Dynamic Flow Model Research Articles

Frequency response function method for dynamic gas flow modeling and its application in pipeline system leakage diagnosis

Timely diagnosing leakages is significant for the safety and reliability of gas pipeline systems, where an efficient and accurate modeling and solution method is critical to identify the leakage rate and location. Here, a frequency response function-based modeling method for dynamic gas flow in pipelines is proposed. Based on the frequency-domain flow resistance of pipelines and Fourier Transform, original dynamic flow model is transformed into the frequency response function model consisting of linear lumped transmission constraints, nonlinear frequency response function, and nonlinear pipeline resistance characteristics, which are efficiently solved according to their different mathematical properties. Dynamic simulation cases show the proposed modeling and solution method greatly improves the computational efficiency keeping the accuracy. Besides, taking the difference between measurements and simulation results under leakage condition as objective, an iterative bilayer optimization algorithm is proposed based on the mathematical categorization of system constraints to efficiently diagnose leakages. Four leakage diagnosis cases in single pipeline and pipeline network with measurement noise and transient boundary conditions are studied to validate the leakage diagnosis method. Absolute location errors in the results are all within 1%, demonstrating the accuracy and robustness of the proposed method for single leakages and multiple leakages occurring successively or simultaneously.

Modelling of hydrological and environmental flow dynamics over a central Himalayan river basin through satellite altimetry and recent climate projections

AbstractThe Himalayan region is vulnerable to climate change, which is triggering extreme hydrological events. To understand the impact of climate emission scenarios on different hydrological flow dynamics of the Himalayan region, a thorough hydrological investigation is needed for improved water resource management. Hence, this research exemplifies the use of remote sensing, modelling techniques, and recent climate projections (CMIP5 and CMIP6) for the assessment of hydrological flow dynamics in the central Himalayan catchment. The SWAT model is calibrated/validated using gauge observation and satellite altimetry‐derived river discharge and used for long‐term hydrological simulations (1979–2100). CMIP6 models predicted that there would be a steady rise in high flows in all the projected scenarios. Flow duration curve for CMIP5 and CMIP6 models depicted that high flow of short duration would be frequent in future. The environmental flow components (EFCs) were calculated using the indicator of hydrologic alteration tool. The SWAT model simulations of hydrological and EFCs under CMIP6 climate projections are found to be significantly more vulnerable to climate warming than CMIP5, which could be due to socioeconomic pathway emission scenarios.

A dynamic continuum route choice model for pedestrian flow with mixed crowds

ABSTRACT This study develops a dynamic route choice model for pedestrian with mixed crowds. Pedestrian flow is regarded as a two-dimensional compressible continuum fluid. Then, characteristic variables are described with mathematical functions. Pedestrians are classified into two classes based on different route choice strategies: reactive and predictive dynamic user-optimal principles. Reactive pedestrians only consider the current information to choose the routes with the minimum instantaneous cost. Predictive pedestrians are assumed to know details about the future and choose routes with the minimum predictive actual travel cost. Two methods are used to solve the models. Numerical simulations are presented to demonstrate the effectiveness of the models and the algorithms. The numerical results show that the evacuation time of predictive pedestrians is shorter than that of reactive pedestrians. Moreover, for low-density situations, predictive pedestrians can help improve the overall evacuation efficiency. However, for high-density situations, predictive pedestrians fail to improve the efficiency.

Homing of mRNA-Modified Endothelial Progenitor Cells to Inflamed Endothelium.

