Introduction

• Extensive importance of fluids simulations
  • Atmospheric flows, heat exchangers
  • Current methods are costly
• Correlation between temperature and velocity
• Machine learning for fluid dynamics [3]
  • Deep learning
  • Generative adversarial networks (GANs)
• Application of machine learning for studyingturbulence

Motivation

• Previous works provided insight on modeling turbulence
  • Fluids modeling: direct numerical simulation (DNS),near-wall physics
  • Artificial intelligence: data-driven models,supervised/unsupervised learning
• Simulate at reduced computational expense
• Improve data-driven models in turbulence research
• Apply deep learning for predicting turbulent heattransfer

Research question

Can turbulent heat transfer be eficiently predicted with wall variables?

• Predict heat transfer from wall variables
• Extrapolate temperature gradients to higher Reynolds numbers

Introduction

• A digital twin (DT) is a combination of integrated multi-physics, multi-scale, probabilistic simulation of a complex product
• Verification and validation (V&V) is the method of determining if the criteria for apart or a system are accurate and complete
• Applications; aerospace, manufacturing, construction, healthcare, automobile, city, agriculture, electricity

Motivation

• Implementing V&V of a single componentinstead of a full system in DT
• Enhancing the model's credibility which is carried out by V&V as an essential initialservice in DT
• Providing quick access to asset data or information and a reduction in manual data transfer effort

Research question

Does implementing V&V using DT improve access to data and reduce the effort of data exchange?

• To conduct a code and calculation verification
• To validate simulations against experiments
• To demonstrate easy access to assetinformation or data

Introduction

• Li-ion batteries are ubiquitous
• Unavailability of battery data makes it difficult to perform prognostics (deep learning)
• Is it possible to predict the remaining use fullife (RUL) of batteries having only three data points without a temporal identifier?

Motivation

• High unpredictability of battery state of healthparameters and experimental data unavailability
• Learning methods can handle complex regression tasks but they require large amounts of high-quality data
• There is a literature gap on predicting the RUL of batteries without temporal information
• By estimating RUL of batteries on an ongoing basis, adaptative actions can be made during the mission to extend the maintenance-free operation window

Research question

Can the remaining useful life(RUL) of a battery be predictedwith no temporal identifier?

Motivation

• Entropy is used to quantify dynamic complexity from an arbitrary time series
• Incipient failure (IF) is an imperfection in the condition of an item from which a degraded or critical failure can be expected to result if corrective action is not taken
• Remaining useful life (RUL) is an estimate of the amount of time that an item is estimated to be able to function before replacement
• Data can be used to better predict IF and RUL
• Avoid catastrophic failure

Research question

Can Multi-scale Divesirty Entropy be used to develop a framework to predict the IF of a system?