scholarly journals Characterisation of Single-Phase Fluid-Flow Heterogeneity Due to Localised Deformation in a Porous Rock Using Rapid Neutron Tomography

2021 ◽  
Vol 7 (12) ◽  
pp. 275
Author(s):  
Maddi Etxegarai ◽  
Erika Tudisco ◽  
Alessandro Tengattini ◽  
Gioacchino Viggiani ◽  
Nikolay Kardjilov ◽  
...  
Keyword(s):  
Fluid Flow ◽  
High Performance ◽  
Shear Bands ◽  
Flow Speed ◽  
Neutron Imaging ◽  
Engineering Industry ◽  
Porous Rocks ◽  
Data Set ◽  
Full Field ◽  
Mechanical Coupling

The behaviour of subsurface-reservoir porous rocks is a central topic in the resource engineering industry and has relevant applications in hydrocarbon, water production, and CO2 sequestration. One of the key open issues is the effect of deformation on the hydraulic properties of the host rock and, specifically, in saturated environments. This paper presents a novel full-field data set describing the hydro-mechanical properties of porous geomaterials through in situ neutron and X-ray tomography. The use of high-performance neutron imaging facilities such as CONRAD-2 (Helmholtz-Zentrum Berlin) allows the tracking of the fluid front in saturated samples, making use of the differential neutron contrast between “normal” water and heavy water. To quantify the local hydro-mechanical coupling, we applied a number of existing image analysis algorithms and developed an array of bespoke methods to track the water front and calculate the 3D speed maps. The experimental campaign performed revealed that the pressure-driven flow speed decreases, in saturated samples, in the presence of pre-existing low porosity heterogeneities and compactant shear-bands. Furthermore, the observed complex mechanical behaviour of the samples and the associated fluid flow highlight the necessity for 3D imaging and analysis.

Integrated seismic and geomechanical/flow modelling study of focused fluid flow

2020 ◽  
Author(s):  
Viktoriya Yarushina ◽  
Assia Lakhlifi ◽  
Hongliang Wang ◽  
David Connolly ◽  
Magnus Wangen ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Fluid Pressure ◽  
Seismic Inversion ◽  
Source Rocks ◽  
Porous Rocks ◽  
Data Set ◽  
Flow Modelling ◽  
Geological Past ◽  
Flow Processes ◽  
Preferential Pathways

<p>The improved resolution of recent seismic surveys has made seismic chimney structures a common observation in sedimentary basins worldwide and on the Norwegian Continental Shelf. Focused fluid flow in vertical chimneys is an important and poorly understood feature in a petroleum system. Oil and gas migrate through preferential pathways from source rocks to structural traps where they form reservoirs. Further migration or leakage from reservoirs leads to formation of shallow hydrocarbon accumulations and gas pockets. In some cases, leakage through preferential pathways can be traced up to the surface or to the sea floor, where it leads to formation of mud volcanoes, mounds and pockmarks. Here, we present results of an integrated case study, which is performed on a 3D seismic data set that covers an area of approximately 3000km2. The seismic sequence stratigraphic interpretation is complemented with a study of seismic fluid migration paths. Detection of seismic chimneys is a challenging task. State-of-the-art chimney cube technology based on self-educating neural networks was used to automatically identify possible structures. The results of seismic inversion in combination with available well data provided a set of surfaces distinguishing various stratigraphic layers and their properties. Obtained geological model was used as a basis for coupled geo-mechanical / fluid flow modelling that reconstructed the fluid flow processes in the geological past that lead to formation of chimney structures. Our numerical model of chimney formation is based on the two-phase theory of fluid flow through (de)compacting porous rocks. Viscous bulk rheology and strong nonlinear coupling of deforming porous rocks to fluid flow are key ingredients of the model. Chimney formation is linked to pressure build-up in the underlying reservoir. We reconstruct the fluid flow processes in the geological past that lead to formation of chimney structures and provide expectations for their present-day morphology, porosity and fluid pressure. Conditions of chimney formation, their sizes, spatial distribution and times of formation are investigated. The fate of the chimney after it has been created and its role as a fluid pathway in the present-day state is studied.</p>

scholarly journals Hugoniot Data and Equation of State Parameters for an Ultra-High Performance Concrete

2021 ◽  
Author(s):  
C. Sauer ◽  
F. Bagusat ◽  
M.-L. Ruiz-Ripoll ◽  
C. Roller ◽  
M. Sauer ◽  
...  
Keyword(s):  
Equation Of State ◽  
High Performance ◽  
High Performance Concrete ◽  
Applied Pressure ◽  
High Strength ◽  
Parameter Study ◽  
Ultra High Performance Concrete ◽  
Data Set ◽  
Shock Response ◽  
Particle Velocities

