Flexible Flow Research Articles

Numerical investigation on vortex-induced vibration response characteristics for flexible risers under sheared-oscillatory flows

Surge motion of top-end platform induced by periodic wave makes marine flexible riser encounter equivalent sheared-oscillatory flow, under which the Vortex-induced Vibration (VIV) response will be more complicated than pure sheared flow or oscillatory flow cases. Based on a time domain force-decomposition model, the VIV response characteristics under sheared-oscillatory flows are investigated numerically in this paper. Firstly, the adopted numerical model is validated well against laboratory experiments under sheared flow and oscillatory flow. Then, 20 sheared-oscillatory flow cases with different oscillation periods and top maximum current velocities are designed and simulated. Under long and short oscillation period cases, the structural response presents several similar features owing to the instantaneous sheared flow profile at each moment, but it also has some different patterns because of the differently varying flow field. Finally, the effects and essential mechanism of oscillation period and top maximum current velocity on VIV response are discussed systematically.

Partially Rated Power Flow Control Converter Modeling for Low-Voltage DC Grids

Scalable and robust low-voltage direct current (LVdc) distribution networks require solutions, allowing flexible power flow control and reliable short-circuit protection. In this paper, the continuous full-order large- and small-signal models of a partially rated power flow control converter (PFCC) are derived utilizing the generalized averaging method. The large-signal model of the PFCC is coupled with a model of the LVdc grid. Due to the state-space representation, the combined model of the PFCC and the LVdc grid is suitable for easy algorithmization, and efficient simulation. These advantages make them essential tools for studying and optimizing of scalable LVdc systems with decentralized power flow control based on the PFCC. The PFCC models provide insights into controller design and stability analysis. The models are experimentally validated, and the functionality of the PFCC is demonstrated in a laboratory-scale microgrid.

Dynamics of flexible plates and flow under impulsive oscillation

The induced flow and the dynamics of flexible plates under heaving characterized by impulsive acceleration patterns Π(t) were experimentally inspected for various Cauchy numbers Ca and fractions τa of the time under acceleration with respect to the period of oscillations T. Simultaneous measurements of the plate motion along their span and the surrounding flow were obtained with particle tracking velocimetry (PTV) and particle image velocimetry (PIV). The Π(t) function was imposed at the root of a low-aspect-ratio plate vertically submerged in a quiescent water tank. Results show that Ca and τa significantly modulated the plate dynamics as well as the induced flow and coherent motions. We developed a simple model to estimate the velocity difference Δu between the root and tip of the plates; it is based on the damped harmonic oscillator and exhibits good performance across the fifteen scenarios inspected (three Ca by five τa). In general, the plate dynamics and flow exhibited three distinctive patterns characterized by flapping-like motions, translation-like motions and another intermediate state. The type of motion was induced by the relative instant at the reverse direction within the forced Π(t)-heaving within one of three characteristic stages; two of them depending on the sign of Δu and the other when Δu→0, i.e., in the limit of pure translation.

The economic lot scheduling problem in limited-buffer flexible flow shops: Mathematical models and a discrete fruit fly algorithm

This paper considers the lot scheduling problem in the flexible flow shop with limited intermediate buffers to minimize total cost which includes the inventory holding and setup costs. The single available mathematical model by Akrami et al. (2006) for this problem suffers from not only being non-linear but also high size-complexity. In this paper, two new mixed integer linear programming models are developed for the problem. Moreover, a fruit fly optimization algorithm is developed to effectively solve the large problems. For model’s evaluation, this paper experimentally compares the proposed models with the available model. Moreover, the proposed algorithm is also evaluated by comparing with two well-known algorithms (tabu search and genetic algorithm) in the literature and adaption of three recent algorithms for the flexible flow shop problem. All the results and analyses show the high performance of the proposed mathematical models as well as fruit fly optimization algorithm.

