The document discusses turbulence near walls and different approaches to modeling it in computational fluid dynamics (CFD). It explains that the boundary layer can be divided into different zones, and CFD requires different considerations depending on whether the viscous sub-layer is solved, the log-law layer is modeled, or the whole boundary layer is solved. It also discusses the use of non-dimensional variables to characterize the boundary layer and describes different near-wall treatments in CFD, including resolving the boundary layer with fine meshes or using wall functions with coarser meshes.
The document discusses reactive flow modeling using the eddy dissipation model (EDM) in ANSYS Fluent. EDM solves conservation equations for chemical species by predicting local mass fractions through a convection-diffusion equation. Reaction rates are assumed to be controlled by turbulence, ignoring chemical timescales. EDM gives the smaller of two expressions to calculate reaction rates, with the chemical reaction rate governed by the large eddy mixing timescale. EDM is computationally cheap but works best for one-two step global reactions, as it cannot capture detailed chemistry effects.
01 reactive flows - governing equations favre averaging Mohammad Jadidi
This document discusses reactive flow modeling in combustion chambers. It covers the equations governing reacting flows, including conservation equations for mass, momentum, molecular species, and energy. It also discusses the equation of state and turbulence transport. The document then covers statistical descriptions of turbulent flows using Reynolds decomposition and Favre averaging. Favre averaging is preferred for reacting flows with variable density as it leads to simpler expressions in the transport equations for continuity, momentum, species, and energy compared to Reynolds averaging. Various terms that arise in the averaged equations require turbulence modeling approaches.
The document discusses the governing equations for reacting flows, including conservation of mass, momentum, molecular species, and energy. It outlines the continuity, momentum, species transport, and energy equations. The species transport equation accounts for convection, diffusion, and chemical reaction sources. The energy equation considers changes in enthalpy due to convection, diffusion, pressure work, and radiation. Simplifications are discussed under certain assumptions, such as a single diffusion coefficient and negligible pressure work/radiation terms, in which case enthalpy behaves as a passive scalar. Other relationships presented include the equation of state and definitions of specific heat capacity and density.
01 reactive flows - finite-rate formulation for reaction modelingMohammad Jadidi
This document discusses equations governing reacting flows as modeled in ANSYS Fluent. It describes how Fluent solves conservation equations for species mass fractions using a convection-diffusion equation, where the chemical source term Ri accounts for reaction rates. Finite-rate kinetics and turbulence-chemistry interaction models are discussed for determining Ri, including the eddy dissipation model. The Arrhenius equation is also presented for calculating forward reaction rate constants based on pre-exponential factors, temperature exponents, and activation energies specified in the kinetic mechanism.
The document discusses different types of multiphase flows. It defines multiphase flow as any fluid system with two or more distinct phases flowing simultaneously in mixture. Multiphase flows are classified into four main categories: gas-liquid flows, gas-solid flows, liquid-solid flows, and three-phase flows. Each category contains different flow regimes depending on factors like particle size and flow rates. Flow maps are used to characterize different flow patterns that can occur for a given system.
This document discusses turbulence modeling in computational fluid dynamics (CFD). It contains three main points:
1. Turbulence models used in CFD simulations like RANS and LES are introduced. Important turbulence concepts such as eddies, length scales, and the energy cascade are explained.
2. Reynolds-averaged Navier-Stokes (RANS) equations are presented along with Reynolds stress tensor and turbulent heat flux terms. Common RANS turbulence models and their governing equations are outlined.
3. Large eddy simulation (LES) is described as an alternative to RANS. Filtering operations in LES to separate large and small scales are discussed. Root-mean-square velocities are presented as a
Large eddy simulation (LES) is a computational fluid dynamics technique that resolves the larger turbulent scales in the fluid flow while modeling the smaller scales. LES aims to directly simulate the larger turbulent scales while parameterizing the effects of smaller scales through a subgrid scale model. LES requires significantly more computational resources than Reynolds-averaged Navier–Stokes (RANS) modeling but provides more detailed turbulent flow information.
