• Journal of computational fluids engineering
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• v.11 no.1 s.32
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• pp.57-66
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• 2006

An optimal shape design approach is presented for a subsonic S-shaped intake using aerodynamic sensitivity analysis. Two-equation turbulence model is employed to capture strong counter vortices in the S-shaped duct more precisely. Sensitivity analysis is performed for the three-dimensional Navier-Stokes equations coupled with two-equation turbulence models using a discrete adjoint method For code validation, the result of the flow solver is compared with experiment data and other computational results of bench marking test. To study the influence oj turbulence models and grid refinement on the duct flow analysis, the results from several turbulence models are compared with one another and the minimum number of grid points, which can yield an accurate solution is investigated The adjoint variable code is validated by comparing the complex step derivative results. To realize a sufficient and flexible design space, NURBS equations are introduced as a geometric representation and a new grid modification technique, Least Square NURBS Grid Approximation is applied With the verified flow solver, the sensitivity analysis code and the geometric modification technique, the optimization of S-shaped intake is carried out and the enhancement of overall intake performance is achieved The designed S-shaped duct is tested in several off-design conditions to confirm the robustness of the current design approach. As a result, the capability and the efficiency of the present design tools are successfully demonstrated in three-dimensional highly turbulent internal flow design and off-design conditions.

• Proceedings of the KOR-KST Conference
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• 1998.09a
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• pp.160-167
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• 1998

Recent world-wide interest in activity-based travel behavior modeling has generated an entirely new perspective on how the profession views the travel demand process. This paper seeks to further promote the case of activity-based travel behavior models by providing some empirical evidence of relationship between travel time and activity duration decision for worker with transportation panel data. The travel time from home to work and from work to home, without activity involvement, is estimated by the Ordinary Least Squares (OLS) method. And, the travel time to and from the selected activity and the activity duration are modeled simultaneously by the Three Stage Least Squares (3SLS) method due to the endogenous relationship between travel time and activity duration. Two kinds of models, OLS and 3SLS, include selectivity bias corrections in a discrete/continuous framework, because of the inter-relationship between the choice of activity type/travel mode (discrete) and the travel time/activity duration (continuous). Estimation is undertaken using a sample of over 1300 household two-day trip diaries collected from the same travelers in the Seattle area in 1989. The behavioral consequences of these models provide interesting and provocative findings that should be of value to transportation policy formulation and analysis.

• Geomechanics and Engineering
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• v.16 no.5
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• pp.495-501
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• 2018

Geogrid application that has proved to be an effective and economic method of reinforcing particles, is widely used in geotechnical engineering. The discrete element method (DEM) has been used to investigate the micro mechanics of the geogrid deformation and also the interlocking mechanism that cannot be easily studies in laboratory tests. Two types of realistically shaped geogrid models with square and triangle apertures were developed using parallel bonds in PFC3D. The calibration test simulations have demonstrated that the precisely shaped triangular geogrid model is also able to reproduce the deformation and strength characteristics of geogrids. Moreover, the square and triangular geogrid models were also used in DEM pull-out test simulations with idealized shape particle models for validation. The simulation results have been shown to provide good predictions of pullout force as a function of displacement especially for the initial 30 mm displacement. For the granular material of size 40 mm, both the experimental and DEM results demonstrate that the triangular geogrid of size 75 mm outperforms the square geogrid of size 65 mm. Besides, the simulations have given valuable insight into the interaction between particle and geogrid and also revealed similar deformation behavior of geogrids during pullout. Therefore, the DEM provides a tool which enable to model other possible prototype geogrid and investigate their performance before manufacture.

• Smart Structures and Systems
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• v.25 no.5
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• pp.569-580
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• 2020

The identification of delays and delay compensation are critical problems in real-time hybrid simulations (RTHS). Conventional delay compensation methods are mostly based on the assumption of a constant delay. However, the system delay may vary during tests owing to the nonlinearity of the loading system and/or the behavioral variations of the specimen. To address this issue, this study presents an adaptive delay compensation method based on a discrete model of the loading system. In particular, the parameters of this discrete model are identified and updated online with the least-squares method to represent a servo hydraulic loading system. Furthermore, based on this model, the system delays are compensated for by generating system commands using the desired displacements, achieved displacements, and previous displacement commands. This method is more general than the existing compensation methods because it can predict commands based on multiple displacement categories. Moreover, this method is straightforward and suitable for implementation on digital signal processing boards because it relies solely on the displacements rather than on velocity and/or acceleration data. The virtual and real RTHS results show that the studied method exhibits satisfactory estimation smoothness and compensation accuracy. Furthermore, considering the measurement noise, the low-order parameter models of this method are more favorable than that the high-order parameter models.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.865-871
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• 2009

In this paper, a novel feedback linearization is proposed for discrete-time nonlinear systems described by discrete-time T-S fuzzy models. The local linear models of a T-S fuzzy model are transformed to a controllable canonical form respectively, and their T-S fuzzy combination results in a feedback linearizable Tagaki-Sugeno fuzzy model. Based on this model, a nonlinear state feedback linearizing input is determined. Nonlinear state transformation is inferred from the linear state transformations for the controllable canonical forms. The proposed method of this paper is more intuitive and easier to understand mathematically compared to the well-known feedback linearization technique which requires a profound mathematical background. The feedback linearizable condition of this paper is also weakened compared to the conventional feedback linearization. This means that larger class of nonlinear systems is linearizable compared to the case of classical linearization.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.6
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• pp.33-43
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• 2021

