Sandia National Laboratories is working with industry partners to develop and validate nondestructive inspection methods for detecting flaws in wind turbine blades. They conducted an experiment using representative blade samples containing realistic flaws to evaluate current inspection techniques and identify ways to improve flaw detection rates. The results showed that inspections could miss flaws but advanced techniques like phased array ultrasound offered improvements over conventional ultrasound. Optimizing factors like inspector training and standardized procedures was also important to ensure reliable inspections.
Damage Detection in Beams Using Frequency Response Function Curvatures Near R...Subhajit Mondal
Structural damage detection from measured vibration responses has gain
popularity among the research community for a long time. Damage is identified in
structures as reduction of stiffness and is determined from its sensitivity towards the
changes in modal properties such as frequency, mode shape or damping values with
respect to the corresponding undamaged state. Damage can also be detected directly
from observed changes in frequency response function (FRF) or its derivatives and
has become popular in recent time. A damage detection algorithm based on FRF
curvature is presented here which can identify both the existence of damage as well
as the location of damage very easily. The novelty of the present method is that the
curvatures of FRF at frequencies other than natural frequencies are used for
detecting damage. This paper tries to identify the most effective zone of frequency
ranges to determine the FRF curvature for identifying damages. A numerical
example has been presented involving a beam in simply supported boundary
condition to prove the concept. The effect of random noise on the damage detection
using the present algorithm has been verified.
Damage Detection in Beams Using Frequency Response Function Curvatures Near R...Subhajit Mondal
Structural damage detection from measured vibration responses has gain
popularity among the research community for a long time. Damage is identified in
structures as reduction of stiffness and is determined from its sensitivity towards the
changes in modal properties such as frequency, mode shape or damping values with
respect to the corresponding undamaged state. Damage can also be detected directly
from observed changes in frequency response function (FRF) or its derivatives and
has become popular in recent time. A damage detection algorithm based on FRF
curvature is presented here which can identify both the existence of damage as well
as the location of damage very easily. The novelty of the present method is that the
curvatures of FRF at frequencies other than natural frequencies are used for
detecting damage. This paper tries to identify the most effective zone of frequency
ranges to determine the FRF curvature for identifying damages. A numerical
example has been presented involving a beam in simply supported boundary
condition to prove the concept. The effect of random noise on the damage detection
using the present algorithm has been verified.
Presentation by Tom DeMint of Owens Corning at CAMX on October 15, 2014. As the wind power energy generation industry continues to develop, one of the main objectives of turbine rotor blade manufacturers is to reduce total energy production cost to align wind power with other energy sources. Energy produced by wind turbines is more widely available than ever before; nevertheless the industry is constantly looking for ways to further optimize the cost of energy (CoE) as one of its foremost goals. The turbine, together with its rotor blades, plays an essential role and is one of the major components of these machines in terms of cost. It generates the torque which drives the generator and is responsible for the range of conditions energy can be extracted from the available wind. Wind farms are now constructed and operate in challenging off-shore as well as on-shore locations with differing wind speed conditions. Glass fiber composite rotor blades have contributed greatly to the success of this sustainable energy source and have allowed the wind industry to make significant advances in recent times, especially in off-shore and in low-wind locations. One of the most important advances has been the progressive technology applied to the properties of glass fiber leading to the development of high modulus glass types for lighter composites offering greatly enhanced resistance to fatigue at an affordable cost. The technological advances in glass fiber properties has resulted in rotor blades of ever greater length - beyond 85m – dimensions deemed unreachable less than a decade ago. This presentationl highlights advances in the material properties of glass fiber to help designers and engineers conceive blades which are lighter yet with increased length, improved aerodynamic performance with resistance to higher, long-term fatigue loads which ultimately enables wind turbines to increase power yield and therefore reduce the cost of energy.
Physics of Failure Electronics Reliability Assurance SoftwareCheryl Tulkoff
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This presentation will cover a recommended solution designed to provide excellent inspection capability, with permanent records. In addition, the overall duration of the inspection process is drastically reduced, and ultrasonic examination provides immediate feedback regarding the welding process.
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In 2014, Whemco Steel Castings Inc., a leader in mill roll manufacturing and service, approached Innerspec Technologies to expand the original Rollmate design with more capabilities.
When challenged to develop a new generation of mill roll inspection systems, Innerspec embraced the task and sought to design a comprehensive solution using the best NDT techniques available for the task.
