Pith. sign in

REVIEW 3 major objections 4 minor 58 references

CFD-based design optimization of a 5 kW ducted hydrokinetic turbine with practical constraints

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A ducted turbine co-optimized around a generator-sized hub reaches 50% efficiency, beating the unducted baseline's 45%.

desk verdict Solid optimization study with a real design result, but the ducted-vs-freestream comparison is not apples-to-apples. read the letter →

arxiv 2411.13492 v1 pith:O2NENSVE submitted 2024-11-20 physics.flu-dyn

Jeongbin Park , Marco Mangano , Sabet Seraj , Bernardo Pacini , Yingqian Liao , Bradford G. Knight , Kartik Naik , Kevin J. Maki
show 3 more authors
Joaquim R.R.A. Martins Jing Sun Yulin Pan
This is my paper · ORCID
classification physics.flu-dyn
keywords ductedhydrokineticturbineCFD-baseddesignoptimizationadjointmethodgradient-basedpowercoefficientclass-shapetransformationpracticalconstraints
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper uses CFD-based gradient optimization to co-design the duct, blades, and hub of a 5 kW hydrokinetic turbine, subject to real-world constraints: a hub large enough to house a generator, a minimum duct thickness for manufacturability, and a rounded duct leading edge for off-design robustness. The optimizer converges to a short, thin, strongly cambered duct with an elongated hub protruding upstream, and the resulting design reaches a power coefficient of about 0.50 in RANS evaluation and 0.48–0.49 in higher-fidelity URANS re-evaluation. That is substantially better than the 0.45 of the freestream reference turbine with the same bulky hub, an advantage that holds across the tested tip-speed-ratio range. The authors take this as evidence that a ducted configuration brings a real hydrodynamic benefit even when practical packaging constraints are included.

What carries the argument

The optimization loop combines a Reynolds-averaged Navier-Stokes solver with a discrete adjoint method for exact gradients, a sequential quadratic programming optimizer, and a feature-based parametric geometry tool that lets all three components — duct, blade, hub — be controlled independently. The duct cross-section is parameterized by class-shape transformation (CST) variables, polynomial-basis coefficients that directly express the leading-edge radius of curvature, making the minimum-thickness and minimum-curvature constraints easy to impose. The blade is defined by chords and twists at nine spanwise stations, and the hub by four control points that are revolved around the axis. This machinery is what allows 37 design variables to be optimized simultaneously without the mesh-distortion and parametrization breakdowns that limited earlier work to a single component.

What would settle it

Optimize the unducted rotor with the same generator-sized hub and the same practical constraints, then evaluate both designs in the same RANS/URANS setup: if the unducted rotor's power coefficient reaches the ducted design's 0.48–0.50, the claimed hydrodynamic benefit of the duct vanishes.

Watch

Extended reading notes

Core claim

The central claim is that simultaneous gradient-based optimization of duct, blade, and hub geometry — with a generator-sized hub and manufacturable duct thickness enforced — yields a ducted turbine whose power coefficient reaches about 50% by RANS and 48–49% by URANS, outperforming the unducted reference turbine with the same hub (about 45%) across the whole range of tip-speed ratios tested. The optimized geometry is counterintuitive in several respects: the duct is thin and strongly cambered, pressed against the minimum-thickness and minimum-leading-edge-curvature constraints; the hub is elongated and protrudes ahead of the duct inlet; and the blade chords grow substantially in the mid-span. Each feature has a plausible physical role — duct camber and cone angle accelerate the flow through the rotor, the protruding hub moves the stagnation point upstream to preserve acceleration distance, and the enlarged chords extract power where the flow is strongest. The authors conclude that the ducted configuration, not just the optimization, is responsible for the efficiency gain.

Load-bearing premise

The claimed advantage over the unducted turbine assumes that the unducted reference, fitted with the same bulky hub, cannot be substantially improved by optimization; the earlier evidence for that comes from a design without a hub.

