What is the best way to validate an energy model for a combustion engine?

Energy models are mathematical representations of the physical and chemical processes that occur in a combustion engine. They can help engineers design, optimize, and test different engine configurations and operating conditions. However, energy models are only as good as their validation, which is the process of comparing the model predictions with experimental data. How can you validate an energy model for a combustion engine effectively and reliably? Here are some tips and best practices to follow.

1 Choose appropriate data

The first step in validating an energy model is to select the experimental data that will be used as the reference for comparison. The data should be relevant, accurate, and representative of the engine performance and behavior. You should also consider the sources of uncertainty and error in the data, such as measurement errors, instrumentation limitations, and environmental factors. Ideally, the data should cover a wide range of engine operating conditions, such as speed, load, fuel type, and emissions.

Add your perspective

2 Define validation criteria

The next step is to define the criteria that will be used to evaluate the agreement between the model and the data. These criteria should be based on the objectives and scope of the model, as well as the level of accuracy and precision required. Common validation criteria include absolute and relative errors, root mean square error (RMSE), coefficient of determination (R-squared), and confidence intervals. Absolute and relative errors measure the differences between the model and data values, expressed as absolute or percentage values. RMSE measures the overall deviation of the model from the data, while R-squared measures how well the model fits the data trend. Finally, confidence intervals measure the uncertainty and variability of the model and data by providing ranges of values that contain true model or data values with a certain probability.

Add your perspective

3 Perform sensitivity analysis

The third step is to perform a sensitivity analysis, which entails examining how the model output changes when the model input parameters vary. This can help you identify the most influential parameters, as well as the ranges and distributions of values that produce acceptable results. Different methods can be used to conduct a sensitivity analysis, such as one-at-a-time (OAT), global, and Monte Carlo. OAT is the simplest method, where you vary one parameter at a time and observe its effect on the model output. Global is a more comprehensive method, where you vary all or a subset of parameters simultaneously and observe their effect on the model output. Monte Carlo is a probabilistic method, where you sample random values from the distributions of the parameters and observe their effect on the model output.

Add your perspective

Mehdi HOUJAMI

Validation Engineering Leader - Multidrive mode System | SFC© certified
Report contribution
By exploring how model output responds to variations in input parameters, you gain valuable insights into the influential factors and acceptable value ranges. Different methods, such as one-at-a-time (OAT), global, and Monte Carlo, offer diverse perspectives. OAT, the simplest, varies one parameter at a time to observe its impact. The global method varies multiple parameters simultaneously for a comprehensive view, while Monte Carlo, a probabilistic approach, samples random values to assess their impact on model output. Each method contributes distinct advantages to refining model accuracy and reliability.

Like

4 Adjust and refine the model

The final step is to adjust and refine the model based on the validation results and the sensitivity analysis. You can modify the model structure, equations, parameters, or assumptions to improve the agreement with the data and reduce the errors and uncertainties. You can also calibrate the model, which is the process of finding the optimal values of the parameters that minimize the errors and maximize the fit. You should repeat the validation and sensitivity analysis until you achieve a satisfactory level of accuracy and reliability for your model.

Add your perspective

Mehdi HOUJAMI

Validation Engineering Leader - Multidrive mode System | SFC© certified
Report contribution
I believe the refining process to be the most critical part about modeling ( regardless if its mechanical or electrical...)- Within, we find the calibration which involves finding optimal parameter values to minimize errors and maximize the model's fit. This fine-tuning ensures that the model aligns closely with real-world observations. The repetition of validation and sensitivity analysis is key, allowing for a continuous improvement cycle until a satisfactory level of accuracy and reliability is achieved. This rigorous approach enhances the model's robustness, making it a more dependable tool for decision-making and analysis.

Like

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

Add your perspective

What is the best way to validate an energy model for a combustion engine?

1

2

3

4

5

1 Choose appropriate data

2 Define validation criteria

3 Perform sensitivity analysis

4 Adjust and refine the model

5 Here’s what else to consider

Mechanical Engineering

Rate this article

Thanks for your feedback

More articles on Mechanical Engineering

More relevant reading

What is the best way to validate an energy model for a combustion engine?

1

2

3

4

5

1 Choose appropriate data

2 Define validation criteria

3 Perform sensitivity analysis

4 Adjust and refine the model

5 Here’s what else to consider

Mechanical Engineering

Rate this article

Thanks for your feedback

Explore Other Skills