Mortality assessment for adult European eels (Anguilla Anguilla) during turbine passage using CFD modelling

Renewable Energy 147 (2020) 1481e1490

Contents lists available at ScienceDirect

Renewable Energy journal homepage: www.elsevier.com/locate/renene

Mortality assessment for adult European eels (Anguilla Anguilla) during turbine passage using CFD modelling Klopries Elena-Maria*, Schüttrumpf Holger Institute of Hydraulic Engineering and Water Resources Management, RWTH Aachen University, Mies-van-der-Rohe-Straße 17, D-52074, Aachen, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 March 2019 Received in revised form 19 August 2019 Accepted 22 September 2019 Available online 24 September 2019

Collisions, shear events and barotrauma are severe causes of fish mortality in a hydroelectric turbine. Fish-adapted turbine management and environmentally enhanced turbines can be mitigation measures. To use those measures efficiently, knowledge about turbine mortality is needed. In this study, a combination of CFD modelling, fish passage modelling and mortality assessment was used to evaluate mortality for different operating points of a Kaplan bulb turbine for adult European eels (anguilla anguilla). Calculated mortality due to collisions varied from 22% to 37%, due to shear events from 7% to 14% and due to barotrauma from 0% to 18%. The operating points with discharges between 70% and 85% of maximum discharge yielded the lowest mortality values. This supports the idea that a fish-adapted turbine management is possible that gives preference to operating points that are less hazardous to fish than others. Based on this approach it is possible to distinguish the locations within a turbine where hazardous hydraulic conditions occur making it a valuable tool in the design process and the biological performance evaluation of a turbine-management plan without needing to implement it first. Furthermore, no animal experiments are necessary for this approach. © 2019 Elsevier Ltd. All rights reserved.

Keywords: CFD modelling Fish passage Kaplan turbine Turbine mortality Turbine evaluation

1. Introduction 1.1. Background A major challenge of hydroelectric power is the reconciliation of its economic and ecological effects. Hydro power plants and their associated structures such as dams pose obstructions to fish populations with regard to habitat connectivity. Turbine entrainment allows for a one-way connectivity for downstream migrating fish but can cause substantial damage and mortality to passing fish. Collisions with moving or stationary parts of the turbine [1,2], rapid pressure changes in the turbine [3,4] and high shear stresses [5] are the three major known mechanisms that cause fish damage. The extent of mortality ranges between 0% and 100% depending on fish species and size as well as the type of turbine [6]. Diadromous fish such as Atlantic salmon (salmo salar) and European eel (anguilla anguilla) must migrate from freshwater rivers to the sea to conclude their life cycle. During their migration they often encounter hydro power plants and the associated turbine

* Corresponding author. E-mail address: [email protected] (E.-M. Klopries). https://doi.org/10.1016/j.renene.2019.09.112 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

induced mortality. This mortality can magnify during their migration when they have to pass several hydro power plants in succession, which can ultimately have population-level effects [7]. Operators and decision makers can choose between several mitigation measures to reduce the effects of turbine entrainment on fish. Environmentally enhanced turbines [8] and fish-adapted turbine management [9] can help to reduce mortality during turbine entrainment. Environmentally enhanced or so-called fish-friendly turbines are purpose-built turbines where passage conditions for fish are improved. These specific designs can for example include gap reduction between the runner blades and the hub [10] to reduce the probability of fish grinding in that gap. Fish-adapted turbine management on the other hand assumes that different operating points of a conventional turbine have varying effects on fish and their mortality. Thus, there may be combinations of operating points that are less harmful than others [9]. Identifying the locations and hydraulic conditions where injuries occur in a turbine is essential for the effectiveness of these measures. In the past, live fish studies have been performed at hydro power plants to assess the mortality of turbines and modes of turbine operation. These studies are costly and can only be conducted after turbines have been placed in operation [11]. Furthermore, fish are deliberately exposed to harmful situations, which is ethically

1482

E.-M. Klopries, H. Schüttrumpf / Renewable Energy 147 (2020) 1481e1490

questionable. Apart from live fish studies, it is possible to use analytical models [12,13] to assess mortality at hydro power plants. These are black-box models that show the relation between input and output parameters of a complex system without giving information on the system’s internal workings. Both methods are applicable to give an estimation of mortality rates but they do not give detailed information on the mechanisms and locations of damage within a turbine. More recent approaches based on sensor fish studies [14,15], laboratory studies [16,17] and computational fluid dynamics (CFD) modelling promise more insight into and quantification of turbine-induced mortality. The general idea of this approach is to describe the flow with a CFD model and determine potential hazards for fish by simulating fish passage through the turbine [18]. Thus, CFD modelling of turbine entrainment and associated mortality typically consists of three major parts: flow modelling, fish passage modelling and mortality assessment. 1.2. Flow modelling CFD modelling uses numerical analysis to describe flow processes. Depending on the equations of movement and the consideration of turbulence the results of CFD models have a different degree of accuracy. Direct numerical simulation (DNS) fully and explicitly resolves all turbulent flow features. To do so, they need very fine computational grids and high computational resources, which makes them unappealing to industrial and engineering use. The Reynolds averaged Navier Stokes (RANS) approach uses timeaveraged velocities and pressure values in combination with surrogate quantities to simulate the effects of turbulence instead of calculating all turbulent fluctuations [19]. Thus, the computational resources and computing times are substantially reduced at the cost of flow accuracy. A compromise between both approaches is the large eddy simulation (LES). Here, turbulent structures that are larger than the size of the computational grid cells are computed directly whereas smaller turbulent structures are modelled by the same surrogate quantities used in RANS [19]. One special version of LES is the detached eddy simulation (DES), where turbulent conditions near the walls are modelled via RANS and in the detached areas away from the solid boundaries the turbulent flow is fully resolved [20]. Romero-Gomez & Richmond [11] and Richmond & Romero-Gomez [18] have shown that simulation of flow and fish passage through hydro power turbines using DES is possible with reasonable computational costs. However, the RANS approach is used frequently in scientific work related with simulation of fish passage through turbines and in the design process of turbines because it is less challenging for users and even less computationally demanding [4,21,22]. The use of the time-averaged RANS approach for modelling the highly time-dependent processes in a turbine might be one of the reasons for the poor correlation between model results and observations in the field [21]. Besides turbulence resolution, the other important aspect with regard to flow modelling in a turbine is the representation of turbine rotation. There are different approaches of how to depict the interaction of rotational and stationary parts in a CFD simulation (frozen rotor, mixing plan, unsteady sliding grid). The frozen rotor approach solves the flow for one position of the rotor and leads to a snapshot of the fluid flow system [23]. The mixing plane approach does not physically resolve the motion of the turbine blades either but uses a multi reference frame for that part of the computational grid that comprises the turbine blades [23]. These approaches are steady-state solutions of a running turbine and thus less computational demanding than resolving the turbine movement with time. The latter aspect is depicted in the unsteady sliding grid approach. Here, adjacent parts of the grid rotate relative to each other, yielding an unsteady flow solution. To ensure undisturbed