The system is fully designed and manufactured in the United States, and serviced and supported by Innerspec offices in the US, Mexico, Europe and China.
Ultrasonic ILI Removes Crack Depth-Sizing LimitsNDT Global
This white paper looks at how the new generation of high-resolution inspection robots overcame the crack-depth sizing limit of previous-generation UT for detection, sizing and location of cracks and crack-like defects in the body and welds of transmission pipelines. Supporting test data is also provided.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
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Topics covered:
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This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
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Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
JMeter webinar - integration with InfluxDB and Grafana
Optimizing Quality Assurance Inspections to Improve the Probability of Damage Detection in Wind Turbine Blades
1. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United
States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.
Dennis Roach, Tom Rice, Josh Paquette
Wind Blade Reliability Center
Sandia National Labs
Optimizing Quality Assurance Inspections to
Improve the Probability of
Damage Detection in Wind Turbine Blades
2. Blade Reliability Collaborative – NDI Objectives
• Develop, evaluate and validate the array of potential nondestructive
inspection methods for the detection of flaws in composite wind turbine
blades
• Plan and implement a national capability – including a physical presence and
methodology - to comprehensively evaluate blade inspection techniques
• Produce optimum deployment of automated or semi-automated NDI to detect
undesirable flaws in blades (time, cost, sensitivity)
• Transfer technology to industry through hardware and technology evaluation,
inspector training, and procedure development
Create the ability for manufacturers to determine the quality
of their product before it leaves the factory & to enhance the
in-service inspection of blades for wind farm operators
3. Optimized InspectionsTraining
Inspectors,
Equipment, &
NDI Techniques
Procedures
NDI Calibration &
Reference Standards
Blade
Maintenance
Programs
Blade Reliability Collaborative -
Program Thrusts to Improve Wind NDI
Enhance factory
reliability, facilitate
repairs before acritical is
reached, minimize
turbine downtime &
increase blade lifetime
Create the ability for manufacturers to determine the
quality of their product before it leaves the factory & to
enhance the in-service inspection of wind blades
4. VoidsVoids
Inspection Areas and Flaw Types of Interest
Flaws include: Ply Waves
Delaminations, Adhesive
Voids, Joint Disbonds,
Snowflaking and Porosity
5. Ultrasonic Transducer
Captured Water Column
Scanning Shoe for
Offset of UT Wave
Plastic Membrane
Weeper Body
Water Inlet
(pumped in from reservoir)
Water Couplant Pool
Inspection Surface
Excess Water Flow
(recovered into reservoir)
To Data Acquisition System
MAUS P-E UT with Focused Probe (1 MHz/2”)
and Adjustable Water Path
New
“Immersion”
Probe Holder
Allows for
Adjustable
Water Path
Flat Bottom HolesPillow Inserts
Pull Tabs
