Muutke küpsiste eelistusi

E-raamat: Wind Energy Modeling and Simulation: Atmosphere and plant, Volume 1

Edited by (National Renewable Energy Laboratory, National Wind Technology Center, USA)
  • Formaat: EPUB+DRM
  • Sari: Energy Engineering
  • Ilmumisaeg: 03-Dec-2019
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781785615221
Teised raamatud teemal:
  • Formaat - EPUB+DRM
  • Hind: 214,50 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Wind Energy Modeling and Simulation: Atmosphere and plant, Volume 1Suurem pilt
  • Formaat: EPUB+DRM
  • Sari: Energy Engineering
  • Ilmumisaeg: 03-Dec-2019
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781785615221
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

In order to optimise the yield of wind power from existing and future wind plants, the entire breadth of the system of a plant, from the wind field to the turbine components, needs to be modelled in the design process. The modelling and simulation approaches used in each subsystem as well as the system-wide solution methods to optimize across subsystem boundaries are described in this reference. Chapters are written by technical experts in each field, describing the current state of the art in modelling and simulation for wind plant design. This comprehensive, two-volume research reference will provide long-lasting insight into the methods that will need to be developed for the technology to advance into its next generation.



Volume 1 covers the computing challenges in full turbine modelling, then discusses bridging scales in the atmosphere and turbulence modelling, wind forecasting, wind plant flow, and plant level controller design.
Preface xv
List of acronyms
xxvii
1 Looking forward: the promise and challenge of exascale computing
1(22)
Michael C. Robinson
Michael A. Sprague
1.1 Introduction
1(5)
1.1.1 Future wind plant technology
2(1)
1.1.2 Physical scales driving HFM and HPC
2(1)
1.1.3 Turbine technology changes requiring HFM and HPC
3(2)
1.1.4 Wind plant performance
5(1)
1.2 Mathematical and numerical modelling pathways
6(6)
1.3 Challenges at petascale and the need for exascale
12(2)
1.4 The challenge of exascale computing
14(4)
1.5 Concluding remarks
18(5)
Acknowledgements
19(1)
References
19(4)
2 Blade-resolved modeling with fluid-structure interaction
23(42)
Ganesh Vijayakumar
James G. Brasseur
2.1 The extraordinary range of length and time scales relevant to wind-turbine operation
26(10)
2.1.1 Impacts of atmospheric "microscale" turbulence
26(5)
2.1.2 The rotor and blade-boundary-layer response length and time scales
31(3)
2.1.3 The wake response length and time scales
34(1)
2.1.4 Influences from atmospheric mesoscales and related weather events
34(1)
2.1.5 Concluding discussion
35(1)
2.2 Essential numerical and modeling elements in blade-resolved simulation of wind turbines
36(17)
2.2.1 CAD model and mesh generation
36(3)
2.2.2 CFD solver
39(5)
2.2.3 Turbulence modeling
44(7)
2.2.4 Fluid-structure interaction
51(2)
2.3 Practical issues in performing blade boundary-layer-resolved simulations
53(2)
2.3.1 Mesh generation
53(1)
2.3.2 Mesh quality
53(1)
2.3.3 Convergence and time step
53(1)
2.3.4 Verification
54(1)
2.3.5 Validation
55(1)
2.4 Conclusions and challenges for future advancement in the state-of-the-art
55(10)
Acknowledgments
58(1)
References
58(7)
3 Mesoscale modeling of the atmosphere
65(52)
Sue Ellen Haupt
Branko Kosovic
Jared A. Lee
Pedro Jimenez
