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What is OpenFAST?

OpenFAST is an open-source wind turbine simulation tool developed by the National Renewable Energy Laboratory (NREL) that builds on the foundations of the previous FAST v8 framework. It integrates key aspects of aerodynamics, hydrodynamics, control systems, and structural dynamics to enable comprehensive, coupled nonlinear simulations of wind turbines in various operational scenarios. This software supports a range of configurations, including onshore, fixed-bottom offshore, and floating offshore turbines, which broadens its applicability. Aimed at fostering a vibrant open-source development community, OpenFAST is designed for collaboration among researchers, industry professionals, and academics, and it features a robust software engineering framework with organized source code and automated testing capabilities. Transitioning from FAST v8.16, OpenFAST introduces a centralized GitHub repository that organizes modules, glue codes, and compiling tools, alongside a new versioning system and enhanced unit testing at the subroutine level. These advancements not only optimize the tool's performance but also create greater opportunities for collaborative engagement among users and developers dedicated to advancing wind energy technologies. Additionally, OpenFAST's commitment to open-source principles encourages innovation and knowledge sharing within the renewable energy sector, further contributing to the growth of sustainable energy solutions.

What is DC-OM DigitalClone for Wind O&M ?

Sentient Science presents DigitalClone for Wind Operations and Maintenance (DC-OM), a validated software-as-a-service (SaaS) solution tailored for wind turbine operators. This innovative platform enables providers and original equipment manufacturers (OEMs) to collaboratively assess the remaining useful life (RUL) and health of essential systems and components within an asset. By enhancing predictive maintenance strategies, DC-OM aims to significantly lower operations and maintenance (O&M) expenses while simultaneously prolonging the lifespan of equipment assets. The versatility of DC-OM allows users to deploy it as an independent monitoring solution or as an enhancement to current condition monitoring systems (CMS) and diagnostic tools, ensuring it is optimized for both scenarios. Notably, DC-OM can autonomously identify damage in assets that are presumed to be healthy, providing actionable insights, and it also tracks the evolution of damage with comprehensive uncertainty quantification. The insights generated by DC-OM are driven by a proprietary fusion framework that leverages physics-informed machine learning, supported by a distinctive architecture for unifying data from multiple sources, making it a unique asset in the wind energy sector. This comprehensive approach not only empowers operators to make informed decisions but also fosters greater efficiency and reliability in wind turbine management.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Free
Free Version

Pricing Information

Upon request
Fleet size dependent

Supported Platforms

Windows
Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

National Renewable Energy Laboratory

Company Location

United States

Company Website

www2.nrel.gov/wind/nwtc/openfast

Company Facts

Organization Name

Sentient Science Corporation

Date Founded

2001

Company Location

United States

Company Website

sentientscience.com

Categories and Features

Wind Farm

Not specified

Categories and Features

Fleet Management

Incident Reporting
Inspection Management
Maintenance Management

Maintenance Management

Predictive Maintenance
Preventive Maintenance

Preventive Maintenance

Condition Monitoring
Inspection Management
Predictive Maintenance

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