Engineering topics

Canuto, V. M.

Publications and source records attributed to Canuto, V. M..

At least 19 records

NASA NTRS2018

Three-Dimensional, Space-Dependent Mesoscale Diffusivity: Derivation and Implications

Recently, we presented a parameterization of an arbitrary tracer 3D mesoscale flux that describes both diabatic and adiabatic regimes without using arbitrary tapering functions. However, we did not parameterize the mesoscale diffusivity, which is the subject of this work. A key difference between the present and previous diffusivity parameterizations is that in the latter, the two main ingredients, mesoscale drift velocity and eddy kinetic energy, were not parameterized but determined using present data, which deprives the models of predictive power. Since winds, stratification, etc., are predicted to change in the future, use of these parameterizations to study future climate scenarios becomes questionable. In this work, we parameterize drift velocity and eddy kinetic energy (vertical–horizontal components), which we first assess with data [WOCE, TOPEX/Poseidon (T/P), and North Atlantic Tracer Release Experiment (NATRE)] and then use in a coarse-resolution stand-alone ocean code under Coordinated Ocean-Ice Reference Experiment I (CORE-I) forcing. We present results for the global ocean temperature and salinity, Atlantic overturning circulation, meridional heat transport, and Drake Passage transport, which we compare with several previous studies. The temperature drift is less than that of five of seven previous OGCMs, and the salinity drift is among the smallest in those studies. The predicted winter Antarctic Circumpolar Current mixed layer depths (MLDs) are in good agreement with the data. Predicting the correct MLD is important in climate studies since models that predict very deep mixed layers transfer more of the radiative perturbation to the deep ocean, reducing surface warming (and vice versa).

Eddies; Mesoscale processes; Mixing; Ocean models;↗

NASA NTRS2018

Parameterization of Mixed Layer and Deep-Ocean Mesoscales Including Nonlinearity

In 2011, Chelton et al. carried out a comprehensive census of mesoscales using altimetry data and reached the following conclusions: "essentially all of the observed mesoscale features are nonlinear" and "mesoscales do not move with the mean velocity but with their own drift velocity," which is "the most germane of all the nonlinear metrics." Accounting for these results in a mesoscale parameterization presents conceptual and practical challenges since linear analysis is no longer usable and one needs a model of nonlinearity. A mesoscale parameterization is presented that has the following features: 1) it is based on the solutions of the nonlinear mesoscale dynamical equations, 2) it describes arbitrary tracers, 3) it includes adiabatic (A) and diabatic (D) regimes, 4) the eddy-induced velocity is the sum of a Gent and McWilliams (GM) term plus a new term representing the difference between drift and mean velocities, 5) the new term lowers the transfer of mean potential energy to mesoscales, 6) the isopycnal slopes are not as flat as in the GM case, 7) deep-ocean stratification is enhanced compared to previous parameterizations where being more weakly stratified allowed a large heat uptake that is not observed, 8) the strength of the Deacon cell is reduced. The numerical results are from a stand-alone ocean code with Coordinated Ocean-Ice Reference Experiment I (CORE-I) normal-year forcing.

Baroclinic flows; Mesoscale processes; Mixing; Sub↗

NASA NTRS2017

Contribution of Sub-Mesoscales to the Vertical Velocity: The Omega-Equation

The oceans ability to regulate carbon dioxide depends on biogeochemical processes that are influenced strongly by eddies. Eddy-resolving simulations have shown that sub-mesoscales (SM) (110 km) generate the highest magnitude vertical velocity and that mesoscales (M) also enhance their contribution to the vertical velocity but to a lesser extent. In this study, we consider the question: can analogous results be obtained using the less numerically demanding -equation? Previously, this question has not been answered because of two reasons: 1) the canonical Hoskins form of the - equation does not include the buoyancy vertical fluxes caused by M and SM; and 2) Giordani et al. (2016) showed how to include an arbitrary vertical buoyancy flux, but no parameterizations were available for the M and SM vertical fluxes. How- ever, the latter are now available together with their assessments, so we consider SM because they make the largest contribution to the vertical velocity. The resulting vertical velocity depends on the extent of the SM regime, the horizontal buoyancy gradient (representing baroclinic instabilities), and the SM eddy kinetic energy. The vertical velocity depends in a linear manner on the wind stress and it may exhibit seasonal variations. The wind stress has two effects on the -equation: indirectly via its contribution to the sub-mesoscale buoyancy flux and directly through the wind stress itself. The results of our sensitivity analysis highlight the range of SM-induced vertical velocities obtained using different input data.

