Michele Guala

IONE, UMN Early Career award

NSF CAREER Award on Geophysical flow control

PFI Grant with Verdant Power IIP-1318201

Extracting energy from wind and water

with contributions by Arvind Singh

Christopher Feist Michael Heisel

Fotis Sotiropoulos Efi Foufoula

Project: DE-FOA-0000415 “High resolution computational algorithms for simulating offshore wind farms”

based on the work of: Mirko Musa Craig Hill Kevin Howard Michael Heislel

Complexity of geophysical flows

Waves

Energy conversion devices

Floating wind turbine

in-stream turbines Complex topography Tidal and river bedforms

All of them immersed in turbulent flows

Wind turbines in complex terrain (NREL)

Instream MHK turbines in complex bathymetry (VP East channel , NewYork)

Common features? 1) horizontal axis turbine producing

power 2) complex incoming flow conditions

(bridge pier, karman vortices, bluff body wakes and turbine wakes)

First goal of a power plant: extract energy efficiently and for a long time with minimal maintenance costs. P = ½ cp A U3 mean power output given a mean incoming flow velocity

representative of the actual flow distribution across the rotor

Turbine lifetime operation and maintenance costs

unsteady loads on the blades, low and high speed shaft, support tower (in general, on all device’s components)

The major source of such unsteadiness comes from the turbulence of the incoming flow (baseline Turbulent Boundary Layer/River + terrain effects + thermal stability effects + wake interactions) the wake induced by an upstream energy converter defines the inflow conditions for the downstream unit(s)

localized erosion downstream of an axial flow hydrokinetic turbine:

scaling and mitigation strategies

... Let us start with one specific physical problem that contains all the complexities we are facing

1 of 11

MHK Research: Experimental Facilities Turbine-Topography Interactions

Small scale (dT = 0.15m) Large scale (dT = 0.5m)

Meandering Channel (dT = 0.15m)

High speed camera

(Chamorro et. al., 2013)

Large-scale Experimental Setup

Hill et al. 2014

Effects of turbine components (at the threshold of motion)

No Structure Tower

Tower & Nacelle Full Turbine

Little erosion occurred with presence of only the tower or the nacelle of the turbine structure; however, presence of full turbine assembly created scour up to ≈30-35% dT.

Hill et al. 2014

Technological barrier 1: device safety, performance and lifetime

Experiment

Small-scale: Ripples

Small-scale: Dunes

Small-scale: Meander

Large-scale: Ripples

Large-scale: Dunes

Local scour

Exp. with migrating bedforms

Hill et al. 2016a satisfactory?

Channel Topography Near-Field Scour and Deposition

Baseline

Ripples

Dunes

Technological barrier 2: Turbine Performance Decreased Performance under migrating Large-Scale Bedforms

1

2

3

4

5

6

7

Chamorro et al. (2015) Phys. Fluids

Hydrograph

ripples dunes

Bedform impact on power fluctuation (and thus... turbine components)

Dunes

Turbine

Flow

Ripples

Best, J. (2005). J. Geophys. Res.

B. Separation zone

C. Expanding flow

D. Shear layer

E. Internal boundary layer

A. Maximum velocity

Hill et al. 2015, Ren. Energy

so if we want improve turbine efficiency and mitigate loads, we need to know/model how bedforms migrate

(~power , topography) correlation

Parameter space Two grain size distributions 1) sand 2) gravel Five flow discharges Froude number in the range of 0.2-0.5 typical river in plain / hilly terrain Observed bedforms: 3D ripples (a,b) transitioning (c) to 2D dunes (d,e)

Estimate the migrating velocity of bedforms to inform turbine control

cart

Repeated 5m long longitudinal transects every 12 seconds along the channel centerline

Spatio temporal resolution Δx = 10mm Lmax = 5m Δt ~ 12s Tmax ~10h

Wake deficit Mean voltage output

A reduced blockage area minimize the spanwise/streamwise distortion of the mean bed (bedform averaged)

By limiting bedform migration within the array the MHK plant behaves like a wind farm

Nacelle hub vortex tip tip vortex

(opposite rotation)

Hong et al. Nature COMM 2014

tip vortex

hub vortex

Howard et al 2015a

Wake flow incoming flow

near wake features

Far wake features : wake meandering

Rotor

Kang and Sotiropoulos 2014, Howard et al 2015b

large scale oscillation of the far wake

envelope of velocity minima low speed meander many meandering paths define the domain of the hub vortex, the region of interaction with the tip vortex, and the onset of wake meandering

