• Title/Summary/Keyword: Cooling schedule

Search Result 46, Processing Time 0.023 seconds

Modified Simulated Annealing Algorithms for Optimal Seismic Design of Braced Frame Struvtures (2차원 가새골조의 최적내진설계를 위한 MSA 알고리즘)

  • Lee, Sang Kwan;Seong, Chang Won;Park, Hyo Seon
    • Journal of Korean Society of Steel Construction
    • /
    • v.12 no.6
    • /
    • pp.629-638
    • /
    • 2000
  • With the positive features of simulated annealing algorithms such as simplicity of the algorithm and the possibility of finding global optimum solution, SA algorithm has been widely applied to structural optimization problems. However, the algorithms are far from practical applications in structural design or optimization of building structures due to requirement of a large number of iterations and dependency on cooling schedule and stopping criteria. In this paper, with the modification of annealing process and stopping criteria, a MSA algorithm is presented in the form of two phase annealing process for optimal seismic design of braced structures. The performance of the proposed algorithm has been illustrated in detail.

  • PDF

Smart Thermostat based on Machine Learning and Rule Engine

  • Tran, Quoc Bao Huy;Chung, Sun-Tae
    • Journal of Korea Multimedia Society
    • /
    • v.23 no.2
    • /
    • pp.155-165
    • /
    • 2020
  • In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.

Simulation of Interim Spent Fuel Storage System with Discrete Event Model (이산 모형을 이용한 사용후 핵연료 중간 저장 시설의 전산기 모사)

  • Yoon, Wan-Ki;Song, Ki-Chan;Lee, Jae-Sol;Park, Hyun-Soo
    • Nuclear Engineering and Technology
    • /
    • v.21 no.3
    • /
    • pp.223-230
    • /
    • 1989
  • This paper describes dynamic simulation of the spent fuel storage system which is described by statistical discrete event models. It visualizes flow and queue of system over time, assesses the operational performance of the system acitivies and establishes the system components and streams. It gives information on system organization and operation policy with reference to the design. System was tested and analyzed over a number of critical parameters to establish the optimal system. Workforce schedule and resources with long processing time dominate process. A combination of two workforce shifts a day and two cooling pits gives the optimal solution of storage system. Discrete system simulation is an useful tool to get information on optimal design and operation of the storage system.

  • PDF

A System Simulation Model of Proton Exchange Membrane Fuel Cell for Residential Power Generation for Thermal Management Study (가정용 연료전지 시스템의 열관리 해석을 위한 시스템 운전 모델 개발)

  • Yu, Sang-Seok;Lee, Young-Duk;Ahn, Kook-Young
    • Transactions of the Korean Society of Mechanical Engineers B
    • /
    • v.34 no.1
    • /
    • pp.19-26
    • /
    • 2010
  • A PEMFC(proton exchange membrane fuel cell) is a good candidate for residential power generation to be coped with the shortage of fossil fuel and green house gas emission. The attractive benefit of the PEMFC is to produce electric power as well as hot water for home usage. The thermal management of PEMFC for RPG is to utilize the heat of PEMFC so that the PEMFC can be operated at its optimal efficiency. In this study, thermal management system of PEMFC stack is modeled to understand the dynamic response during load change. The thermal management system of PEMFC for RPGFC is composed of two cooling circuits, one for controling the fuel cell temperature and the other for heating up the water for home usage. The different operating strategy is applied for each cooling circuit considering the duty of those two circuits. Even though the capacity of PEMFC system (1kW) is enough to supply hot domestic water for residence, heat-up of reservior takes some hours. Therefore, in this study, time schedule of the simulation reflects the heat-up process. Dynamic responses and operating strategies of the PEMFC system are investigated during load changes.

Study and Survey of Operating Efficiency with Cool Storage System (빙축열냉방시스템의 운전효율에 관한 조사연구)

  • 손학식;심창호;김강현;김재철
    • Journal of Energy Engineering
    • /
    • v.11 no.1
    • /
    • pp.1-9
    • /
    • 2002
  • The purpose of this study is to maintain high efficiency and reasonable use of cool thermal storage systems operated in the domestic building sector. As the result of efficiency test from the five types of operated cool storage systems on the condition that COP ranges are 2.6 to 3.4 during the day time and 2.1 to 3.0 during the night time and it decreased by more than 30% of rated COP given 3.8 to 3.0. The Analysis of cool storage rate shows that only 3 (21.4%) systems out of 15 buildings hold to over 40% capacity for its total capacity. To prevent the decrease in operating efficiency, it should correct the malfunction of 3-way valve and expansion valve and the mistake of control values for schedule program and increase cooling tower capacity. In order to improve piping line, it needs bypass brine line off refrigerator, separation of chilled water line with Ice Slurry system at day and night time and speed control of chilled and warm water pumps. This study does require the more studies on improving difficulty of increasing cooling load with Ice on Coil system, waterproofing with Ice Ball system, COP drop during the night time with Ice Lens, low operating temperature during the day time with Ice Slurry and increasing of Power loss due to hot gas de-icing with Ice Harvest in the future.

