Phase Change Materials in Floor Tiles for Thermal Energy Storage

Passive solar systems integrated into residential structures significantly reduce heating energy consumption. Taking advantage of latent heat storage has further increased energy savings. This is accomplished by the incorporation of phase change materials into building materials used in passive applications. Trombe walls, ceilings and floors can all be enhanced with phase change materials. Increasing the thermal storage of floor tile by the addition of encapsulated paraffin wax is the proposed topic of research. Latent heat storage of a phase change material (PCM) is obtained during a change in phase. Typical materials use the latent heat released when the material changes from a liquid to a solid. Paraffin wax and salt hydrates are examples of such materials. Other PCMs that have been recently investigated undergo a phase transition from one solid form to another. During this process they will release heat. These are known as solid-state phase change materials. All have large latent heats, which makes them ideal for passive solar applications. Easy incorporation into various building materials is must for these materials. This proposal will address the advantages and disadvantages of using these materials in floor tile. Prototype tile will be made from a mixture of quartz, binder and phase change material. The thermal and structural properties of the prototype tiles will be tested fully. It is expected that with the addition of the phase change material the structural properties will be compromised to some extent. The ratio of phase change material in the tile will have to be varied to determine the best mixture to provide significant thermal storage, while maintaining structural properties that meet the industry standards for floor tile

Experimental Evaluation of a Simulation Model for Wrap-Around Heat Exchanger, Solar Storage Tanks

The thermal performance of a commercially available 80 gallon, solar storage tank with an integral wrap-around heat exchanger is characterized experimentally an indoor test stand. The experimental results are used to evaluated the accuracy of a previously developed simulation model. Heat input on the collector side of the heat exchanger is held constant causing the heat transfer to reach a quasi-steady state. Temperatures in the heat exchanger and tank increase with time, however, the temperature differences across the heat exchanger remain nearly constant. Several combinations of heat input and collector loop flow are investigated. The development of the tank temperature profiles over time and the overall heat transfer performance predicted by the model are compared with experimental results. The influence of an electric auxiliary heater located in the top of the solar storage tank on the heat exchanger performance is investigated. Experimental normalization of the model is considered and modifications to the model and experiments are recommended

Final Technical Report

The Department of Energy’s Industrial Assessment Center at Colorado State University (CSU IAC) has been helping manufacturers in Colorado and the Rocky Mountain region save energy, reduce waste, and save money while helping to produce highly-trained and highly-capable energy engineers since 1984. The most recent four-year contract continues that trend. This contract ran from September 1, 2002 through May 31, 2007 and included assessments conducted from September 1, 2002 through August 31, 2006. During this contract, the CSU IAC served 77 manufacturers in six Rocky Mountain States and recommended about 311,800 MMBtu/yr in energy savings, 12.6 million gallons of waste water reduction per year, nearly 650,000 pounds of solid waste reduction per year, and more than 5,600 gallons of hazardous solid waste per year, saving more than $9.54 million dollars per year in utility, waste disposal, raw material, and labor costs. Total expenditures for the period were about$814,000 for the period or about $203,500 per year. Thus, the CSU IAC generated almost 12 times more recommended cost savings than the project cost. In addition, the program employed 24 undergraduate mechanical and civil engineering students and seven graduate mechanical engineering students. Of these students, more than 75% have gone on to successful careers in energy engineering or manufacturing, where they continue to provide additional energy and cost savings for industry and the country

Modeling the Heat Gain of a Window With an Interior Shade, How Much Energy Really Gets In?

ABSTRACT Not long ago the ASHRAE Technical Committee on Load Calculation, TC 4.1, had a "bake off' of sorts between different peak air-conditioning load calculation schemes and programs. One of the outcomes of this exercise was the realization that practitioners and software developers make largely different assumptions about how solar energy absorbed by window glass and by window shades contributes to the room solar heat gain. For unshaded glass windows there is general agreement [ASHRAE Handbook of Fundamentals, 2005]. For a shade however, there were two extremes in the models-one assumes that the shade rejects most of the solar energy that it does not transmit, logical if the shade is highly reflective and the glass highly transmissive, the other assumes that all radiation absorbed by the shade is immediately convected into the room. Suffice it to say that we are not the first to derive and present the fundamental equations of this heat transfer problem. What we have done is to avoid any simplifying assumptions in formulating the problem while allowing that some physical constants, convection coefficients in particular, are not well known and need to be parameterized. We whet the readers' appetite by revealing that for a glass/shade system where the glass was 22% transmissive and the shade 52% transmissive, the total heat gain to the room from this window assembly was nearly half of the incident radiation. Of course "It all depends!&quot

Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil

An accurate simulation of a heating coil is used to compare the performance of a PI controller, a neural network trained to predict the steady-state output of the PI controller, a neural network trained to minimize the n-step ahead error between the coil output and the set point, and a reinforcement learning agent trained to minimize the sum of the squared error over time. Although the PI controller works very well for this task, the neural networks do result in improved performance. 1 Introduction Typical methods for designing fixed feedback controllers results in sub-optimal control performance. In many situations, the degree of uncertainty in the model of the system being controlled limits the utility of optimal control design. Building energy systems are particularly troublesome since the process gain is highly variable, depending on the load on components such as heating and cooling coils and on inlet conditions such as air temperature and air volume flow rate. Some of the..

Synthesis of Reinforcement Learning, Neural Networks, and PI Control Applied to a Simulated Heating Coil

An accurate simulation of a heating coil is used to compare the performance of a proportional plus integral (PI) controller, a neural network trained to predict the steady-state output of the PI controller, a neural network trained to minimize the n-step ahead error between the coil output and the set point, and a reinforcement learning agent trained to minimize the sum of the squared error over time. Although the PI controller works very well for this task, the neural networks produce improved performance. The reinforcement learning agent, when combined with a PI controller, learned to augment the PI control output for a small number of states for which control can be improved. Keywords: neural networks, reinforcement learning, PI control, HVAC 1 Introduction Typical methods for designing fixed feedback controllers results in sub-optimal control performance. In many situations, the degree of uncertainty in the model of the system being controlled limits the utility of optimal contro..

Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil

An accurate simulation of a heating coil is used to compare the performance of a PI controller, a neural network trained to predict the steady-state output of the PI controller, a neural network trained to minimize the n-step ahead error between the coil output and the set point, and a reinforcement learning agent trained to minimize the sum of the squared error over time. Although the PI controller works very well for this task, the neural networks do result in improved performance. 1 Introduction Typical methods for designing fixed feedback controllers results in sub-optimal control performance. In many situations, the degree of uncertainty in the model of the system being controlled limits the utility of optimal control design. Building energy systems are particularly troublesome since the process gain is highly variable, depending on the load on components such as heating and cooling coils and on inlet conditions such as air temperature and air volume flow rate. Some of these issu..