Steady-state Network Research Articles

The endoplasmic reticulum as an active liquid network

The peripheral endoplasmic reticulum (ER) forms a dense, interconnected, and constantly evolving network of membrane-bound tubules in eukaryotic cells. While individual structural elements and the morphogens that stabilize them have been described, a quantitative understanding of the dynamic large-scale network topology remains elusive. We develop a physical model of the ER as an active liquid network, governed by a balance of tension-driven shrinking and new tubule growth. This minimalist model gives rise to steady-state network structures with density and rearrangement timescales predicted from the junction mobility and tubule spawning rate. Several parameter-independent geometric features of the liquid network model are shown to be representative of ER architecture in live mammalian cells. The liquid network model connects the timescales of distinct dynamic features such as ring closure and new tubule growth in the ER. Furthermore, it demonstrates how the steady-state network morphology on a cellular scale arises from the balance of microscopic dynamic rearrangements.

Validation of a calibrated steady-state heat network model using measured data

Expanding and modernizing District Heating (DH) systems in cities is a concrete way to foster the decarbonization of the heating sector. Therefore, methods that allow for faster and accurate simulations when limited information about the network is available are required. This paper proposes a method for employing a steady-state model with relatively low computational effort, enabling the creation of a rather coarse model of a heating network. This model is parameterised by making assumptions about the network’s topology and pipe characteristics. Later, the model is calibrated using a heuristic approach, in which the combination of the heat transfer coefficient and the length parameters of each pipe in the network are selected as the calibration target vector, meaning that these initial assumptions of the network are no longer required. The approach is validated using measured data from a specific case study and compares the results of the uncalibrated model with those of a calibrated one. After calibration, the model was found to obtain a mean absolute temperature error below 0.5 °C.

A highly robust gas network simulation approach through an inherently solvable problem formulation for network states far from intended design points

Pipeline networks are an efficient and widespread transportation system for supplying natural gas as well as increasingly green gas to Europe. However, they are exposed to risks arising from environmental factors, accidents, crime and political issues. This work contributes to the assessment of gas transmission networks and enables a targeted improvement of their resilience. Therefore, the objective of this work is to develop a simulation tool that enables gas transmission system operators (TSOs) to identify weak spots, e.g. potential bottle necks, in their pipeline networks. To this end, a steady-state gas network simulation approach is developed that provides fast results for gas supply deficiencies for complete sets of multi-event disruption scenarios, enabling the identification of the most important network elements. This paper describes how to implement a computationally fast, numerically robust, and physically accurate gas network simulation and assessment tool. By solving mass and momentum balance equations, it accounts for key network elements such as flow control valves, pressure control valves, compressor stations, gas sources, and gas consumers. The central feature that distinguishes this approach from classical steady state solvers is the robustness of the gas flow calculation through the choice of a problem boundary condition formulation, which results in an inherently solvable system of equations. This feature is key in ensuring mathematical convergence of predictions for gas network states that strongly deviate from their original design point, where boundary conditions must not become mathematically invalid but need to reflect physically reasonable behavior. The capabilities of the system are demonstrated using a fictitious gas network and a representative national gas transmission network. Finally, the limits of applicability are outlined based on the size of the network under consideration and the types of analyses.

Numerical Solution of the Steady-State Network Flow Equations for a Nonideal Gas

Numerical Solution of the Steady-State Network Flow Equations for a Nonideal Gas

Large-scale closed and generalized networks of ribosome flow model with different site sizes

Dynamical systems play a key role in understanding the different biophysical aspects of many complex transport phenomena. The ribosome flow model with different site sizes (RFMD) is a dynamical system relevant to model various biological and physical processes such as phosphorelay, motion of vehicles along an isolated road, and more. However, many transport processes take place concurrently which give rise to networks comprising multiple tracks e.g., vehicular motion, cargo transport along the cytoskeletal highway, etc. Here, we present two large-scale network models: RFMDs network with a pool (RFMDNP), and the generalized network of RFMDs (RFMDN). The RFMDNP is a closed system that can model the simultaneous movement of particles along tracks having sites of different capacities in a resource-limited environment. We prove that the RFMDNP admits a continuum of linearly ordered steady-state points and it always phase-locks with the periodic excitations in the parameters. Furthermore, we also show that increasing any of the parameters in an RFMD leads to an increase in the output rate of this RFMD, and the output rate of other RFMDs all increase or decrease. Next, we analyze a generalized network of RFMDs that models static connections between the RFMDs and prove the important mathematical properties: (a) the network admits a unique steady-state, and (b) the problem of determining the connecting weights to maximize the steady-state network output rate is a convex optimization problem. We believe that these networks can have wide applicability to model various non-linear phenomena since their behavior is predictable and ordered.

