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Journal articles on the topic 'Time-varying flow'

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Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Egger, Joseph. "Time varying flow over mountains: temperature perturbations at the surface." Meteorologische Zeitschrift 18, no. 1 (March 6, 2009): 101–6. http://dx.doi.org/10.1127/0941-2948/2009/352.

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2

Cai, X., D. Sha, and C. K. Wong. "Time-varying minimum cost flow problems." European Journal of Operational Research 131, no. 2 (June 2001): 352–74. http://dx.doi.org/10.1016/s0377-2217(00)00059-x.

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3

Cai, X., D. Sha, and C. K. Wong. "Time-varying universal maximum flow problems." Mathematical and Computer Modelling 33, no. 4-5 (February 2001): 407–30. http://dx.doi.org/10.1016/s0895-7177(00)00252-1.

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4

Aung Kyaw, NyoNyo, and Sijing Zong. "The Time-varying Cash Flow Sensitivity of Cash." Journal of International Business and Economy 15, no. 2 (December 1, 2014): 1–34. http://dx.doi.org/10.51240/jibe.2014.2.1.

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By using data of US manufacturing companies, we revisit the cash flow sensitivity to cash in two sub-samples of 1993-2000 and 2000-2011 to investigate the time-varying features of the cash flow sensitivity of cash. Our results show a weakening coefficient of US manufacturing firms from 1990s to 2000s. The sensitivity in the later time period is only a half of its original scale. Financially unconstrained firms seem to converge with the constrained firms in the later period, leading to the conclusion that macroeconomic conditions impact more on the cash flow sensitivity of cash than the external financial constraint does. Further, our research identifies that the overall decreasing sensitivity is driven by firms with negative cash flows.

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5

Evcin, Cansu, Ömür Uğur, and Münevver Tezer-Sezgin. "Time varying control of magnetohydrodynamic duct flow." European Journal of Mechanics - B/Fluids 89 (September 2021): 100–114. http://dx.doi.org/10.1016/j.euromechflu.2021.05.007.

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6

Amgain, Dipak Babu, and Tanka Nath Dhamala. "Quickest Flow Algorithms with Time-Varying Attributes." Journal of Institute of Science and Technology 26, no. 1 (June 17, 2021): 63–73. http://dx.doi.org/10.3126/jist.v26i1.37826.

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In many real-world situations, there are numerous network optimization problems where the network attributes depend on time. In this paper, we consider single-source single-sink discrete-time dynamic network flow problems. We review some algorithms for the quickest flow problems in two environments (to the network attributes): time-invariant and time-variant. This paper mainly focuses on the existing algorithms for a later one. In literature, most of the authors have made their objectives to determine the earliest arrival time paths along which a given amount of flow can be sent in the minimum time. Evacuation is the most recent research area of network optimization, where quickest flow models allow the estimation of the minimum time required to bring a given number of evacuees to safety.

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7

FKIRIN, M. A. "On-line time-varying river-flow prediction." International Journal of Systems Science 20, no. 7 (July 1989): 1227–32. http://dx.doi.org/10.1080/00207728908910208.

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8

Nasrabadi, Ebrahim, and S. Mehdi Hashemi. "Minimum cost time-varying network flow problems." Optimization Methods and Software 25, no. 3 (June 2010): 429–47. http://dx.doi.org/10.1080/10556780903239121.

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9

Styrman, Avril. "Relativity vs. absolute simultaneity: Varying flow of time or varying frequency?" Physics Essays 31, no. 3 (September 1, 2018): 256–64. http://dx.doi.org/10.4006/0836-1398-31.3.256.

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10

Hyrkas, Jeremy, Daniel Halperin, and Bill Howe. "Time-Varying Clusters in Large-Scale Flow Cytometry." Proceedings of the AAAI Conference on Artificial Intelligence 29, no. 2 (January 25, 2015): 4022–23. http://dx.doi.org/10.1609/aaai.v29i2.19067.

