GATE MN 2027 | MMT-11 Mock Test L-1 | Blasting Numericals & Linear Programming | By Vaishnavi Ma'am
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GATE MN 2027 | MMT-11 Mock Test L-1 | Blasting Numericals & Linear Programming | By Vaishnavi Ma'am
263 просмотра · 2 нед. назад
MINING GYAN
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263 просмотра · 2 нед. назад
Welcome to Lecture 1 of the MMT-11 Mock Test Discussion for the GATE MN 2027 Exam, presented by Vaishnavi Ma'am! This session tackles a mix of General Aptitude, Operations Research, and Blasting numericals, providing comprehensive solutions for complex exam-style questions.
The lecture begins with quantitative aptitude, calculating unknown product sales quantities using weighted averages and solving geometry problems involving semi-circular arcs. Vaishnavi Ma'am then dives into pattern recognition and logical puzzles, including a challenging sequence involving the states of linearly arranged lightbulbs based on specific neighbor conditions.
Next, the class transitions into core Mining Engineering topics. You will learn how to calculate explosive mass in blast holes using ANFO density and volume formulas. The session also covers the mechanics of detonating cords and NONEL shock tubes, calculating travel times and nominal delays across multiple blast holes. Finally, Vaishnavi Ma'am returns to Operations Research, providing a step-by-step masterclass on solving a resource-constrained Linear Programming (LP) problem to find the maximum weekly profit for a toy manufacturer.
🕒 Video Chapters / Timestamps:
[00:02] Quantitative Aptitude: Finding product sales quantities from average pricing
[08:34] Geometry: Solving the semi-circular string length ratio problem ($x/L$)
[14:50] Pattern Recognition: Identifying missing shapes in sequences
[16:06] Quadratic Equations: Finding curve intersection points and imaginary roots
[20:02] Geometry: Calculating exterior angles formed by intersecting secants on a circle
[25:56] Logical Puzzle: Determining the final on/off states of linearly arranged bulbs
[32:33] Number Theory: Solving $p^2 - q^2 = 13$ using prime factorization
[34:51] Optimization: Maximizing elective credits within constraints
[38:43] Spatial Reasoning: Identifying 3D structures from top and front views
[40:41] Linear Programming (LP): Profit maximization problem (Wooden Soldiers vs. Trains)[43:43] Mapping LP constraints (Finishing hours, Carpentry hours)
[45:57] Solving constraint intersections to find maximum profit
[50:13] Finalizing net profit after subtracting indirect costs (Weekly Rent)
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