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A Level · Cambridge (CIE) · 9709

Calculate kinetic energy and changes of speed

Use ½mv² with positive mass and speed magnitude, convert mass units and distinguish kinetic energy from directed momentum.

Mechanics · Energy, work and power · 4.5
Exam scope and assessment notes

Mechanics (Paper 4, AS & A Level) · Mathematics 9709, 2026–2027, section 4.5. Use g = 10 m s⁻² throughout these problems. Masses are positive and constant. Work by a constant force is Fd cos θ, with θ between force and displacement; scalar-product notation is not required. Kinetic energy is ½mv² and gravitational potential-energy change is mgΔh. State the system and count gravitational work or potential-energy change once, not both. Mechanical energy is conserved only under the stated loss-free model; friction and supplied resistance transfer mechanical energy to other forms. Smooth curved-slide examples use overall energy changes and assume contact throughout, rather than requiring a normal-reaction calculation. Average power is work divided by elapsed time; P = Fv applies to a force along the motion. Constant driving power generally gives variable force and acceleration; instantaneous calculations do not justify constant-acceleration formulae over a whole journey. Check string, contact and motion-state changes before continuing a model. Written practice is self-checked; human subject-teacher review remains pending.

Before you begin

  • Square signed numbers and rearrange a quadratic relation.
  • Convert grams to kilograms and distinguish velocity from speed.

Work on paper. Try each question before revealing help, and explain why your method works.

Quick readiness check

A particle doubles speed without changing mass. What happens to its kinetic energy?

What you will learn

  • Calculate kinetic energy and its change with compatible units.
  • Explain why reversing velocity sign alone does not change kinetic energy.

Kinetic energy depends on the square of speed

A particle of mass m moving at speed v has kinetic energy K = ½mv². With m in kilograms and v in m s⁻¹, K is in joules. A 3 kg particle at 4 m s⁻¹ has K = 24 J; at 6 m s⁻¹ it has K = 54 J. Its increase is 30 J. Doubling speed multiplies kinetic energy by four for the same mass, rather than doubling it.

A 4 kg particle with signed velocity −5 m s⁻¹ has K = ½ × 4 × 25 = 50 J. The negative velocity specifies direction; kinetic energy remains non-negative and is the same at +5. A change of direction can change momentum without changing speed or kinetic energy. Do not use a signed kinetic energy to describe leftward motion.

Changes use final energy minus initial energy

For a 0.5 kg particle slowing from speed 10 to 6 m s⁻¹, initial K is 25 J and final K is 9 J. Thus ΔK = −16 J and the amount lost is positive 16 J. A loss of kinetic energy need not mean energy ceases to exist; it can be transferred to heating, deformation or other forms under the model’s stated interactions.

To recover speed from a known positive energy, use v = √(2K/m) and report the non-negative speed. Energy alone cannot choose a velocity direction. A 250 g particle at 20 m s⁻¹ has mass 0.25 kg and K = 50 J, not 50 000 J. Check both conversion and the squared-speed dependence before using the result in a larger energy balance.

Pause and explain

A 4 kg particle has velocity −5 m s⁻¹. What is its kinetic energy?

Put the idea to work

Worked example

A 3 kg particle increases speed from 4 to 6 m s⁻¹. Find its two kinetic energies and change.

Show the worked solution
  1. Initial K = ½ × 3 × 4² = 24 J.
  2. Final K = ½ × 3 × 6² = 54 J.
  3. The change is 54 − 24 = +30 J.

Answer Initial 24 J; final 54 J; increase 30 J.

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From guided practice to a new situation

Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.

1 · Guided

Find the kinetic energy of a 2 kg particle moving at speed 3 m s⁻¹.

Give me a hint

Use ½mv² and square the numerical speed first.

Compare my reasoning
  1. The squared speed is 9 m² s⁻².
  2. K = ½ × 2 × 9 = 9 J.
  3. The result is a non-negative scalar energy.

K = 9 J.

Look for these in your work

  • Retain the factor ½.
  • Report joules rather than momentum units.
2 · Independent

A 4 kg particle has velocity −5 m s⁻¹. Find its kinetic energy and compare it with the energy at velocity +5 m s⁻¹.

Give me a hint

Both signed velocities have the same square.

Compare my reasoning
  1. At −5, K = ½ × 4 × (−5)² = 50 J.
  2. At +5, K = ½ × 4 × 5² = 50 J.
  3. The energies are equal even though the momenta have opposite directions.

K = 50 J at either velocity; the direction cannot be inferred from K.