3.1 Introduction to meteorology for wind energy modeling
65(6)
3.1.1 Forces and the general circulation of the atmosphere
65(2)
3.1.2 Scales and phenomena in the atmosphere
67(2)
3.1.3 Atmospheric energetics
69(2)
3.1.4 The chaotic nature of atmospheric flow
71(1)
3.2 Basics of atmospheric modeling
71(12)
3.2.1 Historical perspective
71(2)
3.2.2 Governing equations for flows in the atmosphere
73(1)
3.2.3 Numerical resolution requirements
74(1)
3.2.4 Reynolds averaged Navier-Stokes simulation methodology
75(5)
3.2.5 Discretizations
80(1)
3.2.6 Forcing physics and parameterizations
80(3)
3.3 Initial conditions and data assimilation
83(6)
3.3.1 Nudging
83(2)
3.3.2 Variational DA
85(1)
3.3.3 Ensemble Kalman filters
86(1)
3.3.4 EnVar and hybrid DA
87(2)
3.4 Boundary conditions
89(3)
3.4.1 Forcing from global models
89(1)
3.4.2 Top boundary
89(1)
3.4.3 Bottom boundary
89(3)
3.4.4 Coupled models
92(1)
3.5 Using NWP for wind power
92(7)
3.5.1 Resource assessment
93(1)
3.5.2 Forecasting
94(1)
3.5.3 Turbine wake parameterization
94(1)
3.5.4 Postprocessing
95(1)
3.5.5 Assessment
96(3)
3.6 Uncertainty quantification
99(1)
3.6.1 Quantifying parametric uncertainty
99(1)
3.6.2 Quantifying structural uncertainty---ensemble methods
99(1)
3.6.3 Calibrating ensembles
100(1)
3.6.4 Analog ensembles
100(1)
3.7 Looking ahead
100(3)
3.7.1 Storm-scale prediction
101(1)
3.7.2 Scale-aware models
101(1)
3.7.3 Blended global/mesoscale models
101(1)
3.7.4 Seasonal to subseasonal prediction
101(1)
3.7.5 Regime-dependent corrections
102(1)
3.8 Summary and conclusions
103(14)
References
103(14)
4 Mesoscale to microscale coupling for high-fidelity wind plant simulation
117(66)
Jeffrey D. Mirocha
4.1 Introduction
117(3)
4.1.1 Overview of atmospheric simulation at meso and microscales
118(2)
4.2 Large-eddy simulation of the atmospheric boundary layer
120(18)
4.2.1 ABLLES setup
122(6)
4.2.2 LES assessment
128(5)
4.2.3 Unsteady conditions
133(2)
4.2.4 Stable conditions
135(3)
4.3 Enabling multiscale simulation
138(17)
4.3.1 Methods to extend the applicability of periodic LES
138(2)
4.3.2 Coupling LES to mesoscale model output at lateral boundaries
140(12)
4.3.3 Online versus offline coupled simulations
152(3)
4.4 Additional challenges facing high-fidelity multiscale simulation
155(28)
4.4.1 LES SFS models
155(7)
4.4.2 Flow transition at coarse-to-fine LES refinement
162(2)
4.4.3 Bottom boundary condition
164(3)
4.4.4 Data assimilation
167(3)
References
170(13)
5 Atmospheric turbulence modelling, synthesis, and simulation
183(34)
Jacob Berg
Mark Kelly
5.1 Introduction
183(2)
5.1.1 Notation and ensemble averaging
183(1)
5.1.2 Defining the notion of turbulence simulations
184(1)
5.2 Simulating turbulence for wind turbine applications
185(1)
5.3 Turbulence in the atmospheric boundary layer
186(6)
5.3.1 Surface-layer scaling and Monin-Obukhov similarity theory
187(4)
5.3.2 Above the surface layer: typical wind turbine rotor heights
191(1)
5.4 Which characteristics of turbulence affect wind turbines?
192(2)
5.5 Synthetic turbulence and standard industrial approach
194(11)
5.5.1 Statistical attempts
194(1)
5.5.2 Standard spectral models
194(8)
5.5.3 Extensions of the spectral-tensor model
202(3)
5.6 Large eddy simulation
205(6)
5.6.1 The fundamentals
205(2)
5.6.2 SGS models
207(2)
5.6.3 Numerical approach
209(2)
5.7 Final remarks
211(6)
References
211(6)
6 Modeling and simulation of wind-farm flows
217(56)
Matthew J. Churchfield
Patrick J. Moriarty
6.1 Introduction
217(2)
