scholarly journals Assessment of a Multi-column Deep Lake Model for Climate Applications: The Case of Lake Geneva

2021 ◽  
Author(s):  
Romain Gaillard ◽  
Marjorie Perroud ◽  
Stéphane Goyette ◽  
Jérôme Kasparian
Keyword(s):  
Three Dimensional ◽  
Lower Atmosphere ◽  
Thermocline Depth ◽  
Lake Geneva ◽  
Computationally Efficient ◽  
Lake Model ◽  
Efficient Alternative ◽  
Thermodynamics And Dynamics ◽  
Computational Resources ◽  
Inland Water Bodies

Abstract The interaction between large inland water bodies and the atmosphere impacts the evolution of regional weather and climate, which in turn affects the lake dynamics, thermodynamics, ice-formation, and, therefore, ecosystems. Over the last decades, various approaches have been used to model lake thermodynamics and dynamics in standalone mode or coupled to numerical atmospheric models. We assess a turbulence-closure k − ε multi-column lake model in standalone mode as a computationally-efficient alternative to a full three-dimensional hydrodynamic model in the case of Lake Geneva. While it struggles to reproduce some short-term features, the multi-column model reasonably reproduces the seasonal mean of the thermal horizontal and vertical structures governing heat and mass exchanges between the lake surface and the lower atmosphere (stratified period, thermocline depth, stability of the water column). It may therefore allow a two-way coupling with a RCM on timescales or spatial resolutions where full 3D lake models are too demanding in terms of computational resources.

scholarly journals Multi-column modelling of lake Geneva for climate applications

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Romain Gaillard ◽  
Marjorie Perroud ◽  
Stéphane Goyette ◽  
Jérôme Kasparian
Keyword(s):  
Three Dimensional ◽  
Lower Atmosphere ◽  
Thermocline Depth ◽  
Lake Geneva ◽  
Computationally Efficient ◽  
Atmospheric Models ◽  
Lake Model ◽  
Efficient Alternative ◽  
Thermodynamics And Dynamics ◽  
Inland Water Bodies

AbstractThe interaction between large inland water bodies and the atmosphere impacts the evolution of regional weather and climate, which in turn affects the lake dynamics, thermodynamics, ice-formation, and, therefore, ecosystems. Over the last decades, various approaches have been used to model lake thermodynamics and dynamics in standalone mode or coupled to numerical atmospheric models. We assess a turbulence-closure $$k-\epsilon$$ k - ϵ multi-column lake model in standalone mode as a computationally-efficient alternative to a full three-dimensional hydrodynamic model in the case of Lake Geneva. While it struggles to reproduce some short-term features, the multi-column model reasonably reproduces the seasonal mean of the thermal horizontal and vertical structures governing heat and mass exchanges between the lake surface and the lower atmosphere (stratified period, thermocline depth, stability of the water column). As it requires typically two orders of magnitude less computational ressources, it may allow a two-way coupling with a RCM on timescales or spatial resolutions where full 3D lake models are too demanding.

Potential-Flow Modeling for Data Center Applications

2011 ◽  
Author(s):  
Christopher M. Healey ◽  
James W. VanGilder ◽  
Zachary R. Sheffer ◽  
Xuanhang Simon Zhang
Keyword(s):  
Data Center ◽  
Potential Flow ◽  
Stokes Equations ◽  
Three Dimensional ◽  
Wall Friction ◽  
Navier Stokes ◽  
Computationally Efficient ◽  
Navier Stokes Equations ◽  
Comprehensive Comparison ◽  
Efficient Alternative

Potential-flow-based airflow and heat transfer models have been proposed as a computationally efficient alternative to the Navier-Stokes Equations for predicting the three-dimensional flow field in data center applications. These models are simple, solve quickly, and capture much of the fluid flow physics, but ignore buoyancy and frictional effects, i.e., rotationality, turbulence, and wall friction. However, a comprehensive comparison of the efficiency and accuracy of these methods versus more sophisticated tools, like CFD, is lacking. The main contribution of this paper is a study of the performance of potential-flow methods compared to CFD in eight layouts inspired by actual data center configurations. We demonstrate that potential-flow methods can be helpful in data center design and management applications.