Editorial extensions

If this is right

  • A ducted turbine can reach power coefficients near 0.50 even when the hub is sized to contain a real generator, so duct augmentation does not require sacrificing practical packaging.
  • The ducted design outperforms the unducted reference turbine at every tip-speed ratio tested, not just at the design point, which matters for real current variations.
  • The optimized geometry pushes the duct to the minimum thickness and minimum leading-edge curvature allowed; these constraints become the active limit on performance, so relaxing them (with stronger materials or different leading-edge treatment) could yield further gains.
  • The thrust on the rotor is reduced while the total system thrust is increased, meaning more of the anchoring load is carried by the duct structure rather than the rotating blades.
  • The design is reproducible and the key files are provided, so the same optimization loop can be applied to other power scales and flow conditions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If a comparable optimization were applied to the unducted rotor under the same constraints, the efficiency gap could narrow; the paper's baseline is not itself optimized, and earlier optimization evidence for the freestream configuration comes from a design without a hub.
  • The active leading-edge curvature constraint is set for off-design oblique flow; the on-design sacrifice could be quantified by running the same optimization without that constraint and comparing the resulting power coefficients.
  • The elongated hub and thin duct may influence wake recovery and array spacing; the paper does not analyze wake effects, so the net benefit in a multi-turbine array remains an open question.
  • A direct experimental test of the optimized design and an equivalently optimized unducted rotor with the same hub would be the cleanest check; the paper reports that an experimental study is underway, but the results are not yet public.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper presents a gradient-based, adjoint-enabled CFD optimization of a 5 kW ducted hydrokinetic turbine, simultaneously optimizing 37 design variables describing the duct, blades, and hub under practical geometric constraints (minimum duct thickness, leading-edge radius, fixed hub cylindrical section, tip gap, and fixed projected area). The optimized design is reported to reach a power coefficient of 0.501 in RANS and 0.48–0.49 in higher-fidelity URANS, compared with 0.45 for a freestream Bahaj turbine with a comparable hub and 0.321 for the baseline ducted design. The authors argue this demonstrates the hydrodynamic benefit of the ducted configuration and validate their CFD through mesh refinement studies and comparison with experimental data for the freestream case.

Significance. If the central comparative claim is sound, this is a valuable demonstration of practical, manufacturable ducted-turbine optimization: the use of a feature-based CAD parameterization (ESP) with 37 design variables, explicit structural and packaging constraints, and independent URANS re-evaluation is a step beyond prior work. The mesh refinement studies for both the freestream validation and the optimized ducted design, the availability of key files in a public repository, and the two-solver (RANS-MRF and URANS-RS) evaluation are strengths that support the reported efficiency magnitude. The main significance risk is not in the efficiency computation itself but in the fairness of the freestream comparison used to attribute the gain to the duct.

major comments (3)
  1. [Section 4.2 and Abstract] The comparative claim that the optimized ducted turbine 'outperforms the 45% efficiency of the freestream Bahaj turbine featuring the same hub' is not a controlled comparison. The ducted design is the result of jointly optimizing the blade, hub, and duct (37 variables, Table 1), whereas the freestream comparator in Figure 7 is the unoptimized Bahaj rotor with the baseline hub. The only support for the assertion that freestream optimization would not close the gap is reference [10], which, as the authors state in Section 1, was performed without a hub. Since Section 4.2 itself acknowledges that the bulky hub is a significant perturbation and that the baseline ducted design drops to CP ≈ 0.321, part or all of the 0.45-to-0.50 margin could stem from rotor/hub re-optimization rather than from the duct. A same-design-space control is needed: for example, optimizing the freestream rotor with the same hub and constraints, or at least evaluating the ducted design with the baseline hub shape, to isolate the duct's contribution. Without such a control, the conclusion that the work 'demonstrates the hydrodynamic benefits of a ducted configuration' is not fully supported.
  2. [Abstract and Figure 7 vs. Figure 10] The phrase 'featuring the same hub' is imprecise and potentially misleading. The freestream re-evaluation in Figure 7 uses the baseline hemisphere/ellipsoid hub described in Section 2, while the optimized ducted design (Figure 10) has an elongated hub that protrudes upstream of the duct inlet. The two hubs share the same central cylindrical section (D_hub = 0.4 m, L_hub = 0.78 m), but the overall hub shapes differ substantially. The abstract and Section 4.2 should either state that only the generator-sized cylindrical section is the same, or should use a genuinely identical hub shape for the freestream comparison.
  3. [Section 4.2, Table 2, and Abstract] The headline 'up to 50% efficiency when evaluated by RANS/URANS solvers' overstates the URANS evidence. The mesh-converged URANS results in Table 2 give CP in the range 0.48–0.49, with the 19.7M-cell case at 0.480; the only value at or above 0.50 is the RANS value of 0.501. The text in Section 4.2 acknowledges this ('a value of CP up to 50% is expected'), but the abstract's wording is stronger. Reporting the efficiency as '0.48–0.50' or 'approximately 0.49–0.50' would be more consistent with the presented data.
minor comments (4)
  1. [Section 4.1, paragraph 3] The text states 'the thickness constraint of t_duct = 0.0014 m is also active', but Table 1 specifies the constraint as t_duct ≥ 0.014 m. This appears to be a typographical error and should be corrected to 0.014 m.
  2. [Figure 9 and Section 4.1] The optimization stopping criterion is described as 'CP plateaus, despite further mesh adjustments' and is acknowledged as 'not numerically rigorous'. This is a reasonable engineering choice, but the statement that the SNOPT optimality metric decreased to 10^-2.28 should be interpreted with caution; a brief quantitative statement of the plateau tolerance would aid reproducibility.
  3. [Section 2, Eq. (1) and surrounding text] The definition of the reference area A as the maximum projection area of the duct (at the exit) is clear, but the paper should explicitly note that for the freestream Bahaj turbine A equals the rotor swept area, so that the comparison uses the same reference area but not the same physical rotor diameter. The paper does state this implicitly via the diameter calculation, but an explicit sentence would remove ambiguity.
  4. [Section 4.2, paragraph 2] The sentence 'The optimization of the freestream turbine does not provide a significant performance improvement [10]' is presented without acknowledging that reference [10] did not include a hub. The caveat should be stated in the same sentence or the sentence should be removed until a hub-inclusive freestream optimization is performed.

