Research Terms
This instrumented unmanned aerial vehicle (UAV)-based system aids in the non-intrusive measurement of insulation properties of building envelope components such as windows, walls, and roofing systems. This system can be used to evaluate envelope components of multistory buildings including skyscrapers, enabling a citywide evaluation of energy wastage for improving efficiency. The U-value is a quantifiable measurement of a structure’s ability to transfer heat. The lower the U-value, the less heat or energy is lost through insulating materials or structures such as windows on a building. According to the U.S. Department of Energy, up to 30 percent of heating and cooling energy is lost through windows in residential buildings. Studies have shown that replacing single-pane windows with less heat-transferring windows, such as double-pane windows, can save homeowners 20-31 percent in heating and cooling expenses. Technologies for measuring the U-values of non-operable windows or roofing components commonly installed in high-rise buildings are not readily available, leading to energy wastage due to outdated or damaged windows. Similar energy wastage is common for walls and roofing systems as well, owing to the lack of technology for rapid measurements of insulation properties.
Researchers at the University of Florida and WinBuild Inc. have developed an instrumented UAV that uses infrared thermography temperature sensor and anemometry to assess window, wall, and roofing systems’ heat transfer on the exterior of buildings. This integrated system can measure insulation properties to determine if windows or roofing components need replacement or repair to reduce energy wastage.
UAV system estimates heat gain or loss on the exterior façade of buildings remotely and in a non-intrusive manner.
The UAV measures window exterior airflow and temperature in real-time using a hot wire anemometer and temperature using an infrared sensor. For wall and roofing thermal performance applications, the UAV employs infrared thermography (IRT) and hyperspectral cameras to conduct exterior measurements. Utilizing custom-built software, the UAV system calculates the heat gain or loss of windows, walls, and roofing components due to material deterioration. The drone can conduct rapid field measurements to facilitate community-wide mapping of energy wastage. Preliminary tests have been conducted to estimate window heat loss, using spot measurements of UF Rinker Hall’s building envelope.
This algorithm uses low-cost thermal and digital drone images in combination with existing public data to calculate building metrics used for determining flood insurance requirements and building energy efficiency rates.
In the United States in 2019, floods caused $3.75 billion in damage. Only 6 percent of American homeowners have a flood insurance policy from the National Flood Insurance Program. To receive flood insurance, building owners must submit elevation certificates, which can vary in price from $160 to $2000, with the average being $600. The price depends on the quality of the survey, timing, available elevation data, demand, structure type, and occupancy type of the building. The location of the building can extend the time and cost of an elevation certificate if it is far from existing local or USGS benchmarks.
Currently, there is not a technique to let people know if the Lowest Floor Elevation (LFE) is lower than Base Flood Elevation (the local elevation to which water level for a 100-year flood rises) without getting a ground survey done which is expensive and time-consuming. This keeps the building owners in the dark about the likelihood of storm damage on their property and whether they should get flood insurance or not.
Researchers at the University of Florida have developed an algorithm that uses pre-existing data and low-cost drone images to solve this problem. Our method estimates the LFE which can help map the risk of flooding using geospatial analysis. This work will help insurance companies, building owners, to have a preliminary report of the buildings that stand at a risk of flooding.
Algorithm that uses low-cost drone digital and thermal infrared (TIR) images to calculate metrics important in determining flood vulnerability of a building.
The following is the list of possible interested stakeholders to use this algorithm:
This flexible algorithm uses drone-based digital and TIR images and publicly-available data to estimate the Lowest Floor Elevation, an important metric for determining the flood insurance requirements of a building. Using thermal image and image corrections lets the algorithm visualize the elevation of the slab inside the building using non-intrusive measurement using drones from outside. Calibrating the image with the County parcel data yield extremely accurate elevation information. Once LFE is obtained, it is compared with the BFE from the GIS parcel data to estimate the likeliness of flooding. The algorithm uses the same data to estimate wall and roof U-factors that are important for determining the energy efficiency of a building.
This digital twin platform connects to the industry-standard Building Performance Simulation engine, EnergyPlus™, to conduct building energy scenario analysis. It addresses a critical gap in the architecture, engineering, and construction (AEC) industry to perform high-fidelity, real-time energy analysis on complex building systems. Buildings account for a significant amount of global energy consumption and greenhouse gas emissions. At a national level, they represent a significant lever for national energy conservation, with the combined residential and commercial sectors accounting for approximately 37% of total U.S. energy consumption when accounting for electricity-generation losses. Despite the availability of modern Building Information Modeling (BIM), current systems often struggle to bridge the gap between static architectural data and dynamic, physics-based performance analysis. Existing building management approaches and simulation tools are often limited by fragmented data silos and a lack of real-time integration.
