• Structural Engineering and Mechanics
• /
• v.35 no.5
• /
• pp.571-592
• /
• 2010

An energy-based fatigue life prediction framework was previously developed by the authors for prediction of axial, bending and shear fatigue life at various stress ratios. The framework for the prediction of fatigue life via energy analysis was based on a new constitutive law, which states the following: the amount of energy required to fracture a material is constant. In the first part of this study, energy expressions that construct the constitutive law are equated in the form of total strain energy and the distortion energy dissipated in a fatigue cycle. The resulting equation is further evaluated to acquire the equivalent stress per cycle using energy based methodologies. The equivalent stress expressions are developed both for biaxial and multiaxial fatigue loads and are used to predict the number of cycles to failure based on previously developed prediction criterion. The equivalent stress expressions developed in this study are further used in a new finite element procedure to predict the fatigue life for two and three dimensional structures. In the second part of this study, a new Quadrilateral fatigue finite element is developed through integration of constitutive law into minimum potential energy formulation. This new QUAD-4 element is capable of simulating biaxial fatigue problems. The final output of this finite element analysis both using equivalent stress approach and using the new QUAD-4 fatigue element, is in the form of number of cycles to failure for each element on a scale in ascending or descending order. Therefore, the new finite element framework can provide the number of cycles to failure at each location in gas turbine engine structural components. In order to obtain experimental data for comparison, an Al6061-T6 plate is tested using a previously developed vibration based testing framework. The finite element analysis is performed for Al6061-T6 aluminum and the results are compared with experimental results.

• Nuclear Engineering and Technology
• /
• v.52 no.9
• /
• pp.2100-2106
• /
• 2020

This paper newly proposes an efficient analytic springback prediction method to predict the final dimensions of bent cylindrical tubes for a helical tube steam generator in a small modular reactor. Three-dimensional bending procedure is treated as a two-dimensional in-plane bending procedure by integrating the Euler beam theory. To enhance the accuracy of the springback prediction, mathematical representations of flow stress and elastic modulus for unloading are systematically integrated into the analytic prediction model. This technique not only precisely predicts the final dimensions of the bent helical tube after a springback, but also effectively predicts the various target radii. Numerical validations were performed for five different radii of helical tube bending by comparing the final radius after a springback.

• Geomechanics and Engineering
• /
• v.23 no.1
• /
• pp.51-59
• /
• 2020

In this paper, the WEKA platform was used to mine and analyze measured data of floor failure depth and a prediction system of floor failure depth was developed with Java. Based on the standardization and discretization of 35-set measured data of floor failure depth in China, the grey correlation degree analysis on five factors affecting the floor failure depth was carried out. The correlation order from big to small is: mining depth, working face length, floor failure resistance, mining thickness, dip angle of coal seams. Naive Bayes model, neural network model and decision tree model were used for learning and training, and the accuracy of the confusion matrix, detailed accuracy and node error rate were analyzed. Finally, artificial neural network was concluded to be the optimal model. Based on Java language, a prediction system of floor failure depth was developed. With the easy operation in the system, the prediction from measured data and error analyses were performed for nine sets of data. The results show that the WEKA prediction formula has the smallest relative error and the best prediction effect. Besides, the applicability of WEKA prediction formula was analyzed. The results show that WEKA prediction has a better applicability under the coal seam mining depth of 110 m~550 m, dip angle of coal seams of 0°~15° and working face length of 30 m~135 m.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.4
• /
• pp.693-700
• /
• 2024

As of the end of March 2022, the total area of domestic industrial complexes is 606 km2, which is only about 0.6% of the total land area. However, as of 2018, the annual energy consumption of domestic industrial complexes is 110,866.1 thousand TOE, accounting for 53.5% of the country's total energy consumption and 83.1% of the entire industrial sector energy consumption. In addition, industrial complexes have a significant impact on the environment, accounting for 45.1% of the country's total greenhouse gas emissions and 76.8% of industrial sector greenhouse gas emissions. Under this background, in this study, in order to contribute to the energy efficiency of industrial complexes, a prediction study on energy demand and supply for an industrial complex in Korea using machine learning was conducted. In addition, a simulator UI screen was designed to more efficiently convey information on energy demand/supply prediction results and energy consumption status. Among the machine learning algorithms, Multi-Layer Perceptron (MLP) was used, and Bayesian Optimization was applied as an optimization technique for the prediction model. The energy prediction model for the industrial complex built in this study showed a prediction accuracy of 87.90% for compressed air demand and 99.54% for the flow rate available for the public air compressor.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.27 no.3
• /
• pp.137-145
• /
• 2015