Endothelial progenitor cells (EPCs) are one of the most important stem cells for the neovascularization of tissues damaged by ischemic diseases such as myocardial infarction, ischemic stroke, or critical limb ischemia. However, their low homing efficiency in the treatment of ischemic tissues limits their potential clinical applications. The use of synthetic messenger RNA (mRNA) for cell engineering represents a novel and promising technology for the modulation of cell behavior and tissue regeneration. To improve the therapeutic potential of EPCs, in this study, murine EPCs were engineered with synthetic mRNAs encoding C-X-C chemokine receptor 4 (CXCR4) and P-selectin glycoprotein ligand 1 (PSGL-1) to increase the homing and migration efficiency of EPCs to inflamed endothelium. Flow cytometric measurements revealed that the transfection of EPCs with CXCR4 and PSGL-1 mRNA resulted in increased expressions of CXCR4 and PSGL-1 on the cell surface compared with the unmodified EPCs. The transfection of EPCs with mRNAs did not affect cell viability. CXCR4-mRNA-modified EPCs showed significantly higher migration potential than unmodified cells in a chemotactic migration assay. The binding strength of the EPCs to inflamed endothelium was determined with single-cell atomic force microscopy (AFM). This showed that the mRNA-modified EPCs required a three-fold higher detachment force to be released from the TNF-α-activated endothelium than unmodified EPCs. Furthermore, in a dynamic flow model, significantly increased binding of the mRNA-modified EPCs to inflamed endothelium was detected. This study showed that the engineering of EPCs with homing factors encoding synthetic mRNAs increases the homing and migration potentials of these stem cells to inflamed endothelium. Thus, this strategy represents a promising strategy to increase the therapeutic potential of EPCs for the treatment of ischemic tissues.

Dynamic Network Flow Model for Power Grid Systemic Risk Assessment and Resilience Enhancement

Dynamic Network Flow Model for Power Grid Systemic Risk Assessment and Resilience Enhancement

Investigation on variable speed limit control strategy of expressway under adverse weather conditions

In order to effectively improve the capacity of expressways under adverse weather conditions (rainy, foggy and snowy) and reduce the potential safety hazards of expressways, a variable speed limit control strategy is proposed. First, the impact of road alignment on traffic capacity is considered, and road traffic capacity reduction coefficients under different bends and slopes are added so that the cell transmission model and a dynamic traffic flow model can be established; Secondly, the maximum speed limit value under adverse weather conditions is determined according to the influence of rainfall intensity under rainy weather conditions, visibility under foggy weather conditions, and road adhesion coefficient under snowy weather conditions on traffic capacity; Finally, an adaptive genetic algorithm is employed to solve the function that satisfies the safety and efficiency of traffic operation. The results show that the variable speed limit control strategy under adverse weather conditions effectively improves the road capacity, reduces the driving risk of vehicles, and alleviates traffic congestion.

Improved Dynamic Power Flow Model with Frequency Regulation by DFIG Integrated through VSC-HVDC Considering Governor Delay of SG

The doubly-fed induction generators (DFIGs) integrated to the grid through the voltage source converter-high voltage direct current (VSC-HVDC), the cascaded droop control from the system frequency to the DC voltage, then to the active output of the DFIG, was applied to enhance the frequency regulation capability of the power system. The improved dynamic power flow (DPF) model was newly proposed to quantify the frequency response of the coordinated regulation with the inertia of the VSC-HVDC and the DFIGs, and the primary regulation of the synchronous generators (SGs) and the DFIGs. New features of the proposed model include: (i) the SGs’ output in the DPF considering the governor delay, (ii) setting of the virtual inertia of the VSC-HVDC within the DC voltage constraint, and (iii) variable inertia of the DFIGs following changing the kinetic energy of the rotor. The numerical results show the feasibility of the proposed model, and validate the regulation effect and accuracy of the modified inertia of the DFIGs and the maximized virtual inertia of the VSC-HVDC.

Distributionally Robust Multi-Energy Dynamic Optimal Power Flow Considering Water Spillage with Wasserstein Metric

This paper proposes a distributed robust multi-energy dynamic optimal power flow (DR-DOPF) model to overcome the uncertainty of new energy outputs and to reduce water spillage in hydropower plants. The proposed model uses an ambiguity set based on the Wasserstein metric to address the uncertainty of wind and solar power forecasting errors, rendering the model data-driven. With increasing sample size, the conservativeness of the ambiguity set was found to decrease. By deducing the worst-case expectation in the objective function and the distributed robust chance constraints, the exact equivalent form of the worst-case expectation and approximate equivalent form of the distributed robust chance constraints were obtained. The test results of the IEEE-118 and IEEE-300 node systems indicate that the proposed model could reduce water spillage by more than 85% and comprehensive operation cost by approximately 12%. With an increasing number of samples, the model could reduce conservativeness on the premise of satisfying the reliability of safety constraints.

