Attention:The NSF Public Access Repository (PAR) system and access will be unavailable from 5:00 PM ET until 8:00 PM ET on Friday, September 11 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • Minimizing elastic interaction in multi-material wind turbine blade joints using hybrid constitutive modelling and genetic algorithm

This content will become publicly available on December 24, 2026

Title: Minimizing elastic interaction in multi-material wind turbine blade joints using hybrid constitutive modelling and genetic algorithm
A generalized sequence-dependent constitutive model is formulated and coupled with genetic algorithm (GA) to minimize the effect of bolt-on-bolt elastic interaction (EI) of a large size wind turbine blade joint during the process of joint preloading prior to turbine operation. The composite blade flange is bolted to a steel hub flange on the turbine tower side. A Python-based tool is developed for big data sets of elastic interaction among the 54-bolt wind turbine blade-to-hub connection. Trial tightening sequence and patterns include (a) clockwise sequential tightening of six bolts at each apex of three rotated triangles, (b) clockwise sequential tightening of nine bolts at each apex of two rotated triangles, (c) separately tightening one pair of bolts that are located 180° diagonally across each other and repeat 26 more times in a clockwise direction. A genetic algorithm is used to identify feasible tightening sequences that minimize the scatter of bolt preload and achieve the desired level of a uniform joint preload.  more » « less
Award ID(s):
2052658
PAR ID:
10692513
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Sage Journals
Date Published:
Journal Name:
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
ISSN:
0954-4062
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, Mechanical Systems and Signal Processing)
    The safety and integrity of engineered structures are critically dependent on maintaining sufficient preload in their bolted joints. This preload can be dynamically lost due to sustained vibrations or sudden shock that are large enough to induce slip in the threads. While high-fidelity finite element simulations and analytical methods can accurately model the loss of preload for a single bolt, their prohibitive computational expense and complexity render them unfeasible for analyzing large-scale structures with many bolts. This creates a critical need for reduced-order models that capture the essential physics of loosening while remaining computationally efficient. This paper introduces a reduced-order modeling methodology for predicting the loosening of bolted lap joints subjected to transverse shock excitation. The core idea is to treat the bolt tension as a dynamic degree-of-freedom that governs the effective properties of the joint through tension-dependent stiffness and damping that couple the components together. The methodology is applied to a pair of oscillators coupled by a lap joint with a strain-sensing bolt. Three different sets of experimental measurements are used to interrogate the dynamics of the system. Mathematical models are identified for the joint stiffness and damping and the instantaneous tension, which are combined with the equations of motion for the oscillators to simulate and reproduce the experimental measurements. Ultimately, the results validate the treatment of bolt tension as a dynamic degree-of-freedom, such that the methodology provides an effective framework for predicting loosening behavior in bolted joints. 
    more » « less
  2. ; ; ; ; ; ; ; (, Wind Energy)
    Wind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production. 
    more » « less
  3. ; ; ; ; ; ; ; (, Wind Energy)
    Wind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production. 
    more » « less
  4. ; ; ; ; ; ; ; (, Wind Energy)
    Wind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production. 
    more » « less
  5. ; ; ; ; ; ; ; (, Wind Energy)
    Wind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Text Encoded Conversion
  • XML
  • Save / Share this Record
  • Send to Email