Tip vortex locations

Potential tip vortex – meander interaction leading to wake instability

A,

Two flow meander populations have a distinct Stouhdal number

1 2

Let us consider A1 1 Uc1 St1 ~ 0.7 in the range 0.44 ∼ 0.81 explored by Viola, 2014, Iungo, 2014 recognized as the signature of the hub vortex (Kang and Sotiropoulos 2014)

hub vortex

oscillation

Let us consider A2 2 Uc2 St2 ~ 0.3 Medici et al. (2008) reported values in the range of 0.15 ∼ 0.25, Chamorro et al.(2013) observed a peak at 0.28 while Okulov and Sorensen2014 estimated a peak at 0.23, both at x/D ≃ 5. wake

meandering

oscillation

Howard et al. PoF 2015

T1

T4

T7 Non-mobile rectangular channel

fT

We observe: 1) a strong wake meandering signature of rotor angular velocity (~power, under constant torque) and of the cross stream velocity 2) An even stronger peak at lower frequency appears as a signature of a wake superstructure

𝑆𝑡 =𝑓𝑚𝑑𝑇

𝑈ℎ𝑢𝑏= 0.24

Multi-array of Marine Hydrokinetic Devices (In-stream turbines)

Signature of wake meandering down to the fluctuating power

Field measurements – LiDAR deployments

Distance

(x)

Date Start

[cdt]

Duration

[hours]

2.6D1 2013-10-09 16:14 1

3.1D1 2013-10-09 14:45 1

5.1D 2015-10-12 14:53 2.5

1.7D* 2012-07-30 21:34 2.5

4.8D** 2016-09-28 11:00 4

* Met tower in turbine wake

** Pleasant Valley wind farm

1 data acquired by Howard, Guala (2016)

Power plant optimization: wake meandering (field study)

Excess turbulent energy oscillations occur in distinct Strouhal number ranges where St = f∙D/Uhub for turbine diameter D

Wake meandering: St = 0.2 – 0.4 (Medici et al 2008, Howard 2015, among others) Near-wake hub vortex: St ≈ 0.7 (Kang et al 2014, among others)

Wind Tunnel 2.5 MW turbine facility wake meandering as a function of St= f D/U

Eolos

Wind farm

24

3) Field scale PIV using natural snowfall Hong et al Nature COMM, 2014 Gallery of fluid motion 2016

Complex flows in the near wake: Tower and tip vortices

close up view of incoming turbulence (video from browser)

One centered turbine

Two asymmetric turbines Asymmetric placement of turbines alter the channel morphology producing highly 3D patterns far away from the deployment

Bedforms remain fairly 2D with turbines centered in the channel.

Question: can we use MHK turbines / passive /active actuation to re-naturalize rivers while producing energy? that would be (environmentally sustainable)2

MHK river turbines: effects of complex turbine siting

The interaction between flow and energy harvesting devices has POTENTIAL APPLICATIONS FOR CONTROL Future work and some perspective for renewable energy systems

Sensing boundary conditions (approaching bedforms or wind gusts)

Traditional wind energy: We learned where to measure the wind to get the most representative velocity time-series to predict power and blade strain fluctuations (Howard et al. WE 2015)

MHK river turbines: we understand how far upstream bedform migration should be monitored, what signature the turbine leaves on the river bathymetry. Can we imagine any passive / active control strategy to 1. dampen power fluctuations 2. enhance river naturalization

offshore wind energy under ocean waves, floating platform kinematics is two way coupled with the (variable) rotor thrust force: can we use this as a restoring moment in emergency operation?

Temporal Cross-correlation between velocity u(t,z) at different heights and instantaneous power P(t) or blade strain (t)

peak locations(z)

29

At z/zhub~1.3 corresponding to z=zhub+L/4 the wind velocity is mostly correlated with power output and blade strain. This signal can be thus used as a wind input for a predictive control strategy

Eolos

WT

LiDAR at EOLOS field

optimal location

Traditional wind energy:

Howard & Guala WE, 2016

read (t)

prescribe (t) Torque(t)

obtain Thrust(t)

Mitigate pitch

incoming waves H(t)

Enable floating wind turbines with closed loop control for pitch mitigation strategy

(t)

Offshore wind energy:

Ω 𝑡 = 𝐾𝑑

𝑑

𝑑𝑡(𝑡)

Feist et al. 2016

There are three ways to approach the morphodynamic effects of MHK turbines 1) limit the cross-stream blockage of the array

Video

There are three ways to address the morphodynamic effects of MHK turbines 2) embrace the distortion effect to onset meandering motions and sustain the stream naturalization with a MHK vane configuration,

Potential deployment location of single- or multi- unit systems

Provisional patent application :

There are three ways to approach the morphodynamic effects of MHK turbines 3) design turbines to protect the banks from erosion (with Lian Shen , Jeff Marr SAFL)

Take-home messages :