Collaborative evolution of regional green innovation system under the influence of high-speed rail based on Belousov-Zhabotinsky reaction.

As a green transportation mode and carrying factors with high knowledge-intensive characteristics, high-speed rail (HSR) has contributed to the improvement of regional green innovation. However, the relationship between HSR and regional green innovation is still limited. Based on the Belousov-Zhabotinsky (B-Z) reaction model, this study establishes a three-dimensional logistic dynamic evolution model for factor flow, knowledge spillover and green innovation performance. Through linear stability analysis, the threshold conditions for the evolution of the regional green innovation system under the influence of HSR are explored, and the impact of HSR policy on the regional green innovation system under four different initial states is examined. The results reveal four major points: (1) Factor flow is the order parameter and shows a significant collaborative relationship with green innovation performance. (2) The opening of HSR can effectively promote the evolution of the regional green innovation system, and the powerful stimulus of HSR contributes to shortening its evolutionary cycle. (3) The initial state of the regional green innovation system plays a crucial role in the green innovation performance. (4) In the process of collaborative evolution of regional green innovation system, factor flow and knowledge spillover serve as the premise and foundation, respectively, to jointly promote green innovation performance enhancement. Findings not only provide references for decision-makers to implement green innovation strategy and boost the green innovation performance, but also extend the theoretical system of HSR effect and collaborative evolution.

177Lu-DOTA-0-Tyr3-octreotate infusion modeling for real-time detection and characterization of extravasation during PRRT

PurposeGiven the recent and rapid development of peptide receptor radionuclide therapy (PRRT), increasing emphasis should be placed on the early identification and quantification of therapeutic radiopharmaceutical (thRPM) extravasation during intravenous administration. Herein, we provide an analytical model of 177Lu-DOTA0-Tyr3-octreotate (Lutathera®) infusion for real-time detection and characterization of thRPM extravasation.MethodsFor 33 Lutathera®-based PRRT procedures using the gravity infusion method, equivalent dose rates (EDRs) were monitored at the patient’s arm. Models of flow dynamics for nonextravasated and extravasated infusions were elaborated and compared to experimental data through an equivalent dose rate calibration. Nonextravasated infusion was modeled by assuming constant volume dilution of 177Lu activity concentration in the vial and Poiseuille-like laminar flow through the tubing and patient vein. Extravasated infusions were modeled according to their onset times by considering elliptically shaped extravasation region with different aspect ratios.ResultsOver the 33 procedures, the peak of the median EDR was reached 14 min after the start of the infusion with a value of 450 µSv h−1. On the basis of experimental measurements, 1 mSv h−1 was considered the empirical threshold for Lutathera® extravasation requiring cessation of the infusion and start again with a new route of injection. According to our model, the concentration of extravascular activity was directly related to the time of extravasation onset and its duration, a finding inherent in the gravity infusion method. This result should be considered when planning therapeutic strategy in the case of RPM extravasation because the local absorbed dose for β-emitters is closely linked to activity concentration. For selected EDR values, charts of extravasated activity, volume, and activity concentration were computed for extravasation characterization.ConclusionWe proposed an analytical model of Lutathera® infusion and extravasation (gravity method) based on EDR monitoring. This approach could be useful for the early detection of thRPM extravasation and for the real-time assessment of activity concentration and volume accumulation in the extravascular medium.

Modelling cell deformations in bioprinting process using a multicompartment-smooth particle hydrodynamics approach.

