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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Demand Control Ventilation

This documentation focuses on a single end-use savings shape measure - Demand Control Ventilation (DCV). DCV can save energy by reducing the rate at which outdoor air (OA) is delivered during periods of less-than-design occupancy. This measure will enable DCV for air loops using applicable HVAC system types (all except dedicated outdoor air systems [DOAS], packaged systems, or that have an energy recovery ventilator [ERV]) and serving applicable space types (all except kitchens, dining areas, patient spaces, mechanical rooms, stairwells and corridors, or high exhaust space types) using model occupancy schedules to control the DCV. The measure is applicable to 72.7% of the stock floor area. As office buildings outside of California in ComStock are modeled using a single, whole-building space type, DCV is not applied to these building types. The DCV measure demonstrates 2.6% total site energy savings (119 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Ultra-Low SWaP CO 2 Sensing for Demand Control Ventilation (Final Report)

In this project PARC and Energy ETC aimed to develop an ultra-low cost, size, weight, and power (SWaP) printed CO 2 sensor system for occupancy detection to enable Demand Control Ventilation (DCV) on a per-room basis. The CO 2 sensor technology is based on the temperature variation when CO 2 reversibly physisorbs to a highly conductive and high surface area sorbent surface, and is compatible with integration with PARC innovations in printed sensors and flexible electronics, for which PARC is a globally leading research center. The printed CO 2 sensor itself is designed to be compatible with PARC’s “peel-and-stick” platform of ultra-low power, low-cost, distributed sensors, and to facilitate real-time DCV based on overall indoor air quality (IAQ). Previously, PARC has developed flexible hybrid electronics (FHE) compatible materials to measure humidity, temperature, light, strain, and gases such as carbon monoxide, methane, ammonia, and hydrogen sulfide. Through this project, PARC developed FHE-compatible materials to measure CO 2 . Thus, with one <$15 FHE “peel-and-stick” based sensor node, a building management system (BMS) will be able to capture a complete picture of the indoor environment. This includes IAQ, light, temperature, and other comfort factors that impact building operations. Combined with optimized DCV, this low-cost sensor capability can be a key enabler of annual primary energy savings of ~ 0.3-0.4 Quad in commercial buildings while ensuring healthy IAQ. Energy ETC is a leader in supplier-agnostic BMS deployments and will design the commissioning and deployment procedures to maximize system interoperability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Developing a Control Strategy for Minimum Airflow Setting Considering CO2 Level and Energy Consumption in a Variable Air Volume System

In an office building equipped with a Variable Air Volume (VAV) system, this paper introduces a novel method for controlling the minimum supply airflow fraction in each zone’s VAV box, having a capability to consider indoor CO2 level and energy consumption. The EnergyPlus simulation using the medium office prototype model was employed, which evaluated the performance of the energy and CO2 concentration for five VAV box airflow control strategies. The paper focuses on CO2 concentration-based airflow control method and compares it with other four methods including conventional single-max, reduced minimum single-max, demand-controlled ventilation(DCV), and dualmax control methods according to guidelines and common practices. The newly proposed control strategy directly correlates the minimum airflow fraction to CO2 concentration. A general trend emerged when comparing CO2 concentrations—lower minimum airflow fractions were associated with higher concentrations. The proposed control method effectively maintained low CO2 concentrations and enabled a lower airflow fraction contributing to energy consumption reduction. It was confirmed that heating energy consumption in climate zone 4A, 5B, and 6A showed a maximum saving of approximately 30% compared to the conventional single-max and dual max control strategies. It was found that cooling energy consumption in climate zone 4A and 6A can achieve a maximum saving of approximately 10% compared to the conventional control strategies. The proposed CO2 concentration-based control logic is promising as it not only improves the indoor air quality lowering the CO2 concentration in the occupied spaces, but also contributes to HVAC energy savings.

