• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.723-727
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• 2015

Building energy use estimation relies on building characteristics, its energy systems, occupants, and weather. Energy estimation of new buildings is considerably an easy task when compared to modeling existing buildings as they require calibration with actual data. Particularly, when energy estimation of existing building stock is warranted at a city-scale, the problem is exacerbated owing to lack of construction drawings and other engineering specifications. However, as collection of buildings and other infrastructure constitute cities, such predictions are a necessary component of developing and maintaining sustainable cities. This paper uses Artificial Neural Network techniques to predict electricity consumption for residential buildings situated in the City of Gainesville, Florida. With the use of 32,813 samples of data vectors that comprise of building floor area, built year, number of stories, and range of monthly energy consumption, this paper extends the prediction to environmental impact assessment of electricity usage at the urban-scale. Among others, one of the applications of the proposed model discussed in this paper is the study of urban scale Life Cycle Assessment, and other decisions related to creating sustainable cities.

• 한국기계가공학회지
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• 제15권3호
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• pp.44-51
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• 2016

This paper presents the characteristics of the energy absorption in an expansion tube type impact absorber that is applied to a high weight drop tester and the use of a response surface methodology to predict the impact energy absorption. In order to identify the characteristics of the energy absorption, a set of finite element analysis was conducted with Abaqus Explicit. Moreover, the ISCD-II sampling method and a first order polynomial were used to build a response surface. As a result, we demonstrated that the impact energy could be controlled by four main design variables, namely an expansion pipe's thickness, inner radius, pressing die's expansion angle and expansion ratio. Additionally, we observed the relationship between the four main design variables and the impact energy absorbing time, displacement, and maximum impact force.

• Composites Research
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• 제26권3호
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• pp.175-181
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• 2013

고속충격을 받는 복합재 적층판의 충격거동 특성 중에서 관통 후 잔류속도, 시편에 의한 충격흡수에너지 그리고 충격손상영역에 대한 평가와 예측이 중요하다. 본 논문에서는 고속충격을 받는 Carbon/Epoxy 복합재 적층판의 잔류속도와 흡수에너지를 준실험적 방법을 통하여 예측할 수 있는 방법을 제안하였다. 고속충격에 의한 시편의 흡수에너지를 정적에너지와 동적에너지로 구분하였으며 정적에너지는 보강섬유의 파손과 정적 탄성에너지와 관련 있는 준정적압입실험을 통한 관통에너지를 사용하였다. 동적에너지는 고속충격 시 시편 일부의 움직임과 관련한 운동에너지에 대하여 몇 가지 모델을 제안하여 비교하였다. 공압을 이용한 고속충격실험을 수행하고 예측 값과 비교 분석하였다. 시편의 손상영역은 C-scan을 통하여 측정하였다. 관통한계속도보다 큰 초기 속도인 경우, 정적에너지인 관통에너지 뿐 만 아니라 시편의 동적 에너지가 시편 전체 흡수에너지에 크게 기여함을 알 수 있었다.

• Geomechanics and Engineering
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• 제22권6호
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• pp.489-496
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• 2020

This study proposes a new empirical model to effectively predict the excavation performance of a shield tunnel boring machine (TBM). The TBM performance is affected by the geological and geotechnical characteristics as well as the machine parameters of TBM. Field penetration index (FPI) is correlated with rock mass parameters to analyze the effective geotechnical parameters influencing the TBM performance. The result shows that RMR has a more dominant impact on the TBM performance than UCS and RQD. RMR also shows a significant relationship with the specific energy, which is defined as the energy required for excavating the unit volume of rock. Therefore, the specific energy can be used as an indicator of the mechanical efficiency of TBM. Based on these relationships with RMR, this study suggests an empirical performance prediction model to predict FPI, which can be derived from the correlation between the specific energy and RMR.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2003년도 춘계학술발표대회 논문집
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• pp.129-134
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• 2003

The importance of understanding the response of structural composites to impact and CAI cannot be overstated to develop analytical models for impact damage and CAI strength predictions. This paper presents experimental findings observed from quasi-static lateral load tests, low velocity impact tests, CAI strength and open hole compressive strength tests using 3mm thick composite plates ($[45/-45/0/90]_{3s}$ - IM7/8552). The conclusion is drawn that damage areas for both quasi-static lateral load and impact tests are similar and the curves of several drop weight impacts with varying energy levels (between 5.4 J and 18.7 J) fallow the static curve well. In addition, at a given energy the peak force is in good agreement between the static and impact cases. From the CAI strength and open hole compressive strength tests, it is identified that the failure behaviour of the specimens was very similar to that observed in laminated plates with open holes under compression loading. The residual strengths are in good agreement with the measured open hole compressive strengths, considering the impact damage site as an equivalent hole. The experimental findings suggest that simple analytical models for the prediction of impact damage area and CAI strength can be developed on the basis of the failure mechanism observed from the experimental tests.