Flexible risk control strategy based on multi-stage corrective action with energy storage system

This paper presents a flexible risk control strategy with energy storage system to assist in taking a remedy action for removing a line overload in post-contingency. The problem is formulated as a flexible risk constrained-optimal power flow with multi-stage corrective action, which is classified into three types using the difference in ramp rate characteristics of the energy storage system unit and generator. The first-stage minimizes the operating costs in pre-contingency and the remaining stages help to mitigate the risk in post-contingency. With reference to the emergency transmission rating, each line has been set with different risk constraints. Here, the risk index is implemented by different types of a piecewise linear severity function according to the line overload. The proposed strategy can mitigate the risk constraint by taking a rapid corrective action using an storage units with a fast ramp rate, and it is proved using RTS 96 modified 24-bus and 73-bus systems. Based on the results of the case study, in which other strategies are compared, the proposed multi-stage risk control strategy with ESS is found yield a better performance.

Four-dimensional Flow MRI: Principles and Cardiovascular Applications.

In-plane phase-contrast (PC) imaging is now a routine component of MRI of regional blood flow in the heart and great vessels. In-plane PC MRI provides a volumetric, isotropic, time-resolved cine sequence that enables three-directional velocity encoding, a technique known as four-dimensional (4D) flow MRI. Recent advances in 4D flow MRI have shortened imaging times, while progress in big-data processing has improved dataset pre- and postprocessing, thereby increasing the feasibility of 4D flow MRI in clinical practice. Important technical issues include selection of the optimal velocity-encoding sensitivity before acquisition and preprocessing of the raw data for phase-offset corrections. Four-dimensional flow MRI provides unprecedented capabilities for comprehensive analysis of complex blood flow patterns using new visualization tools such as streamlines and velocity vectors. Retrospective multiplanar navigation enables flexible retrospective flow quantification through any plane across the volume with good accuracy. Current flow parameters include forward flow, reverse flow, regurgitation fraction, and peak velocity. Four-dimensional flow MRI also supplies advanced flow parameters of use for research, such as wall shear stress. The vigorous burgeoning of new applications indicates that 4D flow MRI is becoming an important imaging modality for cardiovascular disorders. This article reviews the main technical issues of 4D flow MRI and the different parameters provided by it and describes the main applications in cardiovascular diseases, including congenital heart disease, cardiac valvular disease, aortic disease, and pulmonary hypertension. Online supplemental material is available for this article. ©RSNA, 2019 See discussion on this article by Ordovas .

Polyacrylonitrile‐carbon Nanotube‐polyacrylonitrile: A Versatile Robust Platform for Flexible Multifunctional Electronic Devices in Medical Applications

AbstractFlexible multifunctional electronic devices are of high interest for a wide range of applications including thermal therapy and respiratory devices in medical treatment, safety equipment, and structural health monitoring systems. This paper reports a scalable and efficient strategy of manufacturing a polyacrylonitrile‐carbon nanotube‐polyacrylonitrile (PAN‐CNT‐PAN) robust flexible platform for multifunctional electronic devices including flexible heaters, temperature sensors, and flexible thermal flow sensors. The key advantages of this platform include low cost, porosity, mechanical robustness, and electrical stability under mechanical bending, enabling the development of fast‐response flexible heaters with a response time of ≈1.5 s and relaxation time of ≈1.7 s. The temperature‐sensing functionality is also investigated with a range of temperature coefficient of resistances from −650 to −900 ppm K−1. A flexible hot‐film sensing concept is successfully demonstrated using PAN‐CNT‐PAN with a high sensitivity of 340 mV (m s−1)−1. The sensitivity enhancement of 50% W−1 is also observed with increasing supply power. The low cost, porosity, versatile, and robust properties of the proposed platform will enable the development of multifunctional electronic devices for numerous applications such as flexible thermal management, temperature stabilization in industrial processing, temperature sensing, and flexible/wearable devices for human healthcare applications.

A Dual Heterogeneous Island Genetic Algorithm for Solving Large Size Flexible Flow Shop Scheduling Problems on Hybrid Multicore CPU and GPU Platforms

The flexible flow shop scheduling problem is an NP-hard problem and it requires significant resolution time to find optimal or even adequate solutions when dealing with large size instances. Thus, this paper proposes a dual island genetic algorithm consisting of a parallel cellular model and a parallel pseudo-model. This is a two-level parallelization highly consistent with the underlying architectures and is well suited for parallelizing inside or between GPUs and a multicore CPU. At the higher level, the efficiency of the island GA is improved by exploring new regions within the search space utilizing different methods. In the meantime, the cellular model keeps the population diversity by decentralization and the pseudo-model enhances the search ability by the complementary parent strategy at the lower level. To encourage the information sharing between islands, a penetration inspired migration policy is designed which sets the topology, the rate, the interval, and the strategy adaptively. Finally, the proposed method is tested on some large size flexible flow shop scheduling instances in comparison with other parallel algorithms. The computational results show that it not only can obtain competitive results but also reduces execution time.