Bioprinting using cell-laden bioink is a rapidly emerging additive manufacturing method to fabricate engineered tissue constructs and in vitro models of disease biology. Amongst different bioprinting modalities, extrusion-based bioprinting is the most conveniently adopted technique due to its affordability. Bioinks consisting of living cells are suspended in hydrogels and extruded through syringe-needle assemblies, which subsequently undergo gelation at the collector plate. During the process, pressure is exerted on living cells which may cause cell deaths. Thus, for selected combination of cell and hydrogel, exerted pressure and the extrusion play key roles in determining the cell viability. Experimental evaluation to characterise stresses experienced by the cells in a bioink during bioprinting is a tedious exercise. Herein, computational modelling can be applied efficiently for rapid screening of bioinks. In the present study, a smoothed particle hydrodynamics model is developed for the analysis of stresses exerted on the cells during bioprinting process. Cells are modelled by assigning different mechanical properties to nucleus, cytoskeleton and cell membrane regions of the cell to get a more realistic understanding of cell deformation. The cytoplasm and nucleus are modelled as finite element meshes and a spring model of the cell membrane is coupled to the finite element model to develop a three-compartment model of the cell. Cell deformation is taken as a potential indicator of cell death. Effect of different process parameters such as flow rate, syringe-nozzle geometry and cell density are investigated. A submodeling approach is further introduced to predict deformation with higher resolution in a unit volume containing 104 to 108 cells. Results suggest that the generated bioink flow dynamic model can be a useful tool for the computational study of fluid flow involving cell suspensions during a bioprinting process.

Dynamic Modeling and Flexibility Analysis of an Integrated Electrical and Thermal Energy System With the Heat Pump–Thermal Storage

The integration of high-efficiency heat pump and thermal storage devices is of great significance to realize the synergy between efficient and flexible operation of the integrated electric and thermal energy systems. This article proposed an integrated electric–thermal energy system with heat pump and thermal storage devices and introduced the heat current method for constructing its overall dynamic power flow model by considering energy transfer, conversion, and storage processes. On this basis, we derived the overall system constraint and component constraint equations. Under the objective of wind power curtailment minimization, the comprehensive effects of the dynamic characteristics of the heat pump, thermal storage capacity, new wind power installation, and new heat load on the electric and thermal output are analyzed. The results show that the dispatching accuracy of wind power output can be improved by up to 8% by taking into account the dynamic characteristics of the heat pump. The combination of heat pump and thermal storage device results in a leverage factor of 3.06 and 0.17 for the storage and release processes, respectively, effectively increasing the flexibility of system scheduling. The coordination between the newly added wind power installation and the added new heat load, with the higher operating temperature of the heat pump, is more conducive to promote wind power accommodation and improve the system’s overall flexibility. These results provide the necessary basis for the development of an integrated dispatch plan for the integrated electric and thermal energy systems containing the heat pump–thermal storage.

A general framework for combining traffic flow models and Bayesian network for traffic parameters estimation

This work focuses on traffic parameters estimation based on trajectory data in an arterial corridor with multiple signalized intersections. We develop a general framework that can combine various traffic flow models with Bayesian Network (BN) for estimating the overall traffic parameters using partially observed vehicle trajectory data (with unknown penetration rate). The BN is formulated to establish the probabilistic relationship between the traffic arrival process, traffic states, traffic flow model parameters and observed vehicle trajectories. More specifically, given traffic arrival information (e.g., traffic arrival volume) and fundamental diagram parameters (e.g., capacity, jam density, and free flow speed), vehicle trajectories are derived or simulated based on traffic flow modelling (e.g., shockwave analysis, Cell Transmission Model (CTM), or microscopic traffic simulation model VISSIM). Here, the extracted entry time of an observed vehicle at a pre-defined location upstream of the signal and its travel time are used to establish the probabilistic relationship. On the other hand, they are also the input parameters of the model for the estimation. Then, by combining a dynamic traffic flow model with Bayesian inference, we develop a framework to establish the learning process for traffic parameters estimation, such as traffic volume and traffic flow model parameters. The proposed framework is evaluated with different traffic flow models using the NGSIM dataset of an arterial corridor with three signalized intersections. For the CTM-BN model, the mean absolute percentage error (MAPE) of the estimation is generally below 5% when the penetration rate is above 12%. Regarding the VISSIM-BN model, the MAPE of the estimation is generally below 5% when the penetration rate is above 6%. These results demonstrate the applicability of the framework even under a relatively low penetration rate, and that the fidelity of the dynamic traffic model used does influence the estimation performance.