AbstractThis work aims at the characterization of a modern concrete material. For this purpose, we perform two experimental series of inverse planar plate impact (PPI) tests with the ultra-high performance concrete B4Q, using two different witness plate materials. Hugoniot data in the range of particle velocities from 180 to 840 m/s and stresses from 1.1 to 7.5 GPa is derived from both series. Within the experimental accuracy, they can be seen as one consistent data set. Moreover, we conduct corresponding numerical simulations and find a reasonably good agreement between simulated and experimentally obtained curves. From the simulated curves, we derive numerical Hugoniot results that serve as a homogenized, mean shock response of B4Q and add further consistency to the data set. Additionally, the comparison of simulated and experimentally determined results allows us to identify experimental outliers. Furthermore, we perform a parameter study which shows that a significant influence of the applied pressure dependent strength model on the derived equation of state (EOS) parameters is unlikely. In order to compare the current results to our own partially reevaluated previous work and selected recent results from literature, we use simulations to numerically extrapolate the Hugoniot results. Considering their inhomogeneous nature, a consistent picture emerges for the shock response of the discussed concrete and high-strength mortar materials. Hugoniot results from this and earlier work are presented for further comparisons. In addition, a full parameter set for B4Q, including validated EOS parameters, is provided for the application in simulations of impact and blast scenarios.

An approach for document retrieval using cluster-based inverted indexing

2021 ◽  
pp. 016555152110184
Author(s):  
Gunjan Chandwani ◽  
Anil Ahlawat ◽  
Gaurav Dubey
Keyword(s):  
High Performance ◽  
Clustering Algorithm ◽  
Pearson Correlation ◽  
Relevant Information ◽  
Document Retrieval ◽  
Bhattacharyya Distance ◽  
Data Set ◽  
Query Matching ◽  
Inverted Indexing ◽  
Query Optimisation

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

Error identification in orbital laser altimeter data by machine learning

2021 ◽  
Author(s):  
Oliver Stenzel ◽  
Robin Thor ◽  
Martin Hilchenbach
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Space Science ◽  
Altimeter Data ◽  
Laser Altimetry ◽  
Error Identification ◽  
Laser Altimeter ◽  
Data Set ◽  
Baseline Design ◽  
Solar System Bodies

<p>Orbital Laser altimeters deliver a plethora of data that is used to map planetary surfaces [1] and to understand interiors of solar system bodies [2]. Accuracy and precision of laser altimetry measurements depend on the knowledge of spacecraft position and pointing and on the instrument. Both are important for the retrieval of tidal parameters. In order to assess the quality of the altimeter retrievals, we are training and implementing an artificial neural network (ANN) to identify and exclude scans from analysis which yield erroneous data. The implementation is based on the PyTorch framework [3]. We are presenting our results for the MESSENGER Mercury Laser Altimeter (MLA) data set [4], but also in view of future analysis of the BepiColombo Laser Altimeter (BELA) data, which will arrive in orbit around Mercury in 2025 on board the Mercury Planetary Orbiter [5,6]. We further explore conventional methods of error identification and compare these with the machine learning results. Short periods of large residuals or large variation of residuals are identified and used to detect erroneous measurements. Furthermore, long-period systematics, such as those caused by slow variations in instrument pointing, can be modelled by including additional parameters.<br>[1] Zuber, Maria T., David E. Smith, Roger J. Phillips, Sean C. Solomon, Gregory A. Neumann, Steven A. Hauck, Stanton J. Peale, et al. ‘Topography of the Northern Hemisphere of Mercury from MESSENGER Laser Altimetry’. Science 336, no. 6078 (13 April 2012): 217–20. https://doi.org/10.1126/science.1218805.<br>[2] Thor, Robin N., Reinald Kallenbach, Ulrich R. Christensen, Philipp Gläser, Alexander Stark, Gregor Steinbrügge, and Jürgen Oberst. ‘Determination of the Lunar Body Tide from Global Laser Altimetry Data’. Journal of Geodesy 95, no. 1 (23 December 2020): 4. https://doi.org/10.1007/s00190-020-01455-8.<br>[3] Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. ‘PyTorch: An Imperative Style, High-Performance Deep Learning Library’. Advances in Neural Information Processing Systems 32 (2019): 8026–37.<br>[4] Cavanaugh, John F., James C. Smith, Xiaoli Sun, Arlin E. Bartels, Luis Ramos-Izquierdo, Danny J. Krebs, Jan F. McGarry, et al. ‘The Mercury Laser Altimeter Instrument for the MESSENGER Mission’. Space Science Reviews 131, no. 1 (1 August 2007): 451–79. https://doi.org/10.1007/s11214-007-9273-4.<br>[5] Thomas, N., T. Spohn, J. -P. Barriot, W. Benz, G. Beutler, U. Christensen, V. Dehant, et al. ‘The BepiColombo Laser Altimeter (BELA): Concept and Baseline Design’. Planetary and Space Science 55, no. 10 (1 July 2007): 1398–1413. https://doi.org/10.1016/j.pss.2007.03.003.<br>[6] Benkhoff, Johannes, Jan van Casteren, Hajime Hayakawa, Masaki Fujimoto, Harri Laakso, Mauro Novara, Paolo Ferri, Helen R. Middleton, and Ruth Ziethe. ‘BepiColombo—Comprehensive Exploration of Mercury: Mission Overview and Science Goals’. Planetary and Space Science, Comprehensive Science Investigations of Mercury: The scientific goals of the joint ESA/JAXA mission BepiColombo, 58, no. 1 (1 January 2010): 2–20. https://doi.org/10.1016/j.pss.2009.09.020.</p>