Theoretical Proposal for a New Model of Cylindrical Biogas Combustion Cook Stove

<p>The cook stove is usually constructed from black-painted sheet metal, to better absorb energy. To enhance thermal performance, a theoretical cylindrical design is proposed in this article. This article aims to study this design and compare its performance with the conical design. Theoretical modeling of the cook stove was carried out considering heat transfer by radiation, convection, and conduction based on a steady-state thermal network, and solved using the Matlab R2021b<sup>®</sup> platform under license (License No. 595687). The result of the theoretical analysis predicts a theoretical efficiency of 65%, a pot air temperature <em>T<sub>f</sub></em>=220<sup>o</sup>C and a flame temperature <em>Ta</em>=900<sup>o</sup>C. Similarly, a validation with Sagouong's model on combustion chamber temperature and Kaushik's model on thermal efficiency. A maximum threshold (RMSE) of 4% is observed between the two studies. The <em>T<span style="font-size: 8.33333px;">c</span></em> temperature stagnates rapidly within 5 minutes at 600<sup>o</sup>C and the comparison showed that the firing temperatures of the cylindrical shape are higher than those of the conical-shaped cook stove. Consequently, the performance of the cylindrical-shaped cooking stove can be improved by further experimentation and flue gas analysis.</p>

Combined unbalanced distribution system state and line impedance matrix estimation

To address the challenges that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these strategies require accurate network models to work effectively. However, distribution network data are known to contain errors, and attention has been given to techniques that allow to derive improved network information. This paper presents a novel method to derive line impedance values from smart meter measurement time series, with realistic assumptions in terms of meter accuracy, resolution and penetration. The method is based on unbalanced state estimation and is cast as a non-convex quadratically constrained optimization problem. Both line lengths and impedance matrix models can be estimated based on an exact nonlinear formulation of the steady-state three-phase network physics. The method is evaluated on both the IEEE European Low Voltage Feeder (906 buses) and a real Belgian LV feeder, showing promising results.

A parallel computing architecture based on cellular automata for hydraulic analysis of water distribution networks

Water distribution networks (WDNs) are one of the largest infrastructures in society. Various methods for formulation and hydraulic analysis of water distribution networks, including numerical and non-numerical methods, have been previously proposed. Due to the complexity, the nonlinearity of the hydraulic equations of water distribution networks, and the need for multiple executions and uncertainties in parameters, solving the hydraulic model of water distribution networks has high time complexity. In this paper, a parallel computational architecture based on the concept of cellular automata is proposed to accelerate the numerical solution of the steady-state water distribution network model. Taylor series is proposed to solve hydraulic equations. The presented architecture was implemented as a parallel hardware platform on a field-programmable gate array. The performance of the proposed method was compared with EPANET software for networks with different complexities and topologies. The results show that the proposed parallel algorithm can accelerate the hydraulic analysis of regular water distribution networks up to 700 times and 250 times for small and large networks, respectively.

Applied Pipe Roughness Identification of Water Networks: Consideration of All Flow Regimes

This article presents a mathematically rigorous unification of the pipe roughness identification problem of water distribution networks considering all Reynolds regimes, i.e., laminar, turbulent, as well as transitional flow regimes. Although the identification procedure is based on steady-state hydraulic network equations, the identified roughness parameters are also key for dynamic models that can be used for model-based controller and observer designs. While a three-cycle network simulation example serves to illustrate the presented problem formulation and solution in an extensive manner, the application on a real-world drinking water network is in focus. In addition, vital aspects, such as topology simplifications of the underlying network and the importance of the generation and selection of independent measurement sets, are addressed. We apply root-finding methods instead of methods based on optimization and thereby show that the pipe roughness identification problem may actually be applicable to identify as well as locate leakages.