A dynamic particle scale-driven interphase force model for water-sand two-phase flow in hydraulic machinery and systems

The water–sand two-phase flow in hydraulic machinery and systems has a strong inter-phase interaction due to the effect of rotating turbulence, but many engineering computations do not consider turbulent effects into the interphase force, affecting the prediction accuracy of inner flow characteristics. The objective of this paper is to provide a new dynamic particle scale-driven interphase force model, and its notable features are shown as follows. In the Euler-Euler framework, the time-scale-driven hybrid URANS/LES model is introduced to extend the resolution scale of rotating turbulence. On this basis, (1) the turbulent factor and the wall factor based on a dynamic particle scale are determined to reflect the effects of turbulent fluctuation and wall surface on the drag force; (2) a new turbulent dispersion force model based on a dynamic particle scale is proposed to characterize the following behavior of heavy particles and their interaction with energetic eddies. Four typical flow cases of sediment-laden flows are tested. It is found that the eddy viscosity of water turbulence is effectively adjusted, thereby allowing more turbulent vortices to be resolved on the same URANS grids. The settling motion and inertial motion of sand are reasonably controlled by the improved interphase force model, thereby giving the prediction results of two-phase flow fields with higher accuracy. The new model improves the simulation accuracy of water–sand two-phase flow with rotation and curvature while does not significantly increase the computational cost, which enables efficient engineering computations of water–sand two-phase flow in hydraulic machinery and systems.

Green infrastructure and climate change impacts on the flows and water quality of urban catchments: Salmons Brook and Pymmes Brook in north-east London

Abstract Poor water quality is a widespread issue in urban rivers and streams in London. Localised pollution can have impacts on local communities, from health issues to environmental degradation and restricted recreational use of water. The Salmons and Pymmes Brooks, located in the London Borough of Enfield, flow into the River Lee, and in this paper, the impacts of misconnected sewers, urban runoff and atmospheric pollution have been evaluated. The first step towards finding a sustainable and effective solution to these issues is to identify sources and paths of pollutants and to understand their cycle through catchments and rivers. The INCA water quality model has been applied to the Salmons and Pymmes urban catchments in north-east London, with the aim of providing local communities and community action groups such as Thames21 with a tool they can use to assess the water quality issue. INCA is a process-based, dynamic flow and quality model, and so it can account for daily changes in temperature, flow, water velocity and residence time that all affect reaction kinetics and hence chemical flux. As INCA is process-based, a set of mitigation strategies have been evaluated including constructed wetland across the catchment to assess pollution control. The constructed wetlands can make a significant difference reducing sediment transport and improving nutrient control for nitrogen and phosphorus. The results of this paper show that a substantial reduction in nitrate, ammonium and phosphorus concentrations can be achieved if a proper catchment-scale wetland implementation strategy is put in place. Furthermore, the paper shows how the nutrient reduction efficiency of the wetlands should not be affected by climate change.

A Robust Scheduling Methodology for Integrated Electric-Gas System Considering Dynamics of Natural Gas Pipeline and Blending Hydrogen

As smart grid develops and renewables advance, challenges caused by uncertainties of renewables have been seriously threatening the energy system’s safe operation. Nowadays, the integrated electric-gas system (IEGS) plays a significant role in promoting the flexibility of modern grid owing to its great characteristic in accommodating renewable energy and coping with fluctuation and uncertainty of the system. And hydrogen, as an emerging and clean energy carrier, can further enhance the energy coupling of the IEGS and promote carbon neutralization with the development of power-to-hydrogen (P2H) technology and technology of blending hydrogen in the natural gas system. Dealing with the uncertainty of renewables, a robust schedule optimization model for the integrated electric and gas systems with blending hydrogen (IEGSH) considering the dynamics of gas is proposed and the iterative solving method based on column-and-constraint generation (C&CG) algorithm is implemented to solve the problem. Case studies on the IEGSH consisting of IEEE 39-bus power system and 27-node natural gas system validate the effectiveness of the dynamic energy flow model in depicting the transient process of gas transmission. The effectiveness of the proposed robust day-ahead scheduling model in dealing with the intra-day uncertainty of wind power is also verified. Additionally, the carbon emission reduction resulting from the blending of hydrogen is evaluated.