Circularity Check

1 steps flagged · score 3.0 of 10

The efficiency numbers are CFD-computed, not fitted, so there is no derivation-equals-input circularity; but the central 0.50-vs-0.45 comparison rests the freestream side's near-optimality on self-citation [10], a no-hub study applied to the bulky-hub case, leaving the duct-benefit claim only partially de-confounded.

  1. self citation load bearing [Section 4.2 (Re-evaluation); abstract comparative claim]
    "The freestream turbine with the bulky baseline hub achieved a CP of about 45% according to the RANS and URANS results shown in Figure 7. The optimization of the freestream turbine does not provide a significant performance improvement [10], but adding a duct improves the turbine performance even when the hub is present."

    The de-confounding premise — that the unoptimized freestream's 0.45 is its achievable maximum, so the margin is due to the duct — rests only on the authors' own prior work [10], whose formulation 'excluded a hub' (Sec. 1). This paper makes the bulky hub central (D_hub=0.4 m, L_hub=0.78 m, Sec. 2) and reports the hub drops its baseline ducted CP from the ~0.54 class of [10] to 0.321 (Fig. 8); Sec. 4.1 adds that 'the baseline blade design not considering hub effects [10]' forced large blade changes.

full rationale

This paper's central efficiency numbers are direct CFD outputs, not parameters fitted to the claim: the optimized design reaches CP = 0.501 in the RANS-MRF optimization loop and 0.48-0.49 in URANS-RS re-evaluation, and the comparators (0.45 freestream Bahaj with baseline hub, 0.321 baseline ducted, CT values) are computed in this paper. The URANS re-evaluation is genuinely independent of the optimization (different solver, OpenFOAM vs. DAFoam; different turbulence model, SST vs. SA; different mesh, snappyHexMesh vs. Pointwise; different rotor model, sliding vs. MRF), so the fitted-input-called-prediction pattern does not apply. The sizing convention (A = 5.23 m2 from Bahaj's CP = 0.46) is a stated reference-area choice, and both sides of the comparison use the same A, so no self-definitional loop is present. The one load-bearing step that is not self-contained is the assertion that optimizing the freestream turbine would not close the gap, which is imported from the authors' prior work [10]; that work explicitly 'excluded a hub' (Sec. 1), while this paper's comparison concerns the bulky-hub case and reports the hub is a large perturbation (its own baseline ducted CP drops to 0.321, Fig. 8; 'the baseline blade design not considering hub effects [10]'). Because the freestream side is never optimized in a same-design-space control, the central interpretation 'hydrodynamic benefits of a ducted configuration' relies on a self-citation whose stated assumptions exclude the target condition. Additional caveats that the paper itself states temper the claim without constituting circularity: the optimization stop criterion is 'not numerically rigorous' (Sec. 4.1); the URANS mesh sweep spans 0.450-0.490 for the optimized design, so the 0.48-0.49 vs. 0.45 margin compares with the ducted side's own mesh scatter; and 'the uncertainty in CFD simulations due to mesh and modeling errors must be taken into account' (Sec. 4.2). The abstract's 'same hub' is only the fixed generator cylinder, since the optimized hub is elongated and protrudes upstream of the duct inlet (Sec. 4.1). Verdict: no derivation-equals-input circularity; one partially load-bearing self-citation with a scope mismatch; score 3.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The paper's central claim depends on several engineering design choices and modeling assumptions that are disclosed but not independently validated: the reference area convention, the turbulence models, the practical constraints, and the assumption that the freestream baseline cannot be optimized further. No new physical entities are introduced. The listed numbers are problem inputs chosen by the authors, not fitted to produce the claimed efficiency.