oceans↗

NASA NTRS2011

Comparison of Four Mixed Layer Mesoscale Parameterizations and the Equation for an Arbitrary Tracer

In this paper we discuss two issues, the inter-comparison of four mixed layer mesoscale parameterizations and the search for the eddy induced velocity for an arbitrary tracer. It must be stressed that our analysis is limited to mixed layer mesoscales since we do not treat sub-mesoscales and small turbulent mixing. As for the first item, since three of the four parameterizations are expressed in terms of a stream function and a residual flux of the RMT formalism (residual mean theory), while the fourth is expressed in terms of vertical and horizontal fluxes, we needed a formalism to connect the two formulations. The standard RMT representation developed for the deep ocean cannot be extended to the mixed layer since its stream function does not vanish at the ocean's surface. We develop a new RMT representation that satisfies the surface boundary condition. As for the general form of the eddy induced velocity for an arbitrary tracer, thus far, it has been assumed that there is only the one that originates from the curl of the stream function. This is because it was assumed that the tracer residual flux is purely diffusive. On the other hand, we show that in the case of an arbitrary tracer, the residual flux has also a skew component that gives rise to an additional bolus velocity. Therefore, instead of only one bolus velocity, there are now two, one coming from the curl of the stream function and other from the skew part of the residual flux. In the buoyancy case, only one bolus velocity contributes to the mean buoyancy equation since the residual flux is indeed only diffusive.

Canuto, V. M.↗

NASA NTRS2010

Vertical Diffusivities of Active and Passive Tracers

The climate models that include a carbon-cycle need the vertical diffusivity of a passive tracer. Since an expression for the latter is not available, it has been common practice to identify it with that of salt. The identification is questionable since T, S are active, not passive tracers. We present the first derivation of the diffusivity of a passive tracer in terms of Ri (Richardson number) and Rq (density ratio, ratio of salinity over temperature z-gradients). The following results have emerged: (a) The passive tracer diffusivity is an algebraic function of Ri, Rq. (b) In doubly stable regimes (DS, partial derivative of T with respect to z > 0, partial derivative of S with respect to z < 0), the passive scalar diffusivity is nearly the same as that of salt/heat for any values of Rq < 0 and Ri > 0. (c) In DC regimes (diffusive convection, partial derivative of T with respect to z < 0, partial derivative of S with respect to z < 0, Rq > 1), the passive scalar diffusivity is larger than that of salt. At Ri = O(1), it can be more than twice as large. (d) In SF regimes (salt fingers, partial derivative of T with respect to z > 0, partial derivative of S with respect to z > 0, Rq < 1), the passive scalar diffusivity is smaller than that of salt. At Ri = O(1), it can be less than half of it. (e) The passive tracer diffusivity predicted at the location of NATRE (North Atlantic Tracer Release Experiment) is discussed. (f) Perhaps the most relevant conclusion is that the common identification of the tracer diffusivity with that of salt is valid only in DS regimes. In the Southern Ocean, where there is the largest CO2 absorption, the dominant regime is diffusive convection discussed in (c) above.

