to Monte Carlo (MC) simulations. 

   more » « less