free parameters (7)
  • Reference area A = 5.23 m^2
    Set by the requirement of 5 kW output with CP=0.46 (Bahaj) at U=1.7 m/s; used as the common denominator in CP for ducted and freestream turbines.
  • Inflow velocity U_inf = 1.7 m/s
    Site condition from Mississippi River data [48]; determines the Reynolds number.
  • Rotation rate Omega = 8.26 rad/s
    Corresponds to the peak CP of the baseline ducted design at lambda=4.86; fixed during the optimization.
  • Minimum duct thickness t_duct = 0.014 m
    Manufacturing and structural constraint from 6061 aluminum yield strength; active in the optimized design.
  • Minimum leading-edge curvature radius rho_kappa = 0.0015 m
    Chosen for off-design oblique flow robustness; active in the optimized design.
  • Hub cylindrical section dimensions = 0.4 m diameter x 0.78 m length
    Fixed to house the selected 5 kW generator.
  • Tip gap h = 0.05 m
    Chosen design constraint between duct and blade tips.
assumptions (4)
  • domain assumption RANS with Spalart-Allmaras and k-omega SST turbulence models accurately represent the turbulent flow around ducted turbines at Re approximately 4e6
    The optimization and re-evaluations use these RANS closures; no ducted-turbine experiment is used for validation in this paper.
  • domain assumption The maximum projected area of the duct (exit area) is the appropriate reference area for defining CP
    This convention makes the ducted and freestream turbines comparable by diameter; using rotor area would give a different, higher CP for the ducted turbine.
  • domain assumption The freestream Bahaj turbine with the bulky hub would not benefit significantly from optimization
    The paper relies on [10] for this, but [10] did not include a hub; the bulky hub changes the flow and may alter the optimization potential.
  • domain assumption Uniform steady inflow with a fully turbulent boundary layer
    Inflow is set to the river site condition; waves, turbulence, and oblique flow are not modeled.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CFD-based design optimization of a 5 kW ducted hydrokinetic turbine with practical constraints." pith.science (2026). https://pith.science/paper/O2NENSVE

@misc{pith2026241113492,
  author       = {Pith},
  title        = {Pith review of: CFD-based design optimization of a 5 kW ducted hydrokinetic turbine with practical constraints},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O2NENSVE}},
  note         = {Machine review of arXiv:2411.13492}
}
read the original abstract

Ducted hydrokinetic turbines enhance energy-harvesting efficiency by better conditioning the flow to the blades, which may yield higher power output than conventional freestream turbines for the same reference area. In this work, we present a ducted hydrokinetic turbine design obtained by simultaneously optimizing the duct, blade, and hub geometries. Our optimization framework combines a CFD solver, an adjoint solver, and a gradient-based optimizer to efficiently explore a large design space, together with a feature-based parameterization method to handle the complex geometry. Practical geometrical constraints ensure the manufacturability of the duct in terms of a minimum thickness and the housing of a 5 kW generator within the hub. The optimization converges to a short, thin duct with a rounded leading edge and an elongated hub protruding the duct inlet. The optimized ducted turbine achieves up to 50% efficiency when evaluated by RANS/URANS solvers despite a bulky hub, outperforming the 45% efficiency of the freestream Bahaj turbine featuring the same hub. This work showcases the effectiveness of CFD-based optimization in advancing ducted turbine designs and demonstrates the hydrodynamic benefits of a ducted configuration, paving the way for future research and real-world applications.

Figures

Figures reproduced from arXiv: 2411.13492 by the authors.