Canuto, V. M.↗

NASA NTRS2010

An Attempt to Derive the epsilon Equation from a Two-Point Closure

The goal of this paper is to derive the equation for the turbulence dissipation rate epsilon for a shear-driven flow. In 1961, Davydov used a one-point closure model to derive the epsilon equation from first principles but the final result contained undetermined terms and thus lacked predictive power. Both in 1987 and in 2001, attempts were made to derive the epsilon equation from first principles using a two-point closure, but their methods relied on a phenomenological assumption. The standard practice has thus been to employ a heuristic form of the equation that contains three empirical ingredients: two constants, c(sub 1 epsilon), and c(sub 2 epsilon), and a diffusion term D(sub epsilon) In this work, a two-point closure is employed, yielding the following results: 1) the empirical constants get replaced by c(sub 1), c(sub 2), which are now functions of Kappa and epsilon; 2) c(sub 1) and c(sub 2) are not independent because a general relation between the two that are valid for any Kappa and epsilon are derived; 3) c(sub 1), c(sub 2) become constant with values close to the empirical values c(sub 1 epsilon), c(sub epsilon 2), (i.e., homogenous flows); and 4) the empirical form of the diffusion term D(sub epsilon) is no longer needed because it gets substituted by the Kappa-epsilon dependence of c(sub 1), c(sub 2), which plays the role of the diffusion, together with the diffusion of the turbulent kinetic energy D(sub Kappa), which now enters the new equation (i.e., inhomogeneous flows). Thus, the three empirical ingredients c(sub 1 epsilon), c(sub epsilon 2), D (sub epsilon)are replaced by a single function c(sub 1)(Kappa, epsilon ) or c(sub 2)(Kappa, epsilon ), plus a D(sub Kappa)term. Three tests of the new equation for epsilon are presented: one concerning channel flow and two concerning the shear-driven planetary boundary layer (PBL).

Canuto, V. M.↗

NASA NTRS2010

Ocean Turbulence, III: New GISS Vertical Mixing Scheme

We have found a new way to express the solutions of the RSM (Reynolds Stress Model) equations that allows us to present the turbulent diffusivities for heat, salt and momentum in a way that is considerably simpler and thus easier to implement than in previous work. The RSM provides the dimensionless mixing efficiencies Gamma-alpha (alpha stands for heat, salt and momentum). However, to compute the diffusivities, one needs additional information, specifically, the dissipation Epsilon. Since a dynamic equation for the latter that includes the physical processes relevant to the ocean is still not available, one must resort to different sources of information outside the RSM to obtain a complete Mixing Scheme usable in OGCMs. As for the RSM results, we show that the Gamma-alpha s are functions of both Ri and Rq (Richardson number and density ratio representing double diffusion, DD); the Gamma-alpha are different for heat, salt and momentum; in the case of heat, the traditional value Gamma-h = 0.2 is valid only in the presence of strong shear (when DD is inoperative) while when shear subsides, NATRE data show that Gamma-h can be three times as large, a result that we reproduce. The salt Gamma-s is given in terms of Gamma-h. The momentum Gamma-m has thus far been guessed with different prescriptions while the RSM provides a well defined expression for Gamma-m(Ri,R-rho). Having tested Gamma-h, we then test the momentum Gamma-m by showing that the turbulent Prandtl number Gamma-m/Gamma-h vs. Ri reproduces the available data quite well. As for the dissipation epsilon, we use different representations, one for the mixed layer (ML), one for the thermocline and one for the ocean;s bottom. For the ML, we adopt a procedure analogous to the one successfully used in PB (planetary boundary layer) studies; for the thermocline, we employ an expression for the variable epsilon/N(exp 2) from studies of the internal gravity waves spectra which includes a latitude dependence; for the ocean bottom, we adopt the enhanced bottom diffusivity expression used by previous authors but with a state of the art internal tidal energy formulation and replace the fixed Gamma-alpha = 0.2 with the RSM result that brings into the problem the Ri, R-rho dependence of the Gamma-alpha; the unresolved bottom drag, which has thus far been either ignored or modeled with heuristic relations, is modeled using a formalism we previously developed and tested in PBL studies. We carried out several tests without an OGCM. Prandtl and flux Richardson numbers vs. Ri. The RSM model reproduces both types of data satisfactorily. DD and Mixing efficiency Gamma-h(Ri,Rq). The RSM model reproduces well the NATRE data. Bimodal epsilon-distribution. NATRE data show that epsilon (Ri < 1) approximately equals 10epsilon(Ri > 1), which our model reproduces. Heat to salt flux ratio. In the Ri much greater than 1 regime, the RSM predictions reproduce the data satisfactorily. NATRE mass diffusivity. The z-profile of the mass diffusivity reproduces well the measurements at NATRE. The local form of the mixing scheme is algebraic with one cubic equation to solve.

