L38 Pre-flight#
Block 4: Aircraft Survivability and Mission Planning — Lesson 38: Final Project Work Day Work this before L38. Pre-flights are never collected — in class, anyone may be cold-called to present any question at the board, and that recitation is the participation grade.
Assumed pre-class reading#
The Final Project handout — its Models (aspect, detection contour, route planning), its Use of AI section and the appendix of sample AI prompts, and its Sources and The Paper sections.
The L32 and L34 readings: the aspect a radar sees, the \(R^4\) contour, and why an aspect-aware route graph is directed.
The L38 page — the template, the AI rules for the paper, and how to cite course lessons.
This quiz ties the route to the paper: the first three questions follow one heading through Blocks 1, 3 and 4, and the last two are the paper’s rules for citing the course and for using AI.
Quiz questions (5 items, ~5 minutes)#
Q1. (Multiple choice) An X-band SAM radar sits at the origin. A B-21 is 50 km due east of it (bearing φ = 090° from the radar), on a grid edge that runs east–west. The radar detects the B-21 nose-on out to 48 km; the X-band pattern reads −15 dBsm nose-on, −3 dBsm at the beam and −10 dBsm tail-on. With θ = wrap(φ + 180° − ψ), which aspect does the radar see on each heading, and is the B-21 detected at 50 km?
[ ] (a) Westbound nose-on, R = 48 km: not detected. Eastbound tail-on, R ≈ 64 km: detected.
[ ] (b) Westbound tail-on, R ≈ 64 km: detected. Eastbound nose-on, R = 48 km: not detected.
[ ] (c) Both headings beam-on, R ≈ 96 km: detected either way, since the radar sees the same side.
[ ] (d) Westbound nose-on, R = 48 km: not detected. Eastbound tail-on, R ≈ 85 km: detected.
Q2. (Multiple choice) An aspect-aware planner stores the leg from grid node a to node b and the leg from b back to a as two separate edges, with different costs, although both cover the same stretch of ground. Why?
[ ] (a) Dijkstra’s algorithm runs only on a directed graph, so MATLAB’s
shortestpathneeds each leg stored twice.[ ] (b) An edge’s cost includes the cost already paid to reach its first node, and that differs at a and at b.
[ ] (c) Reversing the leg reverses the heading, so each radar sees another aspect, σ and contour, and another lethal length.
[ ] (d) The aircraft burns more fuel on one of the two, because it flies the ingress heavier than it flies the egress.
Q3. (Multiple choice) T3 also asks for an aspect-blind plan, which prices every edge with the B-21’s nose-on signature. With the X-band pattern of Q1 (and about −28 dBsm near 45° off the nose), which statement is true?
[ ] (a) It is conservative, because nose-on is the largest X-band RCS, so every aspect-aware contour lies inside it.
[ ] (b) It still needs a digraph, because “nose-on” is defined from the heading, and reversing an edge flips it.
[ ] (c) It gives the true \(P_S\) of its own route, since each of that route’s edges was scored when it was planned.
[ ] (d) Each edge costs the same both ways; its route’s true \(P_S\) comes only from re-scoring it aspect-aware.
Q4. (Multiple choice) Four drafts cite course lessons as listed below. Which one meets the Final Project’s citation rule?
[ ] (a) L31, L32, L34 and L36, each cited in the sentence whose specific claim it supports
[ ] (b) L3, L14, L25 and L34, each cited in the sentence whose specific claim it supports
[ ] (c) L3, L14 and L25, each cited for its claim, plus a peer-reviewed journal article on routing
[ ] (d) L3, L14, L25 and L34, cited once together: “this work builds on [1]–[4]”
Q5. (Short answer) You have worked out
your T3 approach on paper: loop over the grid’s edges, take each edge’s heading,
find the aspect each radar sees, and sum λℓ into that edge’s weight in a
digraph. (i) May you ask an AI to turn that approach into MATLAB that calls
FPToolkit.m? Under what conditions? (ii) Name one request about the paper
that the policy allows and one it forbids. (iii) If you used AI at all, what must
you submit, and with what?