Energy, Nuclear & Fusion¶

Plasma¶

Week 1: Single-particle plasma physics¶

Reading/source: MIT 22.611J: plasma parameters and charged-particle motion

Know: Own Debye shielding, plasma frequency, gyro motion and guiding-center scales.

Reconstruct: Derive gyrofrequency/radius and Debye length scaling.

Do: Numerically integrate charged particle in E/B fields and map magnetization regimes.

Context: MIT plasma course covers fusion-relevant charged-particle motion, transport, confinement and MHD.

Defend: Which dimensionless ratios determine whether a plasma model applies?

Gate: Pass if simulation reproduces analytic limits.

Source: source

Week 2: Collisions and transport¶

Reading/source: MIT 22.611J collision/transport notes

Know: Understand Coulomb collisions, mean free paths and classical transport scaling.

Reconstruct: Derive qualitative collision-frequency temperature/density scaling.

Do: Build transport-timescale model and compare confinement requirement.

Context: Fusion must outrun energy/particle losses.

Defend: When is a collisionless model appropriate?

Gate: Pass if dominant transport timescale is identified by scale analysis.

Source: source

Week 3: Fluid and MHD models¶

Reading/source: MIT 22.611J fluid/MHD

Know: Derive plasma fluid equations, frozen-in field intuition and MHD force balance.

Reconstruct: Regenerate continuity/momentum/induction structure from conservation + Lorentz force.

Do: Simulate reduced MHD/Alfvén-wave or pressure-balance toy model.

Context: MHD gives system-scale confinement/stability language.

Defend: What kinetic phenomena disappear in MHD?

Gate: Pass if limitations of fluid closure are explicit.

Source: source

Week 4: Waves and instabilities¶

Reading/source: MIT 22.611J plasma waves/stability

Know: Reason about collective modes, dispersion and instability growth.

Reconstruct: Derive one simple plasma-wave dispersion or two-stream-style instability condition.

Do: Numerically solve dispersion roots vs parameter sweep.

Context: Instabilities can dominate confinement long before equilibrium design matters.

Defend: Why is a stable equilibrium not necessarily dynamically stable?

Gate: Pass if growth rate prediction matches simulation.

Source: source

Fusion¶

Week 5: Magnetic confinement and tokamaks¶

Reading/source: PPPL 2026 intro course + MIT fusion materials

Know: Understand tokamak geometry, safety factor, confinement and fusion-power criteria.

Reconstruct: Derive Lawson/triple-product style condition and fusion power-density scaling.

Do: Build 0-D tokamak performance model with temperature/density/confinement inputs.

Context: PPPL's 2026 public course spans magnetic fusion, materials/technology and deployment.

Defend: What physics and engineering variables are hidden in a single confinement time?

Gate: Pass if model distinguishes plasma gain from plant electric gain.

Source: source

Week 6: Fusion reactions and power balance¶

Reading/source: Fusion seminar/PPPL + nuclear reaction basics

Know: Track reaction rates, alpha heating, radiation/transport losses and recirculating power.

Reconstruct: Derive volumetric fusion power from n1n2<σv>E and simple ignition balance.

Do: Compute D-T power/loss budget over temperature range with uncertainty.

Context: Fusion is a nonlinear power-balance problem.

Defend: Why can Q_plasma>1 still produce a bad power plant?

Gate: Pass if recirculating auxiliaries and heat conversion are included.

Source: source

Week 7: Plasma diagnostics¶

Reading/source: MIT 22.67J 2023 diagnostics syllabus/notes

Know: Understand magnetic, probe, optical/scattering and nuclear diagnostic principles.

Reconstruct: Derive line-integrated vs local measurement distinction and one inversion problem.

Do: Design synthetic diagnostic suite for hidden plasma state and test observability.

Context: Diagnostics determine whether control and scientific inference are possible.

Defend: Which plasma variables are directly measured versus inferred?

Gate: Pass if uncertainty/inversion bias propagates to control variable.

Source: source

Week 8: Plasma control¶

Reading/source: Wave-1 control + plasma models

Know: Design feedback for unstable/uncertain plasma variables with actuator and sensor limits.

Reconstruct: Linearize reduced plasma model; derive state-feedback/observer concept.

Do: Control vertical-position/temperature/current-profile toy dynamics under delay/noise.

Context: High-performance plasmas require real-time estimation and control.

Defend: What instability is too fast for your actuator/sensor loop?

Gate: Pass if bandwidth and saturation limitations are quantified.

Source: source

Engineering¶

Week 9: Magnets and superconducting systems¶

Reading/source: Condensed-matter/materials core + fusion magnet requirements

Know: Connect field strength to current density, stress, cryogenics and stored energy.

Reconstruct: Derive magnetic energy density B²/2μ0 and Lorentz stress scaling.

Do: Design toy toroidal magnet energy/stress/cryogenic budget across B.