Canuto, V. M.↗

NASA NTRS2010

Mixed Layer Sub-Mesoscale Parameterization - Part 1: Derivation and Assessment

Several studies have shown that sub-mesoscales (SM 1km horizontal scale) play an important role in mixed layer dynamics. In particular, high resolution simulations have shown that in the case of strong down-front wind, the re-stratification induced by the SM is of the same order of the de-stratification induced by small scale turbulence, as well as of that induced by the Ekman velocity. These studies have further concluded that it has become necessary to include SM in ocean global circulation models (OGCMs), especially those used in climate studies. The goal of our work is to derive and assess an analytic parameterization of the vertical tracer flux under baroclinic instabilities and wind of arbitrary directions and strength. To achieve this goal, we have divided the problem into two parts: first, in this work we derive and assess a parameterization of the SM vertical flux of an arbitrary tracer for ocean codes that resolve mesoscales, M, but not sub-mesoscales, SM. In Part 2, presented elsewhere, we have used the results of this work to derive a parameterization of SM fluxes for ocean codes that do not resolve either M or SM. To carry out the first part of our work, we solve the SM dynamic equations including the non-linear terms for which we employ a closure developed and assessed in previous work. We present a detailed analysis for down-front and up-front winds with the following results: (a) down-front wind (blowing in the direction of the surface geostrophic velocity) is the most favorable condition for generating vigorous SM eddies; the de-stratifying effect of the mean flow and re-stratifying effect of SM almost cancel each other out,

Canuto, V. M.↗

NASA NTRS2001

New Third-Order Moments for the CBL

Turbulent convection is an inherently non-local phenomenon and a primary condition for a successful treatment of the CBL (convective boundary layer) is a reliable model of non-locality. In the dynamic equations governing the convective flux, the turbulent kinetic energy, etc., non-locality is represented by the third-order moments, TOMs. Since the simplest form, the so-called down gradient approximation (DGA), severely underestimates the TOMs (up to an order of magnitude), a more physical model is needed. In 1994, an analytical model was presented which was derived directly from the dynamical equations for the TOMs. It considerably improved the DGA but was a bit cumbersome to use and, more importantly, it was based on the quasi-normal (QN) approximation for the fourth-order moments. Here, we present a new analytic expression for the TOMs which is structurally simpler than the 1994 expression and which avoids the QN approximation. The resulting fit to the LES data is superior to that of the 1994 model.

Canuto, V. M.↗

NASA NTRS2001

An Improved Model for the Turbulent PBL

Second order turbulence models of the Mellor and Yamada type have been widely used to simulate the PBL. It is however known that these models have several deficiencies. For example, they all predict a critical Richardson number which is about four times smaller than the Large Eddy Simulation (LES) data, they are unable to match the surface data, and they predict a boundary layer height lower than expected. In the present model, we show that these difficulties are all overcome by a single new physical input: the use of the most complete expression for both the pressure-velocity and the pressure-temperature correlations presently available. Each of the new terms represents a physical process that, was not accounted for by previous models. The new model is presented in three different levels according to Mellor and Yamada's terminology, with new, ready-to-use expressions for the turbulent, moments. We show that the new model reproduces several experimental and LES data better than previous models. As far as the PBL is concerned, we show that the model reproduces both the Kansas data as analyzed by Businger et al. in the context of Monin-Obukhov similarity theory for smaller Richardson numbers, as well as the LES and laboratory data up to Richardson numbers of order unity. We also show that the model yields a higher PBL height than the previous models.

Cheng, Y.↗

NASA NTRS2000

Turbulent Convection: Is 2D a good proxy of 3D?

Several authors have recently carried out 2D simulations of turbulent convection for both solar and massive stars. Fitting the 2D results with the MLT, they obtain that alpha(sub MLT) greater than 1 specifically, 1.4 less than alpha(sub MLT) less than 1.8. The authors further suggest that this methodology could be used to calibrate the MLT used in stellar evolutionary codes. We suggest the opposite viewpoint: the 2D results show that MLT is internally inconsistent because the resulting alpha(sub MLT) greater than 1 violates the MLT basic assumption that alpha(sub MLT) less than 1. When the 2D results are fitted with the CM model, alpha(sub CMT) less than 1, in accord with the basic tenet of the model. On the other hand, since both MLT and CM are local models, they should be replaced by the next generation of non-local, time dependent turbulence models which we discuss in some detail.

Canuto, V. M.↗

NASA NTRS2000

Turbulence and Laminar Structures: Can They Co-Exist?

