This document summarizes the steps to perform conjugate heat transfer (CHT) coupling between OpenFOAM and CalculiX using preCICE. The example problem models heat transfer in a heat exchanger with an inner fluid, outer fluid and solid. OpenFOAM is used to simulate the inner and outer fluids while CalculiX simulates the solid. PrecICE is configured to exchange temperature and heat flux boundary condition data between the solvers at mesh interfaces. The workflow involves creating meshes in OpenFOAM and CalculiX, setting up coupling configuration files, and running the coupled simulation over multiple timesteps.
This slide is a trail CHT analysis for relatively complex bodies with chtMultiRegionFoam which is an solver of OpenFOAM. Two methods to make mesh are explained.
This slide is a trail CHT analysis for relatively complex bodies with chtMultiRegionFoam which is an solver of OpenFOAM. Two methods to make mesh are explained.
This slide is about multiphaseEulerFoam which is a CFD solver of OpenFOAM and can analyze multiphase flows. The theory and differences with multiphaseInterFoam are explained.
Spatial Interpolation Schemes in OpenFOAMFumiya Nozaki
The document discusses spatial interpolation schemes in OpenFOAM. It begins by explaining how spatial derivative terms in the finite volume method (FVM) are discretized by integrating over cell volumes and surfaces. It then describes how values at face centers are obtained by interpolating from cell center values using algebraic relationships and weighting factors. Common interpolation schemes in OpenFOAM include upwind, linearUpwind, linear, and limitedLinear. The specification of interpolation schemes on a term-by-term basis is demonstrated. Code examples show how schemes such as upwind, linearUpwind and midPoint calculate interpolated face values and weighting factors differently.
This slide is describing how to set up the OpenFOAM simulations including rotating geometries.
The SRF (Single Rotating Frame) is covered and MRF (Multiple Reference Frame).will be covered in it.
Setting and Usage of OpenFOAM multiphase solver (S-CLSVOF)takuyayamamoto1800
The S-CLSVOF solver in OpenFOAM uses a coupled volume of fluid (VOF) and level set method to simulate multiphase flows. It uses a level set function to track the interface and reinitialize it, improving on the standard VOF method. The solver has been implemented in OpenFOAM versions 2.0.x and higher but boundary conditions for the level set function have not been fully developed. The document provides information on setting up and running a dam break tutorial case using the S-CLSVOF solver by modifying an existing interFoam case.
FlinkDTW: Time-series Pattern Search at Scale Using Dynamic Time Warping - Ch...Flink Forward
DTW: Dynamic Time Warping is a well-known method to find patterns within a time-series. It has the possibility to find a pattern even if the data are distorted. It can be used to detect trends in sell, defect in machine signals in the industry, medicine for electro-cardiograms, DNA…
Most of the implementations are usually very slow, but a very efficient open source implementation (best paper SIGKDD 2012) is implemented in C. It can be easily ported in other language, as Java, so that it can be then easily used in Flink.
We present how we did some slight modifications so that we can use with Flink at even greater scale to return the TopK best matches on past data or streaming data.
In this deck from the GPU Technology Conference, Thorsten Kurth from Lawrence Berkeley National Laboratory and Josh Romero from NVIDIA present: Exascale Deep Learning for Climate Analytics.
"We'll discuss how we scaled the training of a single deep learning model to 27,360 V100 GPUs (4,560 nodes) on the OLCF Summit HPC System using the high-productivity TensorFlow framework. We discuss how the neural network was tweaked to achieve good performance on the NVIDIA Volta GPUs with Tensor Cores and what further optimizations were necessary to provide excellent scalability, including data input pipeline and communication optimizations, as well as gradient boosting for SGD-type solvers. Scalable deep learning becomes more and more important as datasets and deep learning models grow and become more complicated. This talk is targeted at deep learning practitioners who are interested in learning what optimizations are necessary for training their models efficiently at massive scale."
Watch the video: https://wp.me/p3RLHQ-kgT
Learn more: https://ml4sci.lbl.gov/home
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This slide is about multiphaseEulerFoam which is a CFD solver of OpenFOAM and can analyze multiphase flows. The theory and differences with multiphaseInterFoam are explained.
Spatial Interpolation Schemes in OpenFOAMFumiya Nozaki
The document discusses spatial interpolation schemes in OpenFOAM. It begins by explaining how spatial derivative terms in the finite volume method (FVM) are discretized by integrating over cell volumes and surfaces. It then describes how values at face centers are obtained by interpolating from cell center values using algebraic relationships and weighting factors. Common interpolation schemes in OpenFOAM include upwind, linearUpwind, linear, and limitedLinear. The specification of interpolation schemes on a term-by-term basis is demonstrated. Code examples show how schemes such as upwind, linearUpwind and midPoint calculate interpolated face values and weighting factors differently.
