Similarity Criteria of Water Drive Physical Simulation of Pressure-Sensitive Fractured Reservoirs

A mathematical equation of water drive physical simulation of pressure-sensitive fractured reservoirs was established based on previous research results. In this study, the similarity criteria of water drive physical simulation of pressure-sensitive fractured reservoirs were derived according to the similarity theory. First of all, based on the three-dimensional differential equation of rock mechanics, a dimensionless analysis was conducted to determine the similarity relationship between the displacement of oil by water of pressure-sensitive fractured reservoirs, the similarity criterion was obtained, and the similarity criteria were formed. Secondly, according to the similarity criterion, the similar relationship between the stress-strain fields of the real object and the simulated object was worked out. Thirdly, the finite element software COMSOL Multiphysics was applied to model and calculate the multifield coupling process in the percolation of pressure-sensitive fractured reservoirs, verifying the correctness of the established similarity criteria and similarity relationship. The verifying results shows that the similarity between the physical model and the actual model can be realized by magnifying the geometric size N times in a certain direction and adjusting the load and boundary conditions according to the similarity principle, which can be used for the design of the pressure-sensitive fractured reservoir simulation model for a physical indoor test.

Similarity Law between Centrifuge Scale Test and Prototype Underwater Explosion

Shock wave and bubble pulsation caused by underwater explosion destroy the hydraulic structure. However, the realization of the underwater explosion prototype test is restricted by many factors, such as the site environment. Furthermore, the repeatability of the test scheme is not strong. The centrifuge scale test provides a new way of studying the damage degree of the structure under the action of underwater explosion. The similarity relationship refers to the bridge between the scaled model and the prototype, which cannot achieve complete similarity in practice. The centrifuge-scaled model test is performed by increasing the acceleration of a certain multiple. Meanwhile, the model reduces the corresponding ratio in the geometric layout to achieve the geometric similarity with the prototype test. Therefore, the applicability of the centrifuge scaling method in the study of the dynamic response of the structure in underwater explosion needs to be explored further. In this work, the underwater explosion scaling test numerical model for 1 g RDX (equivalent to 1.62 g TNT) charge under different centrifugal acceleration conditions is established, and the calculation results of underwater pressure and dynamic response of the steel plate are compared with the centrifuge test results. A prototype model is established to study the similarity relationship between the centrifuge scale test and the prototype model when the steel plate structure is in the stage of small deformation and linear elasticity. The application of the similarity ratio in the scale test of underwater explosion the centrifuge is discussed. The application of the centrifuge in the study of the failure response of the hydraulic structure in underwater explosion is expanded by establishing the model and comparing with the experimental results.

KEANEKARAGAMAN KULTIVAR SEMANGKA [Citrullus lanatus (THUNB.) MATSUM. & NAKAI] DI SENTRA SEMANGKA NUSAWUNGU CILACAP

Watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai] is a plant of Cucurbitaceae family which is include in an annual plant. In Indonesia, watermelon has diversity in its cultivar as can be seen from the stem, leaf, flower, and fruit morphology. This research is aimed to find out the diversity and the similarity relationship of watermelon cultivar in Nusawungu, Cilacap. A survey method was used in this research by taking the samples with a random sampling technique (purposive sampling). This research parameter consisted of stem, leaf, flower, and fruit morphology of each watermelon cultivar. Data of watermelon cultivar morphology were analyzed descriptively and then analyzed based on the similarity relationship with UPGMA method (Unweighted Pair Group Method with Arithmetic Mean) using the MEGA 5.05 software. The result of this research showed that there were five watermelon cultivars namely C. lanatus ‘Farmers Giant’, C. lanatus ‘Nina’, C. lanatus ‘Black Orange’, C. lanatus ‘Torpedo’, and C. lanatus ‘Bintang’. There were two groups of watermelons based on phenetic analysis namely group I consisting of C. lanatus ‘Torpedo’, C. lanatus ‘Black Orange’, and C. lanatus ‘Nina’ cultivars, group II consisting of C. lanatus ‘Bintang’ and C. lanatus ‘Farmers Giant’. The closest similarity was between C. lanatus ‘Farmers Giant’ and C. lanatus ‘Bintang’ cultivars with the dissimilarity index of 0,516. While the most distantly related was between C. lanatus ‘Nina’ and C. lanatus ‘Farmers Giant’ cultivars with the dissimilarity index of 2,338.

Automatic Scale Parameters in Affinity Matrix Construction for Improved Spectral Clustering

Spectral clustering partitions data into similar groups in the eigenspace of the affinity matrix. The accuracy of the spectral clustering algorithm is affected by the affine equivariance realized in the translation of distance to similarity relationship. The similarity value computed as a Gaussian of the distance between data objects is sensitive to the scale factor [Formula: see text]. The value of [Formula: see text], a control parameter of drop in affinity value, is generally a fixed constant or determined by manual tuning. In this research work, [Formula: see text] is determined automatically from the distance values i.e. the similarity relationship that exists in the real data space. The affinity value of a data pair is determined as a location estimate of the spread of distance values of the data points with the other points. The scale factor [Formula: see text] corresponding to a data point [Formula: see text] is computed as the trimean of its distance vector and used in fixing the scale to compute the affinity matrix. Our proposed automatic scale parameter for spectral clustering resulted in a robust similarity matrix which is affine equivariant with the distance distribution and also eliminates the overhead of manual tuning to find the best [Formula: see text] value. The performance of spectral clustering using such affinity matrices was analyzed using UCI data sets and image databases. The obtained scores for NMI, ARI, Purity and F-score were observed to be equivalent to those of existing works and better for most of the data sets. The proposed scale factor was used in various state-of-the-art spectral clustering algorithms and it proves to perform well irrespective of the normalization operations applied in the algorithms. A comparison of clustering error rates obtained for various data sets across the algorithms shows that the proposed automatic scale factor is successful in clustering the data sets equivalent to that obtained using manually tuned best [Formula: see text] value. Thus the automatic scale factor proposed in this research work eliminates the need for exhaustive grid search for the best scale parameter that results in best clustering performance.