Uncertainty Analysis & Experimentation Course | IIT Delhi Prof. S.R. Kale
Course Details
| Exam Registration | 21 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 8 weeks |
| Categories | Mechanical Engineering, Multidisciplinary, Chemical Engineering |
| Credit Points | 2 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 13 Mar 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 16 Feb 2026 |
| Exam Date | 28 Mar 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Introduction to Uncertainty Analysis and Experimentation: A Foundational Course for Engineers and Researchers
In the precise world of engineering and scientific research, no measurement is perfect. Understanding the "how much" and "why" behind the imperfections in your data is not just academic—it's critical for innovation, quality assurance, and credible results. This is where the rigorous discipline of Uncertainty Analysis comes in.
We are excited to present a detailed overview of the comprehensive course, "Introduction to Uncertainty Analysis and Experimentation," developed and taught by Prof. S. R. Kale of IIT Delhi. This 8-week program is designed to equip students and professionals with the fundamental tools needed to quantify, analyze, and report the uncertainties inherent in any experimental process.
Course Instructor: Learn from an IIT Delhi Veteran
The course is led by Prof. Sunil R. Kale, a distinguished professor with the Department of Mechanical Engineering at IIT Delhi since 1989. With decades of experience, Prof. Kale has developed and taught a wide array of undergraduate and postgraduate courses, including thermodynamics, heat transfer, and experimental methods for thermal engineering.
His extensive research and industry collaboration in heat transfer, fluid mechanics, combustion, and energy conversion ensure that the course content is grounded in both robust theory and practical, real-world application.
Who Should Enroll? (Intended Audience)
This course is meticulously designed for a broad spectrum of learners:
- UG, PG, and PhD students in Engineering (Mechanical, Chemical, Multidisciplinary) and Science streams.
- Engineering faculty members looking to strengthen their curriculum.
- Professionals in industry and R&D laboratories involved in testing, quality control, and research.
Prerequisites: A foundational knowledge equivalent to a bachelor’s degree in engineering or science is recommended, making it ideal for 3rd/4th year undergraduates, masters students, and PhD scholars.
Why is This Course Important? (Course Relevance)
Uncertainty analysis is the backbone of reliable experimentation. This course will teach you how to:
- Express any result with a clear confidence interval.
- Adhere to international standards like ASME PTC 19.1 and ISO GUM, which are crucial for publishing research, legal metrology, and industry compliance.
- Design better experiments by identifying and minimizing key sources of error beforehand.
- Enhance the quality and credibility of your work, a skill highly valued by industries, especially MSMEs.
Detailed 8-Week Course Layout
Here is a week-by-week breakdown of what you will learn:
| Week | Core Topics |
|---|---|
| Week 1 | Course introduction, objectives, and the fundamental concept of expressing results with uncertainty. Overview of ASME and ISO standards. |
| Week 2 | Deep dive into errors and uncertainty: distributions, standard uncertainty, and classification (Random/Systematic, Type A/Type B). |
| Week 3 | The experimentation process: from defining data needs to execution, highlighting the role of uncertainty analysis at every stage. |
| Week 4 | Uncertainty in a single measurement: Identifying elemental error sources and calculating combined & expanded uncertainty. |
| Week 5 | Special cases in measurement: Systematic uncertainties from calibration, physical phenomena (e.g., thermal radiation), and electronic/digitization errors. |
| Week 6 | Uncertainty in a final result: Using the Taylor Series Method (TSM), sensitivity coefficients, and identifying dominant uncertainty sources. |
| Week 7 | Advanced result analysis: Techniques for sensitivity, Pareto charts for contribution analysis, and application in pre-test planning. |
| Week 8 | Data analysis & reporting: Round-off rules, depicting uncertainty on graphs (bars, bands), introduction to correlations, and course summary. |
Key Reference Books and Standards
The course curriculum is aligned with authoritative texts and international standards, including:
- ASME PTC 19.1-2018 “Test Uncertainty”
- ISO JCGM 100:2008 (GUM) – Guide to the expression of uncertainty in measurement
- Coleman & Steele, Experimentation, Validation, and Uncertainty Analysis for Engineers
- Doeblin, Measurement Systems: Application and Design
- Holman, Experimental Methods for Engineers
- Various relevant IS, ISO, ASME, and ASTM standards.
Conclusion: Elevate Your Experimental Rigor
Whether you are a student embarking on a thesis, a researcher publishing papers, or a professional ensuring product quality, a formal understanding of uncertainty analysis is indispensable. This course by Prof. S.R. Kale offers a structured, standard-compliant pathway to mastering this critical skill. By its conclusion, you will not just perform experiments; you will quantify their reliability, design them more efficiently, and report your findings with professional confidence.
Enroll Now →