6.2 Why simulate the flow through wind plants?
219(5)
6.2.1 Improved physical understanding
220(2)
6.2.2 Design
222(1)
6.2.3 Wind-farm control
223(1)
6.2.4 Special cases of interest and forensic analysis
223(1)
6.2.5 Design of experiments
223(1)
6.3 Simulation approaches
224(32)
6.3.1 Noncomputational-fluid-dynamics-based approaches
224(14)
6.3.2 Computational-fluid-dynamics-based approaches
238(18)
6.4 Validation efforts
256(3)
6.5 Future development
259(14)
Acknowledgment
261(1)
References
261(12)
7 Wind-plant-controller design
273(28)
Bart Doekemeijer
Sjoerd Boersma
Jennifer King
Paul Fleming
Jan-Willem van Wingerden
7.1 Introduction
273(4)
7.1.1 Structure of the chapter
273(1)
7.1.2 Current practice in wind farm operation
274(1)
7.1.3 Degrees of freedom in the wind farm control problem
275(1)
7.1.4 Objectives of wind farm control
276(1)
7.2 A classification of wind farm control algorithms
277(3)
7.2.1 Current practice; greedy operation
277(1)
7.2.2 Open-loop model-based controller synthesis
278(1)
7.2.3 Closed-loop model-based controller synthesis
279(1)
7.2.4 Closed-loop model-free controller synthesis
279(1)
7.3 Control-oriented modeling
280(2)
7.3.1 Steady-state surrogate models
280(1)
7.3.2 Control-oriented dynamical surrogate models
281(1)
7.4 Examples
282(8)
7.4.1 Steady-state wind farm model: FLORIS
282(4)
7.4.2 Dynamical wind farm model: WFSim
286(4)
7.5 Software architecture
290(4)
7.5.1 Centralized vs. distributed control
290(3)
7.5.2 Communication with other simulation submodels
293(1)
7.6 Conclusion
294(7)
Acknowledgment
295(1)
References
296(5)
8 Forecasting wind power production for grid operations
301(46)
John W. Zack
8.1 The role of wind-power forecasting
301(1)
8.2 Sense: gathering and ingestion of predictive information
302(4)
8.2.1 Area of influence
303(1)
8.2.2 Observation targeting
304(2)
8.3 Model: translating predictive information into a forecast
306(26)
8.3.1 Physics-based techniques
306(4)
8.3.2 Statistical approaches
310(19)
8.3.3 Power output models
329(1)
8.3.4 Integrated forecast system
330(2)
8.4 Communicate: inform the user for decision-making
332(2)
8.4.1 Deterministic versus probabilistic
332(1)
8.4.2 Time series versus event-based
333(1)
8.5 Assess: evaluation of forecast performance
334(13)
References
344(3)
9 Cost of wind energy modeling
347(30)
M. Maureen Hand
Volker Berkhout
Paul Schwabe
David Weir
Ryan Wiser
9.1 Introduction
347(2)
9.2 Levelized cost of energy (LCOE)
349(1)
9.3 Overview of cost of energy modeling
350(3)
9.4 Modeling investment costs
353(2)
9.5 Modeling energy production
355(3)
9.6 Modeling operational expenditures
358(4)
9.7 Modeling cost of capital
362(3)
9.8 Calculating cost of energy
365(1)
9.9 Estimating future cost of wind energy
366(1)
9.10 Considering the value of wind energy
367(4)
9.11 Conclusion
371(6)
Acknowledgment
371(1)
References
371(6)
Index 377
Paul Veers is the Chief Engineer at NREL's National Wind Technology Center. He has led research on wind energy systems, including atmospheric turbulence simulation, fatigue analysis, reliability, structural dynamics, aeroelastic tailoring of blades, and the evaluation of design requirements. Paul has authored over 70 articles, papers, book chapters, and reports, and for twelve years was the Chief Editor for Wind Energy.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97817856152216e.html
Märksõnad: