Lagrangian statistics of pressure fluctuation events in homogeneous isotropic turbulence

Lagrangian statistics of pressure fluctuation events in homogeneous isotropic turbulence

Mehedi Bappy    Pablo M. Carrica IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA, USA    Gustavo C. Buscaglia Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, Brazil
Abstract

Homogeneous and isotropic turbulent fields obtained from two DNS databases (with equal to 150 and 418) were seeded with point particles that moved with the local fluid velocity to obtain Lagrangian pressure histories. Motivated by cavitation inception modeling, the statistics of events in which such particles undergo low-pressure fluctuations were computed, parameterized by the amplitude of the fluctuations and by their duration. The main results are the average frequencies of these events and the probabilistic distribution of their duration, which are of predictive value. A connection is also established between these average frequencies and the pressure probability density function, thus justifying experimental methods proposed in the literature. Further analyses of the data show that the occurrence of very-low-pressure events is highly intermittent and is associated with worm-like vortical structures of length comparable to the integral scale of the flow.

Homogeneous isotropic turbulence, pressure fluctuations, cavitation

I Introduction

The study of pressure fluctuations in turbulent flows has been the subject of significant theoretical, numerical and experimental work over more than eight decades. Over the years, much knowledge has been gained about the intensity of the fluctuations, their scaling properties, their energy spectrum and probability density function (PDF) as well as about their spatial structure and their relation to other flow variables. The reader is referred, among others, to the works of George et al gba84 (), Pumir pumir94_pf (), Cao et al ccd99_pf (), Gotoh & Rogallo gr99_jfm () and references therein for further details.

Pressure fluctuations have important practical consequences in many physical situations, since they intervene in the forces exerted by the fluid on adjacent and immersed bodies and are also key to acoustic noise. The specific motivation of the study reported herein is the phenomenon of incipient cavitation in turbulent liquid flows, which is a macroscopic consequence of pressure fluctuations on microscopic bubbles (or nuclei) present in the liquid. Though the dynamics of cavitation bubbles has been well established following the pioneering work of Plesset plesset49_jam (), accurate prediction of cavitation inception in technologically relevant flows remains a challenge.

A seminal work on cavitation inception in turbulent flows was published in 1979 by Arndt & George ag79 (). It puts forward the main ingredients for turbulence-induced inception: That the cavitation nuclei are subject to pressure fluctuations as measured in a Lagrangian frame, and that (a) the pressure must dip below a critical level (, the cavitation pressure), and (b) the low pressure must persist below the critical level for a time that exceeds the time scale for bubble growth. The picture has been confirmed and enriched over the years by several authors rk94_jfm (); bfm95_jfm (); lvmb00_pf (), including the role of coherent structures and the possibility of using incipient cavitation bubbles as pressure sensors.

Typically, cavitation inception is defined by the inception cavitation index , where is the value of the reference pressure of the flow at which cavitating nuclei first become observable, is the density and the global velocity scale. By observable it is meant that the frequency of cavitation events growing bubbles to a large enough size is high enough to be experimentally detectable. In view of criteria (a) and (b) above, inception modeling thus requires knowledge about the frequency at which random turbulent fluctuations take the pressure, as experienced by the minute nuclei transported by the flow, below for long enough time. Such knowledge is, however, not as yet available in the literature. The purpose of this contribution is to provide data about in the simplest turbulent flow, homogeneous isotropic turbulence (HIT).

The pressure PDF in HIT is known to be negatively skewed and exhibit an exponential tail at very low pressures pumir94_pf (); ccd99_pf (); gr99_jfm ().On the basis of physical intuition, several authors have argued that the frequency of pressure fluctuations below should also exhibit an exponential tail for large and negative ag79 (); rk94_jfm (); lvmb00_pf (). Mathematically, this is far from obvious. In fact, consider a that exhibits logarithmic pulses of the form where are random times which arise with average frequency . It is easy to check that in such a case has an exponential tail for . However, the frequency is not exponential, as it equals for any (sufficiently negative) . The temporal structure of the pressure excursions is thus crucial in determining the statistics of occurrence of low pressure events.

Having in mind cavitation modeling, in this investigation we perform Lagrangian sampling of two DNS databases counting events at which the particles go below some given pressure threshold for a longer time than some given minimum duration . Notice that each excursion, even if the pressure dives well below for a time much greater than , is counted as a single event. The rationale behind this is that, if the given and are likely to produce cavitation of the particle (nucleus), then the gaseous phase will violently grow and increase the local pressure so that no similar event will take place within the same pressure excursion. The results obtained from OUR Lagrangian counting experiments confirm the exponential dependence for . The factors and are retrieved by fitting the data. We also address the random structure of the pressure fluctuation events by building the PDF of interarrival times. It shows that that low-pressure events do not behave as a Poisson process, exhibiting a marked burstiness that increases as becomes more negative. It is appropriate to point out that, since the tracking method does not incorporate any relative velocity between the Lagrangian particles and the fluid, the results only apply to the smallest cavitation nuclei.

Ii Definitions and methods

Two databases containing Direct Numerical Simulation (DNS) results of Forced Homogeneous Isotropic Turbulence (FHIT) were queried. They consist of fully resolved numerical solutions of the incompressible Navier-Stokes equations in a periodic domain, with forcing applied to a narrow band of low wavenumber modes in such a way that a statistically steady flow develops.

The DNS results are time series of velocity and pressure spatial fields with zero mean from which statistical averages of kinetic energy and viscous dissipation were computed. From these values the basic scales characterizing each flow were defined as:

(1)

where is the kinematic viscosity and unit density is used. The length scale so defined is the Taylor length microscale. With these two basic scales all variables were rescaled (non-dimensionalized), dividing velocities by , lengths by , pressures by , times by , etc. All reference to the DNS data made in what follows concerns the scaled (dimensionless) variables. In particular, the scaled velocity field satisfies

(2)

The non-dimensional parameter that characterizes the flow is the Reynolds number . Two values were considered: ,obtained from the database maintained by J. Jiménez and coworkers at Univ. Politécnica Madrid cvj17_science (), and , obtained from the Johns Hopkins Turbulence Databases lietal08_jt (); yuetal12_jt (); fitdoi ().

Notice that, under this scaling, the Kolmogorov scales are given by

(3)

Notably, is independent of . The Reynolds number based on the Kolmogorov length is then .

The Lagrangian trajectories are computed by numerically solving the equation

(4)

from time to , with initial condition , for a large number of initial positions, i.e., . The adopted time-integration method is a Runge-Kutta scheme of order , with a time step much smaller than the Kolmogorov time scale (). The required spatial interpolation was accomplished using Lagrangian polynomial interpolants of degree three or greater, depending on the case.

Along the set of computed trajectories several statistical estimates were computed by averaging over the Lagrangian samples (parameterized by the set of initial positions) and over time. For these estimates to be meaningful recommended practices were followed. The initial positions were uniformly distributed over the simulation domain, keeping them at least 2.5 grid spacings apart. The integration time extended over several eddy turnover times. Specifically, the field (resolution 512) was sampled with particles during a time (114 turnover times). The field (resolution 1024), on the other hand, was sampled with particles during a time , which corresponds to 5 turnover times.

In incompressible turbulence Lagrangian averages of spatial quantities (e.g., kinetic energy, enstrophy, etc.) coincide with Eulerian averages. Due to the finite number of Lagrangian particles and the finite simulation time, especially in the case with , the estimates obtained by averaging over the particles and over time are not exact. The difference of our estimates with Eulerian averages can be used as a simple test of statistical significance. The error in the rms values of velocity and vorticity were smaller than 2%, value recommended by Yeung y02_arfm (), providing some validation of our sampling procedure.

The Lagrangian “Taylor” timescale of a variable is defined as y02_arfm ()

(5)

where denotes the variance of the corresponding quantity and is the material derivative. The Lagrangian autocorrelation function of a variable with zero mean is defined by

(6)

The Lagrangian integral timescale of , denoted by , is given by

(7)

All the statistical averages in the previous and forthcoming definitions are of course replaced by estimates computed over the numerical approximation of the sampled trajectories.

Finally, the occurrence of low pressure events was investigated as follows: Some negative pressure values were chosen, namely -2, -2.05, -2.1, etc. Notice that these correspond to non-dimensional fluctuations, since . For a given value of , a Lagrangian particle was defined as undergoing a low-pressure event with threshold that starts at time if its pressure satisfies and . The end of the event is defined as the first time such that and . The duration of the event is defined as .

