• Computational Structural Engineering
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• v.18 no.3 s.69
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• pp.15-23
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• 2005

• Journal of computational fluids engineering
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• v.10 no.4 s.31
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• pp.12-17
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• 2005

A computational study of evaluation of current turbulence models is performed for a better prediction of thermal stratification in an upper plenum of a liquid metal reactor. The turbulence models tested in the present study are the two-layer model, the shear stress transport (SST) model, the v2-f model and the elliptic blending mode(EBM). The performances of the turbulence models are evaluated by applying them to the thermal stratification experiment conducted at JNC (Japan Nuclear Corporation). The algebraic flux model is used for treating the turbulent heat flux for the two-layer model and the SST model, and there exist little differences between the two turbulence models in predicting the temporal variation of temperature. The v2-f model and the elliptic blending model better predict the steep gradient of temperature at the interface of thermal stratification, and the v2-f model and elliptic blending model predict properly the oscillation of the ensemble-averaged temperature. In general the overall performance of the elliptic blending model is better than the v2-f model in the prediction of the amplitude and frequency of the temperature oscillation.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.168-173
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• 2000

Design of experiments is utilized for exploring the design space and for building response surface models in order to facilitate the effective solution of multi-objective optimization problems. Response surface models provide an efficient means to rapidly model the trade-off among many conflicting goals. In robust design, it is important not only to achieve robust design objectives but also to maintain the robustness of design feasibility under the effects of variations, called uncertainties. However, the evaluation of feasibility robustness often needs a computationally intensive process. To reduce the computational burden associated with the probabilistic feasibility evaluation, the first-order Taylor series expansions are used to derive individual mean and variance of constraints. For robust design applications, these constraint response surface models are used efficiently and effectively to calculate variances of constraints due to uncertainties. Robust optimization of automotive seat is used to illustrate the approach.

• Korean Journal of Computational Design and Engineering
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• v.9 no.4
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• pp.294-305
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• 2004

Highly detailed geometric models are rapidly becoming commonplace in computer graphics and other applications. These complex models, which is often represented as complex1 triangle meshes, mainly suffer from the vast memory requirement for real-time manipulation of arbitrary geometric shapes without loss of data. Various techniques have been devised to challenge these problems in views of geometric processing, not a representation scheme. This paper proposes the new mesh structure for the compact representation and the efficient handling of the highly complex models. To verify the compactness and the efficiency, the memory requirement of our representation is first investigated and compared with other existing representations. And then we analyze the time complexity of our data structure by the most critical operation, that is, the enumeration of the so-called one-ring neighborhood of a vertex. Finally, we evaluate some elementary modeling functions such as mesh smoothing, simplification, and subdivision, which is to demonstrate the effectiveness and robustness of our mesh structure in the context of the geometric modeling and processing.

• Journal of computational fluids engineering
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• v.12 no.3
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• pp.55-61
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• 2007

A computational study on a strongly stratified natural convection is performed with the elliptic blending second-moment closure. The turbulent heat flux is treated by both the algebraic flux model (AFM) and the differential flux model (DFM). Calculations are performed for a turbulent natural convection in a square cavity with conducting top and bottom walls and the calculated results are compared with the available experimental data. The results show that both the AFM and DFM models produce very accurate solutions with the elliptic-blending second-moment closure without invoking any numerical stability problems. These results show that the AFM and DFM models for treating the turbulent heat flux are sufficient for this strongly stratified flow. However, a slight difference between two models is observed for some variables.

• Wind and Structures
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• v.2 no.4
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• pp.305-323
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• 1999

Accurate turbulence modeling is an essential prerequisite for the use of Computational Fluid Dynamics (CFD) in Wind Engineering. At present the most popular turbulence model for general engineering flow problems is the ${\kappa}-{\varepsilon}$ model. Models such as this are based on the isotropic eddy viscosity concept and have well documented shortcomings (Murakami et al. 1993) for flows encountered in Wind Engineering. This paper presents an objective assessment of several available alternative models. The CFD results for the flow around a full-scale (6 m) three-dimensional surface mounted cube in an atmospheric boundary layer are compared with recently obtained data. Cube orientations normal and skewed at $45^{\circ}$ to the incident wind have been analysed at Reynolds at Reynolds number of greater than $10^6$. In addition to turbulence modeling other aspects of the CFD procedure are analysed and their effects are discussed.

• Wind and Structures
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• v.9 no.2
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• pp.147-158
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• 2006

The purpose of this paper is to find a more accurate method to evaluate pedestrian wind by computational fluid dynamics approach. Previous computational fluid dynamics studies of wind environmental problems were mostly performed by simplified models, which only use simple geometric shapes, such as cubes and cylinders, to represent buildings and structures. However, to have more accurate and complete evaluation results, various shapes of blocking objects, such as trees, should also be taken into consideration. The aerodynamic effects of these various shapes of objects can decrease wind velocity and increase turbulence intensity. Previous studies simply omitted the errors generated from these various shapes of blocking objects. Adding real geometrical trees to the numerical models makes the calculating domain of CFD very complicated due to geometry generation and grid meshing problems. In this case the function of Porous Media Condition can solve the problem by adding trees into numerical models without increasing the mesh grids. The comparison results between numerical and wind tunnel model are close if the parameters of porous media condition are well adjusted.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.139-148
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• 2014

In recent years, computational models using parametric estimation method have been developed and used widely for efficient cost analysis. In this research, by applying experienced data from Guidance and Control Systems in Missile System field, the cost analysis for engineering model and commercial computational model(Price H, HL, M, S) are conducted and its result is analysed, so that the difference between two models and its grounds are apprehended. Comparing the calibrated value of computational model based on the data base of similar equipment and the cost from the engineering estimation, the two results are very close. It means that the credibility of data is enhanced through calibration. Also, for cost analysis of similar components in the future, the method for calibration of the computational models is also examined. When estimating development cost in this research, although many parts have been estimated through uncertain elements, the reliability could have been enhanced by applying computational model which secures objectivity. It is a very reasonable estimation method by utilizing calibration of the computational models based on existing accumulated development data.

• Korean Journal of Computational Design and Engineering
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• v.1 no.1
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• pp.20-32
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• 1996

The boundary evaluation technique for Boolean operation on non-manifold models which is regarded as the most popular and powerful method to create and modify 3-D CAD models has been developed. This technique adopted the concept of Merge and Selection in which the CSG tree for Boolean operation can be edited quickly and easily. In this method, the merged set which contains complete information about primitive models involved is created by merging primitives one by one, then the alive entities are selected following the given CSG tree. This technique can support the hybrid representation of B-rep(Boundary Representation) and CSG(Constructive Solid Geometry) tree in a unified non-manifold model data structure, and expected to be used as a basic method for many modeling problems such as data representation of form features, and the interference between them, and data representation of conceptual models in design process, etc.