A Laplace-Domain Circuit Model for Fault and Stability Analysis Considering Unbalanced Topology

For systems subject to unbalanced faults, analytical model building for stability assessment is a challenging task. This letter presents a straightforward modeling approach. A generalized dynamic circuit representation is achieved by use of the Laplacian transform variable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$s$</tex-math></inline-formula> . We translate the voltage and current relationship at the fault location into the relationship of three subsystems. The final circuit model is an interconnected sequence network with impedances in the Laplace domain. This circuit can be directly converted from a steady-state sequence network. This modeling procedure is illustrated by an example case of an induction motor served by a grid through a series compensated line. Electromagnetic transient simulation results demonstrate that sub-synchronous oscillations can be mitigated when a single-line to ground fault is applied at the motor terminal. Stability analysis results based on the dynamic circuit corroborate the simulation results. What's more, the derived circuit effortlessly reveals why unbalance can enhance stability.

Identifying steady state in the network dynamics of spiking neural networks

Analysis of the dynamics of complex networks can provide valuable information. For example, the dynamics can be used to characterize and differentiate between different network inputs and configurations. However, without quantitatively delineating the network's dynamic regimes, analysis of the network's dynamics is based on heuristics and qualitative signatures of transient or steady-state regimes. This is not ideal because interesting phenomena can occur during the transient regime, steady-state regime, or at the transition between the two dynamic regimes. Moreover, for simulated and observed systems, precise knowledge of the network's dynamical regime is imperative when considering metrics on minimal mathematical descriptions of the dynamics, otherwise either too much or too little data is analyzed. Here, we develop quantitative methods to ascertain the starting point and period of steady-state network activity. Using the precise knowledge of the network's dynamic regimes, we build minimal representations of the network dynamics that form the basis for future work. We show applications of our techniques on idealized signals and on the dynamics of a biologically inspired spiking neural network.

Development and validation of a variational formulation of Two-Phase flow distribution

Nonuniform flow distribution in vertical headers has been an ongoing challenge that limits the advantages of transfer and fluidic technologies, especially in microchannel heat exchangers (MCHXs). This issue has been typically addressed in empirical studies and case-specific computational fluid dynamics (CFD) simulations; however, efforts toward the theoretical understanding and modeling of this phenomenon are lacking in literature. Therefore, this paper presents the development and validation of a theoretical model to reproduce the flow and phase distributions in adiabatic multibranch channels. A variational formulation of the phenomenon is obtained using Prigogine's theorem of minimum entropy production to cope with the inherent high variability of the mathematical problem. The individual flow rates, vapor quality, and pressure distributions yielding the minimum entropy generation rate provide a complete representation of the steady-state fluidic network for an arbitrary structure of the device and any working fluid. This paper presents a first experimental validation of the model when applied to the vertical header of a MCHX circulating R410A refrigerant. The theoretical results provide a coherent representation of the experimental data.

Robust Multi-Task Diffusion Least Mean M-Estimate Adaptive Algorithm and Its Performance Analysis

Multi-task diffusion LMS (MDLMS) is an useful algorithm to infer multiple related parameter vectors concurrently through the cooperation of nodes among clusters. However, it must be pointed out that its optimal estimation can only be realized under Gaussian noise. In practical applications, this ideal noise environment is not easy to meet. For example, there will be some impulsive noise interference. At this time, it is urgent to propose a robust multi-task algorithm against impulsive noise. The modified Huber (MH) function is considered in the new robust multi-task algorithm (i.e., multi-task diffusion least mean M-estimate (MDLMM)) proposed in this brief since its simple structure and outstanding effect of restraining impulsive interference. After that, we analyze the mean and mean square performance of the MDLMM algorithm, and give the stability conditions (in the sense of mean and mean square) and the closed-form expressions of theoretical transient and steady-state network mean square derivation (NMSD) under some assumptions. Numerical simulations verify the accuracy of the theoretical analysis results and the superiority of MDLMM over other robust multi-task algorithms.