flow through the two grid regions, the flow variables are interpolated on the cell faces of the internal interfaces [24]. The approach often used in studies concerning turbine design or fish passage through turbines is the mixing plane approach combined with a multi reference frame simulation due to its reduced computational costs [4,18]. However, the sliding grid approach is also applicable for this kind of studies since it resolves the flow more realistically. 1.3. Fish passage modelling Based on the flow simulation, potential fish pathways through the turbine can be calculated. Richmond & Romero-Gomez [18] state three different ways of modelling the pathways using fish representation methods with varying modelling complexities. Discrete element modelling (DEM) can be used to depict a fish of a certain size and mass (Fig. 1b). A Lagrangian particle approach accounts for the mass of a fish but not its size (Fig. 1c), whereas advected massless particles neither account for size nor mass (Fig. 1d). The latter are used to calculate pathways of fish that equal streamlines. Streamlines are the most common geometric technique to visualize flow, where the resulting curve is tangent to the steady-state flow at every point at a particular time [25]. This means, the fish velocity equals the water velocity. Contrary to that, with DEM and Lagrangian particle modelling the modelled fish paths are affected not only by flow velocity but all body or surface forces that act on the fish such as drag and lift. Richmond & Romero-Gomez [18] tested all three approaches and compared the results to field measurements at the corresponding site. All approaches led to comparable outcomes, though the results derived from streamline calculations unexpectedly agreed best with field measurements. 1.4. Mortality assessment The hydraulic and physical stressors that exist on the modelled pathways of the fish can be assumed to be the stressors that act on the fish during turbine passage and consequently lead to damage or mortality. Thus, it is possible to calculate the probability of exposure to a certain stressor that fish experience. Combining the probability of exposure with the probability of mortal injury (doseresponse-relationship) for every stressor, you can derive a performance score of the turbine tested [4]. This performance score can be a survival rate or a mortality value. It can be used to compare different types of turbines or modes of turbine operations. Relevant stressors identified in recent studies seem to be barotrauma, collision and shear stress. They can be described by different hydraulic and physical parameters. There is no scientific consensus on the right choice of hydraulic and physical values with regard to their effects on fish damage and mortality. Laboratory and field studies have been performed in order to determine the relevant parameters as well as their corresponding dose-responserelationships (Table 1). With regard to shear stress, most recent studies show that acceleration and strain rate acting on fish are most likely the crucial parameters to describe the effect of shear events [16,17]. The associated dose-response-relationships were derived from

Fig. 1. Fish representation of a 10-cm juvenile fish (a) depending on the modelling scheme for fish particles: (b) a composite particle in DEM, (c) a material particle in the Lagrangian scheme, and (d) a massless particle (picture taken from Ref. [18]).

E.-M. Klopries, H. Schüttrumpf / Renewable Energy 147 (2020) 1481e1490

1483

Table 1 Dose-response-relationships for shear stress, barotrauma and collision. Dose-response-relationships in italics are derived from figures given in the publication and are, therefore, only estimations, 1 larger individuals, NV: nozzle velocity [m/s], A: acceleration magnitude [m/s2], SR: strain rate [1/s], N: nadir pressure [kPa], B: buoyancy [-], PA: acclimation pressure [kPa]. Stressor

Hydraulic parameter

Fish species

Shear

NV

Chinook salmon Laboratory test, fish injection into (Oncorhynchus tshawytscha) shear environment

A

Chinook salmon

NV and A

Chinook salmon

NV and SR

Blue gourami (Trichopodus trichopterus)

Laboratory test, fish injection into shear environment

NV and SR

Iridescent shark (Pangasianodon hypophthalmus)

Laboratory test, fish injection into shear environment

Chinook salmon (Yearling)

Laboratory test, mobile aquatic barotrauma laboratory (MABL)

PA and N

Chinook salmon (Subyearling)

MABL

N and B PA and N PA and N

Chinook salmon Chinook salmon Brook lamprey (Lampetra richardonii)

MABL MABL MABL

PA and N

Pacific lamprey (E. tridentatus)

MABL

PA and N

A

Australian bass (Percalates novemaculeata) Carp gudgeon (Hypseleotris spp.) Murray cod (Maccullochella peelii) Silver perch (Bidyanus bidyanus) Juvenile salmon

Hyper- and hypobaric hydrochamber Hyper- and hypobaric hydrochamber Hyper- and hypobaric hydrochamber Hyper- and hypobaric hydrochamber Sensor fish and live fish data

A

Chinook salmon

Sensor fish and live fish data

barotrauma PA and N

PA and N PA and N PA and N Collision

Method

Laboratory test, fish injection into shear environment Laboratory test, fish injection into shear environment

laboratory studies, where test fish were introduced into a jet of water via a pipe (Fig. 2A). The water jet created a shear environment that varied depending on the water velocity at the pipe outlet (nozzle velocity). A number of laboratory studies has been performed on the effects of barotrauma on fish. Fish are exposed to a rapid decompression simulating turbine passage. The ratio of pressure change (RPC) seems to be an adequate predictor variable to explain mortality due to barotrauma [26,27]. RPC consists of the acclimation pressure fish are acclimated to prior to turbine passage and the nadir pressure that describes the lowest pressure fish are exposed to during turbine passage. Dose-response-relationships for barotrauma appear to be highly species specific (Fig. 2B) even under

Dose-response-relationship (mortality)