Look for these in your work

  • Keep mass positive and square the velocity.
  • Separate energy magnitude from velocity direction.
3 · Transfer

A 500 g particle slows from speed 10 to 6 m s⁻¹. Find initial energy, final energy, signed change and amount lost.

Give me a hint

Convert 500 g to kilograms, then subtract initial energy from final energy.

Compare my reasoning
  1. Mass is 0.5 kg, so initial K = ½ × 0.5 × 100 = 25 J.
  2. Final K = ½ × 0.5 × 36 = 9 J.
  3. The signed change is −16 J and the positive amount lost is 16 J.

Initial 25 J; final 9 J; ΔK = −16 J; loss 16 J.

Look for these in your work

  • Convert the mass before calculating SI energy.
  • Distinguish a negative change from a positive loss amount.

Common mistakes

  • Square the speed; do not omit the factor ½.
  • Kinetic energy is non-negative even for negative velocity.
  • Distinguish signed change from the positive amount lost.
Recall without your notes

What can you explain now?

Can kinetic energy alone tell you whether a particle moves left or right?

Compare with the explanation

No; opposite velocities with equal speed have equal kinetic energy.

The squared velocity loses the direction information carried by momentum.

After trying it yourself, choose your next review. This is your self-assessment.

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Make it stick

Use positive mass and squared speed, then calculate energy change as final minus initial.

If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.