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Flow cytometers measure the optical properties of particles to classify microbes. Recent innovations have allowed oceanographers to collect flow cytometry data continuously during research cruises, leading to an explosion of data and new challenges for the classification task. The massive scale, time-varying underlying populations, and noisy measurements motivate the development of new classification methods. We describe the problem, the data, and some preliminary results demonstrating the difficulty with conventional methods.

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11

Altmeyer, Sebastian. "Ferrofluidic Couette flow in time-varying magnetic field." Journal of Magnetism and Magnetic Materials 552 (June 2022): 169205. http://dx.doi.org/10.1016/j.jmmm.2022.169205.

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12

Fujii, Hitoshi, Kunihiko Nohira, Yoshihisa Shintomi, Toshimitsu Asakura, and Takehiko Ohura. "Blood flow observed by time-varying laser speckle." Optics Letters 10, no. 3 (March 1, 1985): 104. http://dx.doi.org/10.1364/ol.10.000104.

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13

Matei, Ion, Assane Gueye, and John S. Baras. "Flow control in time-varying, random supply chains." Transportation Research Part E: Logistics and Transportation Review 77 (May 2015): 311–30. http://dx.doi.org/10.1016/j.tre.2015.01.006.

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14

Nenninger, Steve, and David Rakowski. "Time-varying flow-performance sensitivity and investor sophistication." Journal of Asset Management 15, no. 5 (October 2014): 333–45. http://dx.doi.org/10.1057/jam.2014.32.

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15

Wong, Tommy S. W., and Charng-Ning Chen. "Time of Concentration Formula for Sheet Flow of Varying Flow Regime." Journal of Hydrologic Engineering 2, no. 3 (July 1997): 136–39. http://dx.doi.org/10.1061/(asce)1084-0699(1997)2:3(136).

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16

Grant, B. J., J. M. Fitzpatrick, and B. B. Lieber. "Time-varying pulmonary arterial compliance." Journal of Applied Physiology 70, no. 2 (February 1, 1991): 575–83. http://dx.doi.org/10.1152/jappl.1991.70.2.575.

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We tested the hypothesis that pulmonary arterial compliance (Ca) varies during the ventilatory cycle. Pressure and flow in the main pulmonary artery were measured in open-chest dogs under chloralose anesthesia (n = 12) with a positive-pressure volume-cycled ventilator. Input impedance was calculated from the pressure and flow waves of heart cycles obtained immediately after the start of inspiration (SI) and immediately after the start of expiration (SE). A lumped parameter model was used to calculate Ca from the input impedance spectrum of the main pulmonary artery. Three levels of positive end-expiratory pressure (PEEP) were used before and after meclofenamate (n = 6) or vagotomy (n = 6). Ca was significantly greater at SE than at SI at each level of PEEP. PEEP increased Ca at SE but not at SI. None of these changes was altered by meclofenamate or vagotomy, suggesting that these differences of Ca were due to passive mechanical effects rather than an active neurohumoral mechanisms. We conclude that Ca is time varying during the ventilatory cycle because it is altered by the dynamic increase of lung volume between SI and SE, but not with the quasi-static increase of lung volume induced by raising the level of PEEP. These changes of Ca were unaffected by vagal feedback or inhibition of cyclooxygenase. We suggest that the increased Ca just after the start of expiration may result from dynamic shifts of blood volume from the extra-alveolar to the alveolar vessels.

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17

Ahmadi, Amir Ali, and Bachir El Khadir. "Time-Varying Semidefinite Programs." Mathematics of Operations Research 46, no. 3 (August 2021): 1054–80. http://dx.doi.org/10.1287/moor.2020.1117.

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We study time-varying semidefinite programs (TV-SDPs), which are semidefinite programs whose data (and solutions) are functions of time. Our focus is on the setting where the data vary polynomially with time. We show that under a strict feasibility assumption, restricting the solutions to also be polynomial functions of time does not change the optimal value of the TV-SDP. Moreover, by using a Positivstellensatz (positive locus theorem) on univariate polynomial matrices, we show that the best polynomial solution of a given degree to a TV-SDP can be found by solving a semidefinite program of tractable size. We also provide a sequence of dual problems that can be cast as SDPs and that give upper bounds on the optimal value of a TV-SDP (in maximization form). We prove that under a boundedness assumption, this sequence of upper bounds converges to the optimal value of the TV-SDP. Under the same assumption, we also show that the optimal value of the TV-SDP is attained. We demonstrate the efficacy of our algorithms on a maximum-flow problem with time-varying edge capacities, a wireless coverage problem with time-varying coverage requirements, and on biobjective semidefinite optimization where the goal is to approximate the Pareto curve in one shot.