Author

16.8 m/s: 14% 18.3 m/s: 48% 19.8 m/s: 48% 260 m/s2: 10% 513 m/s2: 10% 15.2 m/s ~550 m/s2: 5% 18.3 m/s ~700 m/s2: 0% 21.3 m/s ~800 m/s2: 10% 22.9 m/s ~950 m/s2: 10% 3.0 m/s e 168 1/s: 27% 6.1 m/s e 339 1/s: 7% 12.2 m/s e 688 1/s: 13% 15.2 m/s e 852 1/s: 27% 18.3 m/s e 1008 1/s: 20% 21.3 m/s e 1185 1/s: 40% 3.0 m/s e 168 1/s: 13% 12.2 m/s e 688 1/s: 0% 18.3 m/s e 1008 1/s: 7% 21.3 m/s e 1185 1/s: 67% 21.3 m/s e 1185 1/s: 13%1 N ¼ 10 kPa PA ¼ 101 kPa: 0% PA ¼ 131 kPa: 0% PA ¼ 161 kPa: 10% PA ¼ 234 kPa: 62% N ¼ 10 kPa PA ¼ 101 kPa: 15% PA ¼ 131 kPa: 10% PA ¼ 161 kPa: 10% PA ¼ 234 kPa: 72% Pmort¼(e-1.132-0.45*Nþ2.4*B)/(1þe-1.132-0.45*Nþ2.4*B) Pmort¼(e-5.56þ3.85*ln(PA/N))/(1þe-5.56þ3.85*ln(PA/N)) Rapid decompression: N ¼ 13.8 kPa PA ¼ 146.2 kPa: 0% Sustained decompression: N ¼ 13.8 kPa PA ¼ 146.2 kPa: 0% Sustained decompression: N ¼ 13.8 kPa PA ¼ 146.2 kPa: 0% Pmort¼(e-5.72þ2.68*ln(PA/N))/(1þe-5.72þ2.68*ln(PA/N))

[30]

Pmort¼(e

-5.70þ1.99*ln(PA/N)

Pmort¼(e

-7.33þ2.79*ln(PA/N)

[31] [17]

[16]

[16]

[32]

[32]

[33] [26] [28]

[28]

[27]

)/(1þe

-5.70þ1.99*ln(PA/N)

)

[27]

)/(1þe

-7.33þ2.79*ln(PA/N)

)

[27]

Pmort¼(e-3.91þ1.39*ln(PA/N))/(1þe-3.91þ1.39*ln(PA/N))

[27]

>95 g: 3,8% - 6,3% (correlation between probability of severe event of [29] sensor fish (>95 g) and mortality in live fish) >95 g: 10.5%e14.1% (correlation between probability of severe event [14] of sensor fish (>95 g) and mortality in live fish)

comparable testing conditions. This is possibly due to physiological differences between species especially with regard to the existence of a swim bladder [28]. Since it is difficult to determine the acclimation pressure of fish and hence the ratio of pressure change, absolute pressure change (kPa) or temporal pressure change rate (kPa/s) might be adequate predictor variables for barotrauma doseresponse-relationships, too [14]. With regard to collisions, the only available data is derived from field studies. Here, results of live fish tests and sensor fish studies are combined [14,29]. However, it is unclear whether the mortality observed on live fish stems from collisions or another stressor. That is why, explicit dose-response-relationships for collision events are not available.

1484

E.-M. Klopries, H. Schüttrumpf / Renewable Energy 147 (2020) 1481e1490

Fig. 2. Dose-response-relationship showing A) the effect of shear events on different fish species, nozzle velocity is the velocity of the jet of water fishes were released into, B) the effect of barotrauma on different fish species, RPC is the ratio of pressure change (acclimation pressure/nadir pressure).

Fig. 3. Physical parameters of the horizontal Kaplan bulb turbine.

1.5. Objectives In the past, works on CFD modelling to assess the biological performance of turbines focused mainly on one or two of the three stressors responsible for mortality. However, in order to assess and compare different modes of turbine operation holistically, it is important to consider all possible hazards. In this work, an example of CFD modelling is introduced that can be used as an engineering design tool to assess different modes of turbine operation with regard to all three causes of fish mortality (shear events, collisions and barotrauma). It is possible to analyse the probability and location of exposure to hazardous mechanisms within a turbine for different operating points. Furthermore, on that basis the feasibility to determine mortality for different operating points and thus, to give recommendations on certain modes of turbine operation that are less hazardous for fish is shown. 2. Materials and methods 2.1. Study object This study focuses on a European river. It flows approximately 520 km from its source to its mouth. All in all, 31 dams and other obstructions are installed along the river, 17 of those are equipped with hydro power generating units. At most of the hydro power plants, Kaplan bulb turbines are used. European eels are native in this river but similar to other European river catchments their population has been declining in the last decades [34]. Since turbine-induced mortality possibly is one factor that contributed to the decline, mitigation measures for European eels are necessary. One idea is to preferably operate the turbines in less harmful operating points. In order to do so, it is essential to assess the biological performance of all possible operating points.

Flow through the Kaplan bulb turbine examined in this study ranges from 15 m3/s to 100 m3/s. Mean head at the site is 6.4 m. Each turbine consists of 20 guide vanes and 4 runner blades. Runner speed is 85 revolutions per minute (rpm) (Fig. 3). To achieve an energetically optimal operating point for each flow through the turbine, the guide vanes as well as the runner blades can be adapted in their angle. That way, energy efficiencies of up to 91% can be achieved. 2.2. CFD model The freely available software package OpenFOAM (v. 2.4.0, OpenFoam Foundation Ltd.) was used to simulate a single turbine unit. To solve the governing equations the pimpleDyMFoam solver was used. This solver uses a combination of PISO and SIMPLE algorithm to solve the pressure-momentum coupling for incompressible, unsteady viscous flows [35]. It also enables dynamic mesh features such as turbine rotation using the unsteady sliding grid approach. Arbitrary mesh interfaces (AMI) allow for the interpolation of flow between the different mesh regions. All cells within the rotational zone rotate at a rotation rate of 8.9012 rad/s equalling 85 rpm. Turbulence was resolved with the RANS approach using a k-u-SST turbulence closure model [36]. A mesh sensitivity test was conducted with a mesh size between 0.2 million cells and 1.8 million cells (Fig. 4). The final mesh consisted of 1.1 million primarily hexahedral cells. Six different operating points were simulated in this study (Table 2). An operating point is defined as a combination of guide vane angle, runner blade angle, head, discharge and the corresponding inflow velocity. Each of the six operating points (30%, 40%, 55%, 70%, 85% and 100% of maximum flow) had a specific geometry and thus, a separate computational grid was constructed for each of them.