Influence of process layouts of multi-roll solid-liquid cast-rolling bonding process on circumferential performance uniformity during fabricating metal cladding materials

The twin-roll casting (TRC) process is typically used to fabricate laminated metal cladding strips with rectangular cross-sections, and it has the advantages of short flow, low energy consumption, green environmental protection, etc. Recently, a twin-roll solid-liquid cast-rolling bonding (TRSLCRB) process was proposed based on the TRC process to fabricate metal cladding materials with round cross-sections. However, the nonuniformity of heat transfer and mass transfer along the circumferential direction finally influences the performance uniformity. Therefore, a multi-roll solid-liquid cast-rolling bonding (MRSLCRB) process was proposed integrating cast-rolling bonding technology and multi-roll groove rolling technology. The steady-state thermal resistance network was established, and the influences of nominal roll radius, groove radius, and molten pool height on the heat transfer and mass transfer were studied systematically. 3-D steady-state thermal-fluid coupled simulation models of twin-roll, three-roll, and four-roll layouts were established. The results indicate that increasing casting roll number can improve the nominal cast-rolling velocity, service life of the casting roll, temperature uniformity, strain uniformity, and strain rate uniformity through regulating the heat transfer and mass transfer. Considering the manufacturing feasibility, the MRSLCRB equipment based on the three-roll layout was designed. Cu/steel cladding bars with uniform circumferential performance were fabricated, and the forming mechanism was revealed. The multi-field coupled analysis method provides a way to control heat and mass transfer uniformity, and the successful attempts of various typical metal cladding materials inspire exploring new forming processes.

The Diffusion Entropy Stochastic Gradient Descent Algorithm With Quasi-Optimal Combiners: Formulation and Analysis

We consider herein the diffusion entropy stochastic gradient descent (DE-SGD) algorithm for non-circular complex-valued signals. We theoretically analyze the mean stability and the steady-state network mean-square-deviation (MSD) of the DE-SGD. We develop both the quasi-optimal static and adaptive combination strategies to enhance the DE-SGD algorithm so as to utilize the spatial variations of the noise circularity coefficients, noise variances, the regressor powers and the step sizes across the entire network. We validate the proposed adaptive combiners theoretically and experimentally in small step size scenarios. We also demonstrate the consistency of the convergence and steady-state performance of both the static combiners and the proposed adaptive combiners. Illustrative simulations and real-world data evaluation validate the superior transient and steady-state performance of the DE-SGD algorithm with the proposed quasi-optimal combination strategies.

ITER assembly phase: Progress toward first plasma

By the end of 2019, ITER had completed about 67% of the overall work required to achieve First Plasma. Significant progress has been achieved since SOFT 2018, most visible in the completion of key buildings and infrastructure on the worksite. The tokamak building will be ready for equipment as of Spring 2020. Commissioning of the connection to the EU grid and the substation for the steady-state electric network is complete. Planning for commissioning of other key plant systems (e.g., secondary cooling water, cryoplant, pulsed power for magnet systems) is well underway. On the manufacturing front, progress is equally impressive. The base and lower cylinder of the cryostat were completed and turned over to ITER in July 2019. The first two poloidal field coils will be turned over to ITER in early 2020, and two more are being wound. Factory tests of the first central solenoid module will be completed in 2020, and five more modules are in the manufacturing process. The first toroidal field coil is to arrive onsite in early 2020, and progress is rapid on additional TF coils. The first sections of the thermal shield are already onsite. The first vacuum vessel sector is complete and will arrive onsite in early 2020. This progress means that, for most of the major first-of-a-kind components, the capability has now been demonstrated to fabricate according to ITER’s demanding specifications. With this progress achieved, ITER will enter Assembly Phase in 2020, remaining on schedule for First Plasma in 2025. The sequence of ITER operation from First Plasma (FP) to the achievement of the Q = 10 project goal has been consolidated in a Staged Approach, with all systems to be installed before the start of full fusion power operation in 2035. The ITER Research Plan, issued publicly in 2018, is consistent with the systems available in each phase. An adjustment of the machine baseline configuration was made in 2019, with two equatorial ports allocated to the Test Blanket Systems and the development of a better performing Disruption Mitigation System. Highlights from each of these areas (manufacturing, commissioning, tokamak assembly) will be presented along with the updated status and plans.