Figure 1
Figure 1. The ducted turbine has a projected area 𝐴 and is subject to inflow 𝑈∞. characterized by the power coefficient 𝐶𝑃 = 𝑃 𝑃avail , (1) where 𝑃 = 𝜏Ω is the mechanical power, with 𝜏 the turbine’s torque and Ω its rotation speed. The conversion of the turbine shaft mechanical power into electrical power through the generator system is subject to losses as follows: 𝑃𝑒 = 𝑃 ⋅ 𝜂, (2) where 𝜂 is the electromechanical efficiency.… view at source ↗
Figure 2
Figure 2. Design variables for (a) duct, (b) blade, and (c) hub. The baseline design is shown in black, with blue and green illustrating two examples of deformed configurations. the front and rear hub ends move axially, primarily driving the hub length. The other two points control the hub front and rear curvature by moving in the radial direction, as shown in figure 2c. Once the locations of the 4 points are given, two cubic… view at source ↗
Figure 3
Figure 3. Flowchart of the optimization and CFD simulation processes. the circular and axisymmetric shape. These limitations become particularly problematic as multiple tightly spaced components require independent and precise control. To overcome these limitations, we use ESP [37] in this work. The entire ducted turbine geometry is generated and parameterized using ESP, with the design variables listed in [PITH_FULL_IMAGE:f… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: ESP model of the ducted turbine. (a) The turbine geometry is created by combining three blades and a hub. The blades are generated by blending NACA 63-8xx hydrofoils along the span, with rotation and scaling applied according to the desired twist and chord distribution…
Figure 5
Figure 5. Figure 5: The mesh generated by using Pointwise predominantly composed of tetrahedral cells. The domain of the mesh is 10.1𝐷 × 10.1𝐷 × 19.4𝐷. The mesh is refined around the ducted turbine geometry, and the near-surface regions are further refined using 15 prism layers with an ex…
Figure 6
Figure 6. Figure 6: The mesh generated using snappyHexMesh predominantly contains hexahedral cells. The domain of the mesh is 12.3𝐷 × 12.3𝐷 × 20.9𝐷. We add a refinement region around the ducted turbine with a size of 3𝐷 × 3𝐷 × 8.2𝐷, with prism layers that further resolve the near-wall reg…
Figure 7
Figure 7. Figure 7: Comparison between RANS (left column) and URANS solvers (right column) with varying mesh resolutions against experimental results for the Bahaj turbine [35] [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Power coefficient 𝐶𝑃 of the baseline ducted turbine as a function of 𝜆 evaluated using RANS-MRF (◦) and URANS-RS (+) with varying mesh resolutions. The right panel provides a close-up view of the results at 𝜆 = 4.86. 4.1. Optimization We solve the optimization problem …
Figure 9
Figure 9. Figure 9: Power coefficient 𝐶𝑃 versus iteration during the optimization process. Red crosses indicate the points where re-meshing becomes necessary due to significant mesh distortion [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Comparison of the optimized duct and hub with the baseline design [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Comparison of the optimized blade with the baseline design. constraint of 𝑡duct = 0.0014 m is also active away from the leading edge, with a smooth transition to thinner airfoil sections enabled by the ESP parametrization. The optimization favors a thin-walled duct ov…
Figure 12
Figure 12. Figure 12: Power coefficient 𝐶𝑃 as a function of time during the URANS-RS simulation using the mesh with 13.4 million cells (left) and the corresponding flow field, illustrating 3D vortex structures using Q-criterion (𝑄 = 0.2) at the stationary state of performance colored by no…
Figure 13
Figure 13. Figure 13: plots the values of 𝐶𝑃 obtained with the URANS solver using the 13.4M cell mesh for a range of 3.86 ≤ 𝜆 ≤ 6.36. The ducted turbine outperforms the 5 kW Bahaj turbine over the whole 𝜆 range despite considering a fixed 𝜆 = 4.86 during the optimization. This further supp…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

58 extracted references · 49 canonical work pages

  1. [10]

    J. Park, B. G. Knight, Y. Liao, M. Mangano, B. Pacini, K. J. Maki, J. R. Martins, J. Sun, Y. Pan, CFD-based design optimization of ducted hydrokinetic turbines, Scientific Reports 13 (2023) 17968

    work page 2023

  2. [1]

    Bahaj, L

    A. Bahaj, L. E. Myers, Fundamentals applicable to the utilisation of marine current turbines for energy production, Renewable energy 28 (2003) 2205–2211

    work page 2003

  3. [2]

    P.T.Jacobson,T.M.Ravens,K.W.Cunningham,G.Scott,AssessmentandMappingoftheRiverineHydrokineticResourceintheContinental United States, Technical Report, Electric Power Research Institute, 2012

    work page 2012

  4. [3]

    Ibrahim, M

    W. Ibrahim, M. Mohamed, R. Ismail, P. Leung, W. Xing, A. Shah, Hydrokinetic energy harnessing technologies: A review, Energy Reports 7 (2021)

    work page 2021

  5. [4]

    Kirby, S

    K. Kirby, S. Ferguson, C. Rennie, I. Nistor, J. Cousineau, Assessments of available riverine hydrokinetic energy: a review, Canadian Journal of Civil Engineering 49 (2022) 839–854

    work page 2022

  6. [5]

    V. G. Nago, I. F. S. dos Santos, M. J. Gbedjinou, J. H. R. Mensah, G. L. Tiago Filho, R. G. R. Camacho, R. M. Barros, A literature review on wake dissipation length of hydrokinetic turbines as a guide for turbine array configuration, Ocean Engineering 259 (2022) 111863

    work page 2022

  7. [6]

    M.I.Yuce,A.Muratoglu, Hydrokineticenergyconversionsystems:Atechnologystatusreview, RenewableandSustainableEnergyReviews 43 (2015) 72–82

    work page 2015

  8. [7]