Analysis of Greenhouse Thermal Environment by Model Simulation (시뮬레이션 모형에 의한 온실의 열환경 분석)

  • 서원명;윤용철
    • Journal of Bio-Environment Control
    • /
    • v.5 no.2
    • /
    • pp.215-235
    • /
    • 1996
  • The thermal analysis by mathematical model simulation makes it possible to reasonably predict heating and/or cooling requirements of certain greenhouses located under various geographical and climatic environment. It is another advantages of model simulation technique to be able to make it possible to select appropriate heating system, to set up energy utilization strategy, to schedule seasonal crop pattern, as well as to determine new greenhouse ranges. In this study, the control pattern for greenhouse microclimate is categorized as cooling and heating. Dynamic model was adopted to simulate heating requirements and/or energy conservation effectiveness such as energy saving by night-time thermal curtain, estimation of Heating Degree-Hours(HDH), long time prediction of greenhouse thermal behavior, etc. On the other hand, the cooling effects of ventilation, shading, and pad ||||&|||| fan system were partly analyzed by static model. By the experimental work with small size model greenhouse of 1.2m$\times$2.4m, it was found that cooling the greenhouse by spraying cold water directly on greenhouse cover surface or by recirculating cold water through heat exchangers would be effective in greenhouse summer cooling. The mathematical model developed for greenhouse model simulation is highly applicable because it can reflects various climatic factors like temperature, humidity, beam and diffuse solar radiation, wind velocity, etc. This model was closely verified by various weather data obtained through long period greenhouse experiment. Most of the materials relating with greenhouse heating or cooling components were obtained from model greenhouse simulated mathematically by using typical year(1987) data of Jinju Gyeongnam. But some of the materials relating with greenhouse cooling was obtained by performing model experiments which include analyzing cooling effect of water sprayed directly on greenhouse roof surface. The results are summarized as follows : 1. The heating requirements of model greenhouse were highly related with the minimum temperature set for given greenhouse. The setting temperature at night-time is much more influential on heating energy requirement than that at day-time. Therefore It is highly recommended that night- time setting temperature should be carefully determined and controlled. 2. The HDH data obtained by conventional method were estimated on the basis of considerably long term average weather temperature together with the standard base temperature(usually 18.3$^{\circ}C$). This kind of data can merely be used as a relative comparison criteria about heating load, but is not applicable in the calculation of greenhouse heating requirements because of the limited consideration of climatic factors and inappropriate base temperature. By comparing the HDM data with the results of simulation, it is found that the heating system design by HDH data will probably overshoot the actual heating requirement. 3. The energy saving effect of night-time thermal curtain as well as estimated heating requirement is found to be sensitively related with weather condition: Thermal curtain adopted for simulation showed high effectiveness in energy saving which amounts to more than 50% of annual heating requirement. 4. The ventilation performances doting warm seasons are mainly influenced by air exchange rate even though there are some variations depending on greenhouse structural difference, weather and cropping conditions. For air exchanges above 1 volume per minute, the reduction rate of temperature rise on both types of considered greenhouse becomes modest with the additional increase of ventilation capacity. Therefore the desirable ventilation capacity is assumed to be 1 air change per minute, which is the recommended ventilation rate in common greenhouse. 5. In glass covered greenhouse with full production, under clear weather of 50% RH, and continuous 1 air change per minute, the temperature drop in 50% shaded greenhouse and pad & fan systemed greenhouse is 2.6$^{\circ}C$ and.6.1$^{\circ}C$ respectively. The temperature in control greenhouse under continuous air change at this time was 36.6$^{\circ}C$ which was 5.3$^{\circ}C$ above ambient temperature. As a result the greenhouse temperature can be maintained 3$^{\circ}C$ below ambient temperature. But when RH is 80%, it was impossible to drop greenhouse temperature below ambient temperature because possible temperature reduction by pad ||||&|||| fan system at this time is not more than 2.4$^{\circ}C$. 6. During 3 months of hot summer season if the greenhouse is assumed to be cooled only when greenhouse temperature rise above 27$^{\circ}C$, the relationship between RH of ambient air and greenhouse temperature drop($\Delta$T) was formulated as follows : $\Delta$T= -0.077RH+7.7 7. Time dependent cooling effects performed by operation of each or combination of ventilation, 50% shading, pad & fan of 80% efficiency, were continuously predicted for one typical summer day long. When the greenhouse was cooled only by 1 air change per minute, greenhouse air temperature was 5$^{\circ}C$ above outdoor temperature. Either method alone can not drop greenhouse air temperature below outdoor temperature even under the fully cropped situations. But when both systems were operated together, greenhouse air temperature can be controlled to about 2.0-2.3$^{\circ}C$ below ambient temperature. 8. When the cool water of 6.5-8.5$^{\circ}C$ was sprayed on greenhouse roof surface with the water flow rate of 1.3 liter/min per unit greenhouse floor area, greenhouse air temperature could be dropped down to 16.5-18.$0^{\circ}C$, whlch is about 1$0^{\circ}C$ below the ambient temperature of 26.5-28.$0^{\circ}C$ at that time. The most important thing in cooling greenhouse air effectively with water spray may be obtaining plenty of cool water source like ground water itself or cold water produced by heat-pump. Future work is focused on not only analyzing the feasibility of heat pump operation but also finding the relationships between greenhouse air temperature(T$_{g}$ ), spraying water temperature(T$_{w}$ ), water flow rate(Q), and ambient temperature(T$_{o}$).

  • PDF