Digital Twin Applications for Gas Network Optimization

Apr 03, 2025

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Digital Twin Applications for Gas Network Optimization

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Network Optimization

Network Optimization. Network optimization models:. Special cases of linear programming models. Important to identify problems that can be modeled as networks because:. (1). network representations make optimization. models easier to visualize and explain. (2).

1.25k views • 25 slides

Network Optimization

Network Optimization. Lectures 15,16 - 1. Taxonomy of TC. 1. 1. 1. 1. Unit Disk Graph. Consisted of a set V of n nodes Distributed in a two-dimensional plane Equipped an omnidirectional antenna with the same P max Unit disk graph, UDG ( V ). V. 0.8. 0.8. 0.4. 0.4. 0.5. 0.5.

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Network Optimization

Network Optimization. Lecture 15,16 - 2. Taxonomy of TC. Direction-based TC. Rely on the ability of the nodes to estimate the relative direction of their neighbors Less accurate information than knowing exact node locations

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Network Optimization

Network Optimization. Lecture 16 - 1. Critical Transmission Range (1/5). Definition 4.0.1 (CTR for connectivity) Suppose n nodes are placed in a certain region R = [0 , l ] d , with d = 1 , 2 , or 3 .

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Network Optimization

Network Optimization. Lecture 14 - 1. Minimum Energy Communication TC. Minimum Energy Communication TC. How to find minimum energy routes between two communication nodes? If global information is available , can be easily found by some shortest path algorithm (e.g. Dijkstra’s algorithm)

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Network Optimization

Network Optimization. Chapter 1 Some Graph Theory Concepts and Trees. 1.1 Some graph theory concepts. Graphs and digraphs

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Network Optimization

Network Optimization. Chapter 2 Transshipment Problem. 2 .1 The Network Simplex Method. G = (V, E) is a digraph. |V|=n , |E|=m. A type of commodity is shipped on the network. For each arc , is the cost of sending one unit of commodity from vertex i to vertex j .

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Network Optimization

Network Optimization. Flow Decomposition. The initial flow. 7. 2. 4. 4. 3. 3. 8. 2. -2. 1. 6. 4. 4. 2. 3. 5. 5. 9. A deficit node (more flow leaving than entering). An excess node (more flow entering than leaving). A balanced node (flow in = flow out).

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Network Optimization

Network Optimization. Lecture 15 - 3. Topology Control (TC). TC & Energy Conservation TC & Network Capacity Definition of TC Taxonomy of TC TC & Routing TC & MAC. Definition of TC (1/2). Topology control

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Airframe Digital Twin

Airframe Digital Twin. Material Scale. Integration of multiple models into a “Digital Twin” of an individual aircraft Integrated database of history Configuration control Virtual Damage Sensor. Structural Scale. 1 mm. Component Scale. Integrated Tool for Realizing CBM+SI.

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Network Optimization

Network Optimization. Lecture 16 - 2. Range Assignment Problems (RA). Problem Definition of RA Approximation for Two-dimension RA Symmetric Versions of RA. Problem Definition of RA (1/3). Range Assignment Problem Nodes can change the transmit power level in many scenarios

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Twin Wheel Applications

Twin Wheel Applications. Cold and dry air in demand!. RH 50 %. 16 º C. 20 º C. RH 75 %. In modern buildings of today, the need for a controlled temperature with an equally controlled humidity is increasing. Typical cooling and dehumidification enviroment.

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Network Optimization

Network Optimization. Chapter 4 Minimum Cost Flow Problems (MCF). 4 .1 The upper-bounded transshipment problem.

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Gas Distribution Network Optimization with Genetic Algorithm

Gas Distribution Network Optimization with Genetic Algorithm. Kuntjoro Adji S. Lala Septem Riza Kusuma Chasanah Widita Febi Haryadi. Introduction. N atural gas plays an important role in providing clean energy for the community .

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Network Optimization

Network Optimization. Chapter 3 Shortest Path Problems. 3 .1 Shortest paths from a single source. In a weighted digraph , a path of minimum weight from vertex v to vertex w is called a shortest path (SP) from v to w, and its length is called the shortest distance (SD) from v to w.

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Digital Twin Industry 2023 Forecasts for Global Regions by Applications & Manufacturing Technology

Global Digital Twin market research report 2018 provides detailed market forecast of Digital Twin, industry share analysis and an overview of key users and top players of the global market with a focus on Chinese market.

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Digital Twin Market

Digital Twin Market, by Application (Product Design and Development, Predictive Maintenance, Machine and Equipment Health Monitoring, and Dynamic Optimization), by End-user Industry (Aerospace and Defense, Healthcare, Home and Commercial, Electronics and Electrical/Machine Manufacturing, Automotive and Transportation, Energy and Utilities, Retail and Consumer Goods, and Others (Agriculture and Chemical)), and by Region (North America, Europe, Asia Pacific, Latin America, Middle East, and Africa) - Global Industry Insights, Trends, Outlook, and Opportunity Analysis, 2018-2026

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Global Digital Twin Market

Global Digital Twin Market was valued US$ 1.56 Bn in 2017 and is expected to reach US$ 17.54 Bn by 2026, at a CAGR of 35.32% during a forecast period.

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Digital Twin Market

Digital Twin Market by Component (Software, Services), Type (Component Twin/Part Twin, Asset Twin, Process Twin, System Twin/Unit Twin), Application, End-use Industry, and Geographyu2014Global Forecast to 2030

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Healthcare Digital Twin

Healthcare Digital Twin Market 2023 Outlook: Industry Trends, Segmentation, consumption by Regional data, Market Growth and Competitive Landscape

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Digital Twin Market

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Digital Twin Market

The Global Digital Twin Market was valued at USD 12.13 billion in 2022 and is estimated to reach approximately USD 225.84 billion by 2031, at a CAGR of 38.9% from 2023 to 2031.

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