Thermal Packaging Temperature Prediction via Machine Learning
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Solution Overview
Problem
Current solutions for shipping temperature-sensitive products are overdesigned, leading to increased costs due to the reliance on worst-case weather conditions for packaging qualification, and existing heat transfer modeling methods are computationally intensive, making them inefficient for rapid simulation.
Innovation Solution
The method involves calculating lane temperature using weather data and machine learning techniques, and employing a pseudo-geometry approach to simplify heat transfer equations, allowing for quick simulations of product temperature and optimal packaging selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If packaging is designed based on worst-case weather conditions (ISTA 7E standard), then product temperature reliability is improved, but packaging cost and complexity increase due to overdesign
Solution Approach 1:
The patent applies dynamics by transitioning from static worst-case design to dynamic, real-time temperature modeling. The system uses machine learning models to continuously predict lane temperatures based on actual weather data, allowing packaging decisions to adapt dynamically to current conditions rather than relying on fixed worst-case scenarios. This enables optimization of packaging for actual expected conditions while maintaining reliability thresholds.
Solution Approach 2:
The patent changes the parameter basis for packaging design from fixed worst-case temperature extremes to dynamically predicted lane temperatures. By using machine learning models that process weather data, origin-destination pairs, and shipment details, the system generates realistic temperature profiles that reflect actual shipping conditions. This parameter transformation allows right-sized packaging selection that maintains product reliability without overengineering.
2Measurement precision
If traditional heat transfer modeling with grid overlay is used, then measurement precision of temperature distribution is improved, but computational time increases significantly
Solution Approach 1:
The patent uses copying by creating simplified thermal models that replicate the essential heat transfer behavior without requiring full finite element analysis. Instead of copying the entire detailed grid model, the system uses machine learning-trained surrogate models that capture the dominant thermal characteristics. These copied models provide sufficient precision for packaging decisions while executing in seconds rather than hours.
Solution Approach 2:
The patent substitutes the mechanical finite element analysis system with a machine learning-based computational system. Rather than solving partial differential equations through numerical methods with grid overlays, the system uses trained neural networks or other ML models that have learned thermal behavior patterns. This substitution maintains measurement precision for practical purposes while dramatically reducing computational time from hours to seconds.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces overdesign in packaging, lowers shipping costs, and enables rapid decision-making by providing accurate temperature predictions and packaging recommendations, improving logistics for temperature-sensitive products.
Implementation Method 1
The standard for heat transfer modeling of thermal packaging is finite element analysis. These models use numerical methods to solve complex heat transfer processes through the use of steady state calculations over small time periods and short distances.
Data Source
AI summary
Systems and methods for modeling a temperature in a thermal package in a lane of commerce using forecast weather data or estimating the actual temperature history in a thermal package using actual weather data. Lane temperature is recognized and used as an intermediate calculated variable derived from weather data. Machine learning techniques estimate the lane temperature to determine a model. Product temperature in thermal packaging is estimated by simultaneously solving a set of heat transfer equations. The above is used to with forecast weather data to calculate a lane temperature and then calculate an expected product temperature based on the time and date of shipment. This product temperature curve is then analyzed with a set of decision rules to improve decision making on when to make a shipment and the best packaging to use.