• Composites Research
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• 제25권5호
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• pp.147-153
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• 2012

본 논문에서는 복합재 적층판의 고속충격 관통에너지를 예측하는 방법 중 하나인 정적압입 관통 실험을 수행하였다. 정적압입 관통 에너지를 정확히 분석하기 위해서 세 가지 방법을 이용하였다. 첫 번째로 AE 센서 신호 변화를 이용해서 압입 관통 지점을 판단하고 관통 에너지를 구하는 방법, 두 번째는 관통된 시편에 다시 관통실험을 수행하여 두 에너지 차를 이용해서 구하는 방법, 세 번째는 재수행한 관통실험의 하중-변위 그래프에서 최대하중지점을 압입 관통 지점으로 판단하고 에너지를 구하는 방법이다. 위 방법들에 의한 관통에너지 예측 결과를 제시하였고 고속충격 실험 결과와 비교하여 타당성을 검증하였다.

• 한국압력기기공학회 논문집
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• 제20권1호
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• pp.56-65
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• 2024

Cast austenitic stainless steels (CASS) and austenitic stainless steel weldments with a ferrite-austenite duplex structure are widely used in nuclear power plants, incorporating ferrite phase to enhance strength, stress relief, and corrosion resistance. Thermal aging at 290-325℃ can induce embrittlement, primarily due to spinodal decomposition and G-phase precipitation in the ferrite phase. This study evaluates the effects of thermal aging by collecting and analyzing various mechanical properties, such as Charpy impact energy, ferrite microhardness, and tensile strength, from various literature sources. Different model expressions, including hyperbolic tangent and phase transformation equations, are applied to calculate activation energy (Q) of room-temperature impact energies, and the results are compared. Additionally, predictive models for Q based on material composition are evaluated, and the potential of machine learning techniques for improving prediction accuracy is explored. The study also examines the use of ferrite microhardness and tensile strength in calculating Q and assessing thermal embrittlement. The findings provide insights for developing advanced prediction models for the thermal embrittlement behavior of CASS and the weldments of austenitic steels, contributing to the safety and reliability of nuclear power plant components.

• Environmental Engineering Research
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• 제21권4호
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• pp.355-364
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• 2016

Beijing, as a cradle of modern industry and the third largest metropolitan area in China, faces more responsibilities to adjust industrial structure and mitigate carbon emissions. The purpose of this study is aimed at predicting and comparing industrial carbon emissions of Beijing in ten scenarios under different policy focus, and then providing emission-cutting recommendations. In views of various scenarios issues, system dynamics has been applied to predict and simulate. To begin with, the model has been established following the step of causal loop diagram and stock flow diagram. This paper decomposes scenarios factors into energy structure, high energy consumption enterprises and growth rate of industrial output. The prediction and scenario simulation results shows that energy structure, carbon intensity and heavy energy consumption enterprises are key factors, and multiple factors has more significant impact on industrial carbon emissions. Hence, some recommendations about low-carbon mode of Beijing industrial carbon emission have been proposed according to simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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• pp.2488-2511
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• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.

• 환경영향평가
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• 제24권5호
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• pp.487-498
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• 2015

기온상승과 인구 및 GDP 증가의 영향으로 인해 에너지 문제가 발생하고 있다. 이러한 문제에 대응하기 위해 에너지 수요에 대한 예측이 필요하다. 따라서 본 연구는 에너지 수요관리, 특히 전력부하를 유발하는 냉난방 에너지 수요 관리에 도움이 되고자 가정 부문 냉난방 에너지의 미래 사용량을 예측하고자 한다. 에너지 사용량을 산정하는데 있어 서비스 수요의 산출이 필요하다. 따라서 서비스 수요 산정식을 이용하여 이를 먼저 도출하고, AIM/end-use 모델을 이용하여 에너지 사용량을 산정하였다. 산정 결과 냉난방 서비스 수요는 2010년에 비해 2050년에 모두 증가하는 추세를 보였다. 하지만 에너지 사용량에서 난방은 감소하고, 냉방은 증가하는 것으로 예측되었다.