Topology Of Solutions Research Articles

Network Robustness Based Transient Stability Optimisation via Scheduling and Switching

Security and transient stability (TS) constrained OPFs do not include network switching and often require excessive computation time due to discretization of the TS model. This paper proposes a non-simulation methodology to optimize active power dispatching and network switching to ensure TS and system security in the preventive control mode. Network switching is introduced in the security constrained OPF and classical TS models, giving a general <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic</i> security and TS constrained OPF. To ensure TS constraints are manageable in the optimization framework, TS is assessed by using proposed non-simulation based proxy measures. To this end, the key factors impacting TS are investigated and new composite robustness metrics for TS assessment are proposed; they consist of two terms, the nodal and network topology metrics. The former models generator features just before a fault, whilst the second considers impact of a fault on units. The general problem is then decomposed into two stages, the first being economic dispatching and the second optimization of network topology for TS via developed network robustness metrics. The minimum cost topology solutions are ranked by the TS performance. The proposed approach is tested on the IEEE68 Bus test system and compared with time-domain TS results.

Adaptive active Brownian particles searching for targets of unknown positions

Developing behavioral policies designed to efficiently solve target-search problems is a crucial issue both in nature and in the nanotechnology of the 21st century. Here, we characterize the target-search strategies of simple microswimmers in a homogeneous environment containing sparse targets of unknown positions. The microswimmers are capable of controlling their dynamics by switching between Brownian motion and an active Brownian particle and by selecting the time duration of each of the two phases. The specific conduct of a single microswimmer depends on an internal decision-making process determined by a simple neural network associated with the agent itself. Starting from a population of individuals with random behavior, we exploit the genetic algorithm NeuroEvolution of augmenting topologies to show how an evolutionary pressure based on the target-search performances of single individuals helps to find the optimal duration of the two different phases. Our findings reveal that the optimal policy strongly depends on the magnitude of the particle’s self-propulsion during the active phase and that a broad spectrum of network topology solutions exists, differing in the number of connections and hidden nodes.

Enabling Quasi-Static Reconfigurable Networks With Robust Topology Engineering

Many optical circuit switched data center networks (DCN) have been proposed in the last decade to attain higher capacity and topology reconfigurability, though commercial adoption of these architectures have been minimal. One major challenge these architectures face is the difficulty of handling uncertain traffic demands using commercial optical circuit switches (OCS) with high switching latency. Prior works have generally focused on developing fast-switching OCS prototypes to quickly react to traffic variations through frequent reconfigurations. This approach, however, adds tremendous complexity overhead to the control plane, and raises the barrier for commercial adoption of optical circuit switched data center networks. We propose, a robust topology and routing optimization framework for reconfigurable optical circuit switched data centers. co-optimizes topology and routing based on a convex set of traffic matrices, and offers strict throughput guarantees for any future traffic matrices bounded by the convex set. For the bursty traffic demands that are unbounded by the convex set, we employ a desensitization technique to reduce performance hit. This enables to generate topology and routing solutions capable of handling unexpected traffic changes without relying on frequent topology reconfigurations. Our extensive evaluations based on Facebook’s production DCN traces show that, even with daily reconfigurations which could be realized by current commercial MEMS-based OCSs from Calient Technologies, achieves about 20% lower max link utilization, and about 32% lower average hop count compared to cost-equivalent static topologies. Our work shows that adoption of reconfigurable topologies in commercial DCNs is feasible even without fast OCSs.

A performance-centric ML-based multi-application mapping technique for regular Network-on-Chip

This research article discusses the challenges faced by the Network-on-Chip (NoC) architecture due to increased integration density and proposes a novel fault-tolerant multi-application mapping approach called ”FANC.” The approach is based on Machine Learning (ML) and can provide solutions for unseen graphs and topologies without prior training. The proposed technique uses an ML-based model to extract relevant information from the search data and incorporate it into the search process. This results in a more robust model with a higher convergence rate and solution quality. The approach is evaluated using a variety of simulation parameters, such as communication cost, network latency, throughput, and power usage. Static simulations are performed in a Python programming environment, while dynamic simulations are performed with a SystemC-based cycle-accurate NoC simulator and the Orion2.0 Power tool. The results show that FANC reduces communication costs by 266%. It also improves network latency by 9%, throughput by 1%, and power consumption by 7%. The approach also simplifies and minimizes the search area in the design exploration process and can be used as an auxiliary component for other optimization algorithms.