Practical electron tomography

1995 ◽  
Vol 53 ◽  
pp. 742-743
Author(s):  
C.L. Woodcock
Keyword(s):  
Electron Tomography ◽  
Tomographic Reconstruction ◽  
Three Dimensional ◽  
3D Shape ◽  
Computational Resources ◽  
Shape Of Particles

Despite the potential of the technique, electron tomography has yet to be widely used by biologists. This is in part related to the rather daunting list of equipment and expertise that are required. Thanks to continuing advances in theory and instrumentation, tomography is now more feasible for the non-specialist. One barrier that has essentially disappeared is the expense of computational resources. In view of this progress, it is time to give more attention to practical issues that need to be considered when embarking on a tomographic project. The following recommendations and comments are derived from experience gained during two long-term collaborative projects.Tomographic reconstruction results in a three dimensional description of an individual EM specimen, most commonly a section, and is therefore applicable to problems in which ultrastructural details within the thickness of the specimen are obscured in single micrographs. Information that can be recovered using tomography includes the 3D shape of particles, and the arrangement and dispostion of overlapping fibrous and membranous structures.

scholarly journals Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 28
Author(s):  
Anna V. Kalyuzhnaya ◽  
Nikolay O. Nikitin ◽  
Alexander Hvatov ◽  
Mikhail Maslyaev ◽  
Mikhail Yachmenkov ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Learning Approach ◽  
Model Structure ◽  
Evolutionary Learning ◽  
Learning Models ◽  
Computationally Efficient ◽  
Performance Models ◽  
Generative Design ◽  
Computational Resources ◽  
Machine Learning Models

In this paper, we describe the concept of generative design approach applied to the automated evolutionary learning of mathematical models in a computationally efficient way. To formalize the problems of models’ design and co-design, the generalized formulation of the modeling workflow is proposed. A parallelized evolutionary learning approach for the identification of model structure is described for the equation-based model and composite machine learning models. Moreover, the involvement of the performance models in the design process is analyzed. A set of experiments with various models and computational resources is conducted to verify different aspects of the proposed approach.

scholarly journals A Study of Features and Deep Neural Network Architectures and Hyper-Parameters for Domestic Audio Classification

2021 ◽  
Vol 11 (11) ◽  
pp. 4880
Author(s):  
Abigail Copiaco ◽  
Christian Ritz ◽  
Nidhal Abdulaziz ◽  
Stefano Fasciani
Keyword(s):  
Network Architecture ◽  
Single Channel ◽  
Classification Performance ◽  
Network Size ◽  
Directed Acyclic Graphs ◽  
Spectral Features ◽  
Audio Classification ◽  
Resource Requirements ◽  
Efficient Alternative ◽  
Computational Resources

Recent methodologies for audio classification frequently involve cepstral and spectral features, applied to single channel recordings of acoustic scenes and events. Further, the concept of transfer learning has been widely used over the years, and has proven to provide an efficient alternative to training neural networks from scratch. The lower time and resource requirements when using pre-trained models allows for more versatility in developing system classification approaches. However, information on classification performance when using different features for multi-channel recordings is often limited. Furthermore, pre-trained networks are initially trained on bigger databases and are often unnecessarily large. This poses a challenge when developing systems for devices with limited computational resources, such as mobile or embedded devices. This paper presents a detailed study of the most apparent and widely-used cepstral and spectral features for multi-channel audio applications. Accordingly, we propose the use of spectro-temporal features. Additionally, the paper details the development of a compact version of the AlexNet model for computationally-limited platforms through studies of performances against various architectural and parameter modifications of the original network. The aim is to minimize the network size while maintaining the series network architecture and preserving the classification accuracy. Considering that other state-of-the-art compact networks present complex directed acyclic graphs, a series architecture proposes an advantage in customizability. Experimentation was carried out through Matlab, using a database that we have generated for this task, which composes of four-channel synthetic recordings of both sound events and scenes. The top performing methodology resulted in a weighted F1-score of 87.92% for scalogram features classified via the modified AlexNet-33 network, which has a size of 14.33 MB. The AlexNet network returned 86.24% at a size of 222.71 MB.

