We invite you to COMSOL User Conference; meet experts in engineering analytics and uncertainty quantification, see demonstrations, and explore how SmartUQ can improve your analysis.
October 2 - 1:00 PM to 2:00 PM
Presented by Gavin Jones, Application Engineer
Advancements in and the rapid proliferation of modeling and simulation have led to new technologies such as digital twins and have given engineers a “data-rich” environment for conducting predictive analytics. However, simulations are a deterministic analysis, failing to consider real-world variability and uncertainties surrounding the simulation process. By accounting for the uncertainties in their simulation models, engineers can develop an accurate predictive model, enabling the performance of advanced analytics, including uncertainty quantification (UQ), design space exploration, trade studies, and predictive maintenance. These predictive capabilities can significantly reduce product development, warranty, and sustainment costs and improve product reliability and durability.
This workshop will show how SmartUQ software can enhance product development and design exploration activities in COMSOL Multiphysics® through the application of predictive analytics and UQ techniques. Using a NACA airfoil CFD simulation and a microwave ablation simulation to demonstrate, this workshop will walk through SmartUQ’s analytics workflow coupled to COMSOL Multiphysics®. The workshop will also highlight additional applications of SmartUQ for other COMSOL® software simulations.
October 3 - 11:00 AM to 11:15 AM
Presented by Gavin Jones, Application Engineer
Part of the
Electromagnetics in the Medical Field Session
This study presents a statistical approach for prediction and uncertainty quantification of a hepatic tumor Microwave Ablation treatment simulation with a spatial and transient response. The study examines the variation in the volume of necrotic tissue in a human liver tissue model during Microwave Ablation/Coagulation therapy due to tissue property variation as a function of wattage and treatment time. The analysis also assesses the effects of tissue variation on the size, shape, and percentage of necrotic tissue as a spatially distributed field rather than as a single-value parameter like cross-sectional area or volume. Data with spatial and functional correlations can often be modeled with fewer training data points using machine learning techniques. This approach also has the advantage of representing the entire spatial-transient field, which allows analysis on a greater range of results such as the percentage of necrotic tissue around an ablation region or areas in which tissue was unaffected. The surrogate models developed in this study are used to perform sensitivity analysis and uncertainty quantification studies to assess the variation in volume and shape of necrotic tissues due to variations in human tissue properties and treatment procedures.
The microwave heating of a cancer tumor model from the COMSOL Multiphysics® Application Library was utilized in COMSOL Multiphysics® simulation software to simulate a region of hepatic cancer tissue. A commonly used coaxial microwave ablation antenna, with the corresponding slotted catheter, is positioned in the center of the cylindrical tissue section. The antenna operates at 2.45 GHz, a frequency widely used in microwave ablation therapy. A Design of Experiments (DOE) containing both biological and treatment factors was used to generate proper training data for the surrogate model. The biological factors included temperature, conductivity, permittivity, and specific heat of cancerous hepatic tissue. The DOE and surrogate model generation, as well as the sensitivity and uncertainty studies, were performed using SmartUQ Software Version 5.0.
The surrogate models developed in this study accurately mimic the model behavior of the COMSOL Multiphysics® simulation. The Continuous Response Emulator, which models summary outputs such as volume of necrotic tissue, percentage of unaffected tissue, etc., converged with a standardized RMSE of less than 0.15 for all output parameters. The Functional Emulator, which models the spatially and transiently distributed field of responses for ablation, converged with a standardized RMSE, averaged across all nodes, of less than 0.1. These surrogate models were then used to produce sensitivity results and uncertainty margins due to expected variation in human tissue properties corresponding to a mock set of liver tumor scenarios.
This study presents a novel methodology for both transient and spatial-transient simulation of tumor ablation treatment using surrogate modeling. This surrogate model will help ensure a microwave ablation device is able to produce predictable ablation zones while keeping ablation time to a minimum. The techniques presented with this case study can be implemented to a wide variety of other medical devices to deliver similar results and improve treatment predictions on a case-by-case basis for patients.
October 3 - 11:30 AM to 11:45 AM
Presented by Gavin Jones, Application Engineer
Part of the
Tools for Optimizing Simulations & Streamlining Workflow Session
Through the use of uncertainty quantification (UQ) and other analytics techniques, this presentation will introduce attendees to the digital twin process workflow.
For a specific part an authoritative digital truth source can be created. This is a digital, interrogatable repository of all the accumulated data and knowledge concerning that part. Using efficiently sampled data from the trade space of a simulation model for the part as well as from physical tests or experimental data an emulator (or surrogate model) of the simulation may be created and calibrated to match real world performance. Statistical calibration is one technique that may be used in this process. Statistical calibration has the advantage over other techniques of accounting for the imperfect nature of all models by assuming discrepancy between the model being calibrated and the physical data set exists. Understanding the discrepancy between the simulation model and physical tests or experiments can help identify model form errors and aid in verification and validation of the simulation.
To illustrate the importance of statistical calibration to creating and running an efficient and accurate digital twin, an example of a statistical calibration using an emulator built for a model created with COMSOL Multiphysics® simulation software will be presented.
