SEARCH · Search NASA

Results for “ventilation controls”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Occupancy-Based Controls for an All-Electric Residential Community in a Cold Climate

In residential buildings, rapid improvements in sensors, communication, and information technology have enabled occupancy-based building controls. These controls utilize occupancy information and modify the operation of the heating, ventilation, and air-conditioning (HVAC) system to minimize excess HVAC energy use, especially when the building is unoccupied. This reduces the total building energy consumption and utility bills while maintaining thermal comfort. In this paper, we present two novel occupancy-driven controls - reactive control and predictive control - and compare their performance. We model an all-electric residential community based on a 27- home community in Basalt, Colorado, in the United States. We simulated various scenarios, considering different temperature setback and control algorithms, to analyze the community-scale impact of these occupancy-based controls. The results show that total HVAC energy savings in a building ranges from 1%- 20% compared to the baseline scenario without occupancy-based controls. The energy-saving potential is highly correlated with the occupancy pattern and temperature setback in the building.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

DOE OSTI2022

Occupancy-Based Controls for an All-Electric Residential Community in a Cold Climate: Preprint

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

DOE OSTI2023

Hardware Specification and Reference Design for the Low-Cost, Interoperable, User-Centric, Supervisory Controller Kit for Small and Medium Size Commercial Buildings

Commercial buildings are responsible for approximately 20 percent of the total United States energy consumption and greenhouse gas emissions. Over 85 percent of these buildings lack building automation systems to manage the various building systems they have. Many of these buildings are small (< 50,000 square feet), underserved, and use rooftop units (RTUs) for heating, ventilation, and air-conditioning needs. Because these buildings lack proper control systems, they have several operational deficiencies that lead to excess energy consumption. Studies have shown that managing the RTU’s heating and cooling setpoints, schedules, setbacks, and optimal start times can result in 20 to 25 percent reduction in electricity consumption in small and medium commercial buildings (SMBs). In addition, improving the demand flexibility of these buildings will result in additional cost savings for the building owner. To address the needs of the SMBs, the Department of Energy’s Building Technologies Office jointly funded Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) to design, develop, test, and validate a low-cost, interoperable, user-centric, retrofit supervisory controller kit (SC-SMB) that can be used to continuously optimize energy consumption and deliver demand flexibility of SMBs, including all-electric buildings, and provide a means for maximizing decarbonization benefits. The team also includes industry partners Edo and Intellimation. Previously, the team drafted an SC-SMB system specification document (Goodman et al. 2023). This document describes a reference design of a low-cost supervisory control system for SMBs, including the various hardware components, software system, and an example implementation of the reference design. The final release of the reference design is planned for December 2025. Section 2.0 of the report documents the relevant building types that the SC-SMB system is suitable for. Section 3.0 documents the various hardware components, their function, cost, whether they are off-the-shelve, support standard communication, etc., and the software system. An example deployment in a 10,000-sf building with six rooftop units, a hot water heater, solar, and storage is described in Section 4.0. The planned next steps are described in Section 5.0, and references are listed in Section 6.0.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Design Requirements and Hardware Specification for the Low-Cost, Interoperable, User-Centric, Supervisory Controller Kit for Small and Medium Size Commercial Buildings

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Analysis of predicted mean vote-based model predictive control for residential HVAC systems

Model Predictive Control (MPC) is an advanced process control method that has attracted much attention in building heating, ventilation, and air conditioning (HVAC) systems. Here, this paper analyzes the optimal precooling performance in residential buildings using MPC with two different comfort indices, namely, temperature and predicted mean vote (PMV). It first formulates, for each comfort index, an optimization problem that accounts for different factors, such as weather, home thermal condition, prediction horizon, time-of use (TOU) utility rate, and rated cooling capacity. The problem is then solved, resulting in an MPC strategy that determines the HVAC on/off control signal and minimizes energy cost over a receding time horizon while maintaining thermal comfort. The energy performance difference between temperature-based and PMV-based MPC strategies is subsequently investigated, especially in light of the interior wall surface temperature and under different combinations of the factors. Extensive simulation results demonstrated that the proposed MPC strategies are adaptive and their performances depend primarily on weather, home thermal condition, and prediction horizon, while the impact of TOU utility rate and rated cooling capacity is relatively small. Because the PMV-based MPC strategy can take advantage of the lower interior wall surface temperature due to precooling, it resulted in 8–45% cost savings for the scenarios investigated and an average increase of 0.042–0.113 in the absolute value of the PMV index compared to the temperature-based MPC strategy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Coupled management of electric vehicle workplace charging and office building loads

