Heat transfer enhancement using double taper microchannel

A three-dimensional numerical study on the combined effect of height as well as width tapering on the thermal performance of double taper microchannel is presented in this paper. The channel inlet width is considered as 300 µm, taper ratio on sidewalls and bottom wall are varied from 0 to 1 and 1 to 3.9, respectively. The thermal resistance ratio, average bottom wall temperature, temperature difference ratio, and pumping power ratio of the channel are evaluated for various flow rates, height, and width tapering. Results showed higher reduction of wall temperature with combined effect height as well as width tapering compared with straight channel. The optimal size of the micro channel to minimize the pumping power and average wall temperature on the constraint of heat flux and footprint area is found. The reduction in average bottom wall temperature is 17.34%, and pumping power ratio is 0.44 (56% power reduction) noted, respectively, at Reynolds number 340. Finally, optimal dimension of double taper microchannel is evaluated for better thermo-hydraulic performance.

Dimensional optimization of rocker-bogie suspension for planetary rover based on kinetostatics and terramechanics

Dimensional optimization is important for planetary rovers to reach good performance, such as high mobility, stability, and low energy consumption. The paper presents a dimensional optimization for a planetary rover with rocker-bogie suspension. During the optimization process, the influence of dimensions on the actuation requirements is studied based on kinetostatics and terramechanics. The objective function is built considering the average driving torque requirements in the most common type of windblown terrain in Mars called megaripples. The optimal dimension design is reached through the genetic algorithm, and the influences of dimensional parameters on rover performances are studied by drawing performance atlases. This work realizes the consideration of energy consumption in the design phase of a planetary rover. Finally, the results guide the design of a rover prototype and are validated by a series of experiments.

Methodology for Establishing Segmentation Strategy for Large Metal Components from Nuclear Power Plants with Consideration of Packaging into Containers

A methodology for segmenting large metal components from nuclear power plants has been developed with a view to minimizing the number of containers to emplace segmented pieces. Spherocylinder-type and rectangular prism-type objects are modeled in shapes, and equations to calculate heights, widths, lengths, or angles for segmentation and the number of pieces are derived using geometric theorems, with a hypothetical ‘virtual rectangle’ being introduced for simplification. Applicability of the new methodology is verified through case studies assuming that each segmented piece is packaged into a 200 L container, and a procedure for adjusting potential overestimation of the segmented pieces due to the virtual rectangle is proposed. The new approach results in fewer segmented pieces but more containers than an existing segmentation study using 3D modeling. It is demonstrated that the number of containers can be further reduced, however, if the generalized methodology is followed by 3D modeling. In addition, it is confirmed that the generalized approach is also applicable to a nonstandard shapes such as ellipsoidal shape but only under limited conditions. Sensitivity analyses are conducted by changing dimensions of the objects and container, which brings about an optimal dimension of container as well. The generalized approach would be utilized either alone in decommissioning planning to estimate waste from segmentation of large metal components or combined with 3D modeling to optimize segmentation operation.

Optimal fast Johnson–Lindenstrauss embeddings for large data sets

Johnson–Lindenstrauss embeddings are widely used to reduce the dimension and thus the processing time of data. To reduce the total complexity, also fast algorithms for applying these embeddings are necessary. To date, such fast algorithms are only available either for a non-optimal embedding dimension or up to a certain threshold on the number of data points. We address a variant of this problem where one aims to simultaneously embed larger subsets of the data set. Our method follows an approach by Nelson et al. (New constructions of RIP matrices with fast multiplication and fewer rows. In: Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1515-1528, 2014): a subsampled Hadamard transform maps points into a space of lower, but not optimal dimension. Subsequently, a random matrix with independent entries projects to an optimal embedding dimension. For subsets whose size scales at least polynomially in the ambient dimension, the complexity of this method comes close to the number of operations just to read the data under mild assumptions on the size of the data set that are considerably less restrictive than in previous works. We also prove a lower bound showing that subsampled Hadamard matrices alone cannot reach an optimal embedding dimension. Hence, the second embedding cannot be omitted.

A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability

To accurately classify the stability of surrounding rock masses, a novel method (VSV-BDA) based on virtual state variables (VSVs) and Bayesian discriminant analysis (BDA) is proposed. The factors influencing stability are mapped by an artificial neural network (ANN) capable of recognizing the model of rock mass classification, and the obtained output vector is treated as VSVs, which are verified as obeying a multinormal distribution with equal covariance matrixes by normal distribution testing and constructed statistics. The prediction variance ratio test method is introduced to determine the optimal dimension of the VSVs. The VSV-BDA model is constructed through the use of VSVs and the optimal dimension on the basis of the training samples, which are divided from the collected samples into three situations having different numbers. ANN and BDA models are also constructed based on the same training samples. The predictions by the three models for the testing samples are compared; the results show that the proposed VSV-BDA model has high prediction accuracy and can be applied in practical engineering.

L-shaped reinforced concrete retaining wall design: cost and sizing optimization

In the context of this study, the design of L-shaped reinforced concrete retaining walls have been scrutinized parametrically depending on the simultaneous analysis of cost and sizing with the use of a recent optimization algorithm. The differences and restrictions of L-shaped reinforced concrete retaining wall design than classical T-shaped walls have been also discussed. The foundation width and the thickness of the wall required for a safe design has been also investigated according to the change of excavation depth, the type of soil dominating field and the external loading conditions. The observed results from optimization analyses shows that the variation of the shear strength angle is the most significant soil geotechnical parameter for supplying an envisaged safe design against sliding, overturning and adequate bearing capacity. Concurrently, the excavation depth is the most important factor that is forming the necessity of the construction of the retaining structure and optimal dimension evaluation. It is also proved that the wall foundation width is the most effected dimension of the retaining structures by the change of design parameters and the cost difference is directly influenced by the change of sizing. A cost-effective wall design can be performed with the use of proposed optimization analysis is capable in a shorter time than the traditional methods. Eventually, it has shown that such optimization methods may be useful to find the optimal design requirements for geotechnical engineering structures.

Long-term financial sustainability: An evaluation methodology with threats considerations

It is shown that one of the directions for increasing the efficiency of managing corporate systems (CS) under the influence of a large number of destabilizing fa-tors ("shocks", threats) is the development of a set of models of estimation and analysis of the long-term stability of CS in proactive contour of management, which allow timely diagnosing a decrease in the company's security level and adopting effective preventive management decisions. A review of existing approa-ches to the formation of such a set of models showed a number of limitations, the result of which is a low forecasting accuracy. The proposed approach, unlike the existing ones, allows to: 1) determine the optimal dimension of the information space of diagnostic factors; 2) find the optimal number of classes of situations for which differentiated management strategies can be developed; 3) determine the period of pre-emption, which does not require updating the models of retrospective diagnostics. This makes it possible to identify the class of not only current, but also forecast situations for a given horizon of proactive management and to choose an adequate preventive strategy.