Simulation Model Construction From Incomplete User Input
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Solution Overview
Problem
Existing simulation methods require complete information about dimensions, physical properties, and connections for constructing simulation models, which is time-consuming and difficult to achieve without prior experience, and existing machine learning approaches need representative data, delaying simulation results when such information is lacking.
Innovation Solution
A simulation device using a processor and storage to construct models with weighting factor data, enabling model construction and simulation from limited information by evaluating user input and outputting relevant data, with optional communication interfaces for acquiring weighting factor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complete information (dimensions, physical properties, connections) is collected for constructing simulation models, then manufacturing precision and reliability are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically generating simulation models from building design data (BIM, CAD) before the user needs to conduct simulations. The model construction unit automatically creates simulation models using building information from design-stage data, eliminating the time-consuming manual data collection and model construction process while maintaining accuracy.
Solution Approach 2:
The system enables self-service by automatically constructing simulation models without requiring manual intervention for data collection and model creation. The model construction unit autonomously processes building design data, extracts necessary information, and generates simulation models, freeing users from the tedious preparatory work while ensuring complete and accurate model construction.
2Productivity
If simulation models are constructed from limited information, then productivity and speed are improved, but manufacturing precision and reliability deteriorate
Solution Approach 1:
The system achieves universality by processing multiple types of building design data (BIM, CAD, floor plans) through a single model construction unit. This unit can handle various data formats and automatically extract necessary simulation parameters, enabling rapid model construction from different information sources while maintaining consistent accuracy through automated parameter extraction and validation.
Solution Approach 2:
The system applies parameter changes by automatically determining simulation parameters from building design data. The model construction unit extracts dimensional information, material properties, and geometric parameters from input data and transforms them into simulation-ready parameters, enabling rapid model construction without manual parameter specification while maintaining accuracy through systematic parameter transformation.
3Manufacturing precision
If sample models are selected and corrected based on user experience, then manufacturing precision is improved, but ease of operation and accessibility deteriorate
Solution Approach 1:
The system enables self-service by automatically constructing simulation models from building design data without requiring users to manually select or correct sample models. The model construction unit autonomously processes input data, extracts necessary parameters, and generates accurate simulation models, making the process accessible to users regardless of their simulation expertise while maintaining high model accuracy.
4Manufacturing precision
If machine learning models are trained with representative data, then manufacturing precision is improved, but loss of time deteriorates due to data preparation requirements
Solution Approach 1:
The system performs preliminary actions by utilizing building design data that is already available from the design stage. Rather than requiring separate data collection and preparation, the model construction unit directly uses dimensional information, material properties, and geometric data from BIM or CAD models, eliminating time-consuming data preparation while maintaining prediction accuracy through automated parameter extraction and validation.
Data Source
AI summary
A simulation device is a device configured to construct a model and perform a simulation using the model. The simulation device includes a processor and a storage device. The storage device stores, as a library, weighting factor data indicating, by a weighting factor, a relationship between information input by a user and an element of the model and/or a calculation condition. The processor evaluates the information acquired by the input of the user, and constructs a model used for simulation using the weighting factor data stored in the storage device. The processor outputs data indicating information related to the constructed model. The processor performs a simulation based on the constructed model, and outputs data indicating a result of the simulation.