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Computing for Neutron Activation Inventory

Tracking which materials become radioactive under neutron exposure, how much, and how it decays, over the life of a plant.

What activation is

When materials absorb neutrons, some nuclei transmute into radioactive isotopes. This activation makes components radioactive during and after operation, which governs handling, maintenance access, and end-of-life material management. Activation inventory computing predicts which isotopes form, in what quantity, and how their activity decays with time.

The calculation

Kronos motion — 14 mev materials test

Why impurities matter

A material chosen for low activation can be spoiled by trace impurities that produce long-lived or highly active isotopes. A few parts per million of the wrong element can dominate the activation picture. This is why reduced-activation materials specify tight impurity limits, and why activation computing must use realistic, not idealized, compositions.

The decay chains

Activation produces isotopes that decay, sometimes into other radioactive isotopes, forming chains. Predicting activity at any future time means solving the coupled decay and transmutation equations, a stiff system spanning half-lives from seconds to years. Specialized inventory codes solve these efficiently.

python
import math

def activity(n0, half_life, t):
    lam = math.log(2)/half_life
    return n0 * lam * math.exp(-lam*t)   # decays per unit time

Kronos framing

Activation inventory informs maintenance planning, shielding, and material choices for the Hyperion breeder. Fusion's advantage here is real, choosing low-activation materials can limit long-lived radioactivity, but it must be demonstrated by calculation with honest impurity assumptions, not asserted. This is design-stage analysis; the machine is not built and construction begins in the second quarter of 2027.