Context: Stronger field helps fusion but magnifies structural/protection challenges.

Defend: Where does increasing B stop being 'free performance'?

Gate: Pass if magnet protection/stored-energy hazard is included.

Source: source

Week 10: First wall, divertor and heat exhaust¶

Reading/source: Thermo/materials + fusion engineering readings

Know: Understand neutron/wall loading, plasma-facing heat flux and material lifetime.

Reconstruct: Derive surface heat-flux/radiator/coolant scaling and damage-per-cycle proxy.

Do: Design divertor/first-wall thermal model with coolant and material temperature limits.

Context: Heat exhaust/material lifetime are central fusion bottlenecks.

Defend: What fails first: temperature, stress, erosion, neutron damage or coolant?

Gate: Pass if competing failure modes are ranked.

Source: source

Week 11: Tritium and fuel cycle¶

Reading/source: Fusion fuel-cycle overview + mass balances

Know: Model breeding, inventory, decay, processing and confinement of tritium.

Reconstruct: Derive inventory dynamic balance and doubling/availability constraints.

Do: Build closed tritium-cycle model with losses and breeding ratio uncertainty.

Context: A D-T plant requires an integrated fuel cycle, not only plasma burn.

Defend: How much hidden inventory is required by process delays?

Gate: Pass if plant operation fails when breeding/process assumptions are stressed.

Source: source

Replication¶

Week 12: Fusion system replication model¶

Reading/source: MIT/PPPL open sources

Know: Integrate 0-D plasma, power, heat, magnet and fuel-cycle models.

Reconstruct: Reconstruct full energy/material flow from blank page.

Do: Replicate a published/reference reactor-point estimate at reduced fidelity.

Context: The goal is system closure, not exact proprietary design.

Defend: Which assumption dominates net electric output?

Gate: Replication Gate: independent parameter audit reproduces your outputs.

Source: source

Fission¶

Week 13: Neutron interactions and criticality¶

Reading/source: MIT 22.05 neutron science/reactor physics

Know: Understand cross sections, moderation, diffusion and multiplication.

Reconstruct: Derive four-factor/k-effective intuition and point-kinetics variables.

Do: Implement simple neutron diffusion/criticality or point-kinetics model.

Context: MIT course explicitly links neutron physics to reactor design.

Defend: What physically changes when k_eff crosses 1?

Gate: Pass if reactivity units/time scales are interpreted correctly.

Source: source

Week 14: Reactor kinetics and feedback¶

Reading/source: MIT 22.05 + 22.06

Know: Model delayed neutrons, temperature/void feedback and control.

Reconstruct: Derive point-kinetics steady/transient relationships and feedback sign.

Do: Simulate power transient with negative/positive feedback and scram.

Context: Reactor safety relies on dynamics, not static criticality alone.

Defend: Why are delayed neutrons operationally transformative?

Gate: Pass if feedback/scram timescales are compared.

Source: source

Week 15: Thermal hydraulics¶

Reading/source: MIT 22.06 / 22.312

Know: Integrate heat generation, conduction, coolant flow, boiling margins and power cycles.

Reconstruct: Derive fuel-centerline/convective heat relations and control-volume coolant energy balance.

Do: Model channel temperature/pressure-drop and safety margin under power ramp.

Context: MIT 22.312 centers thermal-hydraulic/mechanical phenomena in reliable reactor design.

Defend: What thermal quantity is the actual safety constraint?

Gate: Pass if power-to-temperature chain closes.

Source: source

Week 16: Nuclear safety and systems design¶

Reading/source: MIT nuclear systems + safety concepts

Know: Reason about defense-in-depth, decay heat, passive systems, containment and common cause.

Reconstruct: Build fault/event/control structure for loss-of-cooling scenario.

Do: Compare two safety architectures under station blackout assumptions.

Context: Safety is whole-plant control/reliability engineering.

Defend: Which safety function must persist without active power?

Gate: Pass if independent/common-cause failures are represented.

Source: source

Economics¶

Week 17: Plant-level economics and buildability¶

Reading/source: Energy economics + manufacturing core; fusion deployment material

Know: Translate physics into CAPEX, availability, construction time and LCOE-like economics.

Reconstruct: Derive discounted cash-flow and availability effect on unit energy cost.

Do: Cost a stylized fusion/fission plant; sensitivity to capacity factor, build time, financing and component life.

Context: Energy wins by delivered reliable cost, not energy density alone.

Defend: Which technical improvement has highest economic leverage?

Gate: Pass if sensitivity ranks physics and project variables together.

Source: source

Grid¶

Week 18: Firm power in an energy system¶

Reading/source: Wave-1 optimization/control + grid/storage context

Know: Place reactors/fusion in systems with variable renewables, storage and transmission.

Reconstruct: Formulate capacity/dispatch optimization with reliability constraint.

Do: Optimize toy grid under different firm-power costs and outage assumptions.

Context: The value of firm energy depends on the surrounding system.

Defend: When does a technically expensive generator have high system value?

Gate: Pass if comparison uses same reliability/emissions constraints.

Source: source

Research¶

Week 19: Failure/bottleneck map¶

Reading/source: All energy-track models

Know: Rank plasma/heat/material/fuel/plant/construction/economic bottlenecks by leverage and uncertainty.

Reconstruct: Build causal dependency graph with elasticities/sensitivity.

Do: Run global sensitivity/Monte Carlo on integrated model.

Context: This prevents spending a career optimizing a non-binding constraint.

Defend: Which parameter would you pay most to measure better?

Gate: Pass if research priority changes under plausible scenarios.

Source: source

Week 20: Reproduce a fusion or reactor result¶

Reading/source: Choose open paper/course design calculation

Know: Practice real technical reproduction.

Reconstruct: Reconstruct assumptions/equations without code first.

Do: Reproduce one confinement, diagnostic, reactor physics or thermal-hydraulic result.

Context: Use open course material/paper data.

Defend: Which discrepancy is physics versus undocumented convention?

Gate: Extension Gate: reproduction + uncertainty budget + one stress case.

Source: source

Week 21: Independent extension¶

Reading/source: Selected bottleneck literature

Know: Test one mechanism-level intervention.

Reconstruct: Write falsifiable prediction and scaling argument.

Do: Extend model/experiment simulation: control, diagnostic, exhaust, magnet, fuel cycle, reactor feedback or economics.

Context: Novelty comes from reducing an actual system bottleneck.

Defend: What metric improves and what worsens?

Gate: Pass if tradeoff is quantified.

Source: source

Safety¶

Week 22: Integrated plant safety case¶

Reading/source: Wave-2 safety + nuclear/fusion system

Know: Build hazards/controls/reliability around high-energy plant.

Reconstruct: Regenerate containment, shutdown, decay/afterheat and stored-energy hazard map.

Do: STPA/FMEA/reliability analysis for capstone design.

Context: High-energy systems demand evidence before scale.

Defend: What credible single/common-cause event dominates consequence?

Gate: Pass if shutdown/containment conditions are explicit.

Source: source

Capstone¶

Week 23: Integrated energy system design¶

Reading/source: All track sources

Know: Produce physically and economically closed design at conceptual level.

Reconstruct: Reconstruct plasma/neutron -> heat -> power -> auxiliaries -> grid chain.

Do: Capstone: fusion, fission or hybrid enabling subsystem with models, diagnostics/control and cost.

Context: No 'magic materials' or unspecified balance-of-plant.

Defend: Where is your largest unresolved empirical uncertainty?

Gate: Systems Gate: physics + thermal/material + controls + economics reviewers.

Source: source

Week 24: Research program defense¶

Reading/source: Current 2026 PPPL landscape + all work

Know: Choose next-year research program based on system leverage.

Reconstruct: Write propositions, bounds and kill criteria.

Do: Paper + reproducible model + risk register + experimental/validation plan + 12-month milestones.

Context: PPPL's 2026 course explicitly includes deployment/business alongside plasma/fusion science.

Defend: What result in 6 months would cause you to pivot?

Gate: Capstone Gate: defend against scientist, plant engineer and skeptical financier/safety reviewer.

Source: source

Research gates¶

G1 Replication¶

Required performance: Reproduce a fusion/reactor physics, diagnostic, thermal-hydraulic or system calculation.

Minimum artifacts: Equations; units; input sources; uncertainty; code; comparison to source.

Pass criterion: No hidden balance-of-plant or free parameters.

G2 Extension¶

Required performance: Target one binding system bottleneck: stability, heat, materials, fuel, diagnostics/control or economics.

Minimum artifacts: Sensitivity analysis; alternative designs; falsifying regime.

Pass criterion: Improvement must close at plant/system level.

G3 System Closure¶

Required performance: Close core physics->heat->power->auxiliaries/fuel->controls->safety->grid/economics.

Minimum artifacts: Mass/energy flows; dynamics; margins; hazards; plant cost/availability model.

Pass criterion: Net performance must include recirculating power and downtime.

G4 Research Defense¶

Required performance: Prioritize an experiment/program by value of information and deployment leverage.

Minimum artifacts: Concept paper; safety case; experiment/diagnostic plan; 12-month roadmap.

Pass criterion: Must state kill criteria and pivot condition.

Frontier technologies primarily routed here¶

• 4. Cheap, abundant solar electricity: class A
• 6. Planetary smart grid + expanded transmission: class A
• 13. Advanced nuclear fission: class A
• 16. Long-duration energy storage: class B
• 41. Advanced geothermal energy: class A
• 42. Fusion power: class B
• 77. Fusion propulsion: class C
• 95. Large-scale climate engineering: class C