Traditional methods frequently rely on static models that do not account for real-time environmental factors or human-related drivers, such as occupant behavior and operational maintenance; these can influence energy outcomes as significantly as physical building properties. Furthermore, standard simulation engines like EnergyPlus™ typically require bespoke, labor-intensive integrations for each new building model, preventing the scalable, "on-the-fly" analysis necessary for modern facility management. The global market for digital twin technology is rapidly expanding as a crucial concept for improving productivity and reducing downtime across the built environment. By 2023, the significance of building energy profiles has underscored a growing need for robust improvement strategies that can forecast energy demands and simulate complex heating, ventilation, and air conditioning (HVAC) loads with precision. However, the industry still faces a lack of seamless, bi-directional interfaces for visualizing both building states and predicting future performance through automated "what-if" scenario testing.
University of Florida researchers have developed a computing system featuring a bi-directional connector module that interfaces with high-fidelity digital twin platforms, such as NVIDIA™ Omniverse, with the industry-standard EnergyPlus™ simulation engine. This framework leverages a layered architecture, comprising hardware, middleware, and software, to create a dynamic virtual replica of physical assets that is continuously updated with real-time IoT sensor data. By decoupling the digital twin from the simulation engine through a generic connector, this approach enables scalable, real-time decision support and predictive maintenance, offering a transformative solution for energy efficiency and providing building operators with actionable insights to reduce global greenhouse gas emissions. This tool offers valuable data for advancing energy-efficient architectural designs.
Seamless, interactive building energy decision-making platform for conducting building energy scenario analysis and performance optimization and prediction through high-fidelity digital twin visualization and physics-based simulation
Performing building energy analysis involves using a high-fidelity digital twin platform, a bi-directional connector module, and a physics-based simulation engine. Static geometric data from a Building Information Model (BIM) enters the platform, integrates with real-time operational data from IoT sensors, and reaches the simulation engine. Next, a bi-directional loop is established between the virtual replica and the simulation engine, and "what-if" scenarios are executed. Performance results are quantified by analyzing the output data returned from the engine to the digital twin interface. The ratio of energy consumption to specific operational variables is then calculated to provide near real-time visual feedback. These measurements provide valuable insights, enabling building operators to make informed decisions about a building’s energy efficiency.
This HVAC system combines the efficiencies of Variable Air Volume (VAV) and Variable Refrigerant Flow (VRF) systems, simultaneously providing the advantages of both systems. Heating, ventilation, and air conditioning (HVAC) systems are responsible for heating and cooling, and controlling the humidity and purity of the air in an enclosed space. Currently, Variable Air Volume (VAV) and Variable Refrigerant Flow (VRF) systems are popular and widely used HVAC systems.
VAV systems regulate the supply air volume-flow rate using a damper to match the variation of the space-cooling load and maintain the zone air temperature at the set temperature. On the other hand, a VRF is a refrigerant system varying the refrigerant flow rate and temperature to cool down the room with the help of a variable speed compressor and electronic expansion valves (EEV). The EEV uses information received from thermistor sensors in each indoor unit to enable precise control of refrigerant flow variation, providing exact amounts of heating or cooling in each space. VRF systems hold an impressive capacity control range, providing approximately 20-50% higher energy efficiency than traditional VAV systems. However, there is a current movement to increase the efficiency of HVAC systems beyond those of VRF and VAV technologies.
Researchers at the University of Florida have developed the Variable Air Variable Refrigerant Flow (VAVRF) system. This new HVAC system combines the efficiencies of both VAV and VRF systems. The design allows for simultaneous variations of the air volume in conditioned spaces and the refrigerant flow and temperature, increasing efficiencies beyond the conventional VRF and VAV systems.
