2. II - What is pressure?

Umit Aslan, Nick LaGrassa
45-50 minutes
High School


In this lesson, the students learn the basic assumptions of the Kinetic Molecular Theory (KMT) and how they help us conceptualize "pressure as a macro-level property that emerges from the micro-level interactions between many gas particles".

The lesson starts with the basic assumptions of KMT [1]:

1. The gas is composed of a large number of identical molecules moving in random directions, separated by distances that are large compared with their size.
2. Collision between gas particles occur like collisions between billiard balls (i.e. elastic collision). Otherwise, they do not interact. There are no attractive or repulsive forces between the particles.
3. Any energy the particles have is because of their motion only (i.e. kinetic energy).
4. These assumptions are simplifications that describe a theoretical "ideal gas". Most real gases behave qualitatively like an ideal gas.

The students reflect on the assumptions of the KMT and their own gas particle models from Lesson 1.

Then, the students are given an introductory definition of pressure. They are guided through an investigation of this definition using a simple NetLogo model of the bike tire. They observe the effects of adding particles through a valve on the pressure of the tire. They also consider the trade-offs of making simplifications when constructing models of systems.

The lesson ends with a "discrepant event" activity to stimulate transfer: "Can blow up a balloon inside a bottle?".

[1] The students are not expected to learn mathematical representations of the KMT as it is too complex for this grade level.

Underlying Pages


Next Generation Science Standards
  • Physical Science
    • [HS-PS2] Motion and Stability: Forces and Interactions
  • NGSS Crosscutting Concept
    • Patterns
    • Systems
    • Structure and Function
  • NGSS Practice
    • Analyzing Data
    • Constructing Explanations, Designing Solutions
    • Asking Questions, Defining Problems
    • Using Models
    • Arguing from Evidence
    • Conducting Investigations
Computational Thinking in STEM
  • Data Practices
    • Analyzing Data
    • Collecting Data
    • Creating Data
    • Manipulating Data
    • Visualizing Data
  • Modeling and Simulation Practices
    • Assessing Computational Models
    • Designing Computational Models
    • Using Computational Models to Find and Test Solutions
    • Using Computational Models to Understand a Concept
  • Computational Problem Solving Practices
    • Assessing Different Approaches/Solutions to a Problem
    • Computer Programming
    • Troubleshooting and Debugging
  • Systems Thinking Practices
    • Investigating a Complex System as a Whole
    • Thinking in Levels
    • Understanding the Relationships within a System





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