A new computational approach is developed to predict the impact behaviour of fabric panels based on the detailed response of the smallest repeating unit (unit cell) in the fabric. The unit cell is ...constructed and calibrated using measured geometrical (weave architecture, crimp, voids, etc.) and mechanical properties of the fabric. A pre-processor is developed to create a 3D finite element mesh of the unit cell using the measured fabric cross-sectional micro-images. To render an efficient method for simulation of multi-layer packs, these unit cells are replaced with orthotropic shell elements that have similar macroscopic (smeared) mechanical properties as the unit cell. The aim is to capture the essence of the response of a unit cell in a single representative shell element, which would replace the more complicated and numerically costly 3D solid model of the yarns in a crossover. The 3D finite element analysis of the unit cell is used to provide a baseline mechanical response for calibrating the constitutive model in the equivalent shell representation. This shell element takes advantage of a simple physics-based analytical relationship to predict the behaviour of the fabric's warp and weft yarns under general applied displacements in these directions. The analytical model is implemented in the commercial explicit finite element code, LS-DYNA, as a user material routine (UMAT) for shell elements. Layers of fabric constructed from these specialized elements are stacked together to create fabric targets that are then analysed under projectile impact. This approach provides an efficient numerical model for the dynamic analysis of multi-layer fabric structures while taking into account several geometrical and material attributes of the yarns and the fabric.
Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images ...using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.
Crude oil density is an important thermodynamic property in simulation processes and design of equipment. Using laboratory methods to measure crude oil density is costly and time consuming; thus, ...predicting the density of crude oil using modeling is cost-effective. In this article, we develop a neural network-based model to predict the density of undersaturated crude oil. We compare our results with previous works and show that our method outperforms them.
The National Risk Assessment Partnership (NRAP) is a research organization focused on developing methods and tools for long-term quantitative risk assessment for carbon storage. NRAP's approach is to ...divide the carbon storage system into components—reservoir, wells, seals, groundwater, atmosphere—and to develop reduced order models for each of these components. These rapid performance models are trained and/or validated against full physics reservoir models (e.g., TOUGH2, GEM) so that they reproduce similar results but in a fraction of the time of the reservoir model. The different component models can then be combined in an integrated assessment model that can simulate the full system in a matter of seconds or minutes rather than the days, weeks, or longer that a full physics simulation of the entire system would take. The integrated model can then be run in a Monte Carlo mode to assess the probability of failure of a carbon storage system.
In NRAP, part of the focus is on long-term leakage risk, and the rapid performance reservoir models are designed to generate pressures and saturations within the reservoir, and particularly at the reservoir-seal interface, both during injection and for up to 1,000 years post injection. These pressures and saturations can then be used as inputs to wellbore or seal leakage models to predict rates and volumes of leakage of CO2 and/or in situ fluids.
In the past few years, NRAP researchers have developed and applied a number of different reduced order models to saline-, gas-, and oil- bearing storage fields. These models vary significantly in several respects. They range from lookup tables or response surfaces to models such as polynomial chaos expansion to models that rely on data mining and artificial intelligence techniques. Each of these types of rapid performance models has different strengths and weaknesses, depending on the method used, the reservoir type, and the goals.
In all cases, the rapid performance models required a geologic model and at least some traditional reservoir simulation runs for training or validation purposes. Also, in all techniques developed, an initial analysis is performed to reduce the number of input parameters and scenarios needed for the final simulations. The number of reservoir simulation runs needed can vary significantly based on the reduced order model used. The time and sophistication that it takes to develop a reduced order model is another major factor that varies among the different types of models. Some models are easily able to handle a significant number of varying spatial inputs, while others are limited in the number of input parameters available. Additionally, while all of the rapid performance models will run much faster than a reservoir model, there run times can vary from fractions of a second to tens of seconds or longer, depending on the situation being modeled.
This paper will describe the different types of reduced order reservoir models used within NRAP. It will also provide a critical assessment of these rapid performance models, discuss under what circumstances different rapid performance models would be most effective, and evaluate their utility in the context of quantitative risk assessment.
