May 22nd 2024

Improve Building Performance with IES's Dynamic Thermal Simulation Tools

Improve Building Performance with IES's Dynamic Thermal Simulation Tools

Dynamic thermal simulation (DTS) offers a powerful solution for predicting how buildings will perform under various conditions, ensuring energy efficiency and occupant comfort. IESVE's advanced tools analyse thermal behaviour, helping architects and engineers make informed decisions with accurate data.

What is Dynamic Thermal Simulation?

Dynamic Thermal Simulation (DTS) is an essential technique for optimising building performance. In an era where sustainability and energy efficiency are paramount, DTS provides a predictive model to analyse how a building will behave under various conditions. This simulation not only helps in reducing energy consumption but also ensures that the indoor environment remains comfortable for occupants.

What are the Benefits of Dynamic Thermal Simulation?

1. Accurate Energy Predictions

DTS models simulate real-time conditions, providing precise predictions of energy usage. These simulations help design buildings that outperform standard models, ensuring they meet regulations like ASHRAE 90.1 and NECB.

2. Enhanced Comfort

By analysing thermal comfort parameters, DTS ensures that buildings maintain optimal indoor environments, enhancing occupant well-being and productivity. For example, achieving Predicted Mean Vote (PMV) values within the ASHRAE comfort range can increase worker productivity by up to 15%.

3. Lifecycle Cost Analysis

DTS aids in understanding the long-term cost implications of design choices. By evaluating different design scenarios, stakeholders can identify options that reduce operational expenses by 20-40% over the building’s lifecycle.

4. Climate Adaptability

With increasingly unpredictable climate conditions, DTS allows for the design of adaptable buildings that maintain efficiency across diverse environmental scenarios. Buildings can achieve a reduction in energy consumption by 25% through climate-responsive design strategies.

Key Features of IES’s DTS Tools

IESVE’s DTS tools have several key features that make them indispensable for building performance optimisation.

1. Detailed Modelling

IESVE provides highly detailed modelling capabilities, allowing for the simulation of complex building geometries and systems, ensuring an accurate representation of thermal dynamics and energy flows.

2. Integration with Other Tools

Seamlessly integrate DTS with other IESVE tools for comprehensive building performance analysis. This integration supports a unified approach to HVAC design, daylight analysis, and energy modelling, facilitating holistic building optimisation.

3. User-Friendly Interface

The intuitive interface of IESVE makes it accessible for both seasoned professionals and newcomers, streamlining the simulation process and enhancing productivity through efficient workflows.

IESVE Thermal Model

Dynamic Thermal Simulation in Action

IESVE’s DTS tools were used to perform energy modelling and energy analysis as part of an integrative design process for the Walt Disney World Resort McDonald’s Restaurant: the first quick-service restaurant designed to be Net Zero Energy in the U.S. The tools were instrumental in investigating various passive design strategies, enabling detailed modelling of the kitchen equipment loads, and analysing other complex aspects of the design, including the building’s hybrid HVAC system.

IESVE’s DTS tools were used to perform energy modelling and energy analysis for McDonald’s.

Dynamic thermal simulation is a cornerstone of high-performance building design. By leveraging IESVE’s advanced DTS tools, professionals can create buildings that are not only energy efficient but also provide superior comfort and adaptability. Embracing DTS is a step forward in driving innovation and efficiency in building design.

Want to learn more about using dynamic thermal simulation in your projects? Sign up for a free 30-day VE trial or email chris.flood@iesve.com for more information.



References

Attia, S., Gratia, E., De Herde, A., & Hensen, J. L. M. (2012). Simulation-based decision support tool for early stages of zero-energy building design. Energy and Buildings, 49, 2-15.

Chlela, F., Husaunndee, A., Inard, C., & Riederer, P. (2009). A comparative study of predictive control strategies for improving energy efficiency in buildings. Energy and Buildings, 41(2), 147-155.

Costa, A., Keane, M. M., Raftery, P., & O'Donnell, J. (2013). Key factors in modeling energy performance of existing buildings: A case study. Energy and Buildings, 55, 481-491.

National Research Council Canada. (2017). [National Energy Code of Canada for Buildings (NECB)]

Reynders, G., Nuytten, T., & Saelens, D. (2013). Potential of structural thermal mass for demand-side management in dwellings. Building and Environment, 64, 187-199.