    L. Lago, F. Ponta, L. Chen, Advances and trends in hydrokinetic turbine systems, Energy for sustainable development 14 (2010) 287–296

    work page 2010

Show all 58 references
  1. [8]

    M. M. Nunes, A. C. B. Junior, T. F. Oliveira, Systematic review of diffuser-augmented horizontal-axis turbines, Renewable and Sustainable Energy Reviews 133 (2020) 110075

    2020

  2. [9]

    R. Khan, A. Kumar, Performance enhancement of hydrokinetic turbine using augmentation techniques: a review, International Journal of Green Energy 21 (2024) 1667–1694

    2024

  3. [11]

    Lawn, Optimization of the power output from ducted turbines, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 217 (2003) 107–117

    C. Lawn, Optimization of the power output from ducted turbines, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 217 (2003) 107–117

    2003

  4. [12]

    G.J.VanBussel, Thescienceofmakingmoretorquefromwind:Diffuserexperimentsandtheoryrevisited., in:JournalofPhysics:Conference Series, volume 75, IOP Publishing, 2007, p. 012010. doi:10.1088/1742-6596/75/1/012010

    2007 doi

  5. [13]

    P. M. Jamieson, Beating betz: Energy extraction limits in a constrained flow field, Journal of Solar Energy Engineering 131 (2009). doi:10.1115/1.3139143

    2009 doi

  6. [14]

    M. J. Werle, W. M. Presz Jr, Ducted wind/water turbines and propellers revisited, Journal of Propulsion and Power 24 (2008) 1146–1150

    2008

  7. [15]

    Bontempo, M

    R. Bontempo, M. Manna, On the potential of the ideal diffuser augmented wind turbine: an investigation by means of a momentum theory approach and of a free-wake ring-vortex actuator disk model, Energy Conversion and Management 213 (2020) 112794. doi:10.1016/j. enconman.2020.112794

    2020

  8. [16]

    Scherillo, U

    F. Scherillo, U. Maisto, G. Troise, D. Coiro, S. Miranda, Numerical and experimental analysis of a shrouded hydroturbine, in: 2011 International Conference on Clean Electrical Power (ICCEP), IEEE, 2011, pp. 216–222

    2011

  9. [17]

    Shahsavarifard, E

    M. Shahsavarifard, E. L. Bibeau, V. Chatoorgoon, Effect of shroud on the performance of horizontal axis hydrokinetic turbines, Ocean Engineering 96 (2015) 215–225

    2015

  10. [18]

    S. Z. Roshan, S. Alimirzazadeh, M. Rad, Rans simulations of the stepped duct effect on the performance of ducted wind turbine, Journal of Wind Engineering and Industrial Aerodynamics 145 (2015) 270–279

    2015

  11. [19]

    W. Shi, D. Wang, M. Atlar, B. Guo, K.-c. Seo, Optimal design of a thin-wall diffuser for performance improvement of a tidal energy system for an auv, Ocean Engineering 108 (2015) 1–9

    2015

  12. [20]

    D.P.Coiro,E.Daniele,P.DellaVecchia, DiffusershapeoptimizationforGEM,atetheredsystembasedontwohorizontalaxishydroturbines, International Journal of Marine Energy 13 (2016) 169–179

    2016

  13. [21]

    Song, W.-Q

    K. Song, W.-Q. Wang, Y. Yan, Numerical and experimental analysis of a diffuser-augmented micro-hydro turbine, Ocean Engineering 171 (2019) 590–602

    2019

  14. [22]

    M. M. Nunes, R. C. Mendes, T. F. Oliveira, A. C. B. Junior, An experimental study on the diffuser-enhanced propeller hydrokinetic turbines, Renewable energy 133 (2019) 840–848

    2019

  15. [23]

    D.L.Gaden,E.L.Bibeau, Anumericalinvestigationintotheeffectofdiffusersontheperformanceofhydrokineticturbinesusingavalidated momentum source turbine model, Renewable Energy 35 (2010) 1152–1158

    2010

  16. [24]

    M.Shives,C.Crawford, Developinganempiricalmodelforductedtidalturbineperformanceusingnumericalsimulationresults, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 226 (2012) 112–125

    2012

  17. [25]

    C. F. Fleming, R. H. Willden, Analysis of bi-directional ducted tidal turbine performance, International Journal of Marine Energy 16 (2016) 162–173

    2016

  18. [26]

    Belloni, R

    C. Belloni, R. Willden, G. Houlsby, An investigation of ducted and open-centre tidal turbines employing CFD-embedded BEM, Renewable Energy 108 (2017) 622–634

    2017

  19. [27]