This slide is describing how to set up the OpenFOAM simulations including rotating geometries.
The SRF (Single Rotating Frame) is covered and MRF (Multiple Reference Frame).will be covered in it.
Setting and Usage of OpenFOAM multiphase solver (S-CLSVOF)takuyayamamoto1800
The S-CLSVOF solver in OpenFOAM uses a coupled volume of fluid (VOF) and level set method to simulate multiphase flows. It uses a level set function to track the interface and reinitialize it, improving on the standard VOF method. The solver has been implemented in OpenFOAM versions 2.0.x and higher but boundary conditions for the level set function have not been fully developed. The document provides information on setting up and running a dam break tutorial case using the S-CLSVOF solver by modifying an existing interFoam case.
FlinkDTW: Time-series Pattern Search at Scale Using Dynamic Time Warping - Ch...Flink Forward
DTW: Dynamic Time Warping is a well-known method to find patterns within a time-series. It has the possibility to find a pattern even if the data are distorted. It can be used to detect trends in sell, defect in machine signals in the industry, medicine for electro-cardiograms, DNA…
Most of the implementations are usually very slow, but a very efficient open source implementation (best paper SIGKDD 2012) is implemented in C. It can be easily ported in other language, as Java, so that it can be then easily used in Flink.
We present how we did some slight modifications so that we can use with Flink at even greater scale to return the TopK best matches on past data or streaming data.
In this deck from the GPU Technology Conference, Thorsten Kurth from Lawrence Berkeley National Laboratory and Josh Romero from NVIDIA present: Exascale Deep Learning for Climate Analytics.
"We'll discuss how we scaled the training of a single deep learning model to 27,360 V100 GPUs (4,560 nodes) on the OLCF Summit HPC System using the high-productivity TensorFlow framework. We discuss how the neural network was tweaked to achieve good performance on the NVIDIA Volta GPUs with Tensor Cores and what further optimizations were necessary to provide excellent scalability, including data input pipeline and communication optimizations, as well as gradient boosting for SGD-type solvers. Scalable deep learning becomes more and more important as datasets and deep learning models grow and become more complicated. This talk is targeted at deep learning practitioners who are interested in learning what optimizations are necessary for training their models efficiently at massive scale."
Watch the video: https://wp.me/p3RLHQ-kgT
Learn more: https://ml4sci.lbl.gov/home
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document summarizes four presentations from the USENIX NSDI 2016 conference session on resource sharing:
1. "Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics" proposes a framework that uses results from small training jobs to efficiently predict performance of data analytics workloads in cloud environments and reduce the number of required training jobs.
2. "Cliffhanger: Scaling Performance Cliffs in Web Memory Caches" presents algorithms to dynamically allocate memory across queues in Memcached to smooth out performance cliffs and potentially save memory usage.
3. "FairRide: Near-Optimal, Fair Cache Sharing" introduces a caching policy that provides isolation guarantees, prevents strategic behavior, and
A Domain-Specific Embedded Language for Programming Parallel Architectures.Jason Hearne-McGuiness
This document proposes a domain-specific embedded language (DSEL) for programming parallel architectures. The DSEL aims to enable parallelism while avoiding issues like deadlocks, race conditions, and complex APIs. It presents the grammar and properties of the proposed DSEL, including that it generates schedules that are deadlock-free and race-condition free. Examples demonstrating data flow and data parallelism using the DSEL are also provided.
This talk will examine issues of workflow execution, in particular using the Pegasus Workflow Management System, on distributed resources and how these resources can be provisioned ahead of the workflow execution. Pegasus was designed, implemented and supported to provide abstractions that enable scientists to focus on structuring their computations without worrying about the details of the target cyberinfrastructure. To support these workflow abstractions Pegasus provides automation capabilities that seamlessly map workflows onto target resources, sparing scientists the overhead of managing the data flow, job scheduling, fault recovery and adaptation of their applications. In some cases, it is beneficial to provision the resources ahead of the workflow execution, enabling the re-use of resources across workflow tasks. The talk will examine the benefits of resource provisioning for workflow execution.
How HPC and large-scale data analytics are transforming experimental scienceinside-BigData.com
In this deck from DataTech19, Debbie Bard from NERSC presents: Supercomputing and the scientist: How HPC and large-scale data analytics are transforming experimental science.