In this way, given particles evolving in a turbulent field, a stochastic counting process can be defined as the number of low-pressure events (with threshold and duration ) that have . For each threshold and each minimum duration , the number of events was computed from the time history of the Lagrangian particles that are traced over the time of the simulation. From this, we computed an estimate for the rate of such events as

(8)

An analogous proceeding was carried out for high-pressure events, denoting the (positive) threshold by .

Iii Results

iii.1 Basic statistics

We first report some basic statistical quantities obtained from the computed Lagrangian pressure histories. These include the pressure PDFs shown in Fig. 1, together with the quantities summarized Table 1.

Quantity Symbol
Variance of Var 0.78 0.82
Variance of Var 1.48 0.36
Time microscale 0.73 1.51
Integral timescale 2.30 6.21
Table 1: Basic statistics of the computed Lagrangian pressure histories.

The results for are similar to those of Gotoh & Rogallo gr99_jfm ()111The relationship between the non-dimensional variables chosen by Gotoh & Rogallo and those adopted here are: Var., who report a value of about 0.9 at which decreases to about 0.7 at . Cao et al ccd99_pf () report at , while Pumir pumir94_pf () obtained for . Lagrangian Taylor and integral timescales for pressure are not available in the literature, nor is the Lagrangian pressure autocorrelation function, which is shown in Figure 2.

The pressure PDFs exhibit the (approximately) exponential tails previously reported by Pumir pumir94_pf () and Cao et al ccd99_pf (). Specifically, the data show that

(9)
Figure 1: Pressure PDFs obtained from the Lagrangian data at and .
Figure 2: Lagrangian pressure autocorrelation functions at and .

iii.2 Average frequency of pressure fluctuation events


(a)

(b)

Figure 3: Non-dimensional frequency of low-pressure events as function of the threshold pressure for several values of the minimum duration . Results at (a) and (b) .

The main novel results in this contribution are the values of as depicted in Figure 3. Notice that is an average frequency of events per particle. A typical use of these data would be as follows: Consider a volume of fluid in a (homogeneous, isotropic) turbulent flow characterized by given values of , and , and assume that the fluid contains some concentration of small particles (per m) in suspension. Then is the expected frequency (in events/sec) with which the suspended particles will undergo negative pressure fluctuations below (in Pa) that last more than (in sec) within the volume . Of course for this to hold the quantity must be evaluated at the flow’s .

Going back to Figure 3, notice that for any minimum duration the average frequency is approximately exponential in , i.e., , with and depending on and on . In fact, some upward concavity can be observed in the semilog plots for the higher values of (milder pressure fluctuations), but for lower values the plots closely follow straight lines. This can be more easily seen in Fig. 4, in which all plots of corresponding to for both values of (lines) have been put together simultaneously with their exponential fits (symbols).

Min. duration ()
0 0.04 0.80 0.010 0.60
1 0.15 1.35 0.0055 0.68
2 0.08 1.55 0.003 0.72
3 0.07 1.80 0.0025 0.79
4 0.047 1.97 0.0065 1.02
5 0.03 2.10 0.0125 1.25
6 0.014 2.10 0.022 1.50
Table 2: Constants and of the exponential fits of the data, for low-pressure events of duration greater or equal than and for the two datasets ( and 418).
Figure 4: for (lines), for both values of , and their exponential fits (symbols).

The fitted values of and are listed in Table 2. Most relevant is the parameter , i.e., the slope in the semi-log plots. A smaller implies that, as the threshold is lowered, the average rate of excursions below the threshold decreases more slowly. For any minimum duration of the excursions, is smaller for the higher Reynolds number. This is consistent with the pressure PDFs, which also show that lower pressures become more probable at higher , with exponential tails that behave as , with at and at (see Fig. 1).

By direct inspection of Table 2 one notices that is, for both , close to the parameter that corresponds to events of minimum duration between and (i.e., between zero and roughly four Kolmogorov time scales). The experimental consequences of this are quite interesting. Assume that, as proposed by LaPorta et al lvmb00_pf (), one uses the gas nuclei in a liquid as pressure sensors and measures the average rate of cavitation events as a function of the (variable) reference pressure of the flow. Then, if the nuclei’s reaction time is smaller than a few Kolmogorov time scales, the curve cavitation rate vs. will be approximately proportional to and thus, since PDF is exponential, to PDF itself. This has been argued in the literature by several authors ag79 (); rk94_jfm (); lvmb00_pf () on the basis of physical intuition and is herein rigorously confirmed. Notice that the short reaction time of the nuclei is crucial for the proportionality to hold. If the reaction time is larger than, say, 20 (corresponding to ), the dependence of the cavitation rate with pressure will largely differ from that of the pressure PDF. It is worth mentioning that the values of inferred by LaPorta et al lvmb00_pf (), which were in the range 0.14-0.22 for between 1658 and 1880, suggest that keeps decreasing with in much the same way as observed from the DNS databases studied here.


