Adjacent Sub-networks Research Articles

Supramolecular Entanglement in a Hydrogen‐Bonded Organic Framework Enables Flexible‐Robust Porosity for Highly Efficient Purification of Natural Gas

AbstractThe development of porous materials with flexible‐robust characteristics shows some unique advantages to target high performance for gas separation, but remains a daunting challenge to achieve so far. Herein, we report a carboxyl‐based hydrogen‐bonded organic framework (ZJU‐HOF‐8a) with flexible‐robust porosity for efficient purification of natural gas. ZJU‐HOF‐8a features a four‐fold interpenetrated structure with dia topology, wherein abundant supramolecular entanglements are formed between the adjacent subnetworks through weak intermolecular hydrogen bonds. This structural configuration could not only stabilize the whole framework to establish the permanent porosity, but also enable the framework to show some flexibility due to its weak intermolecular interactions (so‐called flexible‐robust framework). The flexible‐robust porosity of ZJU‐HOF‐8a was exclusively confirmed by gas sorption isotherms and single‐crystal X‐ray diffraction studies, showing that the flexible pore pockets can be opened by C3H8 and n‐C4H10 molecules rather by C2H6 and CH4. This leads to notably higher C3H8 and n‐C4H10 uptakes with enhanced selectivities than C2H6 over CH4 under ambient conditions, affording one of the highest n‐C4H10/CH4 selectivities. The gas‐loaded single‐crystal structures coupled with theoretical simulations reveal that the loading of n‐C4H10 can induce an obvious framework expansion along with pore pocket opening to improve n‐C4H10 uptake and selectivity, while not for C2H6 adsorption. This work suggests an effective strategy of designing flexible‐robust HOFs for improving gas separation properties.

Supramolecular Entanglement in a Hydrogen-Bonded Organic Framework Enables Flexible-Robust Porosity for Highly Efficient Purification of Natural Gas.

The development of porous materials with flexible-robust characteristics shows some unique advantages to target high performance for gas separation, but remains a daunting challenge to achieve so far. Herein, we report a carboxyl-based hydrogen-bonded organic framework (ZJU-HOF-8a) with flexible-robust porosity for efficient purification of natural gas. ZJU-HOF-8a features a four-fold interpenetrated structure with dia topology, wherein abundant supramolecular entanglements are formed between the adjacent subnetworks through weak intermolecular hydrogen bonds. This structural configuration could not only stabilize the whole framework to establish the permanent porosity, but also enable the framework to show some flexibility due to its weak intermolecular interactions (so-called flexible-robust framework). The flexible-robust porosity of ZJU-HOF-8a was exclusively confirmed by gas sorption isotherms and single-crystal X-ray diffraction studies, showing that the flexible pore pockets can be opened by C3H8 and n-C4H10 molecules rather by C2H6 and CH4. This leads to notably higher C3H8 and n-C4H10 uptakes with enhanced selectivities than C2H6 over CH4 under ambient conditions, affording one of the highest n-C4H10/CH4 selectivities. The gas-loaded single-crystal structures coupled with theoretical simulations reveal that the loading of n-C4H10 can induce an obvious framework expansion along with pore pocket opening to improve n-C4H10 uptake and selectivity, while not for C2H6 adsorption. This work suggests an effective strategy of designing flexible-robust HOFs for improving gas separation properties.

Topological and dynamic complexity of the Pearl River Delta and its responses to human intervention

