Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

The Quick Building Assessment Tool is a simplified building energy analysis tool designed specifically for users with no experience in buildings.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

PNNL’s building science research and technology deployment initiatives were highlighted at the HUD Innovative Housing Showcase.

Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.

The PNNL-managed Building America Solution Center translates research into actionable considerations for homeowners and builders to provide two solutions in one: increasing energy efficiency while also enhancing disaster resistance.

PNNL welcomes six Department of Energy Office of Science Graduate Student Research awardees from across the nation.

This PNNL project was the focus of Nune’s talk when he delivered the keynote for the Carbon Capture and Utilization track at the 2nd Annual Baker Hughes Energy Frontiers Summit.

A PNNL team’s analysis of new-housing data concludes that single-family homes in lower-income counties are less energy-code-compliant than in higher-income counties, a finding that could shape strategies for enhanced code adoption.

The American Society of Heating, Refrigerating and Air-Conditioning Engineers has given its 2022 Journal Paper Award to Jamie Kono, a PNNL building research engineer.

PNNL’s extensive portfolio of buildings-grid research included three projects that helped answer some of the technical questions related to leveraging energy consumption in buildings to enhance grid operations.

The complex interactions between the land and atmosphere can affect cloud growth.

Assessing observed weather conditions that support or suppress the growth of clouds into deep precipitating storms during the Cloud, Aerosol, and Complex Terrain Interactions experiment.

Multiple concurrent single-particle measurements help develop a quantitative and predictive understanding of secondary organic aerosols.

Applying a novel framework to a climate model reveals new characteristics about how elevated concentrations of aerosols affect clouds.

A deep learning model overcomes persistent challenges in emulating long-term simulations of secondary organic aerosols.

The Northwest Connected Communities Summit brought together representatives of five Department of Energy-funded Connected Communities Projects to share ideas and discuss potential collaboration opportunities.

From energy-efficient buildings to new manufacturing techniques, researchers at PNNL strive for sustainability and a decarbonization.

Performing closure studies using aerosol size, aerosol composition, and cloud condensation nuclei measurements of mixed aerosol from the Southern Great Plains region.

How a model treats gas transfer behavior onto dust and sea salt particles significantly influences nitrate aerosols and their effects on climate.