Design of a 350 kW DC/DC Converter in 1200-V SiC Module Technology for Automotive Component Testing

In this paper, the design and implementation of a DC/DC converter for automotive component testing with state-of-the art performance is described. The converter is the core of a battery emulator for the characterization and development of automotive batteries, electronic chargers, traction inverters, DC-DC converters, E-motors and E-axles. Cutting edge performance, flexibility and compactness are obtained by exploiting 1200-V SiC modules, high switching frequency, planar transformer technology, suitable topology solutions and fast digital control strategies. The implemented system is a liquid-cooled, bidirectional converter with galvanic isolation capable of 350 kW continuous output power, output voltage range 48–1000 V, continuous output current up to 800 A (1600 A peak), voltage/current ramp-up time below 10/2 ms and 0.1% current/voltage accuracy. The entire instrument is implemented in a standard full-height 19-inch rack cabinet.

Simulation models and application options of electric power storage systems in traction power supply

Background: the determination of the energy indicators of the traction power supply system based on the train schedule in the conditions of operation of storage devices is based on the solution of a number of instantaneous schemes, each of which corresponds to the established operating mode. These methods require adaptation to account for non-linear characteristics or indicators, the value of which is associated with the retrospective of the change. In this regard, it is necessary to use calculation methods based on the solution of differential equations to solve problems related to the assessment of performance in transient or emergency modes, taking into account dynamic characteristics. Modeling the operation of storage devices in the traction power supply system allows you to evaluate the change in indicators depending on the parameters of the devices, traction load and circuit solutions for various topologies.&#x0D; Aim: to determine the structure of models of electric power storage devices in DC and AC traction power supply systems, to consider the results of calculating the main energy indicators, to determine ways to improve circuit solutions based on the identified features, to determine the technical requirements for electric power storage devices in traction power supply.&#x0D; Materials and Methods: to obtain the results, methods for solving differential equations corresponding to various variants of the topology of storage devices in traction power supply, implemented in the Matlab software package, are used.&#x0D; Results: the results obtained allow us to determine the structure of storage devices for traction power supply, evaluate models of devices for traction power supply of direct and alternating current, outline the prospects for further research in the field of circuit solutions and required device parameters.&#x0D; Conclusion: the results of the study allow models of electricity storage devices and technical solutions for their implementation aimed at introducing DC and AC traction power supply into the system.

Branching in flow networks with linear congestion

In our modern world, we rely on the proper functioning of a variety of networks with complex dynamics. Many of them are prone to congestion due to high loads, which determines their operation and resilience to failures. In this article, we propose a fundamental model of congestion where travel times increase linearly with the load. We show that this model interpolates between shortest path and Ohmic flow dynamics, which both have a broad range of applications. We formulate the model as a quadratic programme and derive a generalization of Ohm's law, where the flow of every link is determined by a potential gradient in a nonlinear way. We provide analytic solutions for fundamental network topologies that elucidate the transition from localized flow to a branched flow. Furthermore, we discuss how to solve the model efficiently for large networks and investigate the resilience to structural damages.

Biophysical mechanism underlying compensatory preservation of neural synchrony over the adult lifespan

We propose that the preservation of functional integration, estimated from measures of neural synchrony, is a key objective of neurocompensatory mechanisms associated with healthy human ageing. To support this proposal, we demonstrate how phase-locking at the peak alpha frequency in Magnetoencephalography recordings remains invariant over the lifespan in a large cohort of human participants, aged 18-88 years. Using empirically derived connection topologies from diffusion tensor imaging data, we create an in-silico model of whole-brain alpha dynamics. We show that enhancing inter-areal coupling can cancel the effect of increased axonal transmission delays associated with age-related degeneration of white matter tracts, albeit at slower network frequencies. By deriving analytical solutions for simplified connection topologies, we further establish the theoretical principles underlying compensatory network re-organization. Our findings suggest that frequency slowing with age- frequently observed in the alpha band in diverse populations- may be viewed as an epiphenomenon of the underlying compensatory mechanism.