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18

Kouwenhoven, E., I. Vergroesen, Y. Han, and J. A. Spaan. "Retrograde coronary flow is limited by time-varying elastance." American Journal of Physiology-Heart and Circulatory Physiology 263, no. 2 (August 1, 1992): H484—H490. http://dx.doi.org/10.1152/ajpheart.1992.263.2.h484.

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The study examined the influence of left ventricular pressure (PLV) on coronary arterial flow and pressure. In eight anesthetized open-thorax goats with cannulated and artificially perfused left main coronary artery, the PLV was disturbed by aortic occlusions. In the constant pressure perfusion (CPP) protocol the response of systolic arterial inflow on a change in PLV was studied with fixed perfusion pressure and at several perfusion pressure levels. Similarly, in the constant flow perfusion (CFP) protocol the response of systolic perfusion pressure was examined with fixed levels of perfusion flow and repeated for several flow levels. The results show an early systolic response determined by PLV for both protocols. Midsystolic responses were almost absent in the CPP protocol but present in the CFP protocol. At CPP, the effect of a change of PLV on arterial flow in mid systole was only 20% of that on early systolic flow with intact coronary tone and 33% with adenosine-induced vasodilation. At CFP the pulsations in perfusion pressure were 30% of PLV pulsations, both with intact tone and vasodilation; in contrast with the CPP results, no difference for this value was found in different stages of systole. We suggest that stiffness of cardiac muscle determines the influence of PLV on coronary flow. The difference in mid systolic relations between the CPP and CFP protocols is explained by the difference in time constants induced by the perfusion system. The results are best explained by a synthesis between the intramyocardial pump model and the elastance concept.

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19

Day, Steven W., and James C. McDaniel. "PIV Measurements of Flow in a Centrifugal Blood Pump: Time-Varying Flow." Journal of Biomechanical Engineering 127, no. 2 (September 20, 2004): 254–63. http://dx.doi.org/10.1115/1.1865190.

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Measurements of the time-varying flow in a centrifugal blood pump operating as a left ventricular assist device (LVAD) are presented. This includes changes in both the pump flow rate as a function of the left ventricle contraction and the interaction of the rotating impeller and fixed exit volute. When operating with a pulsing ventricle, the flow rate through the LVAD varies from 0-11L∕min during each cycle of the heartbeat. Phase-averaged measurements of mean velocity and some turbulence statistics within several regions of the pump, including the inlet, blade passage, exit volute, and diffuser, are reported at 20 phases of the cardiac cycle. The transient flow fields are compared to the constant flow rate condition that was reported previously in order to investigate the transient effects within the pump. It is shown that the quasi-steady assumption is a fair treatment of the time varying flow field in all regions of this representative pump, which greatly simplifies the comprehension and modeling of this flow field. The measurements are further interpreted to identify the effects that the transient nature of the flow field will have on blood damage. Although regions of recirculation and stagnant flow exist at some phases of the cardiac cycle, there is no location where flow is stagnant during the entire heartbeat.

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20

Kaljurand, Mihkel, and Mihkel Koel. "Analyzing time varying flow composition using multiple input chromatography." Analytica Chimica Acta 348, no. 1-3 (August 1997): 203–14. http://dx.doi.org/10.1016/s0003-2670(97)00239-0.

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21

Ha, Seung-Ji, and Gordon E. Swaters. "Finite-Amplitude Baroclinic Instability of Time-Varying Abyssal Currents." Journal of Physical Oceanography 36, no. 1 (January 1, 2006): 122–39. http://dx.doi.org/10.1175/jpo2838.1.