The early impact behaviour of single and multi-ply Kevlar
® 129 fabric armour systems is investigated using an explicit finite element code, TEXIM, developed in-house. This numerical model is ...carefully validated using continuous temporal data obtained from an instrumented experimental setup. The model is then used to explore the loss in ballistic efficiency of woven fabric targets, as experienced early in the impact event, when either the number of layers in the panel or the yarn denier is increased.
Effect of hereditary obesity on renal expressions of NO synthase, caveolin-1, AKt, guanylate cyclase, and calmodulin.
Obesity has emerged as a major cause of diabetes, cardiovascular disease, and ...renal insufficiency worldwide. Obese Zucker rats exhibit hyperphagia, obesity, insulin resistance, hyperlipidemia, and glomerulosclerosis and are frequently used as a model to study hereditary form of metabolic syndrome. Nitric oxide plays a major role in preservation of renal function and structure. The present study was designed to test the hypothesis that renal disease in this model may be associated with down-regulation of endothelial (eNOS) and neuromal NO synthases (nNOS) in the kidney. The study further sought to explore expressions of caveolin-1, phospho AKt, and calmodulin, which regulate activities of constituitive NOS isoforms, as well as soluble guanylate cyclase (sGC), which is involved in NO signaling.
Twenty-two-week-old male obese and lean Zucker rats were studied. Body weight, serum lipids, urine albumin excretion, and renal tissue abundance of the above proteins were determined.
Serum glucose and arterial pressure were unchanged, whereas urinary NO metabolite (NOχ) excretion and renal tissue nitrotyrosine abundance were markedly reduced (denoting depressed NO production) in the obese versus lean Zucker rats. This was accompanied by significant glomerulosclerosis, tubulointerstitial damage, renal immune cell infiltration, marked down-regulations of renal tissue eNOS and nNOS, mild reduction of caveolin-1, and unchanged calmodulin, phospho-AKt, and sGC.
Hereditary obesity can result in down-regulations of kidney eNOS and nNOS, marked reduction of NO production, and glomerulosclerosis prior to the onset of frank diabetes and hypertension.
Valuing Forests and Rangelands-Ecosystem Services Karimzadegan, H; Rahmatian, M; Dehghani Salmasi, M ...
International Journal of Environmental Research,
09/2007, Letnik:
1, Številka:
4
Journal Article
Recenzirano
Various benefits and services that forests and rangelands offer to the society are studied. Elements of nature are valuable insofar as they serve human being in one-way or another. Utilitarianism ...maintains that natural things have a value to the extent that they confer satisfaction to humans. Without measurable economic valuation comparable with other economic sections of the country, the awful danger of sacrificing the long living of the forests and rangelands for short-term economic benefits is probable. It is concluded that the analysis presents an attempt to value ecosystems and their component species only insofar as they confer benefits, in the form of life support goods and services, to human beings.
Mycophenolate mofetil ameliorates nephropathy in the obese Zucker rat.
The obese Zucker rat has metabolic condition resembling type II diabetes, including hyperlipidemia, obesity, insulin resistance, ...and hyperglycemia. With advancing age, the obese Zucker rat develops glomerulosclerosis, proteinuria, and renal failure. Since immune cells play a central role in the development of chronic renal injury, we evaluated the potential benefit of mycophenolate mofetil (MMF), alone and in combination with angiotensin receptor type 1 blockade (ARB) in the obese Zucker rat.
Thirteen-week-old male obese Zucker rats (fa/fa) were randomly assigned to four experimental groups (five rats each) that received the following treatments for 3 months: (1) losartan (100 mg/L in the drinking water), (2) MMF (20 mg/kg/day), (3) MMF and losartan, and (4) placebo. Lean Zucker rats (N = 5) were included as normal controls. Renal function, biochemical parameters, renal histology, and immunohistology were evaluated.
The placebo-treated obese Zucker rats exhibited proteinuria and significant glomerular and tubulointerstitial injury in association with renal immune cell infiltration. Proteinuria, histologic damage, and renal immune cell infiltration were all reduced by MMF treatment alone or in combination with ARB. The improvement of proteinuria and structural damage was more pronounced in the group that received the combination of MMF and losartan.
MMF treatment alone, and especially in combination with ARB, improves nephropathy in the obese Zucker rat.
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