scholarly journals Experimental study of the dynamic behaviour of High Performance Concrete (HPC) under tensile loading

2018 ◽  
Vol 183 ◽  
pp. 02043 ◽  
Author(s):  
Bratislav Lukić ◽  
Dominique Saletti ◽  
Pascal Forquin
Keyword(s):  
High Speed ◽  
High Performance ◽  
High Performance Concrete ◽  
Imaging System ◽  
Spall Strength ◽  
High Speed Imaging ◽  
Dynamic Tensile Strength ◽  
Full Field ◽  
Local Point ◽  
Measurement Results

This paper presents the measurement results of the dynamic tensile strength of a High Performance Concrete (HPC) obtained using full-field identification method. An ultra-high speed imaging system and the virtual fields method were used to obtain this information. Furthermore the measurement results were compared with the local point-wise measurement to validate the data pressing. The obtained spall strength was found to be consistently 20% lower than the one obtained when the Novikov formula is used.

scholarly journals Large amplitude, short wave peristalsis and its implications for transport

2015 ◽  
Author(s):  
Lindsay D Waldrop ◽  
Laura A. Miller
Keyword(s):  
Fluid Flow ◽  
Large Amplitude ◽  
Compression Wave ◽  
Wave Speed ◽  
Flow Direction ◽  
Short Wave ◽  
Flow Speed ◽  
Biological Pumps ◽  
Flow Speeds ◽  
Flow Compression

Valveless, tubular pumps are widespread in the animal kingdom, but the mechanism by which these pumps generate fluid flow are often in dispute. Where the pumping mechanism of many organs was once described as peristalsis, other mechanisms, such as dynamic suction pumping, have been suggested as possible alternative mechanisms. Peristalsis is often evaluated using criteria established in a technical definition for mechanical pumps, but this definition is based on a small-amplitude, long-wave approximation which biological pumps often violate. In this study, we use a direct numerical simulation of large-amplitude, short-wave peristalsis to investigate the relationships between fluid flow, compression frequency, compression wave speed, and tube occlusion. We also explore how the flows produced differ from the criteria outlined in the technical definition of peristalsis. We find that many of the technical criteria are violated by our model: fluid flow speeds produced by peristalsis are greater than the speeds of the compression wave; fluid flow is pulsatile; and flow speed have a non-linear relationship with compression frequency when compression wave speed is held constant. We suggest that the technical definition is inappropriate for evaluating peristalsis as a pumping mechanism for biological pumps because they too frequently violate the assumptions inherent in these criteria. Instead, we recommend that a simpler, more inclusive definition be used for assessing peristalsis as a pumping mechanism based on the presence of non-stationary compression sites that propagate uni-directionally along a tube without the need for a structurally fixed flow direction.

scholarly journals The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?

2021 ◽  
Vol 4 ◽  
Author(s):  
Stefano Markidis
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Performance ◽  
Critical Role ◽  
Low Frequency ◽  
Accurate Solution ◽  
Limiting Factor ◽  
Data Set ◽  
Linear Solvers ◽  
Computational Performance

Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network. PINNs have emerged as a new essential tool to solve various challenging problems, including computing linear systems arising from PDEs, a task for which several traditional methods exist. In this work, we focus first on evaluating the potential of PINNs as linear solvers in the case of the Poisson equation, an omnipresent equation in scientific computing. We characterize PINN linear solvers in terms of accuracy and performance under different network configurations (depth, activation functions, input data set distribution). We highlight the critical role of transfer learning. Our results show that low-frequency components of the solution converge quickly as an effect of the F-principle. In contrast, an accurate solution of the high frequencies requires an exceedingly long time. To address this limitation, we propose integrating PINNs into traditional linear solvers. We show that this integration leads to the development of new solvers whose performance is on par with other high-performance solvers, such as PETSc conjugate gradient linear solvers, in terms of performance and accuracy. Overall, while the accuracy and computational performance are still a limiting factor for the direct use of PINN linear solvers, hybrid strategies combining old traditional linear solver approaches with new emerging deep-learning techniques are among the most promising methods for developing a new class of linear solvers.

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