REF-STD-6-202-250-SNL-1
1.01"
0.34"
1.35"
0.68"
0.67"
1.35"
0.34"
1.01"
1.35"
USED VECTORPLY ELT 5500
24 PLIES OF MATERIAL (UNIAXIAL FIBER)
2.000"1.000"
2.000"
.40" (10mm) BONDLINE
INSPECTION SIDE
PERCENTAGE OF FULL
THICKNESSAT BONDLINE
(.100" SKINAND .400" BOND
THICKNESS)
25%(OF FULLTHICKNESS)
50%(OF FULLTHICKNESS)
75%(OF FULLTHICKNESS)
FLAT BOTTOM HOLE (FBH)
PILLOW INSERT
EXAMPLES OF VARIOUS FLAW
DEPTHS IN SPAR CAPSECTION
INSPECTION SURFACE
NDI REFERENCE STANDARD 2 FABRICATION DRAWING
SPAR CAPAND SHEAR WEB BLADE SCHEMATIC
(DISBONDS INADHESIVE)
PULLTABS
(DELAMS) (DELAMS) (BASED ON 24 PLIES OF UNIAXIAL MAT'L)
(DISBONDS INADHESIVE)
25%
(.125" MR)
50%
(.25" MR)
1.00" DIA
2.00" DIA
SHEAR WEB
ADHESIVE
FLAT BOTTOMHOLES
1.00" (25mm) FOAM CORE
INTERFACE 2
INTERFACE 1
INTERFACE 1
25%
(B/WPLIES 18 & 19)
75%
(B/WPLIES 6 & 7)
75%
(.375" MR)
4 PLYPILLOWINSERTSFLAT BOTTOMHOLES
25%(B/WPLIES
18 & 19)
50%(B/WPLIES
12 & 13)
75%(B/WPLIES
6 & 7)
25%(.34" MR)50%(.68" MR)75%(1.01" MR)
2.00" DIA
.50" DIA
1.00" DIA
1.50" DIA
1.50" DIA
1.00" DIA
.50" DIA
2.00" DIA
2.00" DIA
.50" DIA
1.00" DIA
1.50" DIA
1.50" DIA
1.00" DIA
.50" DIA
2.00" DIA
2.00" DIA
1.50" DIA
1.00" DIA
.50" DIA
.50" DIA
1.00" DIA
1.50" DIA
2.00" DIA
18.00"
~1.35" (34mm) UNIAXIAL (SPANWISE)
30.00"
__(+45, +45)
2 PLIES OF DOUBLE BIAS (DB)
2 PLIES OF DOUBLE BIAS (DB)
11-30-10
MR = MATERIAL REMAINING
PLY NO. 1 OF SPAR CAP__(+45, +45)
(NOTE: IF USING TEFLON BASED RELEASE FABRIC
(BASED ON 24 PLIES OF UNIAXIALMAT'L)
NOTE: PULLTABS (.007" THK) WILL EXTEND OUT FROM SPECIMEN
EDGE DURING CURE PROCESS, BE SURE TO USE SPECIAL
CARE NOTTO PUNCTURE VACUUM BAG (COVER SHARP
EDGES WITH BREATHER FABRIC) . PULLTABS REMOVED
AFTER CURE PROCESS.
1 of 2NOTES:
1. SPECIMEN CURED USING 14 IN. HG. VACUUM PRESSURE
AND VACUUM LEFT ON OVER NIGHT.
2. POST CURE SPECIMENAT 70 C FOR 10 HOURS.
3. FINAL FLAT BOTTOM HOLE DEPTH MAY CHANGE DEPENDING
ON FINAL PART THICKNESS.
1.875"
2.750"
2.750"
2.750"
2.750"
2.750"
(41)
(42)
(43)
(44)
(45)
(46)
(52)
(51)
(50)
(49)
(48)
(47)
(53)
(54)
(55) (56)
(57)
(58)
(64)
(63)
(62)
(61)
(60)
(59)
(65) (66) (67) (68)
(69)
(70)
(71)
(72)
(73)
(74)
(75)
(76)
(77)
(78)
(79)
(80)
6. Tapered Adhesive Wedge Fiberglass Inspection Surface
Adhesive Bond Line
Out of Spec Thickness
Develop and assess methods to inspect bond line thickness
Phased Array
UT Results
Good Bond
Line Thickness
Anomalies in
Bond Line
Adhesive Thickness Measurements with Phased Array UT
7. Phased Array UT – Display and Deployment
Olympus 1.5Mhz,
42 element probe
Sonatest RapidScan 2
GE Phased Array UT RotoArray
8. On-Blade Phased Array UT Inspections
16 Meter Station on
Fiberglass Spar Cap Blade
Spar Cap Cross Section Schematic
Showing the Spar Cap, Adhesive
Bond Line and Shear Webs
Scanning Direction
Sealed water box and 1.5L16 Phased Array probe was used to
detect missing adhesive in bond lines
Vertical Strip C-Scan Image
Showing Adhesive Void in
Upper Bond Line
Adhesive Void
Between Spar
Cap and
Shear Web
9. Purpose
• Generate industry-wide performance curves to quantify:
Ø how well current inspection techniques are able to reliably find
flaws in wind turbine blades (industry baseline)
Ø the degree of improvements possible through integrating more
advanced NDI techniques and procedures.