River networks of deltas are complex systems, responsible for the transport of water, sediment and nutrients between land and sea. Topological complexity, dynamic complexity and flux vulnerability are three characteristics to describe the material transport within a deltaic river network, and they reflect the channel number and connectivity, flux distribution, and sensitivity of flux to external disturbances, respectively. Here, graph theory was applied to estimate the three characteristics of the Pearl River Delta (PRD) in 1977 and 2008, when the human-induced uneven riverbed downcutting occurred on a large scale. By identifying the channels through which water flows from the apex to each outlet, the eight subnetworks corresponding to the eight outlets were distinguished. Although the topological complexity changes little during this period, the dynamic complexity and vulnerability have great adjustments. The dynamic complexity greatly decreases in the subnetworks that have larger riverbed downcutting comparing to their adjacent subnetworks. We further reveal the positive relation between the decrease in dynamic complexity and human-induced riverbed downcutting under the same topological complexity. Moreover, the local vulnerabilities of channels with relatively high riverbed downcutting increase, leading to the emergence of “high vulnerability channels”. These findings suggest that the human-induced uneven riverbed downcutting is the main reason for the adjustments of dynamic complexity and vulnerability. The results obtained from this study may provide scientific guidance for the further development and utilization in the PRD and other deltaic river systems worldwide subject to intensive human intervention.

Distributed Adaptive Robust Voltage/VAR Control With Network Partition in Active Distribution Networks

This paper proposes a distributed adaptive robust voltage/var control (DAR-VVC) method in active distribution networks to minimize power loss while keeping operating constraints under uncertainties. The DAR-VVC aims to coordinate on-load tap changers, capacitor banks and PV inverters in multiple operation stages through a distributed algorithm. To improve efficiency of the distributed algorithm, an affinity propagation clustering algorithm is employed to divide the distribution network by aggregating “the close nodes” together and setting “the far nodes” apart, leading to the network partition where the information exchange between adjacent sub-networks is reduced. Moreover, the virtual load which describes load characteristics of the sub-networks is applied to enhance the boundary conditions. To fully deal with the uncertainties, the proposed DAR-VVC is formulated in a robust optimization model which considers the worst case to guarantee solution robustness against uncertainty realization. Besides, this paper develops an alternating optimization procedure integrating a column-and-constraint generation algorithm and an alternating direction method of multipliers to solve the DAR-VVC problem. The proposed approach is tested on IEEE 33 and IEEE 123 bus distribution test system and numerical simulations verify high efficiency and full solution robustness of the DAR-VVC.

Range-Based Localization for Sparse 3-D Sensor Networks

Localization plays a pivotal role in wireless sensor networks. Many range-based localization algorithms have been proposed for 2-D sensor networks or densely deployed 3-D sensor networks. However, range-based localization in sparse 3-D sensor networks is still a challenging problem, because the sparseness of the network makes it difficult to obtain a proper order of nodes to be sequentially localized. The patch-and-stitching localization strategy can conquer the sparseness problem in 2-D networks, but for 3-D networks it is still unknown how to uniquely merge two patches when there are not enough common nodes. In this paper, we solve this challenging problem by deriving the conditions under which two subnetworks can be uniquely merged. In the proposed approach, we treat the translation parameters as unknowns and form a set of equations with which the unknowns can be uniquely solved. The novelty of our algorithm also lies in that we exploit both common nodes and connecting edges among adjacent subnetworks to merge them, resulting in very high chances that two subnetworks can be merged. We conduct extensive simulation experiments to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm could localize more than 90% of nodes in sparse 3-D networks with average node degree of 11 and anchor ratio of 5%, while the best existing solution can localize only 52% of nodes in the same situation.

Decentralized Optimal Reactive Power Dispatch of Optimally Partitioned Distribution Networks

This paper proposes a two-stage decentralized optimal reactive power dispatch (D-ORPD) framework, which considers the network partitioning influence in distributed optimization. The first stage is to divide the distribution networks (DNs) into several high-intra-cohesion and low-coupling sub-networks while the mathematical model is constructed based on partition indexes. This optimal partition designation reduces information exchange between adjacent sub-networks. Then, with the reduced information exchange, the distributed computation would be accelerated. Based on the network optimal partition, the D-ORPD model is constructed in a “decomposition-coordination” pattern during the second stage. To enhance the consistency between distributed and centralized optimization, the D-ORPD is improved by the proposed virtual load, which simulates the load characteristic of sub-networks. The alternating directions method of the multipliers (ADMM) algorithm is utilized to solve the mathematical model effectively. Moreover, case studies on the PG&E 69 bus test system and the IEEE 123 bus test system are executed by the MATLAB platform to demonstrate the validity and effectiveness of the proposed method.