    S.Allsop,C.Peyrard,P.R.Thies,E.Boulougouris,G.P.Harrison, Hydrodynamicanalysisofaducted,opencentretidalstreamturbineusing blade element momentum theory, Ocean Engineering 141 (2017) 531–542

    2017

  20. [28]

    P. A. Silva, D. A. R. Vaz, V. Britto, T. F. de Oliveira, J. R. Vaz, A. C. B. Junior, A new approach for the design of diffuser-augmented hydro turbines using the blade element momentum, Energy Conversion and Management 165 (2018) 801–814

    2018

  21. [29]

    T.J.Rezek,R.G.Camacho,N.Manzanares-Filho, Anovelmethodologyforthedesignofdiffuser-augmentedhydrokineticrotors, Renewable Energy 210 (2023) 524–539

    2023

  22. [30]

    G.Tampier,C.Troncoso,F.Zilic, Numericalanalysisofadiffuser-augmentedhydrokineticturbine, Oceanengineering145(2017)138–147

    2017

  23. [31]

    B.Knight,R.Freda,Y.L.Young,K.Maki, Couplingnumericalmethodsandanalyticalmodelsforductedturbinestoevaluatedesigns, Journal of Marine Science and Engineering 6 (2018) 43

    2018

  24. [32]

    Barbarić, Z

    M. Barbarić, Z. Guzović, Investigation of the possibilities to improve hydrodynamic performances of micro-hydrokinetic turbines, Energies 13 (2020) 4560. Park et al.:Preprint submitted to Elsevier Page 15 of 16 CFD-based design optimization of a5 kW ducted hydrokinetic turbine...

    2020

  25. [33]

    T.Rezek,R.Camacho,N.ManzanaresFilho,E.Limacher, Designofahydrokineticturbinediffuserbasedonoptimizationandcomputational fluid dynamics, Applied Ocean Research 107 (2021) 102484

    2021

  26. [34]

    J. R. R. A. Martins, A. Ning, Engineering Design Optimization, Cambridge University Press, Cambridge, UK, 2022. URL:https: //mdobook.github.io. doi:10.1017/9781108980647

    2022 doi

  27. [35]

    Bahaj, A

    A. Bahaj, A. Molland, J. Chaplin, W. Batten, Power and thrust measurements of marine current turbines under various hydrodynamic flow conditions in a cavitation tunnel and a towing tank, Renewable energy 32 (2007) 407–426

    2007

  28. [36]

    D.A.doRioVaz,J.R.Vaz,P.A.Silva, Anapproachfortheoptimizationofdiffuser-augmentedhydrokineticbladesfreeofcavitation, Energy for Sustainable Development 45 (2018) 142–149

    2018

  29. [37]

    Haimes, J

    R. Haimes, J. Dannenhoffer, The Engineering Sketch Pad: A solid-modeling, feature-based, web-enabled system for building parametric geometry, in: 21st AIAA Computational Fluid Dynamics Conference, Fluid Dynamics and Co-located Conferences, American Institute of Aeronautics and...

    2013 doi

  30. [38]

    H. M. Hajdik, A. Yildirim, J. R. R. A. Martins, Aerodynamic shape optimization with CAD-based geometric parameterization, in: AIAA SciTech Forum, National Harbor, MD, 2023. doi:10.2514/6.2023-0726

    2023 doi

  31. [39]

    H. M. Hajdik, B. Pacini, A. Yildirim, B. J. Brelje, J. R. R. A. Martins, Combined systems packaging and aerodynamic shape optimization of a full aircraft configuration, in: AIAA Aviation Forum, San Diego, CA, 2023. doi:10.2514/6.2023-3589

    2023 doi

  32. [40]

    T. W. Sederberg, S. R. Parry, Free-form deformation of solid geometric models, SIGGRAPH Comput. Graph. 20 (1986) 151–160. doi:10.1145/15886.15903

    1986

  33. [41]

    G.K.Kenway,G.J.Kennedy,J.R.R.A.Martins,ACAD-freeapproachtohigh-fidelityaerostructuraloptimization,in:Proceedingsofthe13th AIAA/ISSMOMultidisciplinaryAnalysisOptimizationConference,AIAA2010-9231,FortWorth,TX,2010.doi: 10.2514/6.2010-9231

    2010 doi

  34. [42]

    Dannenhoffer, An overview of the Engineering Sketch Pad, in: AIAA SCITECH 2024 Forum, 2024, p