E.-M. Klopries, H. Schüttrumpf / Renewable Energy 147 (2020) 1481e1490

1485

Fig. 4. Effect of mesh size on simulation results.

Fish passage was modelled using streamlines. They were computed during the post-processing step using the free software paraview (v. 3.6.0, SNL, LANL und Kitware Inc). The seeds of the streamlines were evenly distributed across one quarter of the inflow surface. For each operating point, 300 streamlines were calculated.

Dti ¼

2.3. Mortality assessment The study focuses on mortality evaluation for adult eels at different operating points of a Kaplan bulb turbine. Thus, following the approach described in section 1.4 dose-response-relationships for eels were necessary. However, it was not possible to find eelspecific data in the literature. Therefore, we made assumptions concerning the dose-response-relationships (Table 3 and Table 4). Dose-response-relationships for collision and shear events are identical and roughly based on the findings of Deng et al. [14,29]. Since no acclimation pressure is known for eels at the site studied, the pressure change between lowest and highest simulated pressure on each streamline was used as predictor variable for barotrauma instead of RPC. The interval boundaries and mortality values are assumptions loosely derived from data given in Table 1. The predictor variables for shear stress and collisions was the acceleration on the calculated streamlines. For each point on the streamlines, data on velocity in x, y and z direction was available as well as the coordinates. Based on this data, acceleration for each point on the streamlines is calculated as:


! !

u  u i1

a ¼

iþ1
Dt

(1)

i

(2)

ui qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðXiþ1  Xi1 Þ2 þ ðYiþ1  Yi1 Þ2 þ ðZiþ1  Zi1 Þ2

(3)

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ui;x 2 þ ui;y 2 þ ui;z 2

(4)

Ds ¼ ui ¼

Ds

! where a is the acceleration magnitude (m/s2), u i is the velocity at point i in vector notation (m/s), ui;j is the velocity at point i in index notation for j ¼ x, y and z (m/s), Dti is the time it takes for a particle to get from point i-1 to point iþ1 (s), Ds is the space between point i-1 to iþ1 (m) and Xi ; Yi and Zi are the coordinates in x, y and z direction at point i (m). For each streamline the location and value of the highest acceleration event is determined and defined as the relevant acceleration for that streamline. Following Deng et al. [14,29] shear stress and collisions were told apart depending on the characteristics of the acceleration event. If the duration of the acceleration within 70% of the peak value lasts longer than 0.0075s, the event is defined as a shear event. If the duration is shorter than 0.0075s, it is defined as a collision (Fig. 5).

2.4. Data analysis The CFD results were analysed with the data-analysing software Matlab (2015b, the MathWorks Inc.). For each operating point tested, streamlines were calculated using the freely-available software paraview (v. 3.6.0, SNL, LANL und Kitware Inc) and the streamtracer function. The hydraulic values of the streamlines at all

Table 2 Operating points of a Kaplan bulb turbine tested in CFD modeling. Operating point

Flow (% of maximum flow)

Inflow velocity (m/s)

Guiding vane angle ( )

Runner blade angle ( )

OP01_30 OP02_40 OP03_55 OP04_70 OP05_85 OP06_100

30 40 55 70 85 100

0.7 1.0 1.4 1.7 2.1 2.5

17 25 36 44 51 55

10 14 20 26 31 37

1486

E.-M. Klopries, H. Schüttrumpf / Renewable Energy 147 (2020) 1481e1490

Table 3 Eel-specific dose-response relationship for mortality stressors based on acceleration; values given express the probability of mortality if fish are exposed to accelerations of a given value. Parameter

Stressor

<45 g

45 ge90 g

90 ge135 g

135 ge180 g

180 ge2000g

Acceleration

Shear Collision

0.1 0.1

0.2 0.2

0.6 0.6

0.8 0.8

1.0 1.0

Table 4 Eel-specific dose-response relationship for mortality based on pressure change; values given express the probability of mortality if fish are exposed to pressure changes of a given value. Parameter

Stressor

<40 kPa

40 kPae80 kPa

80 kPae120 kPa

120 kPae160 kPa

160 kPae200 kPa

Pressure change

Barotrauma

0

0.1

0.3

0.6

1.0

points were exported into csv-files and afterwards imported into Matlab. With the help of a script maximum pressure change and acceleration values as well as their locations for all 300 streamlines for each operating point were determined. Afterwards, the probability of exposure for each stressor was computed and multiplied with the corresponding dose-response relationship. Dose-response relationships and probability of exposure were divided into 5 discrete intervals due to simplification. 3. Results and discussion 3.1. Probability of exposure With regard to acceleration events there are only minor differences in the probability of exposure between the six operating points we tested (Fig. 6). Contrary to that, the probability of exposure with regard to pressure change varies between the operating points. For the two lowest operating points the probability of higher pressure changes is increased compared to the other operating points. 3.2. Location of exposure The geometry of the CFD model is divided into three parts e the guide vanes, the runner region and the outlet. Less than 1% of all relevant events were found in the outlet, deeming this region as the least harmful one. The ratio of relevant events in the guide vanes and the runner region highly depends on the stressor and the operating point. For shear events and barotrauma, only 4%e13% and 10%e30% respectively of all relevant events were found in the runner region (Fig. 7). The remaining events occurred in the guide vanes. For collision events, only at the lowest operating point more

relevant events occurred in the guide vanes than in the runner region. For all other operating points, the number of relevant events in the runner region exceeds that in the guide vanes by a factor of 2e36. Collisions seem to happen mainly within the runner region. Deng et al. [2] also found that a majority of severe collision events occur in the runner region. However, small opening angles in the guide vanes at low turbine flows seem to increase the probability of collision events in that region. It is assumed that this can be due to the stronger change of direction at the low angles and the less smooth transition in that region. For barotrauma and shear events the guide vane region seems to be the more hazardous part of the
turbine in general compared to the runner region. Cada et al. [21] also determined that the guide vanes can be among others a hazardous region with regard to shear stress. However, they do not give information on how big the proportion of severe shear events is in the runner region and the guide vanes respectively. For barotrauma, the runner region usually is the most hazardous part of a turbine. This is not in alignment with the findings of this study. Immediately below the runner blades occurs a vast majority of nadirs [4], which is often used to describe the location of barotrauma events. Since in this study the maximum pressure change was used as a predictor variable for barotrauma and not the nadir, this might explain the differences in the location.