Divisional fault diagnosis of large-scale power systems based on radial basis function neural network and fuzzy integral

This paper proposes an effective method for fault diagnosis of large-scale power systems based on radial basis function (RBF) neural network (NN) and fuzzy integral. It aims at effectively diagnosing the tie lines which connect different adjacent sub-networks in the context of divisional fault diagnosis. First, an overlapping network division method is proposed to divide a large-scale power system into a desired number of eligible sub-networks. Then, for each sub-network, a local RBF NN diagnostic module which is constructed by an exhaustive search-assisted forward recursive algorithm is allocated. Finally, a Choquet fuzzy integral fusion module is constructed for any pair of connected sub-networks. When a fault occurs, local RBF NN diagnostic modules will be selectively triggered according to local alarm information. If it involves a tie line, the corresponding Choquet fuzzy integral fusion module will be triggered to fuse the diagnostic outputs derived from the adjacent sub-networks which are connected by the tie line. Case studies with a 14-bus power system are presented to evaluate the feasibility and efficiency of the proposed method under various complex fault scenarios. The diagnostic results demonstrate that this proposed method is efficient in identifying faults within local sub-networks as well as those on the tie lines with strong fault tolerance and high diagnostic accuracy.

Efficient multiway graph partitioning method for fault section estimation in large-scale power networks

Fault section estimation (FSE) of large-scale power networks can be implemented effectively by the distributed artificial intelligence (AI) technique. In this paper, an efficient multiway graph partitioning method is proposed to partition the large-scale power networks into the desired number of connected subnetworks with balanced working burdens in performing FSE. The number of elements at the frontier of each subnetwork is also minimised in the method. The suggested method consists of three basic steps: forming the weighted depth-first-search tree of the studied power network; partitioning the network into connected, balanced subnetworks and minimising the number of the frontier nodes of the subnetworks through iterations so as to reduce the interaction of FSE in adjacent subnetworks. The relevant mathematical model and partitioning procedure are presented. The method has been implemented with the sparse storage technique and tested in the IEEE 14-bus, 30-bus and 118-bus systems, respectively. Computer simulation results show that the proposed multiway graph partitioning method is effective for the large-scale power system FSE using the distributed AI technique.

Scalable WDM access network architecture based on photonic slot routing

This paper introduces an approach to solving the fundamental scalability problem of all-optical packet switching wavelength-division multiplexing (WDM) access networks. Current optical networks cannot be scaled by simply adding nodes to existing systems due to the accumulation of insertion losses and/or the limited number of wavelengths. Scalability through bridging requires, on the other hand, the capability to switch packets among adjacent subnetworks on a wavelength basis. Such a solution is, however, not possible due to the unavailability of fast-switching wavelength sensitive devices. In this paper, we propose a scalable WDM access network architecture based on a recently proposed optical switching approach, termed photonic slot routing. According to this approach, entire slots, each carrying multiple packets (one on each wavelength) are "transparently" routed through the network as single units so that wavelength sensitive data flows can be handled using fast-switching wavelength nonsensitive devices based on proven technologies. The paper shows that the photonic slot routing technique can be successfully used to achieve statistical multiplexing of the optical bandwidth in the access network, thus providing a cost-effective solution to today's increasing bandwidth demand for data transmissions.

An analysis of interfacing between signal subnetworks

AbstractThe objective of this study is to analyze the behavior of offsets between traffic signal subnetworks. If different cycle lengths are used for adjacent subnetworks, the relative timing between the subnetworks will deviate from the original setup. A generalized mathematical model is developed, which demonstrates the cyclic timing patterns of offsets between subnetworks with different cycle lengths. The offset cycle time is equal to the Least Common Multiple (LCM) of the cycle lengths of the adjacent subnetworks. A TRAF‐NETSIM model is used to examine how the cyclic behavior of offsets should be considering in timing the subnetworks, and in reconciling different offset requirements between more than two subnetworks.