Lee, Jong Man↗
DOE OSTI2022

Quantifying the Impact of Advanced Web Platforms on High Performance Computing Usage

The deployment of Science Gateways for High Performance Computing (HPC) systems can alter long-accepted usage patterns on supercomputing systems in positive ways as an ever-increasing number of users migrate their workflows to HPC systems. Idaho National Laboratory (INL) has deployed two separate advanced web platforms, Open OnDemand and NICE DCV, for integration with HPC resources to improve web accessibility for HPC users. Researchers conducted a multi-year study on how HPC usage pat- terns changed in the presence of these platforms. This work reports the results of that study and quantifies the observed impacts, including adoption by visualization and Jupyter Notebook/Lab users, decreased job submission friction, rapid uptake of HPC by Windows users, and increased overall system utilization. The most significant impacts were observed from the deployment of Open OnDemand, and this work also identifies some best practices for Open OnDemand deployment for HPC datacenters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Advanced Rooftop Unit Control

This technical report documents the Advanced Rooftop Unit Control End Use Savings Shapes (EUSS) measure, including providing a description of the technology, modeling methods, and the energy savings calculated from applying the measure to applicable across the US commercial building stock using ComStock to quantify its potential. Advanced rooftop unit controls (ARC) generally consist of a supply fan VFD, and controls to implement demand controlled ventilation (DCV) and air-side economizing, as a retrofit to improve energy efficiency of existing rooftop units (RTUs).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

ComStock/COVID-19: U.S. Commercial Building Stock Analysis of COVID-19 Mitigation Strategies

ComStock is a DOE platform, with NREL as the lead developer, that models the U.S. commercial building stock using OpenStudio/EnergyPlus to perform physics-based building energy modeling simulations. The ASHRAE Epidemic Taskforce (ASHRAE-ETF) has released several COVID-19 mitigation strategies for commercial buildings. ComStock was used to analyze the impact of four strategies on the existing commercial building stock. These strategies are upgrading to MERV-13 filters, disabling demand control ventilation (DCV), increasing minimum outdoor air to 100%, and HVAC flushing mode operation. Results show the impact of each measure on energy consumption, electric consumption, gas consumption and peak electric.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

A Novel Simulation-Based Framework for Sensor Error Impact Analysis in Smart Building Systems: A Case Study for a Demand-Controlled Ventilation System

Sensors are one of the fundamental components for sensor-rich controls in buildings but are prone to different errors. Existing studies show that sensor errors hold a place among top-priority faults in building systems. Before we take countermeasures to mitigate the sensor errors, it is vital to prioritize key sensors and quantify the collective impacts of concurrent sensor errors. In response to this, a simulation-based methodology is introduced to conduct a comprehensive sensor error impact analysis in building systems, which adds a stochastic sensor prioritization through a sensitivity analysis on top of a commonly used deterministic sensor error quantification. The synergies of these two parts help better interpret the sensor error impacts on building energy consumption, ventilation performance, thermal comfort, etc. A sensor-rich CO2-based Demand-Controlled Ventilation system is used as a case study to demonstrate the viability of the methodology as a proof-of-the-concept. The results show that the energy savings potential and ventilation performance are mostly influenced by the accuracy of the AHU outdoor airflow sensors. The accuracy of zone level airflow sensors has a negligible impact on both energy savings and ventilation performance. The accuracy of zone CO2 sensors has more influence on the ventilation performance compared with the accuracy of zone airflow sensors. Compared with the baseline case with zero errors, the largest deviation percentages could reach 16.90% and 94.32%, respectively, in terms of the Heating, Ventilation, and Air-Conditioning (HVAC) annual energy consumption and the Outdoor Air Ratio (OAR) when multiple key sensors suffer from normal error intensities simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Integrated Controls Package for High Performance Interior Retrofit [Slides]

For the last few years, networked lighting control (NLCs) have promised significant energy savings beyond what is achieved through a basic light-emitting diode (LED) lighting retrofit. At the same time, NLCs can substantially increase the cost and complexity of the lighting retrofit. And as lighting system wattage declines because of the increasing efficiency of LEDs, advanced controls have less lighting energy to save and the cost-effectiveness of the NLC investment decreases. But NLCs can be leveraged to achieve significant energy savings and value by enhancing control of other building systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