"Debbie Bard leads the Data Science Engagement Group NERSC. NERSC is the mission supercomputing center for the USA Department of Energy, and supports over 7000 scientists and 700 projects with supercomputing needs. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. She obtained her PhD at Edinburgh University, and has worked at Imperial College London as well as the Stanford Linear Accelerator Center (SLAC) in the USA, before joining the Data Department at NERSC, where she focuses on data-intensive computing and research, including supercomputing for experimental science and machine learning at scale."
Watch the video: https://wp.me/p3RLHQ-kLV
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The document discusses improving the file transfer service (FTS3) at CERN. It describes two aspects: 1) selecting the best source site for file transfers from among multiple replicas by considering factors like throughput and success rate, not just pending files, and 2) maximizing throughput across the WLCG network by increasing TCP buffer sizes either through Linux auto-tuning or manual configuration. Evaluating different techniques for setting optimal TCP buffer sizes could help effectively utilize available network resources and increase FTS3 transfer speeds.
This document discusses developing an online anomaly detection technique for time series data using Bayesian changepoint detection, K-means clustering, and hidden Markov models. The technique would detect changepoints in streaming time series data using Bayesian online changepoint detection. Machine learning models would be built by preprocessing, extracting features, and combining existing approaches. The model would be implemented in a scalable, online, distributed manner using tools like Kafka, Hadoop, Spark and Flink. Key aspects of the technique are discussed including the use of sliding windows in Flink to process streaming data and detect anomalies in new distributions based on learned patterns.
M phil-computer-science-secure-computing-projectsVijay Karan
The document contains details of several M.Phil Computer Science Secure Computing Projects implemented in C#. The projects deal with topics related to privacy, anonymity, and security in databases, distributed systems, wireless sensor networks, and social networks. Some of the project titles and brief descriptions are listed. The projects involve developing algorithms and protocols to privately update databases, assign anonymous IDs, detect clone attacks in wireless sensor networks, and design secure encounter-based mobile social networks.
M phil-computer-science-secure-computing-projectsVijay Karan
The document contains details of several M.Phil Computer Science Secure Computing Projects implemented in C#. The projects deal with topics related to privacy, anonymity, and security in distributed systems and networks. Some of the project titles and brief descriptions included in the document are:
- Privacy-Preserving Updates to Anonymous and Confidential Databases
- Replica Placement for Route Diversity in Distributed Hash Tables
- Robust Correlation of Encrypted Attack Traffic Through Stepping Stones Using Flow Watermarking
- A Policy Enforcing Mechanism for Trusted Ad Hoc Networks
- Adaptive Fault Tolerant QoS Control Algorithms for Maximizing System Lifetime of Query-Based Wireless Sensor Networks
My talk at the Winter School on Big Data in Tarragona, Spain.
Abstract: We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers.
This document provides an overview of Bionimbus and the Open Cloud Consortium (OCC). Bionimbus is an open source cloud for biomedical research that provides services like elastic computing, databases, data transport and analysis pipelines. The OCC operates open clouds and develops standards to bridge private and public clouds. It runs an Open Cloud Testbed and is working to build an Open Science Data Cloud. The OCC aims to develop interoperable cloud architectures and operate infrastructure at data center scale to support open science.
This document summarizes a computer physics communications article about the conditions database system for the COMPASS experiment. The key points are:
1) COMPASS integrated a conditions database system to manage time-dependent detector condition, calibration, and geometry alignment information using software from CERN.
2) The conditions database consists of administration tools, a data handling library, and software to transfer data from detector controls to the database.
3) Performance tests on the COMPASS computing farm showed the conditions database system was able to efficiently manage the large volumes of time-dependent experimental data needed for the COMPASS experiment.
Similar to PreCICE CHT with OpenFOAM and CalculiX (20)
FMU4FOAM is a FMU library of OpenFoam for combined with other solver like OpenModelica. This slide introduce FMU4FOAM outline and report executing the TempControlledFrange.
An individual conducted simulations of a milk crown using the interFoam solver in OpenFOAM on their personal computer. Parameters such as liquid film thickness, droplet velocity, mesh size, and computational domain geometry were varied in the simulations. While a mesh resolution of 0.025mm or finer was needed, the 10GB memory of the personal computer limited simulations to around 8 million cells. Further refinement of the model is still required to fully capture the formation of a milk crown, as only small droplets were observed upon collision in the simulations conducted.
moveEngineTopoChangerMesh is one of solvers in OpenFOAM. This slide is an instruction to use the solver for Kyoto Univ. engine of which piston face isn't flat.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.