What are the best practices for testing and validating your digital twin models before deploying them?

When defining objectives for digital twin testing, consider both short-term and long-term goals. For example, in a manufacturing context, a short-term objective could be optimizing production speed to meet current demand. A long-term objective might involve predicting maintenance needs to reduce downtime and costs. By setting these dual objectives, you ensure that your digital twin addresses immediate needs while also providing a foundation for future improvements and cost savings.

The first step in testing and validating your digital twin models is to define clear objectives and criteria for the simulation. Identify key performance indicators (KPIs) to measure and optimize, expected outcomes and benefits, and the risks and uncertainties to address. Establish how you will compare simulation results with real data. Clear objectives and criteria enable effective and efficient test case design and validation methods.

Awareness and utilization of the 3D model and effectiveness of the data, which leads to successful implementation. Data integration with use cases helps to understand it in a better way. Keep data up to date with the plant changes and MOC.

In my perspective, the primary purpose of a digital twin is to accurately 'represent' reality. This involves providing a realistic interpretation of various variables, thereby yielding actionable insights that can inform physical actions. From my experience, a Digital Twin, even in its most basic deployment state, should align with existing KPIs, insights, and decision-making processes that are currently facilitated by existing tools, platforms, and processes. In essence, the initial step involves emulating existing tools, followed by replacing them, and ultimately, proposing future scenarios.

Los gemelos digitales deben utilizar datos conformes, es decir, es necesario construir un data lake que permita poder historizar los datos de lo que queremos “gemelar” que contenga reglas de calidad y el ciclo de vida de los datos.

Realistic Representation: Utilize data reflecting actual conditions and dynamics of the physical system. Consistency and Accuracy: Ensure data consistency, completeness, and accuracy for reliable modeling. Varied Variables and Scenarios: Cover a sufficient range and frequency of variables and scenarios for comprehensive modeling. Data Sources: Use historical, real-time, or synthetic data based on objectives and availability.

The quality and quantity of data feeding your digital twin models are crucial for validity and reliability. Use realistic, representative data reflecting actual conditions and dynamics of the physical system. Ensure data consistency, completeness, accuracy, and sufficient range and frequency of variables and scenarios. Historical, real-time, or synthetic data can be used, depending on objectives and availability. Always verify and clean data before model integration.

In a complex dynamic environment, often data is generated using physics based models. Depending on the type of physics based model and operation scenario the verification method can be different. All synthetic data must be verified using an established protocol on the specific field.

Definir claramente los protocolos para las pruebas que se van a ejecutar. Estos protocolos de pruebas deben incluir la validación: alcance de los equipos de la planta, balance de masa y energía, sistema SIL, arranque y paro de la planta, validación de escenarios para la validación del modelo digital

Sensitivity and uncertainty analysis are crucial for understanding digital twin model responses to input, parameter, or assumption changes. Sensitivity analysis assesses output variations with different input or parameter values, identifying influential factors and optimizing models for various scenarios. Uncertainty analysis estimates confidence or error levels in outputs, considering input, parameter, or assumption variability. It quantifies model reliability and robustness, addressing data or knowledge gaps and limitations.

Test and validation process can be exhaustive depending on the type of application. It is necessary to define the digital twin system for the intended purpose.

? add the correct size and weight of the product, which is important to define the cycle time. ? Keep in mind that not all movements the robot can simulate are attainable in real life due to singularities.

Augment your data with live data from the physical system to serve as feedback. This real-time feedback loop enhances the model's robustness, making it more adaptable to perturbations and unforeseen circumstances.

Run hazard and operability process Mind modeling constraints, run the twin in different scenarios Incomplete datasets matters and they are of great value. they are due to filtering, multiplicative errors and indirect and implicit acquisition of certain variable