An Experiment to Assess Flaw Detection
Performance in Wind Turbine Blades (POD)
Expected Results - evaluate performance attributes
1) accuracy & sensitivity (hits, misses, false calls, sizing)
2) versatility, portability, complexity, inspection time (human factors)
3) produce guideline documents to improve inspections
4) introduce advanced NDI to industry
10. Wind Blade NDI Probability of Detection Experiment
- Blind experiment: type, location and size of flaws are not know by inspector
- Statistically relevant flaw distribution – Probability of Detection (POD)
- Used to analytically determine the performance of NDI techniques – hits,
misses, false-calls, flaw sizing, human factors, procedures
Experimental Design Parameters
• Representative design and manufacturing
• Various parts of blade such as spar cap,
bonded joints, leading and trailing edge
• Statistically valid POD (number, size of flaws
and inspection area)
• Random flaw location
• Maximum of two days to perform experiment
• Deployment
Fabrication Considerations
• Realistic, random flaw locations
• Portable sample set
• Range of thickness
• Material types (fiberglass and adhesives)
Spar Caps & Shear Web Box Beam
Specimens designs applicable to various blade construction
11. Wind Blade Flaw Detection Experiment -
Probability of Detection Experiment
Benefit to Participants
• Training perspective, inspections on representative
blade structure
• Inspector and production facility received feedback on
how they performed
• POD Value, smallest flaw size detectable with 95%
confidence
• Number of flaws detected & missed
• Number of false calls, if any
• Flaw sizing
• Location and type of flaws missed
NREL
UpWind
DOE
Clipper
LM Wind Power
Gamesa
Molded Fiberglass
SNL
TPI Composites
GE – Global Research
Vestas
Sandia
Review Committee
Ensure representative blade
construction and materials
12. POD Specimen Development and Characterization
Laminate Flaws Include:
Pillow Inserts
Grease Contaminate
Wrinkles – Dry stacked plies
Dry Areas
Flat Bottom Holes
Glass Microballoons
Bond Line Flaws Include:
Pillow Inserts
Pull Tabs
Flat Bottom Holes
Voids
Glass Microballoons
Phased Array UT
40mm Water Box Scan
13. Implementation of Wind POD Experiment
• 11 POD specimens with spar cap and shear web geometry
• Thickness ranges from 8 Plies (0.45” thick laminate, 0.85” thick with
adhesive bond line) to 32 Plies (1.80” thick laminate, 2.20” thick with
adhesive bond line)
• All panels painted with wind turbine blade paint (match inspection surface)
14. Wind Blade Flaw Detection Experiment – Individual
Inspector and Cumulative POD Comparison
All Panels - Spar Cap with Shear Web and Box Spar Construction Types
Conventional Single Element Pulse-Echo
Ultrasonic Inspection Method
15. Wind Blade Flaw Detection Experiment – Various
NDI Performance Attributes Evaluated
Spar Cap with
Shear Web and
Box Spar
Construction Types
Spar Cap with
Shear Web
Construction Types
All Panels - Constant
Thickness Flaws
All Panels - Complex
Geometry Flaws
16. Wind Blade Flaw Detection Experiment –
Improvements Produced by Use of Advanced NDI
C-scan images
produced by single-
element ultrasonic
scanner systems –
easier to interpret data
All Panels,
All Flaw Types –
Conventional NDI
POD 90/95 = 1.333
All Panels,
All Flaw Types –
Advanced NDI
(example only)
POD 90/95 = 1.105
17. Results from Single-Element UT Scanner System
Wind Blade Flaw Detection Experiment –
Optimizing Results with Proper Analysis
Non-optimal use of gate settings can
allow damage to go undetected
Initial Results -
flaw under bondline is
not imaged
Inspector BB – 2” Flaw “Miss” is changed to a “Hit”
(additional data gates used to detect deeper flaws)
Second Analysis – data
reviewed using additional
gate settings; damage
detected
18. • Need to develop array of inspection tools to comprehensively assess
blade integrity
• Consider time, cost, & sensitivity issues (minimize production,
maintenance and operation costs)
• Develop NDI solutions in concert with related studies: effects of defects,
field surveys, analysis, certification, standards
• NDI investigation has produced promising results thus far & may lead to
hybrid approach with multiple NDI tools (e.g. near surface and deep flaws)
• There are sensitive & rapid NDI options available for inspecting wind
blades, both for manufacturing QA and in-service NDI
• Evolution in phased array UT methods & use of C-scan technology
provides the greatest & easiest-to-achieve benefits
• Training, experience (apprenticeships) and optimized procedures are key
factors in determining the overall performance of NDI for detecting
flaws/damage in wind blades
• NDI can help ensure that wind blades meet their design life and possibly
beyond
Wind Blade Flaw Detection Experiment –
Steps to Improve Probability of Flaw Detection
19. If you are interested in participating in the
Sandia Labs wind blade inspection activities:
Tom Rice
Phone: (505) 844-7738
Email: tmrice@sandia.gov
Dennis Roach
Phone: (505) 844-6078
Email: dproach@sandia.gov
Ray Ely
Phone: (505) 284-9050
Email: grely@sandia.gov
Optimizing Quality Assurance Inspections to
Improve the Probability of
Damage Detection in Wind Turbine Blades