The document discusses turbulence near walls and different approaches to modeling it in computational fluid dynamics (CFD). It explains that the boundary layer can be divided into different zones, and CFD requires different considerations depending on whether the viscous sub-layer is solved, the log-law layer is modeled, or the whole boundary layer is solved. It also discusses the use of non-dimensional variables to characterize the boundary layer and describes different near-wall treatments in CFD, including resolving the boundary layer with fine meshes or using wall functions with coarser meshes.
The document discusses reactive flow modeling using the eddy dissipation model (EDM) in ANSYS Fluent. EDM solves conservation equations for chemical species by predicting local mass fractions through a convection-diffusion equation. Reaction rates are assumed to be controlled by turbulence, ignoring chemical timescales. EDM gives the smaller of two expressions to calculate reaction rates, with the chemical reaction rate governed by the large eddy mixing timescale. EDM is computationally cheap but works best for one-two step global reactions, as it cannot capture detailed chemistry effects.
01 reactive flows - governing equations favre averaging Mohammad Jadidi
This document discusses reactive flow modeling in combustion chambers. It covers the equations governing reacting flows, including conservation equations for mass, momentum, molecular species, and energy. It also discusses the equation of state and turbulence transport. The document then covers statistical descriptions of turbulent flows using Reynolds decomposition and Favre averaging. Favre averaging is preferred for reacting flows with variable density as it leads to simpler expressions in the transport equations for continuity, momentum, species, and energy compared to Reynolds averaging. Various terms that arise in the averaged equations require turbulence modeling approaches.
The document discusses the governing equations for reacting flows, including conservation of mass, momentum, molecular species, and energy. It outlines the continuity, momentum, species transport, and energy equations. The species transport equation accounts for convection, diffusion, and chemical reaction sources. The energy equation considers changes in enthalpy due to convection, diffusion, pressure work, and radiation. Simplifications are discussed under certain assumptions, such as a single diffusion coefficient and negligible pressure work/radiation terms, in which case enthalpy behaves as a passive scalar. Other relationships presented include the equation of state and definitions of specific heat capacity and density.
01 reactive flows - finite-rate formulation for reaction modelingMohammad Jadidi
This document discusses equations governing reacting flows as modeled in ANSYS Fluent. It describes how Fluent solves conservation equations for species mass fractions using a convection-diffusion equation, where the chemical source term Ri accounts for reaction rates. Finite-rate kinetics and turbulence-chemistry interaction models are discussed for determining Ri, including the eddy dissipation model. The Arrhenius equation is also presented for calculating forward reaction rate constants based on pre-exponential factors, temperature exponents, and activation energies specified in the kinetic mechanism.
The document discusses different types of multiphase flows. It defines multiphase flow as any fluid system with two or more distinct phases flowing simultaneously in mixture. Multiphase flows are classified into four main categories: gas-liquid flows, gas-solid flows, liquid-solid flows, and three-phase flows. Each category contains different flow regimes depending on factors like particle size and flow rates. Flow maps are used to characterize different flow patterns that can occur for a given system.
This document discusses turbulence modeling in computational fluid dynamics (CFD). It contains three main points:
1. Turbulence models used in CFD simulations like RANS and LES are introduced. Important turbulence concepts such as eddies, length scales, and the energy cascade are explained.
2. Reynolds-averaged Navier-Stokes (RANS) equations are presented along with Reynolds stress tensor and turbulent heat flux terms. Common RANS turbulence models and their governing equations are outlined.
3. Large eddy simulation (LES) is described as an alternative to RANS. Filtering operations in LES to separate large and small scales are discussed. Root-mean-square velocities are presented as a
Large eddy simulation (LES) is a computational fluid dynamics technique that resolves the larger turbulent scales in the fluid flow while modeling the smaller scales. LES aims to directly simulate the larger turbulent scales while parameterizing the effects of smaller scales through a subgrid scale model. LES requires significantly more computational resources than Reynolds-averaged Navier–Stokes (RANS) modeling but provides more detailed turbulent flow information.
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%.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.