Transient Network Model Research Articles

A transient network model for cross-regional layered fire smoke diffusion in high-rise buildings

At present, research on smoke diffusion in high-rise building fires mainly focuses on the single smoke layer propagation within a limited area, with less attention paid to the smoke propagation under the joint action of the fire floor and the vertical shaft area. Moreover, research on fire spread often focuses on the composition of combustible materials and building structures, neglecting natural ventilation. To address the above issues, we propose a transient network model for layered smoke diffusion (CLDTN) based on multi-region integration and complex control parameters. This model considers the smoke stratification mode of the fire floor affected by the fire source and ventilation, as well as the vertical stratification mode of the shaft affected by the stack effect, achieving cross-regional smoke stratification and diffusion modeling through smoke coupling. Then, a time-dependent transient network model is used to analyze furniture materials and open ventilation conditions of doors and windows, and a differential equation for energy conservation in multiple scene layers is constructed to analyze the dynamic behavior of smoke propagation.The comparative experiment of smoke propagation between CLDTN and the Fire Dynamics Simulator (FDS) shows that CLDTN is more efficient in computation time and the model is more accurate.

Impact of control strategies on energy consumption in cold storage facilities

The fast development of global cold chains has led to a rapid increase in cold storage facilities capacity, in which consumes substantial amounts of energy. However, limited research has been conducted on the energy consumed by cold storage facilities, and the time and economic costs of obtaining long-term measurements of annual energy consumption of cold storage facilities are high. Therefore, in this study, short-term field tests and simulations were combined to establish a transient thermal network model for cold storage, using a specific cold storage case study for validation. The model considers the structure of the hall, which strongly impacts the cooling load. The refrigeration system performance was analyzed on the measured data. Literature and field research have shown temperature range control to be the baseline control strategy used in cold storage refrigeration systems, which ensures that the cold storage temperature remains within the set temperature range at all times. In addition, some researchers have proposed a control strategy based on time-of-use tariff that provides cooling almost throughout the valley period. These two control strategies were applied to frozen and chilled rooms, separately; the results indicated that for both frozen and chilled rooms, the latter control strategy consumes slightly less energy than the former strategy. However, the electricity bill for the latter control strategy are 0.35 and 0.5 times lower than those for the former strategy for frozen rooms and chilled rooms, respectively.

Stress-controlled medium-amplitude oscillatory shear (MAOStress) of PVA–Borax

We report the first-ever complete measurement of MAOStress material functions, which reveal that stress can be more fundamental than strain or strain rate for understanding linearity limits as a function of Deborah number. The material used is a canonical viscoelastic liquid with a single dominant relaxation time: polyvinyl alcohol (PVA) polymer solution cross-linked with tetrahydroborate (Borax) solution. We outline experimental limit lines and their dependence on geometry and test conditions. These MAOStress measurements enable us to observe the frequency dependence of the weakly nonlinear deviation as a function of stress amplitude. The observed features of MAOStress material functions are distinctly simpler than MAOStrain, where the frequency dependence is much more dramatic. The strain-stiffening transient network model was used to derive a model-informed normalization of the nonlinear material functions that accounts for their scaling with linear material properties. Moreover, we compare the frequency dependence of the critical stress, strain, and strain-rate for the linearity limit, which are rigorously computed from the MAOStress and MAOStrain material functions. While critical strain and strain-rate change by orders of magnitude throughout the Deborah number range, critical stress changes by a factor of about 2, showing that stress is a more fundamental measure of nonlinearity strength. This work extends the experimental accessibility of the weakly nonlinear regime to stress-controlled instruments and deformations, which reveal material physics beyond linear viscoelasticity but at conditions that are accessible to theory and detailed simulation.

Multiscale simulations of viscoelastic fluids in complex geometries using a finitely extensible nonlinear elastic transient network model

This paper presents a novel implementation of a numerical scheme for predicting complex flows of viscoelastic fluids using a finitely extensible nonlinear elastic (FENE) transient network model. This model extends the FENE model by incorporating chain interactions and accounting for the way in which the maximum chain length, drag, and relaxation time are influenced by entanglement and disentanglement processes. Three different initial networks are considered (disentanglement, entanglement, and aleatory), and the influence of variables such as the kinetic rate constants, elasticity, and chain length on the microstate concentration, stresses, and drag force is investigated. It is shown that although the concentrations of the microstates are independent of the Weissenberg number and the maximum extension length, the stresses and hence the drag are influenced by them.

