Spatial rules that generate urban patterns: Emergence of the power law in the distribution of axial line length

This paper studies emergence/generation of power law in rank-order distribution of axial line length, which is a global pattern observed in real cities, due to interaction of a set of seven simple spatial rules at a local scale. These rules and their interactions form a model expected to simulate the morphological structure of free spaces in unplanned organic pedestrian small cities. Effects of each of the seven rules are discussed through repeated simulations of eight possible combinations of the rules, using a bottom-up process. The results show that the rules generate environments with statistically stable rank-order distribution of axial line length that follows the power law. It means that the axial maps of the simulated environments have a scale-free hierarchical structure such that their distributions lean toward short axial lines. It also represents dominance of local spatial structure, as the model renders a faster rate of growth at a local scale while allowing a steady growth at a global scale.     

Integration of geoelectric and hydrochemical approaches for delineation of groundwater potential zones in alluvial aquifer

Geoelectric and hydrochemical approaches are employed to delineate the ground-water potential zones in District Okara, a part of Bari Doab, Punjab, Pakistan. Sixty-seven VES surveys are conducted with the Electrical Resistivity Meter. The resultant resistivity verses depth model for each site is estimated using computer-based software IX1D. Aquifer thickness maps and interpreted resistivity maps were generated from interpreted VES results. Dar-Zarrouk parameters, transverse resistance (TR), longitudinal conductance (SL) and anisotropy (λ) were also calculated from resistivity data to delineate the potential zones of aquifer. 70% of SL value is ≤3S, 30% of SL value is > 3S. According to SL and TR values, the whole area is divided into three potential zones, high, medium and low potential zones. The spatial distribution maps show that north, south and central parts of study area are marked as good potential aquifer zones. Longitudinal conductance values are further utilized to determine aquifer protective capacity of area. The whole area is characterized by moderate to good and up to some extent very good aquifer protective area on the basis of SL values. The groundwater samples from sixty-seven installed tube wells are collected for hydro-chemical analysis. The electrical conductivity values are determined. Correlation is then developed between the EC (μS/cm) of groundwater samples vs. interpreted aquifer resistivity showing R2 value 0.90.     

Experimental and numerical investigation for energy dissipation of supercritical flow in sudden contractions

Dealing with kinetic energy is one of the most important problems in hydraulic structures, and this energy can damage downstream structures. This study aims to study energy dissipation of supercritical water flow passing through a sudden contraction. The experiments were conducted on a sudden contraction with 15 cm width. A 30 cm wide flume was installed. The relative contraction ranged from 8.9 to 9.7, where relative contraction refers to the ratio of contraction width to initial flow depth. The Froude value in the investigation varied from 2 to 7. The contraction width of numerical simulation was 5~15 cm, the relative contraction was 8.9~12.42, and the Froude value ranged from 8.9~12.42. In order to simulate turbulence, the k-ε RNG model was harnessed. The experimental and numerical results demonstrate that the energy dissipation increases with the increase of Froude value. Also, with the sudden contraction, the rate of relative depreciation of energy is increased due to the increase in backwater profile and downstream flow depth. The experimentation verifies the numerical results with a correlation coefficient of 0.99 and the root mean square error is 0.02.     

Effects of urbanization on river morphology of the Talar River,Mazandarn Province,Iran

In the present study, we investigate the effects of urbanization growth on river morphology in the downstream part of Talar River, east of Mazandaran Province, Iran. Morphological and morphometric parameters in 10 equal sub-reaches were defined along a 11.5 km reach of the Talar River after land cover maps were produced for 1955, 1968, 1994, 2005 and 2013. Land cover types changed extremely during the study period. Residential lands were found to have increased in area by about 1631%, while forest land and riparian vegetation decreased in by approximately 99.9 and 96.2%, respectively. The results of morphometric and morphological factors showed that average channel width (W) for all 11.5 km of the study river decreased by 84% during the study period, while the flow length increased by about 2.14%.     

Particle Swarm Optimization Algorithm for Neuro-Fuzzy Prospectivity Analysis Using Continuously Weighted Spatial Exploration Data

Natural Resources Research - Classification of spatial exploration data for exploration targeting using neuro-fuzzy models means that the many spatial values have to be simplified and assigned to a...     