Coupling of transient matrix diffusion and pore network models for gas flow in coal

Processes of sorption and diffusion play a critical role in the analysis and simulation of coal seam gas (CSG) reservoirs since most of the gas reserves are held by the coal matrix. Thus, it is essential to understand the displacement of gas in coal matrix and fractures. This can be formulated in terms of sorption and diffusion processes inside the matrix and advective flow in the cleats. The multi-scale porous structure and multi-physics nature of gas flow in coal complicate numerical modelling of these phenomena. A novel approach is developed to solve this problem by coupling conventional pore network modelling (PNM) with transient diffusion flow. The coupling is undertaken for each pore network throat separately by utilising the Finite Volume Method and Fick's second law of diffusion. The Langmuir isotherm is employed to describe sorption processes within the coal matrix and to link the concentration and pressure values when coupling the diffusion model with PNM. The proposed approach provides the possibility to account for the transient nature of gas transport across the different scales, while limiting the number of unknowns in the simulation to an effective diffusivity parameter. Our results demonstrate that steady-state pore network models can be effectively coupled with a transient phenomenon such as Fick's diffusion allowing to estimate the diffusive constitute of the matrix to the total gas flow in coal over time. The obtained gas sorption capacity and its release rate can then be used to accurately estimate the time-dependent production profile of CSG reservoirs.

Multienergy Networks Analytics: Standardized Modeling, Optimization, and Low Carbon Analysis

Multienergy systems (MESs) are able to unlock the energy system flexibility using the coupling across multiple energy sectors. Such coupling contributes to improving the overall energy efficiency and promoting the accommodation of renewable energy. Among a wide range of literature, this article provides a perspective of network analytics on how to model, optimize, and conduct low-carbon analysis on MESs. The energy sector coupling involves different levels, for example, from a single building to nationwide. In this article, we categorize multienergy networks into two levels, that is, the district level that covers a relatively small area such as a campus or a community, where the energy conversion and utilization is the major focus, and the multiregion level that covers a relatively large area such as a big city, a province, or the whole country, where the energy transmission is the major concern. We first review the state-of-the-art multienergy networks standardized modeling approaches including: 1) energy hub (EH) model for district level energy networks; 2) network models, including power, heat, and gas steady-state and dynamic network models, for multiregion level energy networks; and 3) load models, including electricity, heat, and gas load forecasting models. Second, we explore the planning and operation methods for both district level and multiregion level energy networks. Third, we introduce a special technique named the carbon emission flow (CEF) model that is able to calculate the equivalent CO2 emission associated with the energy flows in multienergy networks. We also demonstrate how the technique can help multienergy networks planning and operation toward a low carbon society. Finally, we envision several further key research topics in the field of multienergy networks.

PowerModelsDistribution.jl: An open-source framework for exploring distribution power flow formulations

In this work we introduce PowerModelsDistribution, a free, open-source toolkit for distribution power network optimization, whose primary focus is establishing a baseline implementation of steady-state multi-conductor unbalanced distribution network optimization problems, which includes implementations of Power Flow and Optimal Power Flow problem types. Currently implemented power flow formulations for these problem types include AC (polar and rectangular), a second-order conic relaxation of the Branch Flow Model (BFM) and Bus Injection Model (BIM), a semi-definite relaxation of BFM, and several linear approximations, such as the simplified unbalanced BFM. The results of AC power flow have been validated against OpenDSS, an open-source “electric power distribution system simulator”, using IEEE distribution test feeders (13, 34, 123 bus and LVTestCase), all parsed using a built-in OpenDSS parser. This includes support for standard distribution system components as well as novel resource models such as generic energy storage (multi-period) and photovoltaic systems, with the intention to add support for additional components in the future.

Model identification and strategy application for Solid Oxide Fuel Cell using Rotor Hopfield Neural Network based on a novel optimization method

The novelty of this paper is to suggest an effective method according to the application of the Rotor Hopfield Neural Network optimized by the Grey Wolf Optimization (GWO) method for the identification of the Solid Oxide Fuel Cell (SOFC) model. In this literature, the basic required metrics to present the transient models of Solid Oxide Fuel Cell are defined. The proposed model is a hybrid model that is composed of the Rotor Hopfield Neural Network (RHNN) optimized by the GWO algorithm. The hybrid RHNN-GWO model, including a steady-state RHNN Neural Network, ensured by an optimization method. The RHNN algorithm is presented to assess the metrics of the RHNN-GWO model. In contrast to the wavering, the Mean Squared Error (MSE) for the RHNN-GWO model is calculated by 0.0017. The presented model results are examined with some well-known model results. The lowest values for Mean Squared Error belongs to the RHNN-GWO model. Also, the proposed model conserves a tremendous value of calculation time compared to the other models. Also, the proposed model shows a good agreement with SOFC results with lower computational difficulty. For 5000 samples, the variation of the voltage is in the [320,360]V interval, which completely follows the reference voltage of the SOFC.