3.3. Shear events and collision For each streamline the most severe acceleration was determined. Afterwards, they were divided into either shear events (peak duration >0.0075s) or collisions (peak duration <0.0075s). In general, within all relevant acceleration events there were more shear events than collisions based on this definition. However,

Fig. 5. Definition of peak value and duration of peak with regard to distinction between shear event and collision (following 29 and 14).

E.-M. Klopries, H. Schüttrumpf / Renewable Energy 147 (2020) 1481e1490

1487

Fig. 6. Probability of exposure, A) for relevant acceleration events, B) for relevant pressure changes.

Fig. 7. Location of relevant events for all three stressors shear events, collision and barotrauma.

across all operating points 86% of all relevant shear events that were determined had a magnitude of less than 45 g, which leads to minor impacts on fish health. Contrary to that, the majority of relevant collisions (80%) was larger than 90 g (Fig. 8), which leads to more considerable impacts on fish health. This suggests that collisions are more important with regard to fish mortality than shear events. Deng et al. [14] state that acceleration events with a magnitude >95 g are defined as severe. In a variation of in-situ studies with the sensor fish device 16%e29% of all sensors experienced severe collisions [37]. In this study, the proportion of all streamlines that experienced severe collisions (>90 g) was 22%e45% (Table 5). Except for the highest percentage of collision events these values are all within the range of field study results. The highest value of 45% occurs at the lowest operating point which has extraordinarily high values for all stressor parameters. Thus, the proportion of severe collision events seems to be realistic. With regard to shear events Hou et al. [37] found that a proportion of 0%e11% of all sensors released experienced severe shear events. In this study, the percentage of streamlines that experienced severe shear events was 1%e14%. These values are close to those found in Hou et al. [37]. Here as well, the highest value occurred for the lowest operating point. Following the definition of shear events and collisions, it is not possible for one streamline to experience a severe shear event and a severe collision. This could lead to an underestimation of exposure to any acceleration-based mortality. However, it is assumed that this error is small. Studies at other sites show that only about 4% of all sensor fish released experienced a severe collision and a severe shear event [15]. The error of underestimation might be in the same order.

Fig. 8. Proportion of relevant accelerations within each interval of exposure divided into A) shear event and B) collision; The sum of all probabilities e shear event and collision e for one operating point equals one.

3.4. Barotrauma For mortality due to barotrauma the pressure change was used as a predictor variable since no reference value for acclimation pressure for eels at the site studied was available and thus the RPC (acclimation pressure/nadir) could not be used. All calculated relevant pressure changes were smaller than 200 kPa (Fig. 6B). No field data for this study site was available to validate the pressure changes values determined in this study. Compared to values of pressure change that were measured in Francis turbines in field studies these values are much smaller [38]. Values for Francis turbines ranged from approximately 250 kPae860 kPa. This difference might have two reasons. For once, Kaplan turbines and Francis turbines have different modes of operations which can lead to differences in the pressure field. Secondly, the sites that were studied in Fu et al. [38] had much higher heads (56 me94 m) and

1488

E.-M. Klopries, H. Schüttrumpf / Renewable Energy 147 (2020) 1481e1490

Table 5 Percentage of streamlines that experienced severe collisions and severe shear events (acceleration magnitude > 90 g). Operating point

OP01_30

OP02_40

OP03_55

OP04_70

OP05_85

OP06_100

Collision Shear Total

45 14 59

25 9 34

26 6 32

24 1 24

22 2 35

33 1 34

higher rotational speeds (263 rpme400 rpm) compared to this study site (6.4 m head and 85 rpm). 3.5. Mortality Mortality was calculated separately for each operating point and each stressor (shear event, collision and barotrauma). Mortality was highest for the lowest operating point with 51% cumulative mortality due to accelerations and 18% due to barotrauma. This operating point seems to be the most hazardous discharge for the studied Kaplan bulb turbine. Since most of the severe events for this operating point occurred in the guide vanes, it is assumed that the small angle of opening of the guide vanes is the main reason for its poor biological performance. For the remaining operating points mortality was much lower. Mortality due to barotrauma ranged between 0% and 4%. Richmond et al. [4] determined a mortality due to barotrauma of 0.3%e1.7% for juvenile Chinook salmons in a Kaplan turbine. These values lie within the range of the present results. Differences can be explained by the different turbine geometry, different fish species and the different predictor parameter. Mortality due to shear events ranged between 7% and 12% and collision-induced mortality ranged from 22% to 31%. For shear
events Cada et al. [21] predicted mortalities in a Kaplan turbine of
less than 2%. Though they had a similar approach, Cada et al. [21] used a single threshold value to categorise harmful flow condition instead of five intervals with varying probabilities of mortality. This may have an impact on the mortality result. Furthermore, fish species was different in this study. Although the dose-responserelationships might be different between species (see section 1.4), this comparison can still give some level of validation since the path through the turbine and hence the exposure is not species-specific in this study. No species-specific behaviour was implemented in the fish passage modelling. It is not possible to validate the individual mortalities separately with data from live fish studies because in in-situ studies it is hardly possible to determine the cause of mortality and thus to assign mortality values to a stressor. However, there are results available from a live fish study at a very similar hydro power station (Kaplan bulb turbine, runner diameter 4.6 m and runner speed 83 rpm) from 2009 to 2010 conducted with adult European eels (mean body length 70.9 cme76.5 cm) [39]. In the field study, mortality rates of