Factors Influencing the Modelling of Transport Flow Dynamics in Cities

The article defines the regularities in the influence on the traffic flows dynamics in cities using mathematical modelling, taking into account the historical features of this process, as well as the most relevant issues. In the queuing theory obstruction of free movement is considered a service, and the duration caused by this delay is called service duration, which is not applicable to the delay of transport at intersections. By means of mathematical modelling, the dependence of the influence of increasing the congestion degree on the increasein vehicle delays was determined. Results of studying transport flow characteristics in large cities should be considered when determining loading of urban passenger vehicles and calculating duration of delivering passengers to their destination. Application of the proposed mathematical model will help to reduce the probability of traffic congestion in the urban transport network of large cities, providing high-quality public transport services to clients.

Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number

In this article, we present a deep learning-based reduced order model (DL-ROM) for predicting the fluid forces and unsteady vortex shedding patterns. We consider the flow past a sphere to examine the accuracy of our DL-ROM predictions. The proposed DL-ROM methodology relies on a three-dimensional convolutional recurrent autoencoder network (3D CRAN) to extract the low-dimensional flow features from the full-order snapshots in an unsupervised manner. The low-dimensional features are evolved in time using a long short-term memory-based recurrent neural network and reconstructed back to the full-order as flow voxels. These flow voxels are introduced as static and uniform query probes in the point cloud domain to reduce the unstructured mesh complexity while providing convenience in the 3D CRAN training. We introduce a novel procedure to recover the interface description and the instantaneous force quantities from these 3D flow voxels. To evaluate the 3D flow reconstruction and inference, the 3D CRAN methodology is first applied to an external flow past a static sphere at the single Reynolds number of Re = 300. We provide an assessment of the computing requirements in terms of the memory usage, training, and testing cost of the 3D CRAN framework. Subsequently, variable Re-based flow information is infused in one 3D CRAN to learn a symmetry-breaking flow regime (280 ≤ Re ≤ 460) for the flow past a sphere. Effects of transfer learning are analyzed for training this complex 3D flow regime on a relatively smaller time series dataset. The 3D CRAN framework learns the flow regime nearly 20 times faster than the parallel full-order model and predicts this flow regime in time with a reasonable accuracy. Based on the predicted flow fields, the network demonstrates an R2 accuracy of 98.58% for the drag and 76.43% for the lift over the sphere in this flow regime. The proposed framework aligns with the development of a digital twin for 3D unsteady flow field and instantaneous force predictions with variable Re-based effects.

Multi-Period Restoration Model for Integrated Power-Hydrogen Systems Considering Transportation States

This article proposes an innovative integrated power and hydrogen distribution system (IPHDS) restoration model in response to multiple outages caused by natural disasters. During the restoration, repair crews and mobile battery-carried vehicles are considered to repair faulted lines and support critical power loads. Also, the network reconfiguration is taken into consideration in the restoration model to pick up loads. Besides, to address the different response time of hydrogen and power systems, the aerodynamic law-based dynamic hydrogen flow model is applied in the hydrogen system. The proposed model is presented as a mixed-integer linear program, which is verified on a 33-bus-48-node IPHDS with multiple outages. The present results verify the effectiveness of the proposed method.

Global sensitivity analysis with aggregated Shapley effects, application to avalanche hazard assessment

Dynamic models are simplified representations of some real-world entities that change over time. They are essential analytical tools with significant applications, e.g., in environmental and social sciences. Due to physical constraints applied on the outputs, it happens that input parameters are confined to a non-rectangular domain. In order to perform sensitivity analysis in this setting, we introduce the notion of aggregated Shapley effects and we propose an algorithm to estimate them with associated bootstrap confidence intervals. Our procedure is applied to analyze the sensitivity of an avalanche flow dynamic model from an input/output sample obtained by considering only input combinations leading to avalanche events that are both realistic and of interest for risk purposes. More precisely, we analyze the sensitivity in two different settings: (i) little knowledge on the input parameter probability distribution, and (ii) well-calibrated input parameter distribution. This leads insightful results regarding avalanche dynamics and potential related hazard, which demonstrate the usefulness of our approach for practical problems.