(a)

(b)

Figure 5: Non-dimensional frequency of low-pressure events as function of minimum duration for several values of the threshold pressure (). Results at (a) and (b) .

The dependence of with the minimum duration of the events is depicted in Fig. 5. An approximately exponential decay of with is observed for greater than a few (say, ), with a logarithmic slope that becomes more negative as the threshold is lowered. For any the average rate of long events increases significantly with .

It is informative to complement the previous results with further analysis of the distribution of the duration () of the events corresponding to each threshold . The corresponding PDFs are shown in Fig. 6. It is observed that the peak of the PDF is always about 2/3 of and that the PDFs for each have longer tails for the larger . As decreases the distribution becomes narrower, i.e., events that last longer than become increasingly improbable. The means and medians of these PDFs are shown as functions of in Fig. 7. They increase significantly with , contrary to what happens with the peak of the duration PDFs.


(a)

(b)

Figure 6: PDFs of the duration of the low-pressure events for several values of the threshold pressure (). Results at (a) and (b) .
Figure 7: Mean and median duration of the low-pressure events as functions of the threshold pressure .

For the purpose of both completeness and comparison, let us close this section with plots of , corresponding to positive pressure fluctuations. They are shown in Fig. 8. It is evident that positive pressure excursions are much more rare than negative ones, a result that is consistent with the rapid decay of the positive side of the pressure PDF. For minimum duration the rate of events does not change much with , but for events significantly longer than 10 Kolmogorov timescales () the non-dimensional frequency for is much greater than that for , by an order of magnitude or more.


(a)

(b)

Figure 8: Obtained values of non-dimensional frequency of high-pressure events as function of the threshold pressure for several values of the minimum duration . Results at (a) and (b) .

iii.3 Randomness of low-pressure fluctuations

The occurrence of low-pressure fluctuations (of threshold and minimum duration ) in a turbulent flow is certainly a random process. Its stochastic properties can be investigated looking at the arrival process formed by the (monotone increasing) sequence , where is the starting time of the -th event. This generates the stochastic process of interarrival times, . If random particles are seeded into the flow, then by definition the average interarrival time satisfies . A totally random arrival process (Poisson process) exhibits an exponential distribution for , i.e.,

(10)

Whether the sequence of low-pressure events is a Poisson process or not can thus be assessed by inspecting the PDF of interarrival times. This PDF cannot be built from the raw data consisting of all events undergone by the particles because of the temporal resolution limit imposed by the simulation time step. For example, for the case of events with and the total number of such events recorded was 21,072,952 for the simulation at () and 343,070 for that at (). Meanwhile, the number of simulated time steps is 20,000 and 5,000, respectively. This makes the interarrival times to be much smaller than the time step and thus poorly resolved.

The procedure adopted to build was as follows: From the particles in the simulation, batches of particles were extracted at random, selecting the number such that equals 100 simulation time steps. For each batch the interarrival times were computed, and was obtained averaging the histograms of 50,000 such batches.

The results considering events of any duration () for each pressure threshold are shown in Fig. 9, where we plot as a function of . Also shown is the exponential distribution which would correspond to a Poisson process. Analogous PDFs can be built for other values of , but they are not shown since they are quite similar.


(a)

(b)

Figure 9: PDF of the interarrival times of low pressure events of any duration () for different thresholds . The exponential corresponding to a Poisson process is also plotted for comparison. Results at (a) and (b) .

