Associative Memory Fuel Consumption Prediction

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Solution Overview

Problem

Calculating the optimal fuel load for vehicle operations, such as commercial aircraft flights, is challenging due to variations in methods and assumptions, leading to either insufficient fuel resulting in unscheduled stops or excessive weight reducing efficiency and increasing costs.

Innovation Solution

A computer-implemented method using an associative memory system to predict fuel consumption by identifying similar historical vehicle operations based on weighted attributes, allowing for accurate calculation of fuel needs for planned operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional methods and assumptions are used to estimate fuel load, then the calculation process is simple, but the accuracy is insufficient leading to either too little fuel (requiring unscheduled stops) or too much fuel (increasing weight and cost)

Engineering Contradiction:

Improvefuel consumption prediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent uses associative memory to store and retrieve historical vehicle operation data as copies of actual flight patterns. By copying real historical operations and matching them with current operation plans through attribute comparison, the system achieves accurate fuel consumption predictions without requiring complex theoretical models, thus improving measurement precision while keeping the system relatively simple

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent introduces an intermediary associative memory system that mediates between operation plans and fuel consumption calculations. This intermediary stores historical data and provides similar historical operations as references, enabling accurate predictions without direct complex calculations, thereby resolving the contradiction between accuracy and system complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If more historical data and complex analysis methods are used, then fuel consumption prediction accuracy improves, but processing time and computational resources increase

Engineering Contradiction:

Improvefuel consumption prediction accuracyVSAvoidcalculation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary action by pre-storing historical vehicle operation data in associative memory during off-peak times or in advance. When a new operation plan needs fuel estimation, the system quickly retrieves similar historical operations from pre-organized data rather than performing complex real-time analysis, significantly reducing calculation time while maintaining high accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

By using copies of historical operations stored in associative memory, the system can rapidly retrieve and compare similar past operations without reprocessing large datasets. This copying approach enables fast accurate predictions by leveraging pre-computed historical patterns rather than performing time-consuming real-time calculations

Inventive Principle:

Principle #26Copying

3Reliability

If fuel load is increased to ensure sufficient fuel supply, then the risk of unscheduled stops is reduced, but vehicle weight increases reducing efficiency and increasing cost

Engineering Contradiction:

Improveoperational reliabilityVSAvoidvehicle weight

Core Design Contradiction:

ReliabilityVSWeight of moving object

Solution Approach 1:

The patent replaces traditional mechanical/mathematical calculation methods with an associative memory-based information retrieval and pattern matching system. This substitution enables more accurate fuel consumption predictions by analyzing actual historical operation patterns, allowing the system to determine the precise minimum fuel load needed without excessive margins, thus improving operational reliability while minimizing unnecessary weight

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system uses feedback from historical operation data stored in associative memory to continuously improve fuel consumption predictions. By comparing actual historical fuel consumption with operation parameters, the system refines its predictions and can determine optimal fuel loads with higher confidence, reducing both the risk of insufficient fuel and the waste of carrying excessive fuel weight

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS10417651B2Fuel consumption predictions using associative memories

Publication Date: 2019.09.17 THE BOEING CO

AI summary

A method, system, and computer program product for predicting fuel consumption for a vehicle operation is provided. An associative memory device is accessed, using one or more attributes of an operation plan for the planned vehicle operation, to determine at least one historical vehicle operation from a plurality of historical vehicle operations that is similar to the planned vehicle operation. The associative memory device contains data for a plurality of attributes and collected from the plurality of historical vehicle operations. The at least one historical vehicle operation is determined by applying a respective weight defined within the associative memory device to data values for each of the plurality of attributes. Fuel consumption data for the planned vehicle operation is predicted, based on historical fuel consumption data corresponding to the at least one historical vehicle operation. The predicted fuel consumption data is output.