Schwarzschild first suggested that the laminar structures observed in the high Reynolds number Re = UL/nu approx. = (10(exp 12)) solar photosphere are the result of turbulence rather than a proof of its absence. He reasoned that since turbulence generates large turbulent viscosities nu(sub t) much greater than nu, the "effective" Reynolds number Re = UL/nu(sub t) approx. = O(1). Schwarzschild's argument is, however, incomplete for it assumes that the entire role of the non-linear interactions is to "enhance" viscosity. While this is not true in general, we present a proof of how and why it may occur, thus completing Schwarzschild's argument. We further discuss the fact that the same model non-local turbulence models have been shown to reproduce LES data for a variety of flows pertaining to astrophysics, geophysics and laboratory situations (at a fraction of the time).

Canuto, V. M.↗

NASA NTRS2000

New Third-Order Moments for the PBL

Turbulent convection is inherently non-local and a primary condition for a successful treatment of the PBL is a reliable model of non-locality. In the dynamic equations governing the convective flux, turbulent kinetic energy, etc, non-locality enters through the third-order moments, TOMs. Since the simplest form, the so-called down gradient approximation (DGA , severely underestimates the TOMs (by up to an order of magnitude), a more physical model is needed. In 1994, an analytical model was presented which was derived directly from the dynamical equations for the TOMs. It considerably improved the DGA but was a bit cumbersome to use. Here, we present a new analytic expression for the TOMs which is considerably simpler than the 1994 expression and which at the same time yields a much better fit to the LES data.

Canuto, V. M.↗

NASA NTRS1999

Turbulent Diffusivities for Momentum, Heat, Salt and Passive Scalars

A program began ten years ago to build a turbulence model to describe high Reynolds numbers flows. Specifically, the aim was to devise a model that would satisfy two basic conditions: reproduce available turbulence data (laboratory, numerical simulations, etc.) concerning shear driven flows, buoyancy driven flows, 2D turbulence, freely decaying turbulence etc., and yet be manageable so as to be used, for example, in GCM'S. The model was presented in a series of papers that have appeared in Physics of Fluids since 1996. A total of about 80 turbulence statistics were reproduced. The model has no adjustable parameters. The next step was to apply the model to construct the vertical diffusities K for moment K(sub m), temperature K(sub h), salt K(sub s), and passive scalars K(sub c). First, we took K(sub c) = K(sub h) and tested the model using the GFDL ocean model. The results for the profiles of T and S vs. depth are indistinguishable from those derided using the latest model, the KPP model by the NCAR group. Presently, we are running the same GFDL code relaxing the assumptive K(sub c) = K(sub h). Indeed, the turbulence model yelds a salt diffusivity that depends on R(sub i) and R(sub rho) = ((beta)(delta)S/(alpha)(delta)T/(delta)z) in such a way that K(sub s) may be quite different from K(sub h). Salt fingers and double diffusivity laboratory data are reproduced. Results from the ocean model will be available shortly. Finally, we are trying to derive the horizontal diffusivities with the goal of providing a physically acceptable representation of mesoscale eddies. The recently suggested GMW parameterization has improved several O-GCM results and the goal here is to try to derive/justify it from a turbulence model and/or propose improvements/modifications. Theoretical work is in progress.

Canuto, V. M.↗

NASA NTRS1999

Turbulent Diffusivities for Momentum, Heat, Salt and Passive Scalars

A program began ten years ago to build a turbulence model to describe high Reynolds numbers flows. Specifically, the aim was to devise a model that would satisfy two basic conditions: reproduce available turbulence data (laboratory, numerical simulations, etc.) concerning shear driven flows, buoyancy driven flows, 2D turbulence, freely decaying turbulence etc., and yet be manageable so as to be used, for example, in General Circulation Models (GCM'S). The model was presented in a series of papers that have appeared in Physics of Fluids since 1996. A total of about 80 turbulence statistics were reproduced. The model has no adjustable parameters. The next step was to apply the model to construct the vertical diffusities K for moment K(sub m), temperature K(sub h), salt K(sub s). and passive scalars K(sub c). First, we took K(sub s) = K(sub h) and tested the model using the GFDL ocean model. The results for the profiles of T and S vs. depth are indistinguishable from those derived using the latest model, the KPP model by the NCAR group. Presently, we are running the same Geophysical Fluid Dynamics Laboratory (GFDL) code relaxing the assumptive K(sub s) = K(sub h). Indeed, the turbulence model yields a salt diffusivity that depends on Ri and R rho (= Beta delta S/delta z/alpha delta T/delta z) in such a way that K(sub s) may be quite different from K(sub h). Salt fingers and double diffusivity laboratory data are reproduced. Results from the ocean model will be available shortly. Finally, we are trying to derive the horizontal diffusivities with the goal of providing a physically acceptable representation of mesoscale eddies. The recently suggested GMW parameterization has improved several O-GCM results and the goal here is to try to derive/justify it from a turbulence model and/or propose improvements/modifications. Theoretical work is in progress.