In this study, the actual energy consumption of the secondary side of District Heating System (DHS) with different hot water supply temperature control methods are compared. Three methods are Set-point Control, Outdoor Temperature Reset Control and Outdoor Temperature Prediction Control. While Outdoor Temperature Reset Control has been widely used for energy savings of the secondary side of the system, the results show that Outdoor Temperature Prediction Control method saves more energy. In general, Outdoor Temperature Prediction Control method lowers the supply temperature of hot water, and it reduces standby losses and increases overall heat transfer value of heated spaces due to more flow into the space. During actual energy consumption monitoring, Outdoor Temperature Prediction Control method saves about 7.1% in comparison to Outdoor Temperature Reset Control method and about 15.7% in comparison to Set-point Control method. Also, it is found that at when partial load condition, such as daytime, the fluctuation of hot water supply temperature with Set-point Control is more severe than Outdoor Temperature Prediction Control. Therefore, it proves that Outdoor Temperature Prediction Control is more stable even at the partial load conditions.

• Nuclear Engineering and Technology
• /
• v.52 no.1
• /
• pp.14-18
• /
• 2020

Korea Atomic Energy Research Institute (KAERI) has carried out various neutronics experiments in the commissioning stage of the Jordan Research and Training Reactor (JRTR), and this paper introduces the results of first criticality prediction and experiment for the JRTR. The Monte Carlo Code for Advanced Reactor Design and analysis (McCARD) with the ENDF/B-VII.0 nuclear library was used for prediction calculations in the process of the first criticality approach, which was performed to provide reference for the first criticality experiment. In the experiment, fuel loading was carried out by measuring the inverse multiplication factor (1/M) to predict the number of fuel assemblies at the first criticality, and the first critical was reached on April 25, 2016. Comparing the first criticality prediction and experiment, the calculated and measured CAR (Control Absorber Rod) heights for the first criticality were 575 mm and 570.5 mm, respectively, that is, the difference between the two results was approximately 5 mm. From this result, it was confirmed that JRTR manufacturing and various experiments had successfully progressed as designed.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.11 no.2
• /
• pp.75-80
• /
• 2011

The main objective of this research is to develop fatigue prediction model for flexible pavements using energy ratio factor and phase angle. The two parameters are considered as fundamental properties of time and temperature dependent viscoelastic asphalt concrete materials. The energy ratio factor is defined as the ratio of the pseudo-total cumulative dissipated energy to the cumulative dissipated energy to failure during the test. The phase angle between the stress and strain ware signals stems from the intrinsic the dependent asphalt mixture behavior. The phase angle was computed and the relationship between the initial mixture stiffness and the initial phase angle is presented. As a result, fatigue prediction model for flexible pavements was proposed using intrinsic properties of viscoelastic asphalt concrete materials.

• Journal of Korea Multimedia Society
• /
• v.10 no.1
• /
• pp.139-146
• /
• 2007

Many researches had been gone about method to track moving object using wireless sensor network. We examined tradeoffs that exist between quantity of energy and correctness of tracking, and we confirmed that can get more energy sayings through improved motion prediction method. The consumed energy in the tracking is used by sensor node for sensing the object, and tracking correctness is a differ once of actual object position from calculated value by sensing. Some tracking methods and controlling parameters causes a variation of tracking correctness and energy consuming, we can get best energy effectiveness by motion prediction algorithm. Furthermore, we get better tracking quality and energy effectiveness through using a motion prediction algorithm that consider acceleration. By the simulation, we know that if we use an accurate motion prediction algorithm, node activation range that is used for target's predicted position should be restricted to sensing range of sensor is better.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.3
• /
• pp.175-181
• /
• 2021

This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.

• Proceedings of the SAREK Conference
• /
• 2009.06a
• /
• pp.940-945
• /
• 2009

Energy planning for hybrid energy system is important to increase the flexibility in the urban community and national energy systems. Expected maximum loads, load profiles and yearly energy demands are important input parameters to plan for the technical and environmental optimal energy system for a planning area. The method for energy demand prediction has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. This method can produce 10% of errors hourly load profile from individual building to urban community. As the results of this paper, energy demand prediction system has been developed based on simulink.