A60 class bulkhead penetration piece is a fire resistance system installed on a bulkhead compartment to protect lives and to prevent flame diffusion in a fire accident on a ship and offshore plant. This study focuses on the approximate optimization of the fire resistance design of the A60 class bulkhead penetration piece using a multi-island genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class bulkhead penetration piece. For approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were considered discrete design variables; moreover, temperature, cost, and productivity were considered constraint functions. The approximate optimum design problem based on the meta-model was formulated by determining the discrete design variables by minimizing the weight of the A60 class bulkhead penetration piece subject to the constraint functions. The meta-models used for the approximate optimization were the Kriging model, response surface method, and radial basis function-based neural network. The results from the approximate optimization were compared to the actual results of the analysis to determine approximate accuracy. We conclude that the radial basis function-based neural network among the meta-models used in the approximate optimization generates the most accurate optimum design results for the fire resistance design of the A60 class bulkhead penetration piece.

• Korean Journal of Ecology and Environment
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• v.46 no.1
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• pp.1-9
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• 2013

Algal blooms in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. In addition to precipitating taste and odor problems, blooms damage the environment, and some classes like cyanobacteria (blue-green algae) release toxins that can threaten human health, even causing death. There is a recognized need in the water industry for models that can accurately forecast in real-time algal bloom events for planning and mitigation purposes. In this study, using data for an interconnected system of rivers and reservoirs operated by a New Jersey water utility, various ANN models, including both discrete prediction and classification models, were developed and tested for forecasting counts of three different algal classes for one-week and two-weeks ahead periods. Predictor model inputs included physical, meteorological, chemical, and biological variables, and two different temporal schemes for processing inputs relative to the prediction event were used. Despite relatively limited historical data, the discrete prediction ANN models generally performed well during validation, achieving relatively high correlation coefficients, and often predicting the formation and dissipation of high algae count periods. The ANN classification models also performed well, with average classification percentages averaging 94 percent accuracy. Despite relatively limited data events, this study demonstrates that with adequate data collection, both in terms of the number of historical events and availability of important predictor variables, ANNs can provide accurate real-time forecasts of algal population counts, as well as foster increased understanding of important cause and effect relationships, which can be used to both improve monitoring programs and forecasting efforts.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.127-136
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• 2009

Modeling and Simulation, especially in mobile ad hoc network(MANET), are the most effective way to analyze performance or optimize system parameters without establishing real network environment. Focusing mainly on overall network behaviors in MANET concerns dynamics of network transport operations, which can efficiently be characterized with event based system states rather than execution details of protocols. We thus consider the network as a discrete event system to analyze dynamics of network transport performance. Zeigler's set-theoretic DEVS(Discrete Event Systems Specification) formalism can support specification of a discrete event system in hierarchical, modular manner. The DEVSim++ simulation environment can not only provide a rigorous modeling methodology based on the DEVS formalism but also support modelers to develop discrete event models using the hierarchical composition methodology in object-orientation. This environment however hardly supports to specify connection paths of network nodes, which are continuously altered due to mobility of nodes. This paper proposes a DEVS-based modeling and simulation methodology of enabling node mobility, and develops DEVS models for the mobile ad hoc network. We also simulate developed models with the DEVSim++ engine to verify the proposal.

• International Journal of Highway Engineering
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• v.15 no.5
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• pp.31-45
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• 2013

PURPOSES : Determination of particle packing model variables that can be used for formulation of new DEM based particle packing model by examining existing particle packing models METHODS : Existing particle packing models are thoroughly examined by analytical reformulation and sensitivity analysis in order to set up DEM based new particle packing model and to determine its variables. All model equations considered in this examination are represented with consistent expressions and are compared to each others to find mathematical and conceptual similarity in expressions. RESULTS : From the examination of existing models, it is observed that the models are very similar in their shapes although the derivation of the models may be different. As well, it is observed that variables used in some existing models are comprehensive enough to estimate particle packing but not applicable to DEM simulation. CONCLUSIONS : A set of variables that can be used in DEM based particle packing model is determined.

• Structural Engineering and Mechanics
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• v.52 no.2
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• pp.369-389
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• 2014

Two-layer coupled or composite beams with discrete shear connectors of finite dimensions are commonly encountered in pre-fabricated construction. This paper presents the development of simplified closed-form solutions for such type of coupled beams for practical applications. A new coupled beam element is proposed to represent the unconnected segments in the beam. General solutions are then developed by an inductive method based on the results from the finite element analysis. A modification is subsequently considered to account for the effect of local deformations. For typical cases where the local deformation is primarily concerned about its distribution over the depth of the coupled beam, empirical modification factors are developed based on parametric calculations using finite element models. The developed analytical method for the coupled beams in question is simple, sufficiently accurate, and suitable for quick calculation in engineering practice.