Design Optimisation/Simulation TRIZ Cases

A design support device calculates contribution degrees to prioritize improvement plans from a case database.

A component model uses natural vibration behavior to determine mesh fineness, applying coarse elements where deformation is low.

Automated validation of multi-component design constraints eliminates manual verification errors in complex capital projects.

A Power Estimation Block calculates semiconductor power consumption using real clock signals and design parameters.

A morphed mesh displaces boundary nodes and interpolates interior positions to update finite element models.

Finite element analysis and computational fluid dynamics models predict fluid containment to resolve accuracy versus complexity trade-offs.

Finite element sub-models extract local temperature rises on chip wire segments to resolve inaccuracies from foundry formulas that ignore layout geometries.

Simulating sensor orientation defines an operational zone by intersecting sensitivity and coverage areas, reducing false positives in complex environments.

Partitioning circuit networks into subtrees enables parallel simulation, reducing computation time while maintaining accuracy for complex integrated circuits.

An integrated carbon neutral strategy reduces energy consumption through machine learning models while managing high implementation costs.

A neural network predicts particle positions and velocities using density tensors to enable large-scale fluid simulation.

Dynamic grid refinement targets high-resolution cells only where events occur, reducing computational cost while maintaining prediction precision.

Plate-shaped internal electrode models replicate impedance characteristics to shorten simulation time while maintaining crosstalk accuracy.

Machine-trained networks predict parasitic effects and OPC costs during routing to reduce timing violations and signal integrity issues.

A mixed-signal partitioning method assigns analog or digital disciplines to domainless nets using user-defined overrides.

System analyzes casting designs to minimize residual stress and geometric distortion by simulating solidification and heat treatment processes.

Hierarchically defining boundary points and segments from CAD data eliminates multi-camera imaging errors while maintaining high recognition accuracy.

A configurable hardware architecture template generates design parameters to process streaming input data, reducing backpressure and power consumption.

A computing system applies multiple material failure models to geometric data for composite structures.

An ontology-based framework generates and tracks faults across hardware, software, and system domains to simulate propagation paths.

A 3D data center model displays measured IT rack inlet temperatures alongside computational fluid dynamics predictions.

A NoC simulation system generates transaction sequences with multiple messages to model full SoC behavior.

Monte Carlo back projection updates seismic velocity models, resolving cycle skipping and coherent noise artifacts in subsurface imaging.

A topography simulation apparatus calculates material surface changes using discrete time steps and interpolation.

A feature calculation device segments communication graph nodes into representative and non-representative groups to compute values selectively.

Simulating mask shifts and overlay misalignment generates techfiles with dielectric constants to predict parasitic resistive capacitance variations.

Multilevel simulation coarsens computational meshes by zeroing low-weight connections, reducing communication costs in reservoir modeling.

A computer-implemented method generates a tree data structure to manage virtual garment assembly tasks.

A simulation system models material deformation using a spring-mass network and Verlet integration for real-time tactile feedback.

An evaluation system measures AIOps predictive capability using historical data simulations and accuracy scores.

An LSTM model predicts water reservoir storage capacity using historical climate and operational data streams.

A machine learning optimization system manages electrolyzer load and current densities in caustic soda production.

Genetic algorithms evaluate cost functions from sensor data to rapidly determine actuator placement, avoiding extensive trial-and-error testing.

A computer-implemented method models compressor components as non-adiabatic solids to predict performance accurately.

A model transition system combines outputs from executing multiple mathematical models during a specified transition period.

Deep neural networks predict job execution times to optimize scheduling, reducing design cycles for new materials.

Curved port tube geometry minimizes shear stress to delay flow separation and improve low frequency output.