Canuto, V. M.↗

NASA NTRS1999

Overshooting in Massive Stars: Conceptual Problems and Suggested Solution

Overshooting in massive stars faces a basic difficulty: what theoretical models offer is not what stellar structure studies need. The former use Delta(sub mu) = 0 and define the OV where the negative convective flux J vanishes while the latter need to know where the concentration flux J(sub C) vanishes. We suggest that J may be dynamically irrelevant and derive the new dynamic equations for J(sub C). A new feature emerges: for large concentration gradients VC, J(sub C) is no longer proportional to VC, as always assumed.

Canuto, V. M.↗

NASA NTRS1999

Ocean Turbulence I: One-Point Closure Model Momentum and Heat Vertical Diffusivities

Since the early forties, one-point turbulence closure models have been the canonical tools used to describe turbulent flows in many fields. In geophysics, Mellor and Yamada applied such models using the 1980 state-of-the art. Since then, no improvements were introduced to alleviate two major difficulties: 1) closure of the pressure correlations, which affects the correct determination of the critical Richardson number Ri(sub cr) above which turbulent mixing is no longer possible and 2) the need to express the non-local third-order moments (TOM) in terms of lower order moments rather than via the down-gradient approximation as done thus far, since the latter seriously underestimates the TOMs. Since 1) and 2) are still being dealt with adjustable parameters which weaken the credibility of the models, alternative models, not based on turbulence modeling, have been suggested. The aim of this paper is to show that new information, partly derived from the newest 2-point closure model discussed, can be used to solve these shortcomings. The new one-point closure model, which in its simplest form is algebraic and thus simple to implement, is first shown to reproduce a variety of data. Then, it is used in a Ocean-General Circulation Model (O-GCM) where it reproduces well a large variety of ocean data. While phenomenological models are specifically tuned to ocean turbulence, the present model is not. It is first tested against laboratory data on stably stratified flows and then used in an O-GCM. It is more general, more predictive and more resilient, e.g., it can incorporate phenomena like wave-breaking at the surface, salinity diffusivity, non-locality, etc. One important feature that naturally comes out of the new model is that the predicted Richardson critical value Ri(sub cr) is Ri (sub cr approx. = 1) in agreement with both Large Eddy Simulations (LES) and empirical evidence while all previous models predicted Ri (sub cr approx. = 0.2) which led to a considerable underestimate of the extent of turbulent mixing and thus to an incorrect mixed layer depth. The predicted temperature and salinity profiles (vs. depth) are presented and compared with those of the Kolmogorov-Petruvsky-Piskunuv (KPP) model and Levitus data.

Canuto, V. M.↗

NASA NTRS1999

Ocean Turbulence: Mesoscale Modeling in Isopycnal Coordinates the role of the Spectrum of Vertical Shear - Part 4

We study the tracer subgrid term in isopycnal coordinates, S(sub I). We employ two ingredients: the experimental data on vertical spectra of ocean turbulence measured by Gargett et al.(1981) and the stochastic approach recently developed by Dukowicz and Smith (1997). Our result confirms that S(sub I) is made of two parts: an advection and a diffusion term. However, the tracer bolus velocity u** consists of two terms u** = u(sub 1) + u(sub 2) while in the GM model there is only a term related to u(sub 1) which is shown to be: u(sub 1) = k(bar-q)(sup -1)(delta)(sub rho) where bar-q is the thickness weighted average potential vorticity, a result in agreement with the recent suggestions by Treguier et al. (1997), Lee et al. (1997) and Greatbatch (1998). The second component u(sub 2) IS new. We compute it in the geostrophic approximation using the Gargett et al. data (1981) on ocean vertical turbulence. We find that u(sub 2) much greater than u(sub 1) and that u(sub 2) is orthogonal to u(sub 1).

Canuto, V. M.↗