ArchiLambda Lab is based in the Pennsylvania State University's Department of Architectural Engineering and has its roots in both illumination and mechanical system options. This laboratory’s goal is to understand the thermal and optical dynamics and implications of interactions among buildings, occupants, and external climatic stimuli. The current research efforts encompass three main aspects: bridging the gaps between radiometric and photometric studies in the building environment, integrating spectrally-selective smart material and structures into building envelopes and fenestration systems, and discovering human responses to the interaction between thermal and visual elements. Our research methodology combines experimental, computational, and subjective methods.
This project proposes to develop and evaluate the impact of a smart ambient bright light (SABL) intervention, including both daylighting and electrical lighting, to reduce agitation and other behavioral and psychological symptoms of dementia (BPSD) for nursing home (NH) residents living with Alzheimer’s disease and related dementias (ADRD). (09/01/2022)
Window coatings for energy savings
We have been recently interviewed by the Penn State News team for discussing the recent work published in Energy Conversion and Management. This work examined the energy-saving properties of a coating comprising nanoscale components that can reduce heat loss and better absorb heat. They also completed the first comprehensive energy savings analysis of the material at the building scale (02/2022).
New PhD Graduate
Qiuhua Duan has completed her doctoral research in May 2021 and accepted the Assistant Professor position in the Civil, Construction, and Environmental Engineering Department at the University of Alabama (07/2021).
IEE SEED Grant
The lab's proposal of “Building Energy Savings by Tuning Indoor Lighting” is selected for funding by the Institutes of Energy and the Environment (IEE) Seed Grant Program of Penn State University. The project is to discover the potential thermal-visual interactions in the controllable indoor environment. We will be collaboratively working with Dr. Anne-Marie Chang in Biobehavioral Health and Nursing and Dr. Javad Khazaei in Electrical Engineering on this interdisciplinary project. (04/01/2020)
NSF CBET Fund
Compounding the effects of the building (envelopes and systems) and built environment characteristics, the social and behavioral characteristics of households and urban-rural inequality can result in differing levels of vulnerability to extreme temperature events. This NSF-funded research aims to build an urban-rural regional assessment, preparedness, and response system for extreme temperature events for use during the transition to global sustainability. (08/01/2022)
Podcast - lighting for energy savings
On the latest episode of the Growing Impact podcast, Julian Wang and Anne-Marie Chang have discussed their seed grant project that investigates how indoor lighting can be adjusted to affect user thermal responses to save energy on a building’s heating and cooling and positively impact human health (09/2021).
NSF CMMI Fund
We receive a new research grant from the NSF CMMI program for investigating new building envelope technologies with dual-modality characteristics: generating thermal radiation in winter via the photothermal effect and reducing summer solar heat gains in summer via the photovoltaic effect. See the details in this link. (07/13/2020)
USDA CIG Project
A research project ($598,715 three years) is supported by the Conservation Innovation Grants program of the USDA for studying the application of spectrally selective materials in greenhouses. (04/20/2020)
NSF CAREER Project
The lab has started the NSF CAREER research project ($500,000 for five years) since Oct. 2019. This project is to produce dynamic windows that adapt to climate conditions and spectrally respond to solar irradiance. (02/25/2020)
Renewable and Sustainable Energy Reviews
Data-driven personal thermal comfort prediction: A literature review
Personal thermal comfort prediction modeling has become a trending topic in efforts to improve individual indoor comfort, a notion that is closely related to the design and performance of building systems, especially in sustainable and smart buildings. This research provides a comprehensive overview of data-driven approaches and processes