A (21150, 19050) GC-LDPC Decoder for NAND Flash Applications

In this paper, a (21150, 19050) globally-coupled low-density parity check (GC-LDPC) code designed for NAND flash memories is presented. The proposed LDPC code comprises three disjoint subcodes which can be decoded independently. This highly structural parity check matrix contributes to efficient decoder implementation and flexible decoding flow control. Moreover, a two-phase local/global decoding procedure optimized for the proposed GC-LDPC code is introduced. Scenarios of collaborative decoding that leverages the special code structures are discussed. In the proposed decoder architecture, the pipelined processing elements with scheduling are employed to reduce the critical path and decoding latency as well. Implemented in UMC 65 nm process, the post-layout simulation shows a maximum decoding throughput of 4.32 Gb/s with the chip area 3.376 mm2.

Multi‐level, multi‐stage lot‐sizing and scheduling in the flexible flow shop with demand information updating

AbstractMotivated by a real‐world problem in an automobile manufacturing firm, we study the multi‐level, multi‐stage lot‐sizing and scheduling problem with demand information updating. The objective is to determine the quantities and sequences of production on different production stages to minimize the total costs of production and inventory. We employ the martingale model for forecast evolution to model the evolving demand over time and build a mixed‐integer programing (MIP) model for the problem using a hybrid period approach (i.e., combining micro‐ and macro‐periods). To solve this NP‐hard problem, we propose three heuristic algorithms based on the idea of “relax‐and‐fix” within the rolling horizon framework. We conduct computational experiments to test the performance of the heuristic algorithms as well as compare the performance of the proposed heuristics with the benchmark procedure (truncated procedure for MIP) for both small‐ and large‐scale problem instances. The computational results show that Heuristic 1 performs the best among the three heuristic algorithms.

Energy Flows Management of Multiple Electric Vehicles in Smart Grid

This paper presents energy flows management of multiple electric vehicles charging and its impact on a microgrid. The studied low voltage grid comprises also two photovoltaic generators and non-dispatchable loads. Three concepts using electric vehicles batteries as a distributed storage and bidirectional energy transfer in the Smart Grid are considered: Vehicle to Home, Vehicle to Vehicle and Vehicle to Grid. The potential for implementation of flexible microgrid energy flow management strategy for reduction of peaks in power consumption, absorption or production of reactive power (when required) and improving the power quality in the studied microgrid are estimated. For this purpose a set of usual load profiles of electric vehicle charging modes are studied and presented. Simulation results demonstrate the potential of electric vehicles as a flexible load and energy storage for peak load shaving in microgrids.DOI: 10.5755/j01.eie.25.1.22730

History Matching the Full Norne Field Model Using Seismic and Production Data

SummaryIn this paper, we use a combination of acoustic impedance and production data for history matching the full Norne Field. The purpose of the paper is to illustrate a robust and flexible work flow for assisted history matching of large data sets. We apply an iterative ensemble-based smoother, and the traditional approach for assisted history matching is extended to include updates of additional parameters representing rock clay content, which has a significant effect on seismic data. Further, for seismic data it is a challenge to properly specify the measurement noise, because the noise level and spatial correlation between measurement noise are unknown. For this purpose, we apply a method based on image denoising for estimating the spatially correlated (colored) noise level in the data. For the best possible evaluation of the workflow performance, all data are synthetically generated in this study. We assimilate production data and seismic data sequentially. First, the production data are assimilated using traditional distance-based localization, and the resulting ensemble of reservoir models is then used when assimilating seismic data. This procedure is suitable for real field applications, because production data are usually available before seismic data. If both production data and seismic data are assimilated simultaneously, the high number of seismic data might dominate the overall history-matching performance.The noise estimation for seismic data involves transforming the observations to a discrete wavelet domain. However, the resulting data do not have a clear spatial position, and the traditional distance-based localization schemes used to avoid spurious correlations and underestimated uncertainty (because of limited ensemble size), are not possible to apply. Instead, we use a localization scheme that is based on correlations between observations and parameters that does not rely on physical position for model variables or data. This method automatically adapts to each observation and iteration.The results show that we reduce data mismatch for both production and seismic data, and that the use of seismic data reduces estimation errors for porosity, permeability, and net-to-gross ratio (NTG). Such improvements can provide useful information for reservoir management and planning for additional drainage strategies.