C Hill, M Musa, M Guala “Interaction between instream axial flow hydrokinetic turbines and unidirectional flow bedforms” Renewable Energy 86, 409-421 (2016) C Hill, Kozarek J. Sotiropoulos F, M Guala “ Interaction between instream axial flow hydrokinetic turbines and uni-directional flow bedforms” WRR in press (2016) KB Howard, M Guala “Upwind preview to a horizontal axis wind turbine: a wind tunnel and field‐scale study” Wind Energy in press (2016) KB Howard, JS Hu, LP Chamorro, M Guala “Characterizing the response of a wind turbine model under complex inflow conditions “ Wind Energy 18 ( 4), 729-743, (2015) C Hill, M Musa, LP Chamorro, C Ellis, M Guala “Local Scour around a Model Hydrokinetic Turbine in an erodible Channel” ASCE Journal of Hydraulic Engineering 140 (8), 04014037, (2014) C. Feist, T. Calderer, K. Ruehl, F. Sotiropoulos and M. Guala “Platform kinematics and wake evolution of a floating wind turbine” submitted Journal of Fluid Mech. J. Hong, M Toloui, Chamorro, Guala KB.Howard J Tucker and F Sotiropoulos “Nature COMM (2014) M Guala, A Singh , N BadHeartBull E Foufoula-Georgiou “Spectral description of migrating bedform and sediment transport” J. Geophys. Res. (2014) A Singh, KB Howard M Guala On the homogeneization of turbulence in a turbine wake flow” Physics of fluids, 2015

1) MHK instream turbines impose a scour deposition pattern that has local and (in some cases) a non-local effects on mean bed elevation and bedform evolution.

2) Migrating bedforms have an effect on the instantaneous power and on wake meandering 3) At the power plant scale: power fluctuations (and likely unsteady loads) depend on the

coupling between wake interactions and bed evolution

Mitigation strategies are possible: involve both flow preview and turbine control.

For tidal or river (MHK) turbines, the key environmental impact is the morphodynamic equilibrium of the sediment bed (affect bedform-induced roughness, near-bed velocity, wall shear stress , fish life cycle, biota)

changes in bathymetry are easily measurable and can be monitored for decades. It is the most robust metric we can envision to assess environmental impact

Future research

we are developing a turbine scour (ys) model , as a function of sediment (d) and rotor (D) diameter, incoming flow Froude number (F), river depth (y) and turbine operating conditions (thrust coefficient CT , induction factor or power coefficient. (Inspired by Gioia & Bombaredelli 2002, Chakrabothy & Gioia 2004, Manes & Brocchini, 2015)

how did we get there?

𝜏

𝜌= − 𝑢′𝑤′ ~ 𝑢𝑑𝑉

grain scale dynamics

scour scale dynamics

where ys is the scour depth and Fd is the drag force

ys , V

How do we couple those scales?

through turbulence kinetic energy dissipation, assuming steady conditions, K41 small scale isotropy, and the establishment of the inertial range

ys , V

at the sediment interface instead...

obtain V = f( Fd Uhub CT ....)

accounting for the specific turbine scour assuming that the key length scale is the scour depth and the velocity scale in the scour hole is defined by the turbine drag

; with 𝐹𝑑 = ½ 𝑐𝑇 𝐴 𝑈2ℎ𝑢𝑏

oops! not equal, but proportional to... some exp. calibration is necessary

~

Thank you, question?

acknowledge the other member of the team

wake meandering at the EOLOS turbine & (hopefully, depending on IONE funding) a new design of river flow energy converters: OCT gave us green light and support for a provisional patent

Microcystis growth and metabolism in lake-equivalent turbulence, light and temperature conditions (with Miki Hondzo)

Anne Wilkinson Michael Heisel

... the engineering staff at SAFL (Chris, Jim2, Jeff, Ben ) made all this possible during the renovation

... the faculty: Efi , Fotis , Miki, and many others

Chawdhary et al. 2015

Baseline flow: Verdant power large scale deployment

Asymmetric turbine patch (array) investigation

Experimental Set-up: Wind Tunnel Testing

Case study utilizing PIV and voltage output Varying Thermal stratifications:

1) Turbine in baseflow

2) Two turbines in series

3) Sinusoidal hill upwind

of turbine

Simultaneous PIV measurement windows

Flow

Karman vortex shedding

DC motor voltage

𝜆 = 𝜏𝑢𝑐𝑜𝑛𝑣𝑒𝑐𝑡𝑖𝑜𝑛 𝜌𝑥𝑦(𝜏) =𝑣𝑥(𝑡)𝑣𝑦(𝑡 + 𝜏)

𝑣𝑥2 𝑣𝑦

2

T

RESULTS: Turbine and Turbine interactions

~6-10 consistent with VLSM

Adding complexity: Varying thermal stability regimes

stable neutral convective

stable

convective neutral

Top tip

Bottom tip

o – Neutral □ - Stable ◊ - Convective

(Howard K.B. , Chamorro L.P., Guala M. AIAA 2012, to be submitted to BLM)

Stab

le

Co

nve

ctiv

e

U/Uhub

RESULTS: thermal stability regimes coupled with topographic effects

baseflow-neutral hill - stable

see also Singh, Howard, Guala PoF 2014

Spectral analysis of bedform topography indicates little difference on reach-scale sediment transport characteristics and bedform size with or without

turbines;

Far downstream effect of centered MHK turbine(s) on bedforms