The first immediate observation from the plots of PDF is that low-pressure events do not take place as a totally random, Poisson process that would entail an exponential PDF. The semilog-plots of the PDFs of interarrival times exhibit an upward concavity, or “heavy tail”, which becomes more prominent as the threshold is decreased. This heavy tail is characteristic of processes that exhibit burstiness, for which a popular quantitative measure in the literature is the “burstiness parameter” gb08_epl () defined by

(11)

where is the standard deviation of . Notice that for a periodic process (), for a Poisson process (), and for a highly bursty process (). The graphs of vs. fixing the minimum duration to are shown in Fig. 10 for both considered. Clearly, the low-pressure events are more bursty for larger fluctuations (more negative ). To visualize this, in Fig. 11 we plot, as a function of time, the number of events that start in temporal bins of 50 time steps for and in the simulation with . The number of events per bin with threshold -2 oscillates moderately around its mean value, while that with threshold -11 is most of the time near zero with intermittent bursts that reach 30 or more events per bin. It is remarkable that burstiness parameters of value 0.3 and higher are observed. Such values are not frequent in natural phenomena, and can be found in highly intermittent human activities such as e-mail sending gb08_epl ().

Figure 10: Burstiness parameter of the low-pressure events as a function of the threshold pressure for the two considered.
Figure 11: Number of events that start within a time bin (size of the bin: 50 time steps) as a function of time. The plots correspond to events of threshold and -11, with minimum duration , as recorded in the simulation.

The high burstiness of very-low-pressure events is an indication of large flow structures being involved in them, such that when one of these structures appears many particles go through it and bursts of events take place. This picture is consistent with the intermittent structures of intense vorticity, or worms, first described by Jiménez et al jwsr93_jfm (), which have lengths of the order of the integral scale of the flow. To confirm this, we looked at the pressure isosurfaces of the simulation, for which a burst of low-pressure events takes place for non-dimensional times between 6 and 18 (the burst thus lasts about 46 Kolmogorov timescales). The isosurfaces are shown in Fig. 12 at some selected instants. The lightest surfaces, corresponding to , are present in all snapshots as expected from the low value of for . At time a vortical structure develops, which is most evident at the peak of the burst (between and 11.8, third and fourth frames in the figure). It is within this structure that very low pressures (-8 or lower) occur and affect numerous Lagrangian particles. By time 14.4 this structure is dissolving away and after the burst, at , the flow has recovered an isosurface pattern similar to the one observed before the burst. We have checked that the vertical low-pressure structure is indeed a high vorticity region, and its shape and length are in agreement with the intermittent worms reported in the literature (notice that the integral lengthscale for this flow is 12.1, roughly 1/5 the box edgelength) jwsr93_jfm ().

Figure 12: Pressure isosurfaces of value , -4, -6, -8 and -10 during a burst of very-low-pressure events. The corresponding times are, from left to right and top to bottom, , 6.4, 9.1, 11.8, 14.4 and 25.1. The size of the periodic box is 55.6 and the integral scale is 12.1.

Iv Conclusions

Motivated by cavitation inception modeling, this work reports some Lagrangian statistics of the pressure field in forced homogeneous isotropic turbulence. It is clear that for a cavitation nucleus to grow up to detectable size a pressure fluctuation that takes it to sufficiently low pressures for long enough time is necessary. However, the frequency of such low-pressure events was not available in the literature and is first reported herein for two values of , namely 150 and 418. The main result consists of the average frequency with which a Lagrangian particle undergoes a fluctuation that takes below some threshold for a time longer than some minimum duration . This average frequency, for any , is observed to have an exponential tail (i.e., to behave as ) towards very low pressure thresholds. Furthermore, the value of the logarithmic slope that corresponds to is roughly coincident with that of the exponential tail of the pressure PDF. Also reported is the PDF of the duration of the low-pressure events, which shows that the most probable duration is smaller than the Kolmogorov timescale and quite insensitive to for both considered. On the other hand, the mean and median duration of the pressure excursions grow significantly with and depend strongly on , but only for moderate values of this variable. The analysis of the interarrival times between low-pressure events shows that their occurrence departs from that of a totally random homogeneous stochastic process (Poisson process). This departure becomes more and more accentuated as the threshold is lowered. The distribution of interarrival times is heavy-tailed, indicative of a bursty process. In fact, a quantitative indicator of burstiness was computed, yielding values indicative of a highly intermittent process. This suggests that the bursts of low-pressure events are associated with intermittent large-scale vortical structuresjwsr93_jfm (), as confirmed by examination of the pressure isosurfaces at the time of the bursts.

The reported results provide useful quantitative data to predict the frequency, intensity and duration of pressure fluctuations experienced by very small particles that are passively transported by a turbulent flow. The behavior at higher Reynolds numbers and in other turbulent flows should certainly be explored to gain further understanding. Also, the relative velocity that develops between non-neutrally-buoyant particles of finite size and the surrounding liquid could attract bubbles toward vortex cores and strongly affect the computed frequencies. These issues are the subject of ongoing work.