    J. Dannenhoffer, An overview of the Engineering Sketch Pad, in: AIAA SCITECH 2024 Forum, 2024, p. 1315

    2024

  35. [43]

    doi:10.5194/wes-4-163-2019

    M.H.A.Madsen,F.Zahle,N.N.Sørensen,J.R.R.A.Martins, Multipointhigh-fidelityCFD-basedaerodynamicshapeoptimizationofa10 MW wind turbine, Wind Energy Sciences 4 (2019) 163–192. doi:10.5194/wes-4-163-2019

    2019 doi

  36. [44]

    Mangano, J

    M. Mangano, J. R. R. A. Martins, Multipoint aerodynamic shape optimization for subsonic and supersonic regimes, Journal of Aircraft 58 (2021) 650–662. doi:10.2514/1.C036216

    2021 doi

  37. [45]

    P. He, C. A. Mader, J. R. R. A. Martins, K. J. Maki, DAFoam: An open-source adjoint framework for multidisciplinary design optimization with OpenFOAM, AIAA Journal 58 (2020). doi:10.2514/1.J058853

    2020 doi

  38. [46]

    doi:10.1137/S0036144504446096

    P.E.Gill,W.Murray,M.A.Saunders, SNOPT:AnSQPalgorithmforlarge-scaleconstrainedoptimization, SIAMReview47(2005)99–131. doi:10.1137/S0036144504446096

    2005 doi

  39. [47]

    Jasak, A

    H. Jasak, A. Jemcov, Z. Tukovic, et al., OpenFOAM: A C++ library for complex physics simulations, in: International workshop on coupled methods in numerical dynamics, volume 1000, Dubrovnik, Croatia), 2007, pp. 1–20

    2007

  40. [48]

    https://waterdata.usgs.gov/ monitoring-location/07374000/

    USGS water data for Mississippi River at Baton Rouge, LA, Accessed: 10.28.2024. https://waterdata.usgs.gov/ monitoring-location/07374000/

    2024

  41. [49]

    Tariquzzaman, P

    M. Tariquzzaman, P. Li, S. J. Barton, A. P. Thurlbeck, T. Kilgore, T. K. Brekken, Y. Cao, Multi-physics and multi-timescale modeling of hydrokinetic turbine energy conversion system, IEEE Journal of Emerging and Selected Topics in Power Electronics (2024)

    2024

  42. [50]

    Fundamental

    B. Kulfan, J. Bussoletti, "Fundamental" parametric geometry representations for aircraft component shapes, in: 11th AIAA/ISSMO multidisciplinary analysis and optimization conference, 2006, p. 6948

    2006

  43. [51]

    Venters, B

    R. Venters, B. T. Helenbrook, K. D. Visser, Ducted wind turbine optimization, Journal of Solar Energy Engineering 140 (2018) 011005

    2018

  44. [52]

    H. M. Hajdik, A. Yildirim, N. Wu, B. J. Brelje, S. Seraj, M. Mangano, J. L. Anibal, E. Jonsson, E. J. Adler, C. A. Mader, G. K. W. Kenway, J. R. R. A. Martins, pyGeo: A geometry package for multidisciplinary design optimization, Journal of Open Source Software 8 (2023) 5319. d...

    2023 doi

  45. [53]

    Spalart, S

    P. Spalart, S. Allmaras, A one-equation turbulence model for aerodynamic flows, in: 30th Aerospace Sciences Meeting and Exhibit, 1992. doi:10.2514/6.1992-439

    1992 doi

  46. [54]

    E. Luke, E. Collins, E. Blades, A fast mesh deformation method using explicit interpolation, Journal of Computational Physics 231 (2012) 586–601. doi:10.1016/j.jcp.2011.09.021

    2012 doi

  47. [55]

    Secco, G

    N. Secco, G. K. W. Kenway, P. He, C. A. Mader, J. R. R. A. Martins, Efficient mesh generation and deformation for aerodynamic shape optimization, AIAA Journal 59 (2021) 1151–1168. doi:10.2514/1.J059491

    2021 doi

  48. [56]

    F. R. Menter, M. Kuntz, R. Langtry, et al., Ten years of industrial experience with the SST turbulence model, Turbulence, heat and mass transfer 4 (2003) 625–632

    2003

  49. [57]

    doi:10.7302/2703

    B.Brelje,MultidisciplinaryDesignOptimizationofElectricAircraftConsideringSystemsModelingandPackaging,Ph.D.thesis,University of Michigan, Ann Arbor, MI, 2021. doi:10.7302/2703

    2021 doi

  50. [58]

    K. P. Naik, M. Mangano, B. Knight, S. Seraj, Y. Liao, J. Park, Y. Pan, J. Martins, K. Maki, J. Sun, Experimental performance comparison of ducted and freestream hydrokinetic turbines, Available at SSRN 4973480 (2024). Park et al.:Preprint submitted to Elsevier Page 16 of 16

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.