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pump-performance-db

Aggregate manufacturer pump curves and calculate BEP and operating points.

45|14|Updated Nov 7, 2025
Search Tags:#performance#database#interpolation#selection#curves#pump
One-click install
npx skills add https://github.com/Soljourner/claude-engineering-skills --skill pump-performance-db
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pump-performance-db
Source: https://github.com/Soljourner/claude-engineering-skills/tree/main/skills/databases/pump-performance-db
Command: npx skills add https://github.com/Soljourner/claude-engineering-skills --skill pump-performance-db

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Centralize and standardize pump performance data by aggregating manufacturer curves and specifications, enabling faster, data-driven pump selection and system design.

Core Features & Use Cases

  • Consolidated access to head, flow, efficiency, power, and NPSH curves from multiple manufacturers.
  • BEP identification, point querying, and comparison to support shortlisting and design decisions.
  • Use Case: quickly evaluate several pump options for a given duty point and export a ready-to-use dataset for simulations.

Quick Start

Load example pump data and run a BEP query for a chosen flow.

Frequently Asked Questions about pump-performance-db

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compare pump performance curves from multiple manufacturers for selection tasks?▼

You can compare pump performance curves by aggregating head, flow, and efficiency data from multiple manufacturers into a centralized dataset. This enables direct evaluation of different pump options for a specific duty point.

How do I calculate the Best Efficiency Point (BEP) for a pump using flow and head data?▼

Calculating the Best Efficiency Point (BEP) involves querying consolidated pump performance data and applying curve interpolation to identify the optimal operating point. This validates the BEP against flow and efficiency metrics.

Does Python with numpy and scipy support pump curve interpolation and unit normalization?▼

Yes, Python with numpy and scipy supports pump curve interpolation and unit normalization. These dependencies provide the mathematical functions required to process and standardize performance data for accurate calculations.

Can I use aggregated pump data for NPSH checks and system design simulations?▼

Yes, you can use aggregated pump data for NPSH checks and system design simulations. The Skill exports ready-to-use datasets containing head, flow, and power data directly applicable to fluids engineering workflows.

What is the best way to identify and shortlist pumps for a specific mechanical engineering duty point?▼

The best way to shortlist pumps for a specific duty point is to run point queries against standardized manufacturer curves. This allows you to compare efficiency and power data to support design decisions.

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