Keep learning Mathematics

Course scope and assessment

312 sequenced lessons provide 936 guided, independent and application problems covering the 160 objectives in sections 1.1–1.8, 2.1–2.6, 3.1–3.9, 4.1–4.5, 5.1–5.5 and 6.1–6.5. The map lists all 38 sections across six alternative components; all eight Pure Mathematics 1 sections and all six Pure Mathematics 2 sections, from Paper 2 Algebra through Numerical solution of equations, currently have full teaching sequences. Paper 3 Algebra, section 3.1, links the six shared foundation lessons plus six lessons on partial fractions and rational binomial expansions; Paper 3 logarithms, trigonometry and numerical equations use the shared lessons, with the Paper 1 graph foundation linked into trigonometry. Paper 3 differentiation combines the shared methods with its inverse-tangent derivative lesson. Paper 3 Integration links four shared standard and identity lessons plus seven advanced lessons, covering all six section 3.5 requirements. Eight Vectors lessons cover all six section 3.7 requirements, with 2D and 3D line and solid applications. Nine Differential equations lessons cover all four section 3.8 requirements, including Paper 3 integration techniques, initial conditions, equilibrium solutions and conditional model interpretations. Eleven Complex numbers lessons cover all eight section 3.9 requirements, with seven Argand diagrams and explicit argument and boundary checks. All nine Pure Mathematics 3 sections now have teaching linked to every mapped requirement. Nine Forces and equilibrium lessons cover all seven section 4.1 requirements, with six force diagrams, signed incline-friction intervals, reaction-dependent pull and push limits and separate-body third-law checks. Nine Kinematics lessons cover the four official section 4.2 requirements, with the graph requirement mapped to three relationships. Six motion diagrams support directed quantities, graph slopes and signed areas, Paper 1 time-calculus methods, reversal distances, constant-acceleration stages and shared-clock particle meetings. Six Momentum lessons cover both section 4.3 requirements, including signed system totals, conservation assumptions, outgoing velocities, coalescence, unknown masses and successive impacts. Four diagrams show system momentum through the stipulated events, and supplied kinetic-energy checks distinguish conservation of momentum from changes in kinetic energy. Twelve Newton’s laws lessons cover all four section 4.4 requirements, with vertical support and release stages, rough horizontal and incline motion, inferred parameters, hanging and surface pulley systems, rigid tow-bar thrust and slack-string transitions. Seven force diagrams support the separate-particle balances. Nine Energy, work and power lessons cover all five section 4.5 requirements, with angle-dependent work, kinetic and reference potential energy, signed net-work balances, smooth curved slides, rough and driven slopes, ideal connected-system energy and instantaneous power on hills. Five diagrams distinguish the energy inputs, losses and valid positive-speed force model. All five Mechanics sections now have teaching and staged practice linked to their mapped requirements. Thirteen Representation of data lessons cover all five section 5.1 requirements, including choice and limitations of displays, ordered and back-to-back stems, raw summaries and box plots, unequal-width density histograms, cumulative-frequency interpolation, descriptive moment calculations, grouped estimates, coding, combining two datasets and correcting totals. Six accessible diagrams retain repeated observations and distinguish numerical spread, frequency area and estimated readings. Ten Permutations and combinations lessons cover both official section 5.2 requirements, with selection and ordered roles, repeated objects, adjacency blocks, non-adjacency gaps, leading-zero cases, labelled multi-row seating and mixed restrictions. Six accessible diagrams show outcome trees, ordered versus unordered choices, repeated copies, blocks, gaps and distinct row seats. Eleven Probability lessons cover all four section 5.3 requirements, with equiprobable elementary outcomes, selection-based probabilities, complements and disjoint regions, independence tests, restricted sample spaces, two-way tables, replacement and removal trees, reversed conditions and unknown parameters. Seven accessible diagrams show dice outcomes, random subsets, event regions, a conditioned grid, a count table and two probability trees. Twelve Discrete random variables lessons cover all three official section 5.4 requirements, with complete probability tables, moment and parameter checks, binomial assumptions, exact probabilities and moments, geometric first-success probabilities, tails, expectation and integer reliability thresholds. Six accessible diagrams distinguish outcome grouping, weighted moments, fixed trials, discrete probability bars, waiting conventions and integer tail deadlines. Twelve Normal distribution lessons cover all three official section 5.5 requirements, with continuous-area interpretation, MF19 cumulative tables, full standardisation, tail and interval regions, conditional probabilities, percentiles, one- and two-parameter inference, binomial suitability, continuity corrections and approximate integer thresholds. Seven accessible diagrams show model areas, table reading, aligned scales, intervals, quantiles, matched binomial and normal shapes and whole integer cells. All five Statistics 1 sections now have teaching and staged practice linked to their mapped requirements. Fourteen Poisson distribution lessons cover all five official section 6.1 requirements, with homogeneous event-count assumptions, exposure scaling, exact masses and tails, conditional count events, moments, parameter inference, probability duration targets, disjoint and overlapping windows, rare-count binomial approximations, matched normal approximations and continuity corrections. Nine accessible diagrams show event exposures, unit conversions, discrete bars, complete tail complements, population moments, shared windows, approximate count models and integer cells. Exact checks distinguish approximate and exact close-target decisions. Fourteen Linear combinations lessons cover the six listed results in official section 6.2, with affine moments, signed scales and constant cases, expectation without independence, independent squared variance contributions, fixed and random fees, independent copies versus reused values, affine normal events, independent normal sums and differences, weighted and conditional areas, coefficient inference, exact independent Poisson sums and overlapping exposure limits. Nine accessible diagrams distinguish scale changes, variance contributions, component costs, copy mechanisms, aligned normal scales, summed areas, Poisson count pairs and unsupported distribution claims. Fourteen Continuous random variables lessons cover both official section 6.3 requirements, with density validity and units, support-aware intervals, complements and conditional regions, first and second moments, percentiles by direct density areas, symmetry checks, parameter inference, infinite-support power and exponential integrals, divergent-moment limits and combined applications. Nine accessible diagrams distinguish density height from probability, normalisation, negative-density failures, central and conditional areas, mean versus median, percentile locations and partial infinite tails. Explicit cumulative distribution function knowledge is not required. Eighteen Sampling and estimation lessons cover all eight official section 6.4 requirements, with population and frame definitions, random-number selection, sampling-quality limits, independent sample-mean moments, normal means, CLT conditions, SE scaling, raw, summarised and coded unbiased estimates, known-variance and large-sample mean intervals, repeated-sampling interpretation, precision planning, proportion intervals, sparse-outcome limits and contextual applications. Ten accessible diagrams distinguish selection frames, number rejection, repeated sample means, population versus mean density, CLT models, variance divisors, interval targets, illustrative coverage, integer size checks and proportion margins. Eighteen Hypothesis tests lessons cover all five official section 6.5 requirements, with pre-specified alternatives, inclusive count tails, explicit equal-tail conventions, integer critical regions and actual significance, exposure-aware Poisson tests, continuity-corrected count approximations, normal and large-sample mean tests, confidence-interval connections, error classifications, fixed-region binomial, Poisson and normal error probabilities and sample-size power comparisons. Nine accessible diagrams show the testing procedure, exact count regions, unequal Poisson tails, discrete significance jumps, corrected count boundaries, matching mean intervals and alternative error distributions. All shared lessons are counted once in the course total. All 38 sections now link teaching and practice. Eight earlier overview lessons remain available, but do not count as full section coverage. This is a content-availability measure, not full programme completion or exam readiness. Written practice is self-checked, not officially marked. Check the 2028–2030 syllabus separately if sitting an examination from 2028; this teaching map uses 2026–2027 requirements.

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