ASSESSING THE ENERGY LOSSES ON BACKHAUL OF CELLULAR COMMUNICATION SYSTEMS

A mathematical model is proposed for assessing the energy consumption and energy efficiency of cellular communication systems, taking into account the possible energy losses on backhaul. It is shown that these losses depend on the applied technology options (fiber, microwave), topology solutions (star, tree, ring), and the area throughput.

DeepPlacer: A custom integrated OpAmp placement tool using deep models

Mechanisms towards the automatic analog integrated circuit layout design have been an intensive research topic in the past few decades. Still, the industrial environment has no automatic approach established. The advances of machine learning applications in electronic design automation come with the promise to change this reality. This paper proposes a deep learning generative model for the placement “optimization” of analog integrated circuit basic blocks. The model behaves as an argmin operator for the placement cost function and can provide placement solutions instantly. Moreover, the model can be fed with unlabeled data, greatly facilitating data collection. A generic and innovative circuits’ representation at the network’s input layer is proposed, encoding the devices’ dimensions, connectivity, and topological constraints. Besides, the randomness found in generative models is embedded directly into the feature vector, as the order of the features per device is shuffled in the input vector. Shuffling the order of the devices’ features in the input not only brings multi-modality but also solves a generalization problem, as there is not any natural order defined to place devices in the feature vector. As a proof of concept, a deep artificial neural network capable of proposing different placement solutions, in less than 150 ms each, for six amplifier topologies and, in multiple technology nodes ranging from 350 nm down to 65 nm, is demonstrated. DeepPlacer was capable of producing correct solutions for topologies and technology nodes not present in the training set, showing good generalization while not hindering circuit performance due to the placement.

Analysis of Carrier-Based Aircraft Catapult Launching Based on Variable Topology Dynamics

The catapult process of a carrier-based aircraft includes multiple links such as catapult tensioning, separation of the holding rod, dragging and running, separation of the catapult and drag shuttle, and free running. The connection relationships between the front landing gear of the carrier-based aircraft and other related components in each link are different, therefore, it is necessary to adjust the topological relationships of the dynamic model in real time, when solving the catapult dynamics of a carrier-based aircraft. In this paper, a dynamic model of the multibody system of the catapult take-off is established, and a variable topology solution is carried out for the dynamic model by adjusting dynamic augmentation equations; in addition, a dynamic analysis of a carrier-based aircraft catapult and take-off process is carried out. A catapult dynamics model and variable topology solution method were established, which solved the changes at the moment of the restraining rod separation, catapult rod separation, and catapult tackle during the aircraft catapult take-off. After the restraining rod was separated from the front landing gear, the catapult force was transmitted to the rear strut, which instantly increased the load of the rear strut by 238.5 kN. In addition, after the carrier-based aircraft reached the end of the catapult’s stroke, the catapult rod was separated from the catapult tow shuttle then unloaded, and the load of the rear strut was reduced from 486.2 kN to −20.3 kN. Under the protruding effect of the nose gear, the pitch angle of the carrier-based aircraft increased rapidly from −0.93° and reached 0.54° when the carrier-based aircraft rushed out of the deck.

Multi-component topology optimization for thin-walled structures

This article presents a multi-component topology optimization method for thin-walled structures, which can be applied to two-dimensional plate components and three-dimensional tube components. Based on the theory of multi-component topology optimization for stamped sheet metal assemblies, an accurate calculation method for the Young’s modulus of joint elements is provided. To avoid the discontinuous derivative and improve the optimization efficiency, a non-zero gradient-based material interpolation scheme with penalization is constructed. The cost of die-set materials can be controlled effectively by the maximum size of components. Several topology designs based on minimized compliance confirm the effectiveness of the proposed method. The topology solutions illustrate that the proposed method can realize the structural design of thin-walled structures which can meet the die-set material cost requirement.