HVAC system combines the benefits of Variable Air Volume (VAV) and Variable Refrigerant Flow (VRF) technologies, providing higher efficiencies and energy savings than traditional HVAC systems
This Variable Air Variable Refrigerant Flow (VAVRF) system depicts an innovative HVAC system, combining the benefits of both VAV and VRF technologies simultaneously. In contrast to VRF systems, consisting of several indoor units with individual fans for providing air movement, the VAVRF system encompasses a central ventilation Air Handling Unit (AHU) responsible for this task. The AHU is a VAV system with supply, relief, ventilation, return, and mixing sections. A VRF-type system handles the heating and cooling of air from the unit supplies into the building using ductwork. Each zone has a volume-modulating air damper (VAVRF indoor unit), maintaining a predetermined volume of air flowing through it. A VRF coil inside this unit provides heating or cooling in response to the space thermostat, and a few usual heat recovery/branch controller boxes serve multiple VRF coils. The VAVRF system's easy packaging and centralized airflow lead to savings in energy and cost, better air cleaning, and improved control.
This asthma inhaler case device collects environmental data and coordinates with a centralized evaluation and mapping service to provide real-time air quality measurements at high resolution. Asthma is a common chronic condition in which inhaled substances can irritate the airways and trigger allergic reactions. It can also exacerbate other diseases and lead to hospitalizations. Among children, asthma is the main reason for missed school days. Select air quality monitoring stations are placed around the United States in areas with small populations. Though somewhat helpful, these stations are limited in the types of pollutants they can sense and the areas they can accurately monitor. They cannot identify ground-level air pollutants in densely populated areas that also pose a threat for people with asthma.
Researchers at the University of Florida have created a device for inhalers that gathers high-resolution local air quality data in real-time and transfers the calibrated data to an online mapping service for data dissemination. The tool measures the air-quality risk level of the local environment to determine appropriate medication dosage. It can produce higher-quality environmental data useful for planning any construction of infrastructure (buildings, paved roads, etc.) and for personal or public health decisions.
Portable smart air quality sensors embedded in inhaler cases communicate with each other in a network to measure air pollutant levels at high resolution in the local environment.
The multisensory attachment case for asthma inhalers gather high-resolution, spatially, and temporally scaled air quality measurements. This information then transmits to a centralized mapping service that processes it into real-time, actionable environmental data. Sensors in the inhaler attachments measure local levels of O3, PM2.5, NO2, temperature, and relative humidity at their various locations. The devices share the air quality data with patients wirelessly in real-time for precise dosing; they are battery charged and come with a carrying case.
This non-invasive system measures the argon gas content and thermal properties of double-glazed windows. Double-glazed windows play a crucial role in regulating building temperatures, but over time, and with prolonged sunlight exposure, their energy-efficient properties degrade. As awareness of global energy consumption grows, so does the demand for energy-efficient solutions to help manage building temperatures. The global energy-efficient windows market was valued at USD 15.19 billion in 2023 and is projected to grow by 7.7% annually from 2024 to 20301. Conventional methods of assessing window glazing face significant barriers, underscoring the need for a cost-effective, accurate alternative.
Available methods for assessing window deterioration often require invasive techniques that can damage installed windows or necessitate their removal for lab testing. Traditional instruments are often expensive and complex, making this system a simpler and more accessible solution for glazing assessments. With buildings accounting for a substantial portion of global energy consumption, predominantly through heating and cooling requirements, enhancing fenestration energy efficiency is essential. In an era where global energy policies are increasingly directed towards sustainability and efficiency, understanding the dynamics of solar light interaction with fenestration systems is crucial.
Researchers at the University of Florida, WinBuild Inc., and SunPine Inc. have developed a system to determine the thermal properties of double-glazed windows. By combining a full-spectrum camera, light source, and filter system, the energy efficiency of double-glazed windows can be measured with precision and accuracy on-site.
Full-spectrum camera system for non-method measuring argon gas content and thermal properties of double-glazed windows
Window glaze assessments provide critical information about how the solar spectrum interacts with double-glazed glass. Measuring argon gas content and thermal properties involves using a light source that simulates sunlight, a filter system, and a full-spectrum camera. Light enters from the top, passes through the filter, and reaches the camera. Next, a glass sample is placed between the camera and the filter, and an additional photograph is taken. Argon gas content is quantified by analyzing a sectioned-off square of pixels in the full-spectrum photograph. The ratio of the glass intensity value to the total intensity value is then calculated. These measurements provide information about the argon gas content and thermal properties of double glazing, which provide valuable insights, enabling window examiners to make informed decisions about a building’s energy efficiency. By delivering key insights into light and glazing interactions, this tool offers valuable data for advancing energy-efficient architectural designs.