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Abstract The weakly nonlinear baroclinic instability characteristics of time-varying grounded abyssal flow on sloping topography with dissipation are described. Specifically, the finite-amplitude evolution of marginally unstable or stable abyssal flow both at and removed from the point of marginal stability (i.e., the minimum shear required for instability) is determined. The equations governing the evolution of time-varying dissipative abyssal flow not at the point of marginal stability are identical to those previously obtained for the Phillips model for zonal flow on a β plane. The stability problem at the point of marginally stability is fully nonlinear at leading order. A wave packet model is introduced to examine the role of dissipation and time variability in the background abyssal current. This model is a generalization of one introduced for the baroclinic instability of zonal flow on a β plane. A spectral decomposition and truncation leads, in the absence of time variability in the background flow and dissipation, to the sine–Gordon solitary wave equation that has grounded abyssal soliton solutions. The modulation characteristics of the soliton are determined when the underlying abyssal current is marginally stable or unstable and possesses time variability and/or dissipation. The theory is illustrated with examples.

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22

Sasaki, Shigeru, and Morito Shiohara. "A Real-Time Optical Flow Processing using Time-Varying Image Processor: ISHTAR." Journal of the Robotics Society of Japan 13, no. 3 (1995): 331–34. http://dx.doi.org/10.7210/jrsj.13.331.

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23

Opasanon, Sathaporn, and Elise Miller-Hooks. "Noisy Genetic Algorithm for Stochastic, Time-Varying Minimum Time Network Flow Problem." Transportation Research Record: Journal of the Transportation Research Board 2196, no. 1 (January 2010): 75–82. http://dx.doi.org/10.3141/2196-08.

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24

Selvakumar, Jhanani, and Efstathios Bakolas. "Robust Time-Optimal Guidance in a Partially Uncertain Time-Varying Flow-Field." Journal of Optimization Theory and Applications 179, no. 1 (June 14, 2018): 240–64. http://dx.doi.org/10.1007/s10957-018-1326-1.

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25

Honda, Takuto, Mayuka Kanaya, Isao T. Tokuda, Anne Bouvet, Annemie Van Hirtum, and Xavier Pelorson. "Experimental study on the quasi-steady approximation of glottal flows." Journal of the Acoustical Society of America 151, no. 5 (May 2022): 3129–39. http://dx.doi.org/10.1121/10.0010451.

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To examine the quasi-steady approximation of the glottal flow, widely used in the modeling of vocal fold oscillations, intraglottal pressure distributions were measured in a scaled-up static vocal fold model under time-varying flow conditions. The left and right vocal folds were slightly open and set to a symmetric and oblique configuration with a divergence angle. To realize time-varying flow conditions, the flow rate was sinusoidally modulated with a frequency of 2 and 10 Hz, which correspond to 112.5 and 562.5 Hz, respectively, in real life. Measurements of the intraglottal pressures under both steady and time-varying flows revealed that the pressure profiles of the time-varying flow conditions are non-distinguishable from those of the steady flow conditions as far as they have the same subglottal pressure as an input pressure. The air-jet separation point was also non-distinguishable between the steady and the time-varying flow conditions. Our study therefore suggests that the time-varying glottal flow can be approximated as a series of steady flow states with a matching subglottal pressure in the range of normal vocalization frequencies. Since the glottal closure was not taken into account in the present experiment, our argument is valid except for such a critical situation.

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26

Yi, Bin, Lu Chen, Hansong Zhang, Vijay P. Singh, Ping Jiang, Yizhuo Liu, Hexiang Guo, and Hongya Qiu. "A time-varying distributed unit hydrograph method considering soil moisture." Hydrology and Earth System Sciences 26, no. 20 (October 20, 2022): 5269–89. http://dx.doi.org/10.5194/hess-26-5269-2022.