A self-stabilizing Hashed Patricia Trie

While many research in distributed computing has covered solutions for self-stabilizing computing and topologies, there is far less work on self-stabilization for distributed data structures. However, when peers in peer-to-peer networks crash, a distributed data structure may not remain intact. We present a self-stabilizing protocol for a distributed data structure called the Hashed Patricia Trie (Kniesburges and Scheideler WALCOM'11) that enables efficient prefix search on a set of keys. The data structure has many applications while offering low overhead and efficient operations when embedded on top of a Distributed Hash Table. Especially, longest prefix matching for x can be done in O(log⁡|x|) hash table read accesses. We show how to maintain the structure in a self-stabilizing way, while assuring a low overhead in a legal state and an asymptotically optimal memory demand of Θ(d) bits, where d is the number of bits needed for storing all keys.

Large-scaled truss topology optimization with filter and iterative parameter control algorithm of Tikhonov regularization

There are recently some advances in solving numerically topology optimization problems for large-scaled trusses based on ground structure approach. A disadvantage of this approach is that the final design usually includes many bars, which is difficult to be produced in practice. One of efficient tools is a so-called filter scheme for the ground structure to reduce this difficulty and determine several distinct bars. In detail, this technique is valuable for practical uses because unnecessary bars are filtered out from the ground structure to obtain a well-defined structure during the topology optimization process, while it still guarantees the global equilibrium condition. This process, however, leads to a singular system of equilibrium equations. In this case, the minimization of least squares with Tikhonov regularization is adopted. In this paper, a proposed algorithm in controlling optimal Tikhonov parameter is considered in combination with the filter scheme due to its crucial role in obtaining solution to remove numerical singularity and saving computational time by using sparse matrix, which means that the discrete optimal topology solutions depend on choosing the Tikhonov parameter efficiently. Several numerical examples are investigated to demonstrate the efficiency of the filter parameter control algorithm in terms of the large-scaled optimal topology designs.

Multi-Objective optimal design of a cable driven parallel robot for rehabilitation tasks

This paper addresses the topological and dimensional synthesis of cable-driven parallel robots. A combined methodology for type and size optimization is proposed and applied to a cable driven parallel robot, which is intended for upper-limb rehabilitation exercises. The proposed approach deals with a set of deterministic and non-deterministic parameters within a multi-objective genetic algorithm. The proposed study aims to select a suitable architecture having optimal dimensions by considering several optimality criteria such as minimizing the cable tensions and achieving the smallest footprint. An illustrative application of the proposed synthesis approach is developed for LAWEX, a cable driven parallel robot for upper limb rehabilitation. Four different topology solutions have been considered for LAWEX robot. It is also proposed to use an additional safety criterion to select a solution on the obtained Pareto front.

ILP formulations for monitoring-cycle design based on segment routing

The minimum cycle cover problem in segment routing is to find a set of monitoring cycles such that (a) they originate from the same vantage point, (b) cover every directed link in the network, (c) each cycle can be encoded by a segment list smaller than a given maximum size, and (d) the total length of all cycles is minimized. To solve the problem using Integer Linear Programming (ILP), the key challenge is how to capture all the subtle features of segment routing using linear constraints. In this paper, five original ILP formulations, numbered from ILP1 to ILP5, are designed. In ILP1, the classic flow-based approach is adopted for constructing cycles. In ILP2, a more efficient voltage-based approach is adopted. We then extend ILP2 to ILP3 to jointly minimize the total segment list size. Since the time required to detect a network failure is determined by the longest cycle, we also extend ILP2 to ILP4 to jointly minimize the length of the longest cycle. As finding optimal solutions for large-sized topologies is time-consuming, we propose to limit the set of candidate segments for constructing cycles. Although the resulting ILP5 cannot guarantee optimal solutions, we show that it always outperforms the best heuristic algorithm. Finally, the performance gain of using a dedicated monitoring topology for probe packets, as adopted by all existing heuristic algorithms, is studied.

