Course overview
What SLOP3358 is, who it's for, what you should already know, and what you should be able to do by the end of it.
What this course is
SLOP3358 treats Chinese cooking as one connected teaching laboratory, not a set of decorative examples: the same soft-matter physics runs through all twelve weekly modules, grouped into four three-modulestages. Across them you move from describing a food's texture in words to measuring it, explaining it, predicting it and redesigning it — in the same terms a materials scientist would use for any soft material. The course reuses standard, established models — Newtonian and power-law flow, Fourier conduction, Arrhenius kinetics, Flory–Stockmayer percolation, the Gordon–Taylor equation — rather than inventing new theory. Depth comes from applying real models honestly to real food systems, not from piling on unfamiliar terms.
Measures and models this course puts to use:
- storage modulus, G′
- loss modulus, G″
- flow curve
- transport coefficient
- glass transition, Tg
- percolation threshold
Who it's for
A third-year student who wants to see thermodynamics, transport and rheology applied to something concrete rather than another abstract system — no prior cooking experience, food science background, or Chinese-language knowledge is assumed. Every food system used in the course is introduced from first principles, in translation, before any model is applied to it.
Learning outcomes
By the end of SLOP3358, you should be able to:
Identify which physical state (fluid, viscoelastic solid, amorphous glass, crystalline solid) a given cooked or processed food occupies, and justify that classification from its mechanical or thermal behaviour.
Select and apply an appropriate flow model (Newtonian, power-law, Herschel–Bulkley), thermal model (sensible or latent heat, Biot-number regime), or network model (percolation, Gordon–Taylor glass transition) to a specified food material, stating the governing equation and every variable in it.
Fit a model to a dataset, report a goodness-of-fit measure, and read that measure honestly — recognising when a model does not fit well rather than reporting a fit regardless.
State, for any quantitative claim you make about a food material, whether the underlying value is measured, drawn from published literature, or a labelled illustrative/synthetic estimate — and why that distinction matters.
Use a validated model in reverse: given a target texture or shelf-life outcome, propose a process or formulation change and justify it from the model rather than from taste alone.
How the course runs
Each week pairs one written module — the material itself — with a lecture that applies it through a demonstration, comparison or worked example rather than repeating it — twelve of each, run in the fixed four-stage order set out in the modules index. Rather than a simulation for every module, the course concentrates its interactive work into two instruments — the Virtual Rheometer and the Crispness Phase Diagram, both introduced mid-course and both run entirely in the browser — used repeatedly across the Labs and the Laboratory Portfolio, rather than once each and set aside.
Expected workload
The reading and the lecture for a given week are sized to be worked through in that week, alongside whichever staged assessment is currently open — the four pieces of graded work land in weeks 4, 10, 11 and 12, so the heavier sustained effort concentrates there rather than spreading evenly. The two instruments are meant to be returned to more than once before the Laboratory Portfolio is due, not used in a single sitting.
Tools and equipment
A computer with a modern browser is all this course requires. Both virtual instruments run entirely client-side — nothing to install, no account, no special software or hardware beyond that.
How assessment supports these outcomes
Each piece of graded work is built to exercise specific outcomes above, not to test recall of the modules in general — see Assessment for every brief, spec and marking criterion in full.
- Material Characterisation Report
Marked 35% on correct use of a Stage 1 governing model and 45% combined on honest assumptions and real, labelled sources — outcomes 2 and 4 above, applied to one material you choose yourself.
- Modelling Problem Sets
Half the mark is correct application of the relevant transport, kinetics or phase-transition model — outcome 2 — with the rest on shown working and a discussion of the model's limits.
- Laboratory Portfolio
Both instruments are marked on fit quality and state interpretation (outcomes 1 and 3), and 15% of the mark is awarded for stating assumptions and data provenance throughout (outcome 4).
- Inverse Material Design Project
The capstone: a validated model run backwards from a target outcome, judged holistically on whether that target was genuinely measurable and the inversion honestly reported (outcome 5, drawing on all the others).