• 한국생산제조학회지
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• 제8권5호
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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.

• 설비공학논문집
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• 제3권4호
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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.

• 항공우주시스템공학회지
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• 제10권4호
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• pp.26-34
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• 2016

본 논문에서는 다공성 단열재의 정확도가 높은 유효 열전도율 예측 모델을 새롭게 제안하고, 기존 예측 모델 및 시험 결과와 비교 검증하였다. 이를 위해 기존 유효 예측 모델들을 다공성 단열재의 고체 부피율에 따른 열전도율 시험 결과 값과 비교하였다. 그리고 고체의 부피율에 따른 유효 열전도율 시험결과와 비교하여 가장 높은 정확도를 가진 Zehner-Schlunder 모델 및 시험 결과 데이터를 기반으로 고체-유체의 부피율과 열전도율 비로 구성된 다항식을 추가하여, 새로운 유효 열전도율 예측 모델을 정의하였다. 예측 모델을 시험 결과와 비교하여 검증하였다. 또한 예측 모델을 적용하여 열방어구조의 과도 열전달 해석을 수행하였으며, 열전달 시험 결과와의 비교를 통해 유효 열전도율 예측 모델의 유효성을 확인하였다.

• 한국연소학회:학술대회논문집
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• 한국연소학회 2014년도 제49회 KOSCO SYMPOSIUM 초록집
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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.

• 한국항공우주학회지
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• 제45권3호
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• pp.163-172
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• 2017

다공성 단열재는 탁월한 단열 효과로 단열공간을 최소화하여, 기존 단열재 대비 내부 공간을 활용할 수 있어 여러 산업 분야에서 사용되고 있다. 특히 높은 단열 효과뿐만 아니라 경량화가 요구되는 항공우주 산업분야에서는 이와 같은 다공성 단열재의 수요가 증가하고 있다. 본 논문에서는 다공성 단열재의 정확도가 높은 유효 열전도율 예측 모델을 새롭게 제안하고, 기존 예측 모델 및 시험 결과와 비교 검증하였다. 이를 위해, 기존 유효 열전도율 예측 모델에 대하여 문헌조사를 수행하였고, 다공성 단열재의 고체 부피율에 따른 열전도율 시험결과 값과 비교하였다. 또한 유효 열전도율 시험 결과와 비교하여 가장 높은 정확도를 가진 Zehner-Schlunder 모델 및 시험 결과 데이터를 기반으로 새로운 유효 열전도율 예측 모델을 정의하였으며, 시험 결과 데이터와 비교하여 기존 유효 열전도율 예측 모델보다 유사한 정확도를 나타내는 것을 확인하였다. 또한, 개선된 유효 열전도율 예측 모델을 적용하여 초고속 비행체 열방어구조의 과도 열전달 해석을 수행하였으며, 열전달 시험 결과와의 비교를 통해 예측 모델의 유효성을 확인하였다.

• 문화기술의 융합
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• 제9권6호
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• pp.883-888
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• 2023

데이터센터는 24시간 365일 IT 서비스를 제공하는 곳이기 때문에, 2030년에는 데이터센터의 전력 소비량은 약 10%로 증가될 것으로 예측되고, 고밀도 IT장비들의 도입이 점차 증가하면서, IT장비가 안정적으로 운영될 수 있도록 냉방 에너지 절감 및 이를 위한 에너지 관리가 갖춰져야 하기에 다양한 연구가 요구되고 있는 상황이다. 본 연구는 데이터센터의 에너지 절약을 위해 다음과 같은 과정을 제안한다. 데이터센터를 CFD 모델링하고, 인공지능기반 열환경 예측 모델을 제안하였으며, 실측 데이터와 예측 모델 그리고 CFD 결과를 비교하여 최종적으로 데이터 센터의 열관리 성능을 평한 결과 전처리 방식은 정규화 방식으로 사용되었고, 정규화에 따른 RCI, RTI 및 PUE의 예측값 또한 유사한 것을 확인할 수 있다. 따라서 본 연구에서 제안하는 알고리즘으로 데이터센터에 적용될 열환경 예측 모델로 적용 및 제공할 수 있을 것으로 판단된다.

• 대한기계학회논문집
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• 제17권7호
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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.

• 한국신뢰성학회지:신뢰성응용연구
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• 제7권1호
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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.

• 환경영향평가
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• 제20권4호
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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.

• 한국공간구조학회논문집
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• 제23권4호
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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.