A multi-item multi-packaging model to minimise cost of lost units, unpacking cost and CO2 emissions

This paper presents two multi-objective models that integrate packaging size and production scheduling problems for a flexible flow shop system. The main objective is to find the packaging size of finished product per item and the production schedule that would minimise cost of lost units, unpacking cost, inventory cost, earliness/tardiness penalties and kilograms of carbon dioxide emitted by resources operation. Since the complexity of the proposed models, a Pareto-based hybrid genetic algorithm (HGA) is also developed. A case study was developed to analyse the performance of both models using different instances. Numerical results indicate that the outperformance of one model over the other depends on the demand and the packaging size.

Numerical study of the effect of a square block on the deformation of channel walls

ABSTRACT In this paper, the flow around a square block in a two-dimensional channel with flexible walls and steady laminar flow is analysed by the one-way fluid-structure interaction numerical method. Flow analysis is carried out by FLUENT and the wall deformations are studied with the assistance of finite element software ANSYS. The study aims to investigate the effects of Poisson’s ratio, blockage ratio, channel wall thickness, and elastic modulus on hydrodynamic parameters and ultimately the deformation of channel walls. The results show that an increase in the blockage ratio increases the wall displacement, the total drag coefficient and the friction coefficient on the block. In contrast, an increase in the elastic modulus or Poisson’s ratio decreases the displacement of the walls.

Performance optimization of a flexible manufacturing system using simulation: the Taguchi method versus OptQuest

The Taguchi method is widely used in the field of manufacturing systems performance simulation and improvement. On the other hand, Arena/OptQuest is one of the most efficient contemporary simulation/optimization software tools. The objective of this paper is to evaluate and compare these two tools applied to a flexible manufacturing system performance optimization context, based on simulation. The principal purpose of this comparison is to determine their performances based on the quality of the obtained results and the gain in the simulation effort. The results of the comparison, applied to a flexible manufacturing system mean flow time optimization, show that the Arena/OptQuest optimization platform outperforms the Taguchi optimization method. Indeed, the Arena/OptQuest permits one, through the lowest experimental effort, to reliably minimize the mean flow time of the studied flexible manufacturing system more than the Taguchi method.

Sustainable Intralogistics due to Uniform Software and Modular Transport Entities

The current trend in internal value stream structures shows a clear development towards a combination of the specific advantages of the operation-oriented and the process-oriented production systems. All scientific approaches have in common that flexible intralogistics production networks require an autonomous and intelligent transportation systems that is not designed for a specific task, but can take on different transport roles due to their versatility. However, a comprehensive analysis of current designs and research projects in the field of AGVs and intralogistics transport robots show that there is neither the necessary interoperability of different systems nor a system architecture that addresses a flexible material flow independent of the physical design of the transportation system. AGVs mainly have a central control system, which manages the generation of transport orders and their disposition on the vehicles. In addition, the routes are specified centrally for the vehicles, even if they have free navigation. The individual transport unit is therefore only an executing machine that processes the assigned transport orders without any influence on order dispatching or route planning. Therefore, these AGV approaches do not have the flexibility and autonomy required for an intralogistics production network. The research project addresses the necessary interoperability of logistics systems and the abandonment of proprietary standalone solutions. For this purpose, methods for digitizing the workspace, route planning and path execution using a service-oriented architecture (SOA) were developed. A central component of this architecture is the approach described in the paper to enable decentralized path planning on the vehicles. Therefore, the path planning must be applicable to different target platforms without implementation effort and the vehicle software must be designed independently of the application. This approach allows intralogistics systems to be flexible and adaptable, resulting in a more sustained use of these systems.