An Improved Prediction-Area Plot for Prospectivity Analysis of Mineral Deposits

Natural Resources Research - In this paper an improved prediction-area plot has been developed. This type of plot includes performance measures similar to other existing methods (receiver operating...     

Assessment of Estimated Bedrock and Stream Sediment Geochemical Backgrounds in Catchment Basin Analysis

Natural Resources Research - The weighted mean and the multiple regression techniques are two methods that are employed to estimate elemental background concentration of lithologies upstream of...     

The Temperature – Magnetic Field Relation in Observed and Simulated Sunspots

Observations of the relation between continuum intensity and magnetic field strength in sunspots have been made for nearly five decades. This work presents full-Stokes measurements of the full-split (\(g = 3\)) line Fe i 1564.85 nm with a spatial resolution of \(0.5^{\prime\prime}\) obtained with the GREGOR Infrared Spectrograph in three large sunspots. The continuum intensity is corrected for instrumental scattered light, and the brightness temperature is calculated. Magnetic field strength and inclination are derived directly from the line split and the ratio of Stokes components. The continuum intensity (temperature) relations to the field strength are studied separately in the umbra, light bridges, and penumbra. The results are consistent with previous studies, and it was found that the scatter of values in the relations increases with increasing spatial resolution thanks to resolved fine structures. The observed relations show trends common for the umbra, light bridges, and the inner penumbra, while the outer penumbra has a weaker magnetic field than the inner penumbra at equal continuum intensities. This fact can be interpreted in terms of the interlocking comb magnetic structure of the penumbra. A comparison with data obtained from numerical simulations was made. The simulated data generally have a stronger magnetic field and a weaker continuum intensity than the observations, which may be explained by stray light and limited spatial resolution of the observations, and also by photometric inaccuracies of the simulations.     

Full-scale experimental investigations on the response of a flooded passenger vehicle under subcritical conditions

Natural Hazards - Vehicles can be easily swept away by floodwaters once the flow velocity and depth reach certain critical limits, with probabilities toward fatality reported to be nearly 50%....     

Acceleration of thermodynamic computations in fluid flow applications

Reservoir simulators model the highly nonlinear partial differential equations that represent flows in heterogeneous porous media. The system is made up of conservation equations for each thermodynamic species, flash equilibrium equations and some constraints. With advances in Field Development Planning (FDP) strategies, clients need to model highly complex Improved Oil Recovery processes such as gas re-injection and CO2 injection, which requires multi-component simulation models. The operating range of these simulation models is usually around the mixture critical point and this can be very difficult to simulate due to phase mislabeling and poor nonlinear convergence. We present a Machine Learning (ML) based approach that significantly accelerates such simulation models. One of the most important physical parameters required in order to simulate complex fluids in the subsurface is the critical temperature (T_crit). There are advanced iterative methods to compute the critical point such as the algorithm proposed by Heidemann and Khalil (AIChE J 26,769–799, 1980) but, because these methods are too expensive, they are usually replaced by cheaper and less accurate methods such as the Li-correlation (Reid and Sherwood 1966). In this work we use a ML workflow that is based on two interacting fully connected neural networks, one a classifier and the other a regressor, that are used to replace physical algorithms for single phase labelling and improve the convergence of the simulator. We generate real time compositional training data using a linear mixing rule between the injected and the in-situ fluid compositions that can exhibit temporal evolution. In many complicated scenarios, a physical critical temperature does not exist and the iterative sequence fails to converge. We train the classifier to identify, a-priori, if a sequence of iterations will diverge. The regressor is then trained to predict an accurate value of T_crit. A framework is developed inside the simulator based on TensorFlow that aids real time machine learning applications. The training data is generated within the simulator at the beginning of the simulation run and the ML models are trained on this data while the simulator is running. All the run-times presented in this paper include the time taken to generate the training data and train the models. Applying this ML workflow to real field gas re-injection cases suffering from severe convergence issues has resulted in a 10-fold reduction of the nonlinear iterations in the examples shown in this paper, with the overall run time reduced 2- to 10-fold, thus making complex FDP workflows several times faster. Such models are usually run many times in history matching and optimization workflows, which results in compounded computational savings. The workflow also results in more accurate prediction of the oil in place due to better single phase labelling.