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Abstract. The distributed unit hydrograph (DUH) method has been widely used for flow routing in a watershed because it adequately characterizes the underlying surface characteristics and varying rainfall intensity. Fundamental to the calculation of DUH is flow velocity. However, the currently used velocity formula assumes a global equilibrium of the watershed and ignores the impact of time-varying soil moisture content on flow velocity, which thus leads to a larger flow velocity. The objective of this study was to identify a soil moisture content factor, which, based on the tension water storage capacity curve, was derived to investigate the response of DUH to soil moisture content in unsaturated areas. Thus, an improved distributed unit hydrograph, based on time-varying soil moisture content, was obtained. The proposed DUH considered the impact of both time-varying rainfall intensity and soil moisture content on flow velocity, assuming the watershed to be not in equilibrium but varying with soil moisture. The Qin River basin and Longhu River basin were selected as two case studies, and the synthetic unit hydrograph (SUH), the time-varying distributed unit hydrograph (TDUH) and the current DUH methods were compared with the proposed method. Then, the influence of time-varying soil moisture content on flow velocity and flow routing was evaluated, and results showed that the proposed method performed the best among the four methods. The shape and duration of the unit hydrograph (UH) were mainly related to the soil moisture content at the initial stage of a rainstorm, and when the watershed was approximately saturated, the grid flow velocity was mainly dominated by excess rainfall. The proposed method can be used for the watersheds with sparse gauging stations and limited observed rainfall and runoff data.

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27

Burlingham, Charlie S., and David J. Heeger. "Heading perception depends on time-varying evolution of optic flow." Proceedings of the National Academy of Sciences 117, no. 52 (December 16, 2020): 33161–69. http://dx.doi.org/10.1073/pnas.2022984117.

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There is considerable support for the hypothesis that perception of heading in the presence of rotation is mediated by instantaneous optic flow. This hypothesis, however, has never been tested. We introduce a method, termed “nonvarying phase motion,” for generating a stimulus that conveys a single instantaneous optic flow field, even though the stimulus is presented for an extended period of time. In this experiment, observers viewed stimulus videos and performed a forced-choice heading discrimination task. For nonvarying phase motion, observers made large errors in heading judgments. This suggests that instantaneous optic flow is insufficient for heading perception in the presence of rotation. These errors were mostly eliminated when the velocity of phase motion was varied over time to convey the evolving sequence of optic flow fields corresponding to a particular heading. This demonstrates that heading perception in the presence of rotation relies on the time-varying evolution of optic flow. We hypothesize that the visual system accurately computes heading, despite rotation, based on optic acceleration, the temporal derivative of optic flow.

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28

Qi, Zhanfeng, Lishuang Jia, Yufeng Qin, Jian Shi, and Jingsheng Zhai. "Propulsion Performance of the Full-Active Flapping Foil in Time-Varying Freestream." Applied Sciences 10, no. 18 (September 8, 2020): 6226. http://dx.doi.org/10.3390/app10186226.

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A numerical investigation of the propulsion performance and hydrodynamic characters of the full-active flapping foil under time-varying freestream is conducted. The finite volume method is used to calculate the unsteady Reynolds averaged Navier–Stokes by commercial Computational Fluid Dynamics (CFD) software Fluent. A mesh of two-dimensional (2D) NACA0012 foil with the Reynolds number Re = 42,000 is used in all simulations. We first investigate the propulsion performance of the flapping foil in the parameter space of reduced frequency and pitching amplitude at a uniform flow velocity. We define the time-varying freestream as a superposition of steady flow and sinusoidal pulsating flow. Then, we study the influence of time-varying flow velocity on the propulsion performance of flapping foil and note that the influence of the time-varying flow is time dependent. For one period, we find that the oscillating amplitude and the oscillating frequency coefficient of the time-varying flow have a significant influence on the propulsion performance of the flapping foil. The influence of the time-varying flow is related to the motion parameters (reduced frequency and pitching amplitude) of the flapping foil. The larger the motion parameters, the more significant the impact of propulsion performance of the flapping foil. For multiple periods, we note that the time-varying freestream has little effect on the propulsion performance of the full-active flapping foil at different pitching amplitudes and reduced frequency. In summary, we conclude that the time-varying incoming flow has little effect on the flapping propulsion performance for multiple periods. We can simplify the time-varying flow to a steady flow field to a certain extent for numerical simulation.

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29

Camlibel, Kanat, and Aneel Tanwani. "A discretization algorithm for time-varying composite gradient flow dynamics." IFAC-PapersOnLine 54, no. 9 (2021): 558–63. http://dx.doi.org/10.1016/j.ifacol.2021.06.116.