32%e35% were measured for turbine discharges varying from 80% to 100% of maximum discharge. For similar operating points (OP04_70, OP05_85 and OP06_100) computed mortalities in this study lie within a range of 30%e37% and 0%e1% for accelerationinduced mortalities and barotrauma-induced mortalities respectively. These results show that the calculated mortalities lie within realistic ranges (Fig. 9B). With regard to a fish-adapted turbine operation it is obvious that operating points OP04_70 and OP05_85 yield the lowest mortality. On the other hand, for operating point OP01_30 a much higher mortality was determined. These findings can be used to find biologically optimal combinations of operating points. The preference of operating points is only possible if a hydro power site has more than one turbine and the total flow through all turbines can be divided flexibly between all turbines. Besides biological performance, operating hours, change-over points and reaction times are further parameters that need to be considered regarding turbine operation. 3.6. Discussion Streamlines were calculated to evaluate the hydraulic conditions fish experience during the passage of a Kaplan bulb turbine. Each streamline represents the path a fish would take during turbine passage. However, streamlines neither account for the mass nor the body length of a fish. Especially for adult European eels that can reach a body length of more than 0.8 m, this simplification might lead to an underestimation of mortality. Streamlines were chosen because of the low computational costs they cause making them suitable as an engineering design or evaluation tool. The geometry tested in this study did not include the inflow region and draft tube of the hydro power plant. That geometry was not available. The negligence of the draft tube might lead to an underestimation of mortality since the region directly following the runner and the draft tube can cause hazardously turbulent flow conditions [21]. The inflow region and the fish distribution therein strongly influence the path a fish takes through a turbine [4]. The path through the turbine in turn has an impact on the stressors a fish is exposed to. Although no proof was found for a dependency of mortality to seed location of streamlines, the missing inflow region might have an effect on mortality values.

Fig. 9. A) Calculated mortality separated by stressors, cumulative mortality due to acceleration is the sum of mortality due to shear events and collisions, B) comparison of calculated mortality and results of fields studies [40].

E.-M. Klopries, H. Schüttrumpf / Renewable Energy 147 (2020) 1481e1490

CFD simulation was performed as a basis of the hydraulic conditions fish experience within a turbine. No field data on the hydraulics within the turbine that was tested was available to calibrate the CFD model. However, the results concerning mortality and probability of exposure that were compared to similar sites and studies showed a good agreement. Head at the study site usually is 6 me7 m depending on the flow. The simulated head in the numerical model was calculated from the difference in pressure between the model inlet and outlet. The resulting pressure drop is a function of the geometry and the flow and corresponds to a head of 3.2 me16.1 m depending on the operating point. The calculated head and the head at the study site are in the same order of magnitude. This gives a level of validation to the numerical model. The under- and overestimation of the calculated head might be explained by the negligence of the draft tube and the inflow region in the numerical model. 4. Conclusions Understanding the mechanisms in a hydro turbine that lead to fish damage and the fish’ responses to these mechanisms is crucial for the development of more fish-friendly turbines and fishadapted turbine managements. This study highlights the potential of CFD modelling and mortality assessment as a biological performance evaluation tool of turbines or a turbine-management plan. It is possible to calculate a mortality value for migrating adult European eels due to collisions, shear events and barotrauma. The mortality agrees well with registered mortality values from field studies, giving strong validation to the results. The combination of CFD models and mortality assessment offers great advantages compared to conventional approaches of building or implementing a turbine or turbine-management plan respectively. Firstly, CFD modelling is less expensive than building a full-size turbine and conducting live-fish studies. Secondly, once established, CFD modelling and mortality assessment have no need of live fish testing. Governmental regulations such as animal protection laws often demand a reduction in the extent of animal experiments due to ethical reasons. The approach shown in this paper is the only approach to evaluate biological performance of turbines without using live fish but at the same time creating knowledge on the mechanisms and locations of damage within a turbine. Furthermore, this approach allows compliance of boundary conditions and thus facilitates comparisons of scenarios or turbine types. Field studies usually have the disadvantage of varying boundary conditions and thus are less suitable for fundamental research. In this study, differences in the biological performance of different operating points of a Kaplan bulb turbine were determined. The operating points at a discharge of 70%e85% of maximum discharge yielded the lowest mortality for adult eels. From a biological view, these operating points could be preferable in a fish-adapted turbine-operation plan. The interval of the lowest mortality coincides with the discharge interval that yields the highest energy efficiencies for the turbine tested. For discharges of 70%e85% of maximum discharge there can be energy efficiencies of 90.6%e91.1%. A part of the energy is irreversibly converted into heat due to friction (continuous energy losses). Furthermore, energy is lost in the guide vanes and runner region as a consequence of changes in the cross section and deflection of water flow (local energy losses). If the water exiting the runner has a lot of spin, more energy is lost than at a straight outflow. Kaplan turbines can adjust their guide vanes as well as their runner blades so that for each turbine discharge there is an ideal combination of blade angles. However, each turbine has an interval of discharges where the guide-vane-runner-blade-combination is optimal and the resulting energy losses are minimal. It might be that small energy losses -

