Monolithic aerogels of high molecular weight polyethylene (Mw=3×106- 6×106 g/mol) have been prepared by solvent extraction with supercritical carbon dioxide from thermoreversible gels prepared in decalin. These low density and highly porous aerogels present an apparent POROSITY up to 90%. The aerogel morphology observed by scanning electron microscopy (SEM) is characterized by spherulitic structures being interconnected by fibers. The X-ray diffraction experiments show that PE aerogels are highly crystalline with a degree of crystallinity of c.a.80% and PE chains being packed into the typical orthorombic unit cell. The combined SEM and N2 sorption investigations show that PE aerogels are essentially macroporous with a small amount of mesopores. The oil-sorption performance of polyethylene aerogels has been also evaluated in this study in order to assess a possible use of these materials for oil spillage recovery and results show that aerogel macropores allow a very fast sorption kinetics with a 100% oil weight uptake obtained in less than 1 min.

Natural ventilation is one of the most essential issues in the concept of high-performance architecture. The POROSITY has a lot to do with wind-phil architecture to meet high efficiency in integrated architectural design and materialization a high-performance building. Natural ventilation performance in porous buildings is influenced by a wide range of interrelated factors including terrace depth, POROSITY distribution pattern, POROSITY ratio, continuity or interruption of the voids and, etc. The main objective of this paper is to investigate the effect of POROSITY distribution pattern on natural ventilation performance in a mid-rise building. One solid block and six porous residential models based on unit, row and combined relocation modules with different terrace depths (TD = 1. 2, 1. 5 m) were analyzed by computational fluid dynamics (CFD). The evaluations are based on grid sensitivity analysis and a validation of wind tunnel measurements. Investigations indicated that introducing the velocity into a solid block would enhance the building natural ventilation performance up to 64 percent compared to the solid case. However, it is demonstrated through simulations that the POROSITY distribution pattern as an architectural configuration has a significant effect on ventilation efficiency. Unit-Relocation models (U-RL) have approximately 1. 64 times the mean airflow of the solid block, 1. 1 times of Row-Relocation (R-RL) and 1. 22 times of Combined-Relocation models (CO-RL). U-RL models are also able to achieve approximately 1. 26 times the maximum air velocity inside the blocks compared to the solid case. This value is about 1. 05 times of R-RL cases and 1. 1 times of CO-RL cases. The results clearly indicated that POROSITY distribution pattern is a factor that could be modified by architects to fulfill most of architectural and environmental requirements.

In this study, an attempt is made to predict effective POROSITY in one of the oil fields in the Persian Gulf by designing a probablistic neural network (PNN) and simultanusely making use of seismic attributes and effective POROSITY logs in the reservoir window. This was done by deriving a multiattribute transformation between an optimum subset of seismic attributes and the effective POROSITY logs.The geophysical data used in this study consist of 3D seismic pre-stack time migrated (PSTM) data with 12.5*12.5 m grid size and a 4 ms sampling rate. The length of the seismic traces are two seconds. Well logs of five vertical wells in the study area, including Sonic (DT), Density (RHOB), Effective POROSITY (PHIE) and Seismic Well Velocity Surveys (Check Shots), were used. The reservoir layer is a Mishrif member of the Sarvak formation with Cretaceous age, which is common in oil reservoirs in the Persian Gulf. The top of the Mishrif is adjusted with the Middle Turonian Unconformity and covered with shaley Laffan formation. The Mishrif Reservoir in study area contains two reservoir zones. The lower zone with higher clay content is separate from the upper zone. The upper zone consists of clean limesone with better reservoir properties. Seismic traces close to the well locations were used to generate seismic attributes. Effective POROSITY logs at the reservoir area were the target logs in this study.The designed neural network consists of one input layer, one hidden layer with four processing units (neuron), and one output layer with one neuron. In order to prepare training samples for the neural network, PHIE logs were converted to time domain using a time-depth relationship calculated from the DT logs and check shot curves for each well location. Subsequently, these logs were filtered (using a Hanning filter with 4 ms length) and resampled with seismic sampling rate (4 ms). Finally, a set of seismic attributes, including sixteen sample-based seismic attributes, were generated using HRS software. Training samples in this study consisted of 57 samples (selected seismic attributes and their related effective POROSITY from PHIE logs in the time domain). For training the network, the samples were divided into three data sets: the training samples, cross validation samples and testing samples. The training data were used for adjusting the weights of the network; the cross validation data were used to prevent overtraining theneural network; and the testing data were used to ensure generalizabillity of the network output.A forward stepwise regression process was used to determine an optimum subset of attributes for use in the training of the neural networks. The optimum subset of attributes in this study consists of the Dominant Frequency, Amplitude Weighted Frequency, Integrated Absolute Amplitude and Filter 45-60 Hz.After the network was trained using training and cross validation data sets, it was used to predict the testing data. The results show a good correlation between real and predicted data, with 92% correlation. Finally, in order to attain a better generalization of the network, testing data sets were inserted to trained data and the network was trained again. This network was then used to predict effective POROSITY in well locations which increased the correlation coefficient to 95%. This study shows the ability of the PNN networks to predict effective POROSITY even with a paucity of training examplares.

Investigation of Hydrocarbon reservoir is important, so it is essential to predict and explore them precisely. One of the methods is well logging, which can transfer the probe or tool in the well to measure one or more characteristics. Nuclear well logging includes radioisotope source and at least one detector. In this work, emission direction of neutrons from the 241Am-Be neutron source toward the calcite formation has been investigated using MCNPX 2. 6 to obtain the best precision in determining the liquid POROSITY. The results show that emission direction of 20 in degree with the resolution POROSITY of 3% in counts is recorded in detectors; as well; there is no decrease in depth penetration, providing speed of tools.

A dynamic two dimensional problem of thermoelasticity with double porous structure has been considered to investigate the disturbance due to normal force and thermal source. Laplace and Fourier transform technique is applied to the governing equations to solve the problem. The transformed components of stress and temperature distribution are obtained. The resulting expressions are obtained in the physical domain by using numerical inversion technique. Numerically computed results for these quantities are depicted graphically to study the effect of POROSITY. Results of Kumar & Rani [42] and Kumar & Ailawalia [43] have also been deduced as special cases from the present investigation.

Steel fibres have gained popularity in recent decades for use in concrete at relatively low volume fractions. They are mainly used to enhance toughness, flexural strength and resistance to shrinkage-induced cracking. However, little information is available about the effects of fibers on permeability and POROSITY, which play an important role in long –term durability of concrete materials. This paper presents the results of an experimental study that was carried out to examine The influences of steel fiber addition on the permeability and POROSITY of a concrete prepared mainly from local materials. The test results are discussed in this paper, the interpretation of the test results is reported as well as conclusions regarding the effects of steel fibers on the Water and gas permeability of concrete.