• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.5
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• pp.108-114
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• 1999

The thermal environmental chamber has been using in maintaining weather condition keeping thermal capacity under heating and cooling load fluctuation and for the performance testing of cooling system or air-conditioner on artificial envi-ronment. In ordder to make the various environmental conditions in the thermal environmental chamber the proper cooling system is necessary to eliminate the heating load produced inside the chamber and to maintain the designed environmental condition. For this reason the optimal design of cooling system and the prediction of performance is also required. This paper describes the prediction of performance of cooling system in the thermal environmental chamber with the capacity of 37,000kcal/hr which is developed for the test of performance in heating mode of heat pump system, In the results this paper is trying to develop simulation program on the base of mathematical models and which can be applied effectively to the optimal design of cooling system and prediction of performance to the inside and outside change of envi-ronmetal load.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.3 no.4
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• pp.297-305
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• 1991

Recently, automotive air conditioning system manufacturers have been made a great efforts on the system compactness and high efficiency. This growing interest comes improvements in evaporator thermal performance, one of the most important factors affecting the performance of air conditioning system. In order to improve design of compact type evaporator, this study executes performs to develop a computer program for evaporator thermal performance prediction of automotive air conditioning system. The brief summaries of this study are as follows: 1) To predict the overall thermal performance of serpentine type evaporator, the new simulating method is developed. 2) The calculations are performed as functions of oil mass concentration and refrigerant two-phase distribution at inlet manifold of evaporator. 3) The validity of this simulating program is confirmed by comparing the predicted thermal performance results to experimental results of practical available evaporator. 4) Based on these results, suggestions are made to improve the thermal performance of evaporator.

• Journal of Aerospace System Engineering
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• v.10 no.4
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• pp.26-34
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• 2016

In a number of industries, porous insulations have been frequently used, reducing thermal insulation space through excellent performance of the thermal insulation's characteristics. This paper suggests an effective thermal conductivity prediction model. Firstly, we perform a literature review of traditional effective thermal conductivity prediction models and compare each model with experimental heat transfer results. Furthermore, this research defines the effectiveness of thermal conductivity prediction models using experimental heat transfer results and the Zehner-Schlunder model. The newly defined effective thermal conductivity prediction model has been verified to better predict performance than other models. Finally, this research performs a transient heat transfer analysis of a thermal protection system with a porous insulation in a high speed vehicle using the finite element method and confirms the validity of the effective thermal conductivity prediction model.

• 한국연소학회:학술대회논문집
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• 2014.11a
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• pp.19-20
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• 2014

Boiler design should be desinged to maximize thermal efficiency of the system under imposed load requirement and a boiler should be validated for transient operation. If a proper prediction is possible on the transient behavior and transient characteristics of a boiler, one may asses the performance of boiler component, control logics and operation procedures. In this work, dynamic modeling method of boiler is presented and dynamic simulation of load runback scenario was carried out on suprecritical oil-firing boiler.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.3
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• pp.163-172
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• 2017

Porous insulation have been frequently used in a number of industries by minimizing thermal insulation space because of excellent performance of their thermal insulation. This paper devices an effective thermal conductivity prediction model. First of all, we perform literature survey on traditional effective thermal conductivity prediction models and compare each other model with heat transfer experimental results. Furthermore this research defines advanced effective thermal conductivity prediction models model based on heat transfer experimental results, the Zehner-Schlunder model. Finally we verify that the newly defined effective thermal conductivity prediction model has better performance prediction than other models. Finally, this research performs a transient heat transfer analysis of thermal protection system with a porous insulation using the finite element method and confirms validity of the effective thermal conductivity prediction model.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.883-888
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• 2023

Since data centers are places that provide IT services 24 hours a day, 365 days a year, data center power consumption is expected to increase to approximately 10% by 2030, and the introduction of high-density IT equipment will gradually increase. In order to ensure the stable operation of IT equipment, various types of research are required to conserve energy in cooling and improve energy management. This study proposes the following process for energy saving in data centers. We conducted CFD modeling of the data center, proposed an artificial intelligence-based thermal environment prediction model, compared actual measured data, the predicted model, and the CFD results, and finally evaluated the data center's thermal management performance. It can be seen that the predicted values of RCI, RTI, and PUE are also similar according to the normalization used in the normalization method. Therefore, it is judged that the algorithm proposed in this study can be applied and provided as a thermal environment prediction model applied to data centers.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.7 s.94
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• pp.1851-1863
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• 1993

A procedure for the prediction of the off-design performance of a gas turbine engine is proposed. The system performance at off-design speed is predicted by coupling the thermodynamic models of a compressor and a turbine. The off-design performance of a compressor is obtained using the stage-stackimg method, while the Ainlay-Mathieson method is used for a turbine. The procedure is applied to a single-shaft gas turbine and its predictability is found satisfactory. The results also show that the net work output increases with the increase of the turbine inlet temperature, while the thermal efficiency is marginal. The maximum thermal efficiency at design point is obtained between the highest pressure ratio and design pressure ratio.

• Journal of Applied Reliability
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• v.7 no.1
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• pp.31-43
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• 2007

The function of O-ring seals is to prevent leakage during the service life of the components in which they are installed. The life prediction of O-ring is very important at various industry fields. Generally, to evaluated the long-term performance of O-ring in severe environments has applied a life prediction technique based on accelerated life test (ALT). In this work, Accelerated thermal aging test(l20, 130, 140, $150^{\circ}C$) of O-ring was applied for life prediction of O-ring. The property changes after thermal aging test was measured using TGA, DSC, FT - IR, Video Microscope and SEM. Shape parameter and life prediction were obtained using MINITAB program.

• Journal of Environmental Impact Assessment
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• v.20 no.4
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• pp.435-442
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• 2011

In this study, a regression model was developed for prediction of inflow temperature to support an effective thermal stratification simulation of Yongdam Reservoir, using the relationship between gaged inflow temperature and air temperature. The effect of reproductability for thermal stratification was evaluated using EFDC model by gaged vertical profile data of water temperature(from June to December in 2005) and ex-developed regression models. Therefore, in the development process, the coefficient of correlation and determination are 0.96 and 0.922, respectively. Moreover, the developed model showed good performance in reproducing the reservoir thermal stratification. Results of this research can be a role to provide a base for building of prediction model for water quality management in near future.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.