1489

especially local losses at the guide vanes and runner blades and the losses as a consequence of spin below the runner region - lead to biologically positive flow conditions as well. Fish are subjected to less turbulent flows and thus might be exposed to less acceleration events. These findings support the assumption that along with the runner region the guide vanes are a hazardous part of a turbine. Thus, in biological performance considerations this part should be taken into consideration as well. This also applies to the development of environmentally enhanced turbines. In this study, collisions were the most severe cause of mortality (22%e37%). This aligns with early assumptions that collisions are the most significant cause of mortality [13]. However, mortalities up to 18% for barotrauma and shear events occurred as well. This shows that these causes of mortality should not be neglected in biological-performance considerations either. All in all, the combination of CFD modelling and dose-response relationships facilitates the determination of mortality values for fish passing through a turbine. Thus, it is possible to calculate the probability that a fish that enters a turbine exits it without damage. That way, a comparison of fish survival between different modes of turbine operation is possible. Combined with the estimated costs of a mitigation measurement, the ecological and economic effects can be calculated. Thus, CFD modelling is a valuable tool to give recommendations on the most efficient fish protection measurements. Acknowledgement The authors wish to thank Voith Hydro Holding GmbH & Co. KG for providing of the turbine geometry used in this study. The authors would also like to thank the Eel Protection Initiative Rhineland-Palatinate/Innogy SE for providing data on field studies with adult eels and innogy SE (former RWE Innogy) for providing a Doctoral scholarship to Elena-Maria Klopries. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Elena-Maria Klopries was granted a Doctoral scholarship from innogy SE (former RWE Innogy AG) during her first three years of her dissertation which is related to this work. References [1] J.K. Davies, A review of information relating to fish passage through turbines: implications to tidal power schemes, J. Fish Biol. 33 (sA) (1988) 111e126, https://doi.org/10.1111/j.1095-8649.1988.tb05565.x. [2] Z. Deng, T.J. Carlson, G.R. Ploskey, M.C. Richmond, D.D. Dauble, Evaluation of blade-strike models for estimating the biological performance of Kaplan turbines, Ecol. Model. 208 (2e4) (2007) 165e176, https://doi.org/10.1016/ j.ecolmodel.2007.05.019. [3] R.S. Brown, A.H. Colotelo, B.D. Pflugrath, C.A. Boys, L.J. Baumgartner, Z.D. Deng, et al., Understanding barotrauma in fish passing hydro structures: a global strategy for sustainable development of water resources, Fisheries 39 (3) (2014) 108e122, https://doi.org/10.1080/03632415.2014.883570. [4] M.C. Richmond, J.A. Serkowski, L.L. Ebner, M. Sick, R.S. Brown, T.J. Carlson, Quantifying barotrauma risk to juvenile fish during hydro-turbine passage, Fish. Res. 154 (2014) 152e164, https://doi.org/10.1016/j.fishres.2014.01.007.
[5] D.A. Neitzel, D.D. Dauble, G.F. Cada, M.C. Richmond, G.R. Guensch, R.P. Mueller, et al., Survival estimates for juvenile fish subjected to a laboratory-generated shear environment, Trans. Am. Fish. Soc. 133 (2) (2004) 447e454, https:// doi.org/10.1577/02-021. [6] B.M. Pracheil, C.R. DeRolph, M.P. Schramm, M.S. Bevelhimer, A fish-eye view of riverine hydropower systems: the current understanding of the biological response to turbine passage, Rev. Fish Biol. Fish. 26 (2) (2016) 153e167, https://doi.org/10.1007/s11160-015-9416-8. [7] T.K. McCarthy, P. Frankiewicz, P. Cullen, M. Blaszkowski, W. O’Connor, D. Doherty, Long-term effects of hydropower installations and associated river regulation on River Shannon eel populations: mitigation and management, Hydrobiologia 609 (1) (2008) 109e124, https://doi.org/10.1007/s10750-0089395-z.
[8] T.W. Hogan, G.F. Cada, S.V. Amaral, The status of environmentally enhanced hydropower turbines, Fisheries 39 (4) (2014) 164e172, https://doi.org/ 10.1080/03632415.2014.897195.

1490

E.-M. Klopries, H. Schüttrumpf / Renewable Energy 147 (2020) 1481e1490

[9] B. Becker, F. Notermanns, C. Reuter, H. Schüttrumpf, Entwicklung einer fischfreundlichen Turbinensteuerung für Laufwasserkraftwerke an der Mosel, Hydrol. Wasserbewirtsch. 53 (1) (2009) 4e12.
[10] G.F. Cada, The development of advanced hydroelectric turbines to improve fish passage survival, Fisheries 26 (9) (2001) 14e23, https://doi.org/10.1577/ 1548-8446(2001)026<0014:TDOAHT>2.0.CO;2. [11] P. Romero-Gomez, M.C. Richmond, Simulating blade-strike on fish passing through marine hydrokinetic turbines, Renew. Energy 71 (2014) 401e413, https://doi.org/10.1016/j.renene.2014.05.051. [12] A.W.H. Turnpenny, S. Clough, K.P. Hanson, R. Ramsey, D. McEwan, Risk Assessment for Fish Passage through Small, Low-Head Turbines: ETSU H/06/ 00054/REP, 2000. €digung von Fischen durch Turbinen, [13] K. von Raben, Zur Frage der Bescha Wasserwirtschaft 48 (4) (1957) 97e100. [14] Z.D. Deng, T.J. Carlson, J.P. Duncan, M.C. Richmond, D.D. Dauble, Use of an autonomous sensor to evaluate the biological performance of the advanced turbine at Wanapum Dam, J. Renew. Sustain. Energy 2 (5) (2010) 53104, https://doi.org/10.1063/1.3501336. [15] J.J. Martinez, Z. Daniel Deng, E.-M. Klopries, R.P. Mueller, P. Scott Titzler, D. Zhou, et al., Characterization of a siphon turbine to accelerate low-head hydropower deployment, J. Clean. Prod. 210 (2019) 35e42, https://doi.org/ 10.1016/j.jclepro.2018.10.345. [16] A.H. Colotelo, R.P. Mueller, R.A. Harnish, J.J. Martinez, T. Phommavong, K. Phommachanh, et al., Injury and mortality of two Mekong River species exposed to turbulent shear forces, Mar. Freshw. Res. 69 (12) (2018) 1945, https://doi.org/10.1071/MF18126. [17] Z. Deng, R.P. Mueller, M.C. Richmond, G.E. Johnson, Injury and mortality of juvenile salmon entrained in a submerged jet entering still water, N. Am. J. Fish. Manag. 30 (3) (2010) 623e628, https://doi.org/10.1577/M09-153.1. [18] M.C. Richmond, P. Romero-Gomez, Fish passage through hydropower turbines: simulating blade strike using the discrete element method, IOP Conf. Ser. Earth Environ. Sci. 22 (6) (2014) 62010, https://doi.org/10.1088/17551315/22/6/062010. [19] R. Schwarze, CFD-Modellierung: Grundlagen und Anwendungen bei €mungsprozessen, Springer Vieweg, Berlin, 2013, ISBN 978-3-642-24377Stro 6. [20] P.R. Spalart, Detached-eddy simulation, Annu. Rev. Fluid Mech. 41 (1) (2009) 181e202, https://doi.org/10.1146/annurev.fluid.010908.165130.
[21] G.F. Cada, J. Loar, L. Garrison, R. Fisher, D. Neitzel, Efforts to reduce mortality to hydroelectric turbine-passed fish: locating and quantifying damaging shear stresses, Environ. Manag. 37 (6) (2006) 898e906, https://doi.org/10.1007/ s00267-005-0061-1. [22] E. Zangiabadi, I. Masters, A.J. Williams, T.N. Croft, R. Malki, M. Edmunds, et al., Computational prediction of pressure change in the vicinity of tidal stream turbines and the consequences for fish survival rate, Renew. Energy 101 (2017) 1141e1156, https://doi.org/10.1016/j.renene.2016.09.063. [23] M. Beaudoin, H. Nilsson, M. Page, R. Magnan, H. Jasak, Evaluation of an improved mixing plane interface for OpenFOAM, IOP Conf. Ser. Earth Environ. Sci. 22 (2) (2014) 22004, https://doi.org/10.1088/1755-1315/22/2/022004. [24] D. Wilhelm, Rotating flow simulations with OpenFOAM, IJASAR (2015) 1e7, https://doi.org/10.19070/2470-4415-SI01001. [25] T. McLoughlin, R.S. Laramee, R. Peikert, F.H. Post, M. Chen, Over two decades of integration-based, geometric flow visualization, Comput. Graph. Forum 29 (6) (2010) 1807e1829, https://doi.org/10.1111/j.1467-8659.2010.01650.x. [26] R.S. Brown, T.J. Carlson, A.J. Gingerich, J.R. Stephenson, B.D. Pflugrath, A.E. Welch, et al., Quantifying mortal injury of juvenile Chinook salmon exposed to simulated hydro-turbine passage, Trans. Am. Fish. Soc. 141 (1) (2012) 147e157, https://doi.org/10.1080/00028487.2011.650274.

