Miniaturized engineers building cooling towers on an LED chip landscape to cool down LEDs.

Cooling the Future: How Advanced Thermal Models Are Revolutionizing LED Technology

Lena Kashyap in Tech & Innovation September 2025 • 4 min read.

"Explore the latest advancements in thermal management for LEDs and how cutting-edge modeling techniques are enhancing efficiency and longevity."

In today's world, light-emitting diodes (LEDs) are essential to numerous illumination products. However, managing the heat LEDs produce is a critical challenge. To tackle this, engineers use compact models that capture time-dependent behavior. These dynamic compact thermal models (DCTMs) are vital for simulating LED performance efficiently, and they are a key focus in projects like the European ECSEL Delphi4LED.

Model order reduction (MOR) has become a crucial technique across various fields, including scientific computing and systems control. The primary goal of MOR is to simplify complex computational models, enabling faster simulations and, in some cases, making previously impossible simulations feasible. MOR achieves this by identifying and preserving the most significant aspects of the model while discarding unnecessary details.

While methods for linear problems are well-established, ongoing research addresses more complex nonlinear, parameterized, and coupled problems. One persistent challenge is whether a mathematically simplified model can be structured as an RC network—a critical consideration for applications in electronic device automation (EDA) and LED modeling, where thermal models ideally mimic RC systems.

The Science of Keeping LEDs Cool

Miniaturized engineers building cooling towers on an LED chip landscape to cool down LEDs.

The heat transfer within an LED package is governed by partial differential equations that describe how temperature changes over time and space. These equations consider factors like thermal conductivity, heat source distribution, and boundary conditions. To simplify these complex calculations, engineers use methods like the finite volume method to convert the problem into a set of ordinary differential equations.

These equations create a system where the temperature at any point in the LED package can be predicted based on the heat inputs and material properties. This system is represented mathematically, allowing engineers to simulate different conditions and optimize the design for better thermal performance. The challenge is that these models can be computationally intensive, requiring significant processing power and time.

To address the challenges, here are some key points:

Thermal Conductivity
Heat Source Distribution
Boundary Conditions
Material Properties

To tackle the computational demands, advanced MOR techniques based on Krylov subspaces are employed. These methods aim to reduce the complexity of the model while preserving its accuracy, allowing for faster simulations and more efficient design optimization. One such method is the Interpolatory Rational Krylov Algorithm (IRKA), which intelligently selects important points in the frequency domain to create a simplified model that accurately represents the thermal behavior of the LED.

Looking Ahead: The Future of LED Thermal Management

The development of innovative methodologies for creating reduced order models will drive the future of LED technology. Methods like IRKA show great promise in enhancing the speed and efficiency of simulations for LED packages. As research progresses, several open questions remain, including how to best construct RC compact models from Krylov subspace-generated models and how to incorporate measured data into these models. Ongoing efforts in structure-preserving MOR and direct RC model construction promise further advancements in LED thermal management, paving the way for cooler, more reliable, and energy-efficient lighting solutions.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1109/nemo.2018.8503163, Alternate LINK

Title: Model Order Reduction For Dynamic Thermal Models Of Led Packages

Journal: 2018 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO)

Publisher: IEEE

Authors: W.H.A. Schilders, S. Lungten

Published: 2018-08-01

Everything You Need To Know

Why are dynamic compact thermal models (DCTMs) important in LED technology, and what role do they play in projects like the European ECSEL Delphi4LED?

Engineers use dynamic compact thermal models (DCTMs) to efficiently simulate LED performance by capturing time-dependent thermal behavior. These models are crucial for projects like the European ECSEL Delphi4LED, which focus on enhancing LED technology.

What is model order reduction (MOR), and how does it address the challenges of complex computational models in LED thermal management?

Model order reduction (MOR) simplifies complex computational models by preserving the most significant aspects while discarding unnecessary details. This allows for faster simulations, making previously infeasible simulations possible. While linear problem methods are well-established, research continues into nonlinear, parameterized, and coupled problems.

How is heat transfer described within an LED package, and what key factors must engineers consider when modeling thermal behavior?

Heat transfer within an LED package is described by partial differential equations that account for temperature changes over time and space. Key factors include thermal conductivity, heat source distribution, boundary conditions, and material properties. Engineers use methods like the finite volume method to convert these equations into a system that predicts temperature at any point in the LED package based on heat inputs and material properties. Addressing the computational intensity of these models is a key challenge.

How do advanced MOR techniques like the Interpolatory Rational Krylov Algorithm (IRKA) improve the efficiency of simulations for LED packages?

Advanced MOR techniques, such as those based on Krylov subspaces like the Interpolatory Rational Krylov Algorithm (IRKA), reduce model complexity while maintaining accuracy. IRKA selects important points in the frequency domain to create a simplified model representing the LED's thermal behavior. This allows for faster simulations and efficient design optimization.

What are the ongoing research areas and open questions in LED thermal management, and how might they impact the future of LED technology?

The future of LED thermal management involves innovative methodologies for creating reduced order models. Methods like IRKA show promise in enhancing simulation speed and efficiency for LED packages. Open questions remain, such as how to construct RC compact models from Krylov subspace-generated models and incorporate measured data. Structure-preserving MOR and direct RC model construction are also promising research areas.