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30

Desse, Jean-Michel. "Effect of Time-Varying Wake Flow Characteristics Behind Flat Plates." AIAA Journal 36, no. 11 (November 1998): 2036–43. http://dx.doi.org/10.2514/2.304.

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31

Jong-Chul Yoon, In-Kwon Lee, and H. Kang. "Video Painting Based on a Stabilized Time-Varying Flow Field." IEEE Transactions on Visualization and Computer Graphics 18, no. 1 (January 2012): 58–67. http://dx.doi.org/10.1109/tvcg.2011.47.

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32

Chen, Mingcheng, Shawn C. Shadden, and John C. Hart. "Fast Coherent Particle Advection through Time-Varying Unstructured Flow Datasets." IEEE Transactions on Visualization and Computer Graphics 22, no. 8 (August 1, 2016): 1960–73. http://dx.doi.org/10.1109/tvcg.2015.2476795.

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33

Burlingham, Charlie, and David Heeger. "Heading Perception Depends on Time-Varying Evolution of Optic Flow." Journal of Vision 18, no. 10 (September 1, 2018): 47. http://dx.doi.org/10.1167/18.10.47.

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34

Ho, Jiann-Min, Pi-Cheng Hsiu, and Ming-Syan Chen. "Deadline Flow Scheduling in Datacenters with Time-Varying Bandwidth Allocations." IEEE Transactions on Services Computing 13, no. 3 (May 1, 2020): 437–50. http://dx.doi.org/10.1109/tsc.2017.2701363.

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35

Martinez, R. "Sources of flow noise due to time‐varying tip relief." Journal of the Acoustical Society of America 113, no. 4 (April 2003): 2244. http://dx.doi.org/10.1121/1.4780379.

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36

Kim, Jeongsim, Bara Kim, Jerim Kim, and Yun Han Bae. "Stability of flow-level scheduling with Markovian time-varying channels." Performance Evaluation 70, no. 2 (February 2013): 148–59. http://dx.doi.org/10.1016/j.peva.2012.08.005.

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37

Rajabzadeh, Yalda, Amir Hossein Rezaie, and Hamidreza Amindavar. "Short-term traffic flow prediction using time-varying Vasicek model." Transportation Research Part C: Emerging Technologies 74 (January 2017): 168–81. http://dx.doi.org/10.1016/j.trc.2016.11.001.

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38

Zhang, Qin, Scott Draper, Liang Cheng, and Hongwei An. "Scour below a subsea pipeline in time varying flow conditions." Applied Ocean Research 55 (February 2016): 151–62. http://dx.doi.org/10.1016/j.apor.2015.10.003.

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39

Desse, Jean-Michel. "Effect of time-varying wake flow characteristics behind flat plates." AIAA Journal 36 (January 1998): 2036–43. http://dx.doi.org/10.2514/3.14083.

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40

Salehi Fathabadi, H., S. Khodayifar, and M. A. Raayatpanah. "Minimum flow problem on network flows with time-varying bounds." Applied Mathematical Modelling 36, no. 9 (September 2012): 4414–21. http://dx.doi.org/10.1016/j.apm.2011.11.067.

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41

Techy, Laszlo. "Optimal navigation in planar time-varying flow: Zermelo’s problem revisited." Intelligent Service Robotics 4, no. 4 (June 17, 2011): 271–83. http://dx.doi.org/10.1007/s11370-011-0092-9.

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42

Krishnan, H., C. Garth, and K. Joy. "Time and Streak Surfaces for Flow Visualization in Large Time-Varying Data Sets." IEEE Transactions on Visualization and Computer Graphics 15, no. 6 (November 2009): 1267–74. http://dx.doi.org/10.1109/tvcg.2009.190.

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43

Khodayifar, S., M. A. Raayatpanah, and P. M. Pardalos. "A polynomial time algorithm for the minimum flow problem in time-varying networks." Annals of Operations Research 272, no. 1-2 (February 28, 2017): 29–39. http://dx.doi.org/10.1007/s10479-017-2450-2.