[27] B.D. Pflugrath, C.A. Boys, B. Cathers, Predicting hydraulic structure-induced barotrauma in Australian fish species, Mar. Freshw. Res. 69 (12) (2018) 1954, https://doi.org/10.1071/MF18137. [28] A.H. Colotelo, B.D. Pflugrath, R.S. Brown, C.J. Brauner, R.P. Mueller, T.J. Carlson, et al., The effect of rapid and sustained decompression on barotrauma in juvenile brook lamprey and Pacific lamprey: implications for passage at hydroelectric facilities, Fish. Res. 129e130 (2012) 17e20, https://doi.org/ 10.1016/j.fishres.2012.06.001. [29] Z.D. Deng, T.J. Carlson, J.P. Duncan, M.C. Richmond, Six-Degree-of-Freedom sensor fish design and instrumentation, Sensors 7 (12) (2007) 3399e3415, https://doi.org/10.3390/s7123399. [30] Z. Deng, G.R. Guensch, C.A. McKinstry, R.P. Mueller, D.D. Dauble, M.C. Richmond, Evaluation of fish-injury mechanisms during exposure to turbulent shear flow, Can. J. Fish. Aquat. Sci. 62 (7) (2005) 1513e1522, https:// doi.org/10.1139/F05-091. [31] M.C. Richmond, Z.D. Deng, C.A. McKinstry, R.P. Mueller, T.J. Carlson, D.D. Dauble, Response relationships between juvenile salmon and an autonomous sensor in turbulent flow, Fish. Res. 97 (1e2) (2009) 134e139, https:// doi.org/10.1016/j.fishres.2009.01.011. [32] R.S. Brown, T.J. Carlson, A.E. Welch, J.R. Stephenson, C.S. Abernethy, C.A. McKinstry, et al., Assessment of Barotrauma Resulting from Rapid Decompression of Depth-Acclimated Juvenile Chinook Salmon Bearing Radio Telemetry Transmitters: Final Report, Pacific Northwest National Laboratory, 2007. [33] J.R. Stephenson, A.J. Gingerich, R.S. Brown, B.D. Pflugrath, Z.D. Deng, T.J. Carlson, et al., Assessing barotrauma in neutrally and negatively buoyant juvenile salmonids exposed to simulated hydro-turbine passage using a mobile aquatic barotrauma laboratory, Fish. Res. 106 (3) (2010) 271e278, https://doi.org/10.1016/j.fishres.2010.08.006. [34] ICES, EIFAAC/ICES WGEEL REPORT 2013, in: Report of the Joint EIFAAC/ICES Working Group on Eels (WGEEL); 18-22 March 2013 in Sukarietta, Spain, 4-10 September 2013 in Copenhagen, Denmark, 2013 [cited 2014 Sep 26]. Available from: URL, http://www.ices.dk/sites/pub/Publication%20Reports/Expert% 20Group%20Report/acom/2013/WGEEL/wgeel_2013.pdf. [35] T. Holzmann, Mathematics, Numerics, Derivations and OpenFOAM®: Holzmann CFD, 2016. [36] F. Menter, Zonal two equation k-w turbulence models for aerodynmaic flows, in: 24th Fluid Dynamics Conference, 1993. [37] H. Hou, Z. Deng, J. Martinez, T. Fu, J. Duncan, G. Johnson, et al., A hydropower biological evaluation toolset (HBET) for characterizing hydraulic conditions and impacts of hydro-structures on fish, Energies 11 (4) (2018) 990, https:// doi.org/10.3390/en11040990. [38] T. Fu, Z.D. Deng, J.P. Duncan, D. Zhou, T.J. Carlson, G.E. Johnson, et al., Assessing hydraulic conditions through Francis turbines using an autonomous sensor device, Rev. Fish. Sci. 99 (2016) 1244e1252, https://doi.org/10.1016/ j.renene.2016.08.029. €rgensen, S. Teggers-Junge, H. Schüttrumpf, 20 Jahre [39] E.-M. Klopries, L. Kroll, L. Jo aktive Partnerschaft für den Aal an Mosel und Saar: Aalschutz-Initiative Rheinland-Pfalz & innogy SE, Landesamt für Umwelt Rheinland-Pfalz, 2016 [cited 2018 Mar 5]. Available from: URL: https://iam.innogy.com/-/media/ innogy/documents/ueber-innogy/innogy-Innovation-und-Technik/ aalschutzinitiative-komrimierte-pdf-version.ashx. [40] E.-M. Klopries, Ethohydraulische und hydronumerische Untersuchungen an Rechen und Kaplanturbinen als Beitrag zur Reduktion der Aalsch€ adigung an Laufwasserkraftanlagen [Dissertationsschrift]. 1. Auflage. Herzogenrath: shaker. Mitteilungen des Lehrstuhls und Instituts für Wasserbau und Wasserwirtschaft der RWTH Aachen 173 (2018). ISBN: 978-3-8440-6202-1.