Research on limited buffer scheduling problems in flexible flow shops with setup times

In order to solve the limited buffer scheduling problems in flexible flow shops with setup times, this paper proposes an improved whale optimization algorithm (IWOA) as a global optimization algorithm. Firstly, this paper presents a mathematic programming model for limited buffer in flexible flow shops with setup times, and applies the IWOA algorithm as the global optimization algorithm. Based on the whale optimization algorithm (WOA), the improved algorithm uses Levy flight, opposition-based learning strategy and simulated annealing to expand the search range, enhance the ability for jumping out of local extremum, and improve the continuous evolution of the algorithm. To verify the improvement of the proposed algorithm on the optimization ability of the standard WOA algorithm, the IWOA algorithm is tested by verification examples of small-scale and large-scale flexible flow shop scheduling problems, and the imperialist competitive algorithm (ICA), bat algorithm (BA), and whale optimization algorithm (WOA) are used for comparision. Based on the instance data of bus manufacturer, simulation tests are made on the four algorithms under variouis of practical evalucation scenarios. The simulation results show that the IWOA algorithm can better solve this type of limited buffer scheduling problem in flexible flow shops with setup times compared with the state of the art algorithms.

Flow shop scheduling with variable processing times based on differential shuffled frog leaping algorithm

In the problem of flexible flow shop scheduling with variable processing times, the change of processing speed often affects product quality and causes fluctuations in capacity, which makes it difficult to solve the scheduling problem. In the light of the flexible flow-shop scheduling problem with variable processing times (FFSP-VPT), the FFSP-VPT mathematical model is established. The improved differential shuffled frog leaping algorithm (DSFLA) serves as the global optimisation algorithm. It overcomes the shortcomings of the adaptive shuffled frog leaping algorithm which easily falls into local optimum and converges slowly. The two-stage coding method is used to determine the online sequence of the job and processing speed of the stage with variable processing times. The simulation experiments confirm the improvement of the shuffled frog leaping algorithm in global search ability and its effectiveness in solving the flexible flow shop scheduling problem with variable processing times.

Optimization for energy-efficient flexible flow shop scheduling under time of use electricity tariffs

With the development of society and economy, the global energy problem has become increasingly critical. Methods for time of use electricity tariffs have been introduced to balance the demand for resource and increase the energy efficiency in manufacturing industry. The scheduling problem for flexible flow shop in the environment of TOU is studied in this paper. An energy consumption model of machine tools is established which involves the processing energy, standby energy and set-up energy for a detailed description of electricity cost in shop-floor. The Improved Strength Pareto Evolutionary Algorithm is employed in which a novel gene encoding considering processing sequence, machine tool and processing time simultaneously is proposed to obtain the Pareto Front of the makespan and electricity cost. A case study is presented and the result shows that the proposed method is applicable for trade-off of electricity cost and delivery time for the purpose of enhanced energy efficiency.

Research on limited buffer scheduling problems in flexible flow shops with setup times

In order to solve the limited buffer scheduling problems in flexible flow shops with setup times, this paper proposes an improved whale optimization algorithm (IWOA) as a global optimization algorithm. Firstly, this paper presents a mathematic programming model for limited buffer in flexible flow shops with setup times, and applies the IWOA algorithm as the global optimization algorithm. Based on the whale optimization algorithm (WOA), the improved algorithm uses Levy flight, opposition-based learning strategy and simulated annealing to expand the search range, enhance the ability for jumping out of local extremum, and improve the continuous evolution of the algorithm. To verify the improvement of the proposed algorithm on the optimization ability of the standard WOA algorithm, the IWOA algorithm is tested by verification examples of small-scale and large-scale flexible flow shop scheduling problems, and the imperialist competitive algorithm (ICA), bat algorithm (BA), and whale optimization algorithm (WOA) are used for comparision. Based on the instance data of bus manufacturer, simulation tests are made on the four algorithms under variouis of practical evalucation scenarios. The simulation results show that the IWOA algorithm can better solve this type of limited buffer scheduling problem in flexible flow shops with setup times compared with the state of the art algorithms.