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44

Li, Z., B. J. Grant, and B. B. Lieber. "Time-varying pulmonary arterial input impedance via wavelet decomposition." Journal of Applied Physiology 78, no. 6 (June 1, 1995): 2309–19. http://dx.doi.org/10.1152/jappl.1995.78.6.2309.

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Wavelet decomposition is proposed as a novel approach for determining pulmonary arterial input impedance throughout the breathing cycle. The canine pulmonary arterial input impedance was evaluated throughout the ventilatory cycle at 5, 10, and 15 cmH2O of positive end-expiratory pressure. The impedance spectrum was obtained by Fourier transformation of wavelets generated by decomposing the pulmonary arterial pressure and flow waveforms. With wavelet decomposition, each heart beat is viewed individually as a transient pulse rather than as an interval within a continuous function of pressure and flow. The advantage of using this approach is the ability to obtain stable estimates of input impedance spectra with high-frequency resolution over the entire frequency range with only a limited data set of pressure and flow decomposed to wavelets as short as singular extrapolated cardiac cycles. This method was used to define the changes of input impedance that occur during the ventilatory cycle. Results show that the impedance spectrum undergoes notable changes during the breathing cycle and demonstrate the utility of the proposed method.

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45

Seggiani, M. "Modelling and simulation of time varying slag flow in a Prenflo entrained-flow gasifier." Fuel 77, no. 14 (November 1998): 1611–21. http://dx.doi.org/10.1016/s0016-2361(98)00075-1.

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46

Yang, Xianfeng, and Gang-Len Chang. "Estimation of Time-Varying Origin–Destination Patterns for Design of Multipath Progression on a Signalized Arterial." Transportation Research Record: Journal of the Transportation Research Board 2667, no. 1 (January 2017): 28–38. http://dx.doi.org/10.3141/2667-04.

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Most state-of-the-art control strategies for coping with arterial congestion provide progression for heavy through-traffic flows. However, such strategies cannot tackle arterial congestion caused by both heavy turning and through-traffic flows, where turning-traffic volumes often spill over their designated bay length and cause link blockage. An effective approach is to offer a progression band to each of those critical path flows that can be identified from the arterial origin–destination (O-D) flow patterns. This study proposes three models for estimating such information from available traffic measurements. The estimated time-varying O-D distributions yield both the number of critical path flows and their respective volume ranks for design of their progression bands. Based on the principle of flow conservations, the first model captures the relationships between link counts and dynamic O-D flows, whereas the second model directly takes turning flows at each intersection as the primary model input. To consider further the impact of traffic signal plans on O-D flow patterns, the third model incorporates a set of additional measurements—the time-varying queue length information—to improve the estimation accuracy. Comparisons of the actual O-D flows and the estimated results demonstrate the effectiveness of the proposed models for identifying the heavy flow paths and their respective volumes.

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El-Sherbeny, Nasser A. "AN ALGORITHM OF A MINIMUM COST FLOW PROBLEM WITH TIME-VARYING AND TIME-WINDOWS." Journal of Mathematical Sciences: Advances and Applications 55, no. 1 (February 10, 2019): 1–20. http://dx.doi.org/10.18642/jmsaa_7100121950.

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Dai, J. G., and Pengyi Shi. "A Two-Time-Scale Approach to Time-Varying Queues in Hospital Inpatient Flow Management." Operations Research 65, no. 2 (April 2017): 514–36. http://dx.doi.org/10.1287/opre.2016.1566.

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van der Wall, B. G., and J. G. Leishman. "On the Influence of Time‐Varying Flow Velocity on Unsteady Aerodynamics." Journal of the American Helicopter Society 39, no. 4 (October 1, 1994): 25–36. http://dx.doi.org/10.4050/jahs.39.25.

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Aksikas, Ilyasse, Adrian M. Fuxman, and J. Fraser Forbes. "Control of Time-Varying Distributed Parameter Plug Flow Reactor by LQR." IFAC Proceedings Volumes 41, no. 2 (2008): 11955–60. http://dx.doi.org/10.3182/20080706-5-kr-1001.02023.

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