Multi-time scale thermal tribological dynamics coupling model of the helicopter intermediate reducer under severe loss of lubrication

There is no lubrication in the contact area, which leads to a significant influence of the thermal characteristics on the friction dynamics and life of the helicopter intermediate reducer because the helicopter experiences severe loss of lubrication. In this study, for evaluating the non-linear friction dynamics and transient thermal network models of the intermediate gearbox, a multi-time scale coupling method was proposed to establish a proportional allocation method between the oil film region and the rough peak contact region of the tooth surface by considering the effect of temperature and to study the adsorption film under the condition of severe lubrication loss. The coupled thermal and kinetic characteristics of the severe lubrication loss condition were obtained by calculating the friction coefficients in the lubricated and dry-friction regions. The results showed that the friction coefficient of the bevel gear tooth surface fluctuated between 0.1 and 0.6, and the degree of the gear axis trajectory shift was more evident owing to the increase in the friction force excitation caused by the increase in the friction coefficient.

Chaotic Neural Network with Nonlinear Delayed Self-feedback and Applications

We propose a novel transient chaotic neural network model with a nonlinear delayed self-feedback term, aiming to leverage its complex dynamical behavior for algorithm optimization. To avoid the influence of chance, we conduct experiments using randomly generated data and analyze the model’s chaotic dynamical behavior by visualizing the stability of the neurons through inverse bifurcation diagrams and maximum Lyapunov exponent diagrams. The simulation results demonstrate the remarkable effectiveness of this new model in network optimization, with a success rate even reaching 100%, surpassing previous chaotic neural network models. Notably, in the following sections, all nonlinear terms are represented using trigonometric functions.

The Multiple Frequency Conversion Sinusoidal Chaotic Neural Network and Its Application

Aiming at the problem that the global search performance of a transiently chaotic neural network is not ideal, a multiple frequency conversion sinusoidal chaotic neural network (MFCSCNN) model is proposed based on the biological mechanism of the brain, including multiple functional modules and sinusoidal signals of different frequencies. In this model, multiple FCS functions and Sigmoid functions with different phase angles were used to construct the excitation function of neurons in the form of weighted sum. In this paper, the inverted bifurcation diagram, Lyapunov exponential diagram and parameter range of the model are given. The dynamic characteristics of the model are analyzed and applied to function optimization and combinatorial optimization problems. Experimental results show that the multiple frequency conversion sinusoidal chaotic neural network has better global search performance than the transient chaotic neural network and other related models.

Multiscale modeling of complex fluids under SAOS and LAOS using a combined FENE transient network model

The multiscale modeling of complex fluids under small and large amplitude oscillatory shear flow using non-linear kinetic and transient network models is presented. The kinetics of microstates is analogous to chemical kinetics, which defines the physical macromolecule interaction in a Newtonian fluid, and the concentration of microstates defines a variable maximum length of extension for each microstate. The effect of important parameters like viscosity ratio, chain length, viscoelasticity, kinetic rate constants, for different initial entanglement scenarios (entangled, disentangled and aleatory) are analyzed. The Lissajous curves for the shear stress and the first normal stress difference versus the instantaneous strain or strain-rate are shown. The self-intersection of the Lissajous curves or secondary loops is shown to depend on the kinetic rate constants, the maximum extension length, and the elasticity.

A Generalized Mechano-statistical Transient Network Model for Unravelling the Network Topology and Elasticity of Hydrophobically Associating Multiblock Copolymers in Aqueous Solutions

In this contribution, we unravel the transient network topology and elasticity of micellar networks formed by hydrophobically associating multiblock copolymers in aqueous solutions. Unlike studies on conventional triblock copolymers bearing hydrophobic blocks as end groups, our research focuses on alternating poly(ethylene oxide) (PEO) and poly(propylene oxide) (PPO) multiblock copolymers having multiple hydrophobic PPO blocks along the chain. We adopt a combinatorics approach to extend and generalize the mechano-statistical transient network model developed by Annable et al. for telechelic triblock copolymers [ Journal of Rheology, 1993, 37, 695] to multiblock copolymers. The model allows one to predict the concentration dependent elasticity of networks formed by multiblock copolymers with known molecular characteristics by using knowledge of the micellar network microstructure. The spatial distribution of the hydrophobic nodes is inferred from Small-Angle X-ray Scattering (SAXS) by converting the structure factor to the radial distribution function. The number of closely neighboring micellar cores between which an elastic bridge can be formed (nm) is calculated by spherical integration of the radial distribution function up to a distance of the radius of gyration of an intermediate soluble PEO block. Using the evolution of nm with concentration as an input for the model, the predictions show good agreement with experimental elasticity data, as inferred from the plateau modulus in linear shear rheology. The network evolves from loop-dominated, poorly elastic with cross-linking nodes with low functionality at low concentrations to bridge-dominated, highly elastic with higher node functionalities at more elevated concentrations. It is anticipated that our generalized mechano-statistical transient network model can also be used for equally spaced, multisticker associating polymers forming networks by multifunctional interactions other than micellar aggregation.