Research on Hybrid Cascaded Multi-terminal HVDC Transmission and Its Dynamic Model Test

Hybrid cascaded multi-terminal DC transmission topology scheme adopts LCC converter at the rectification side, inverter side uses high-end LCC converters in series with low-end multiple VSC converters. This topology solution can make full use of the advantages of LCC and VSC technology. As a new generation of HVDC transmission technology, hybrid cascade multiterminal still has many key technologies to broken. Through the development of hybrid cascade multiterminal HVDC dynamic simulation test system, the start-up, shutdown, high and low valves switching-back and DC fault self-cleaning of hybrid cascade multiterminal HVDC transmission are studied and tested. It provides technical support for the engineering application of hybrid cascade multi-terminal HVDC transmission technology.

Optimization of compliant adaptive structures in the design of a morphing droop nose

A design procedure for the synthesis of active camber morphing wing devices is proposed. A topology optimization initially defines the internal structure that is further enhanced by structural size and shape optimizations, and these optimizations are based on the distributed compliance concept. The size optimization enables the adaption of the topology solution to other materials and geometries while refining the topology solution to improve the shape quality of the skin deformation. Then, the structural shape optimization enables the reduction of the stress peaks inside the compliant structure and the finalization of the details to obtain a solution that is closer to the manufacturing process stage. The proposed methodology is used in the design of an adaptive droop nose to be installed on a reference regional aircraft, and two different design applications are considered. The first application is the validation of the procedure at the full–scale level using a superelastic material for the internal structure. The second application is the design of a corresponding 3D–printed prototype, in which both geometry and material changes are considered, for experimental validation. The results show satisfactory shape quality and the achievement of structural feasibility. The experimental functional test of the scaled prototype demonstrates the effectiveness of the adopted morphing solution.

OPENING DESIGN FOR DOUBLE-LAYER CONCRETE BOX GIRDER BASED ON TOPOLOGY OPTIMIZATION

The double-deck concrete box girder is composed of two kinds of material, steel and concrete, and their working properties can be represented by a tie rod-compression rod model composed of steel and concrete. A new topology optimization method called progressive structure optimization is introduced to establish the tie rod-compression rod model reflecting the working mechanism of the structure, which provides theoretical guidance for simplifying the opening design of the double-layer concrete box girder web. This paper introduces the optimal topology solution to establish the tie rod-compression rod model and how to complete the structural reinforcement design. The result shows that the topological optimal solution obtained by the windowed progressive structure optimization algorithm can better represent the internal stress state of the structure; and by the optimization method, the opening ratio of I-beam structure is increased by 5.5 times compared with the experimental design, the ultimate load is increased by 1.2 times, but the amount of steel is reduced by 30%.

Conceptual Insightful Synthesis of Spatial Compliant Mechanisms Using the Load Flow Formulation

Abstract Conceptual design of spatial compliant mechanisms with distinct input and output ports may be hard because of its complex interconnected topology and is currently accomplished by computationally intensive automated techniques. This paper proposes a user insightful method for generating conceptual compliant topology solutions. The method builds on recent advances where the compliant mechanism deformation is represented as load flow in its constituent members. The nature of load flow enables functional decomposition of compliant mechanisms into maximally decoupled building blocks, namely, a transmitter member and a constraint member. The proposed design methodology seeks to synthesize spatial compliant designs by systematically combining transmitter-constraint members first, identifying kinematically feasible transmitter load paths between input(s) and output(s), and then selecting appropriate constraints that enforce the load path. The paper proposes four design steps to generate feasible solutions and four additional guidelines to optimize load paths and constraint orientations. The method is applied with equal ease to three spatial complaint mechanism examples that belong to single-input single-output, multiple-input single output, and single-input multiple-output mechanisms.