Integrated Computational Element Design Optimization

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Solution Overview

Problem

Conventional ICE design optimization methods require extensive computational resources and time due to the large number of preliminary designs and simulations, often necessitating high-intensity computing power and hardware, while using single-objective functions that may conflict with fabricability considerations.

Innovation Solution

A genetic algorithm is employed to evolve the thickness of ICE layers using a constrained multi-objective function, reducing the need for derivative calculations and enabling efficient design optimization with a significant reduction in computing requirements and cost.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If conventional ICE design optimization methods are used with gradient-based routines and extensive simulations, then design optimization thoroughness is improved, but computational resources and time requirements increase dramatically

Engineering Contradiction:

Improvedesign optimization thoroughnessVSAvoidcomputation time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent replaces the mechanical gradient-based optimization routine with a genetic algorithm, which is a biologically-inspired computational method. This substitution eliminates the need for derivative calculations and allows parallel evaluation of multiple design candidates, significantly reducing computation time while maintaining optimization quality

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent performs preliminary filtering of design candidates based on fabrication constraints before full optimization. By pre-screening designs for manufacturability, the system reduces the number of candidates requiring extensive simulation and optimization, thereby reducing overall computational time

Inventive Principle:

Principle #10Preliminary action

2Manufacturing precision

If conventional ICE design optimization methods are used with extensive simulations, then design quality is improved, but hardware requirements and computing power increase

Engineering Contradiction:

Improvedesign qualityVSAvoidhardware requirements

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent segments the optimization process into multiple independent stages: preliminary filtering, genetic algorithm optimization, and final evaluation. Each stage can be performed independently and on smaller computational resources, eliminating the need for massive parallel computing clusters

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent uses simplified models and surrogate functions to approximate complex optical simulations during the optimization process. These simplified copies allow rapid evaluation of design candidates without requiring full-scale electromagnetic simulations for every candidate

Inventive Principle:

Principle #26Copying

3Ease of operation

If single-objective functions are used for ICE design optimization, then optimization simplicity is improved, but fabricability considerations are compromised

Engineering Contradiction:

Improveoptimization simplicityVSAvoidfabricability

Core Design Contradiction:

Ease of operationVSEase of manufacture

Solution Approach 1:

The patent merges multiple objectives (optical performance and fabrication constraints) into a single multi-objective fitness function used by the genetic algorithm. This unified function evaluates both performance metrics and manufacturability criteria simultaneously, ensuring that optimized designs are both high-performing and fabricable

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS10430542B2System, method and computer program product for integrated computational element design optimization and performance evaluation

Publication Date: 2019.10.01 HALLIBURTON ENERGY SERVICES INC

AI summary

A system for integrated computational element (“ICE”) design optimization and analysis utilizes a genetic algorithm to evolve layer thickness of each fixed ICE structure using a constrained multi-objective merit function. The system outputs a ranked representative group of ICE design candidates that may be used for further fabricability study, ICE combination selection, efficient statistical analysis and/or feature characterization.