Transient thermal network model for train‐induced wind cooling dry‐type on‐board traction transformer in electric multiple units

AbstractThermal characteristic is one of the key performances of dry‐type on‐board traction transformer (D‐OBTT), which directly affects the operation safety of the new generation electric multiple units. However, due to the particularity of train‐induced wind cooling mode of D‐OBTT, the existing efficient thermal modelling methods cannot be used under fluctuating air velocity and load. A time‐saving transient thermal model for D‐OBTT is proposed. First, the framework of the transient thermal network model (TTNM) is proposed considering the coexistence of non‐fully and fully developed state of the train‐induced wind and the addition heat flow in the solid domain. Then, a novel formula for calculating the length of non‐fully developed section considering the geometric dimensions and heat flux density is proposed based on computational fluid dynamics (CFD) parametric sweeps for the first time. The accuracy of TTNM is validated by a D‐OBTT experimental setup, and the time consumption is significantly reduced compared with CFD.

Research on equivalent thermal network modeling for rare-earth giant magnetostrictive transducer

Of crucial importance for giant magnetostrictive transducers (GMTs) design is to quickly and accurately analysis the temperature distribution. With the advantages of low calculation cost and high accuracy, thermal network modelling has been developed for thermal analysis of GMT. However, the existing thermal models have their limits to describe these complicated thermal behaviors in the GMTs: most of researches focused on steady-state which is incapable of capturing temperature variances; the temperature distribution of giant magnetostrictive (GMM) rods is generally assumed to be uniform whereas the temperature gradient on the GMM rod is remarkable due to its poor thermal conductivity; the non-uniform distribution of GMM’s losses is seldom introduced into thermal model. Therefore, a transient equivalent thermal network (TETN) model of GMT is established in this paper, considering the aforementioned three aspects. Firstly, based on the structure and working principle of a longitudinal vibration GMT, thermal analysis was carried out. Following this, according to the heat transfer process of GMT, the TETN model was established and the corresponding model parameters were calculated. Finally, the accuracy of the TETN model for the temporal and spatial analysis of the transducer temperature are verified by simulation and experiment.

Identification of High Resolution Transient Thermal Network Model for Power Module Packages

A transient thermal network model is utilized to design and evaluate the transient thermal characteristics of power modules. Static test method identifies the transient thermal network model from the time response of the junction temperature, which is obtained by using the temperature dependency of I-V characteristics for power devices. This paper experimentally evaluates the effect of the sampling frequency and the resolution of the AD conversion on the accuracy of the obtained transient thermal network model. The high sampling frequency and the high resolution in the obtained time response of the junction temperature enable to clearly identify the transient thermal network model of the power module with the direct bonding copper substrate.

Evaluation of effects of molecular chain structure and crystallinity on mechanical properties of polyamide by finite element method using transient network model

Polyamide (PA) is a semi-crystalline polymer in which main chain is configured by repeating units of amide bonds (-NHCO-). The strength of PA is achieved by intermolecular hydrogen bond between hydrogen and oxygen atoms of amide bonds. In this study, the effects of the crystallinity and crystallite size were investigated by uniaxial tensile tests of commercial PA6 and PA-MXD10. Furthermore, the time-dependent nonlinear mechanical model based on the transient network model was proposed to predict the mechanical behavior of PAs with different crystallinities. From experimental results, the stress-strain curves of the PAs with small crystallinity showed the sharp drop after the maximum stress and the necking occurred due to local deformation. In contrast, near the maximum stress of PA with large crystallinity, a gradual change in stress was observed, and the local deformation was suppressed. Simulation results indicated that the modified transient network model can predict the mechanical behavior of PAs with different crystallinities.

The effect of fire location and the reverse stack on fire smoke transport in high-rise buildings

In this paper, smoke transport in high-rise buildings through elevator shafts and stairwells is investigated for various fire location and stack effect conditions. For this purpose, a transient network model, Fire-STORM, is upgraded and used. The results are benchmarked by using a computational fluid dynamics (CFD) model. Six scenarios are tested, which are 1st floor, mid-floor, and top-most floor fires under normal stack (cold environment) and reverse stack (hot environment) conditions. For each scenario, the time history of pressures, temperatures, and soot mass fractions in the fire floors, elevator shafts, and stairwells and the average soot mass fraction in all stories of the building are presented. Overall, Fire-STORM has reasonably good accuracy compared to CFD with significantly faster computation times (90 s on a single core vs. 4 days on 32 cores in parallel). One of the intended uses of this fast low-order model is a data generation engine for neural network modeling of high-rise building fires. As such, one of the unique features of this work is the development of a realistic random heat release rate (HRR) modeling approach created using a Gaussian process.