Acknowledgments

The authors are thankful to Adrián Lozano-Durán for his help in accessing and processing the database at Univ. Politécnica de Madrid. This research was sponsored by the US Office of Naval Research through MURI grant N00014-17-2676, Univ. of Minnesota lead institution, Dr. Ki-Han Kim program manager. GCB acknowledges support from the São Paulo Research Foundation (FAPESP, Brazil), grant 2018/08752-5.

References

  • [1] W. George, P. Beuther, and R. Arndt. Pressure spectra in turbulent free shear flows. J. Fluid Mech., 148:155–191, 1984.
  • [2] A. Pumir. A numerical study of pressure fluctuations in three-dimensional, incompressible, homogeneous, isotropic turbulence. Physics of Fluids, 6:2071–2083, 1994.
  • [3] N. Cao, S. Chen, and G. Doolen. Statistics and structures of pressure in isotropic turbulence. Physics of Fluids, 11(8):2235–2250, 1999.
  • [4] T. Gotoh and R. Rogallo. Intermittency and scaling of pressure at small scales in forced isotropic turbulence. J. Fluid Mech., 396:257–285, 1999.
  • [5] M. Plesset. The dynamics of cavitation bubbles. Journal of Applied Mechanics, 16:277–282, 1949.
  • [6] R. Arndt and W. George. Pressure fields and cavitation in turbulent shear flows. In Twelfth Symposium on Naval Hydrodynamics, Technical paper no. 91, Series A, pages 327–339, 1979.
  • [7] B. Ran and J. Katz. Pressure fluctuations and their effect on cavitation inception within water jets. J. Fluid Mech., 262:223–263, 1994.
  • [8] B. Belahadji, J. Franc, and J. Michel. Cavitation in the rotational structures of a turbulent wake. J. Fluid Mech., 287:383–403, 1995.
  • [9] A. La Porta, G. Voth, F. Moisy, and E. Bodenschatz. Using cavitation to measure statistics of low-pressure events in large-reynolds-number turbulence. Phys. Fluids, 12:1485–1496, 2000.
  • [10] J. Cardesa, A. Vela-Marín, and J Jiménez. The turbulent cascade in five dimensions. Science, 357:782–784, 2017.
  • [11] Y. Li, E. Perlman, M. Wan, Y. Yang, R. Burns, C. Meneveau, R. Burns, S. Chen, A. Szalay, and G. Eyink. A public turbulence database cluser and applications to study Lagrangian evolution of velocity increments in turbulence. Journal of Turbulence, 9:1–29, 2008.
  • [12] H. Yu, K. Kanov, E. Perlman, J. Graham, E. Frederix, R. Burns, A. Szalay, G. Eyink, and C. Meneveau. Studying Lagrangian dynamics of turbulence using on-demand fluid particle tracking in a public turbulence database. Journal of Turbulence, 13:1–29, 2012.
  • [13] Forced Isotropic Turbulence Dataset (Extended). https://doi.org/10.7281/T1KK98XB.
  • [14] P. Yeung. Lagrangian investigations of turbulence. Annu. Rev. Fluid Mech., 34:15–42, 2002.
  • [15] The relationship between the non-dimensional variables chosen by Gotoh & Rogallo and those adopted here are: Var.
  • [16] K. Goh and A. Barabási. Burstiness and memory in complex systems. EPL, 81:48002, 2008.
  • [17] J. Jiménez, A. Wray, P. Saffman, and R. Rogallo. The structure of intense vorticity in isotropic turbulence. J. Fluid Mech., 255:65–90, 1993.
Comments 0
Request Comment
You are adding the first comment!
How to quickly get a good reply:
  • Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made.
  • Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements.
  • Your comment should inspire ideas to flow and help the author improves the paper.

The better we are at sharing our knowledge with each other, the faster we move forward.
""
The feedback must be of minimum 40 characters and the title a minimum of 5 characters
   
Add comment
Cancel
Loading ...
363508
This is a comment super asjknd jkasnjk adsnkj
Upvote
Downvote
""
The feedback must be of minumum 40 characters
The feedback must be of minumum 40 characters
Submit
Cancel

You are asking your first question!
How to quickly get a good answer:
  • Keep your question short and to the point
  • Check for grammar or spelling errors.
  • Phrase it like a question
Test
Test description