Direct simulation of evolution and control of three-dimensional instabilities in attachment-line boundary layers

1995 ◽  
Vol 291 ◽  
pp. 369-392 ◽  
Author(s):  
Ronald D. Joslin
Keyword(s):  
Boundary Layer ◽  
Stokes Equations ◽  
Three Dimensional ◽  
Plate Boundary ◽  
Navier Stokes ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Simulation Results ◽  
Dimensional Simulation ◽  
Attachment Line

The spatial evolution of three-dimensional disturbances in an attachment-line boundary layer is computed by direct numerical simulation of the unsteady, incompressible Navier–Stokes equations. Disturbances are introduced into the boundary layer by harmonic sources that involve unsteady suction and blowing through the wall. Various harmonic-source generators are implemented on or near the attachment line, and the disturbance evolutions are compared. Previous two-dimensional simulation results and nonparallel theory are compared with the present results. The three-dimensional simulation results for disturbances with quasi-two-dimensional features indicate growth rates of only a few percent larger than pure two-dimensional results; however, the results are close enough to enable the use of the more computationally efficient, two-dimensional approach. However, true three-dimensional disturbances are more likely in practice and are more stable than two-dimensional disturbances. Disturbances generated off (but near) the attachment line spread both away from and toward the attachment line as they evolve. The evolution pattern is comparable to wave packets in flat-plate boundary-layer flows. Suction stabilizes the quasi-two-dimensional attachment-line instabilities, and blowing destabilizes these instabilities; these results qualitatively agree with the theory. Furthermore, suction stabilizes the disturbances that develop off the attachment line. Clearly, disturbances that are generated near the attachment line can supply energy to attachment-line instabilities, but suction can be used to stabilize these instabilities.

On the numerical study of thermohydrodynamics and biochemistry of inland water bodies

2021 ◽  
Author(s):  
Daria Gladskikh ◽  
Evgeny Mortikov ◽  
Victor Stepanenko
Keyword(s):  
Mixed Layer ◽  
Numerical Study ◽  
Three Dimensional ◽  
Water Bodies ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
One Dimensional ◽  
Lake Model ◽  
Upper Mixed Layer ◽  
The One

<p>The study of thermodynamic and biochemical processes of inland water objects using one- and three-dimensional RANS numerical models was carried out both for idealized water bodies and using measurements data. The need to take into account seiche oscillations to correctly reproduce the deepening of the upper mixed layer in one-dimensional (vertical) models is demonstrated. We considered the one-dimensional LAKE model [1] and the three-dimensional model [2, 3, 4] developed at the Research Computing Center of Moscow State University on the basis of a hydrodynamic code combining DNS/LES/RANS approaches for calculating geophysical turbulent flows. The three-dimensional model was supplemented by the equations for calculating biochemical substances by analogy with the one-dimensional biochemistry equations used in the LAKE model. The effect of mixing processes on the distribution of concentration of greenhouse gases, in particular, methane and oxygen, was studied.</p><p>The work was supported by grants of the RF President’s Grant for Young Scientists (MK-1867.2020.5, MD-1850.2020.5) and by the RFBR (19-05-00249, 20-05-00776). </p><p>1. Stepanenko V., Mammarella I., Ojala A., Miettinen H., Lykosov V., Timo V. LAKE 2.0: a model for temperature, methane, carbon dioxide and oxygen dynamics in lakes // Geoscientific Model Development. 2016. V. 9(5). P. 1977–2006.<br>2. Mortikov E.V., Glazunov A.V., Lykosov V.N. Numerical study of plane Couette flow: turbulence statistics and the structure of pressure-strain correlations // Russian Journal of Numerical Analysis and Mathematical Modelling. 2019. 34(2). P. 119-132.<br>3. Mortikov, E.V. Numerical simulation of the motion of an ice keel in stratified flow // Izv. Atmos. Ocean. Phys. 2016. V. 52. P. 108-115.<br>4. Gladskikh D.S., Stepanenko V.M., Mortikov E.V. On the influence of the horizontal dimensions of inland waters on the thickness of the upper mixed layer // Water Resourses. 2021.V. 45, 9 pages. (in press) </p>

Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes

2021 ◽  
pp. gr.275777.121
Author(s):  
George W Armstrong ◽  
Kalen Cantrell ◽  
Shi Huang ◽  
Daniel McDonald ◽  
Niina Haiminen ◽  
...  
Keyword(s):  
Carbon Footprint ◽  
Phylogenetic Diversity ◽  
Alpha Diversity ◽  
Previous Method ◽  
Metagenomic Data ◽  
Efficient Computation ◽  
Computationally Efficient ◽  
Dataset Size ◽  
Computational Resources ◽  
Older Populations

The number of publicly available microbiome samples is continually growing. As dataset size increases, bottlenecks arise in standard analytical pipelines. Faith’s phylogenetic diversity is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's Phylogenetic Diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

A Fast View Synthesis Implementation Method for Light Field Applications

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Wei Gao ◽  
Linjie Zhou ◽  
Lvfang Tao
Keyword(s):  
Neural Networks ◽  
Light Field ◽  
Three Dimensional ◽  
Super Resolution ◽  
View Synthesis ◽  
Compact Representation ◽  
Light Weight ◽  
Computationally Efficient ◽  
Full Resolution ◽  
Time Systems

View synthesis (VS) for light field images is a very time-consuming task due to the great quantity of involved pixels and intensive computations, which may prevent it from the practical three-dimensional real-time systems. In this article, we propose an acceleration approach for deep learning-based light field view synthesis, which can significantly reduce calculations by using compact-resolution (CR) representation and super-resolution (SR) techniques, as well as light-weight neural networks. The proposed architecture has three cascaded neural networks, including a CR network to generate the compact representation for original input views, a VS network to synthesize new views from down-scaled compact views, and a SR network to reconstruct high-quality views with full resolution. All these networks are jointly trained with the integrated losses of CR, VS, and SR networks. Moreover, due to the redundancy of deep neural networks, we use the efficient light-weight strategy to prune filters for simplification and inference acceleration. Experimental results demonstrate that the proposed method can greatly reduce the processing time and become much more computationally efficient with competitive image quality.

Prediction of Slamming Loads on Catamaran Wetdeck Using CFD

2015 ◽  
Author(s):  
Ahmed Swidan ◽  
Giles Thomas ◽  
Dev Ranmuthugala ◽  
Irene Penesis ◽  
Walid Amin ◽  
...  
Keyword(s):  
Model Simulation ◽  
Three Dimensional ◽  
Grid Method ◽  
Computationally Efficient ◽  
Overset Grid ◽  
Force Magnitude ◽  
Overset Grid Method ◽  
Drop Tests ◽  
Transient Velocity ◽  
Transient Velocity Profile

Wetdeck slamming is one of the principal hydrodynamic loads acting on catamarans. CFD techniques are shown to successfully characterise wetdeck slamming loads, as validated through a series of controlled-speed drop tests on a three-dimensional catamaran hullform model. Simulation of water entry at constant speed by applying a fixed grid method was found to be more computationally efficient than applying an overset grid. However, the overset grid method for implementing the exact transient velocity profile resulted in better prediction of slam force magnitude. In addition the splitting force concurrent with wetdeck slam event was quantified to be 21% of the vertical slamming force.

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