Analysis on heat transfer and start-up performance of mercury heat pipe

Mercury heat pipe has the advantages of good thermal stability and low saturated vapor pressure, which is the best choice for the transition from water heat pipe to liquid metal heat pipe. The effects of heating power and heat pipe structure on start-up time and steady-state heat transfer performance of mercury heat pipe were studied by using transient thermal network model. The results showed that: 1) Increasing the length of condenser is beneficial to reducing the start-up time and thermal resistance; 2) Increasing the heating power or wall thickness will reduce the thermal resistance, but increase the start-up time, and increasing the porosity of wick is just the opposite; 3) Increasing the thickness of wick can increase both the start-up time and the thermal resistance.

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.

Terminal Flow of Cluster-Forming Supramolecular Polymer Networks: Single-Chain Relaxation or Micelle Reorganization?

We correlate the terminal relaxation of supramolecular polymer networks, based on unentangled telechelic poly(isobutylene) linear chains forming micellar end-group clusters, with the microscopic chain dynamics as probed by proton NMR. For a series of samples with increasing molecular weight, we find a quantitative agreement between the terminal relaxation times and their activation energies provided by rheology and NMR. This finding corroborates the validity of the transient-network model and the special case of the sticky Rouse model, and dismisses more dedicated approaches treating the terminal relaxation in terms of micellar rearrangements. Also, we confirm previous results showing reduction of the activation energy of supramolecular dissociation with increasing molecular weight and explain this trend with an increasing elastic penalty, as corroborated by small angle x-ray scattering data.

Thermal-Fluid-Solid Coupling in Thermal Characteristics Analysis of Rolling Bearing System Under Oil Lubrication

Abstract It is necessary to use the thermal network method for thermal analysis of the bearing, but there are still some shortcomings. In this paper, a novel thermal network model of the bearing transient temperature is developed considering the thermal-fluid-solid coupling effects. First, the quasi-static analysis of the bearing is carried out considering the thermal expansion effect, and the heat generation, heat transfer, and heat dissipation are studied. Then, the coupling effects between the oil characteristics, heat generation, structure parameters, and temperature (thermal-fluid-solid) during the operation of the bearing are discussed, and the transient thermal network model of the bearing-shaft-bearing housing system is established. Test results indicate that the existing models (without thermal-fluid-solid coupling) have large temperature deviation, while the proposed model in this paper considering the thermal-fluid-solid coupling effects is much more accurate. Finally, the effects of rotational speed, load, oil temperature, and oil flowrate on the temperature rise are all achieved and discussed.

A memristor-based transient chaotic neural network model and its application

Transient chaotic neural networks (TCNNs) have shown promise in solving optimization problems but still suffer from slow convergence and being difficult to implement in hardware. In this paper, the HP memristor is introduced to a TCNN to develop a memristor-based transient chaotic neural network (MTCNN) model that is highly efficient, converges quickly, and has significant prospects for physical implementation. The proposed MTCNN makes full use of the nonlinearity and memory-related characteristics of memristors, and their conductance values are used as self-feedback connection weights that can be adjusted dynamically according to the annealing algorithm. The MTCNN model was applied to solve combinatorial optimization problems, including the channel assignment problem (CAP) of four cells and the traveling salesman problem (TSP) of 10 cities. In 500 runs, the MTCNN algorithm delivered a 5% higher optimal solution rate than the TCNN algorithm while using only 70% of its number of iterations in the CAP, and achieved a shorter average distance and a 40% higher convergence speed than the TCNN algorithm in the TSP.

Fire Smoke Transport and Opacity Reduced-Order Model (Fire-STORM): A New Computer Model for High-Rise Fire Smoke Simulations

The problem of smoke spread through elevator shafts in high rise buildings is analyzed theoretically and numerically in this paper. While experiments and computational fluid dynamics (CFD) models have been used for such exercises, there is a need for fast reduced-order models for such scenarios. Towards this goal, a transient network model called High-rise fire smoke transport and opacity reduced-order model (Fire-STORM) was developed to investigate heat and mass transfer through the elevator shaft during fires. The model numerically solves the coupled set of differential equations of the fire floor in conjunction with the steady state conservation equations of the elevator shaft. The model is validated in two stages. First, the stack effect in a non-fire scenario is analyzed. Pressure differences through exterior doors and elevator doors are compared with experimental data available in the literature and results of a computational fluid dynamics tool. Then, a first-floor fire scenario is considered for the same high-rise building in four different cases which are combinations of different building tightness and ambient temperatures. The results are compared with CFD simulations. For the four different building envelope and ambient thermal conditions, the soot mass fractions and optical visibilities were calculated and compared to CFD predictions. Overall, Fire-STORM is a simple and fast tool to model the evolution of heat and mass transfer in a high-rise building affected by fire. While Fire-STORM is excellent in predicting transient smoke transport for buildings with loose envelopes, it should be used with caution for buildings with tight envelopes since the errors for these cases are relatively high. Despite this, the relative computational speed difference between Fire-STORM and the CFD model highlights the utility of a reduced-order model for firefighter decision making and building control system design.