Significant building energy savings are realized through heating, ventilation, and air conditioning (HVAC) setpoint adjustment and daylighting control. Workplace charging (WPC) enables colocation of electric vehicles (EVs) with office building loads. We proposed managing energy use of workplace EV charging and the office building and determined the number of EVs that building energy savings can facilitate charging. We simulated building energy savings in typical medium offices in Chicago IL, Baltimore MD, and Houston TX, spanning three US climate regions. Considering the EV hosting capacity of the saved building energy and travel patterns of roundtrip commuting, we minimized EV charging costs under time-of-use electricity pricing. Managed WPC can reduce charging electricity bills compared to first-come, first-served charging. The ratio of EVs to chargers, the coincident period of commuters’ dwell time and lower electricity prices, and the number of EVs in the office impacted the economic benefits achieved through charging management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Performance assessment of a real water source heat pump within a hardware-in-the-loop (HIL) testing environment

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while also maintaining the thermal comfort of building occupants. As the pivotal point between building electricity consumption and indoor thermal comfort, high-efficiency electrical vapor-compression heat pumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on heat pump operation and performance when subjected to different weather conditions, building loads, and grid requests calls for investigation and verification via experimental testing with actual heat pumps integrated with real-time building and grid responses. This study introduces a Water-Source Heat Pump (WSHP) Hardware-in-The-Loop (HIL) Test Facility that is the first of its kind. This testbed utilizes a 2-ton variable speed water-to-air heat pump that is capable of interacting with a virtual environment currently comprised of an EnergyPlus (E+) building simulation, an agent-based occupant behavioral model, and a single U-tube ground-loop heat exchanger (GLHE) model. Detailed descriptions of the testbed’s physical design and operation, virtual environment, as well as their mutual communication is provided. An uncertainty analysis is also performed under manufacturer specified heating and cooling design conditions. This analysis shows that the total load across the WSHP’s demand side heat exchanger, i.e., the sum of its latent and sensible components, can be measured with a relative uncertainty of ± 10.4% and ± 3.6% in cooling and heating mode respectively. The WSHP’s coefficient of performance (COP) can be measured with relative uncertainties of ± 10.4% in cooling mode, and ± 3.7% in heating mode. A preliminary 24-h experimental demonstration is then performed utilizing the DOE prototype small commercial office building model in E+. The simulation takes place in Atlanta, GA on the date of 08/26/15 from 12:00 AM to 11:59 PM using TMY3 weather data. Here, the results from this demonstration show that over the course of this experiment the simulated outputs of zone dry-bulb temperature, zone humidity ratio, and WSHP inlet water temperature can be tracked by testbed emulators up to a root mean squared error (RMSE) of ± 0.27 °C, ± 0.376 g/kg, and ± 0.85 °C respectively. The WSHP’s dynamic behavioral characteristics and performance are also captured, and correspond well with the authors’ previous understanding of heat pump efficiency as a function of evaporator and condenser fluid inlet conditions respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2000

Reliability Modeling of Microelectromechanical Systems Using Neural Networks

Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.

Perera. J. Sebastian↗

NASA NTRS2013

Maintaining Adequate Carbon Dioxide Washout for an Advanced Extravehicular Mobility Unit

Over the past several years, NASA has realized tremendous progress in technology development that is aimed at the production of an Advanced Extravehicular Mobility Unit (AEMU). Of the many functions provided by the spacesuit and portable life support subsystem within the AEMU, delivering breathing gas to the astronaut along with removing the carbon dioxide (CO2) remains one of the most important environmental functions that the AEMU can control. Carbon dioxide washout is the capability of the ventilation flow in the spacesuit helmet to provide low concentrations of CO2 to the crew member to meet breathing requirements. CO2 washout performance is a critical parameter needed to ensure proper and sufficient designs in a spacesuit and in vehicle applications such as sleep stations and hygiene compartments. Human testing to fully evaluate and validate CO2 washout performance is necessary but also expensive due to the levied safety requirements. Moreover, correlation of math models becomes challenging because of human variability and movement. To supplement human CO2 washout testing, a breathing capability will be integrated into a suited manikin test apparatus to provide a safe, lower cost, stable, easily modeled alternative to human testing. Additionally, this configuration provides NASA Johnson Space Center (JSC) the capability to evaluate CO2 washout under off-nominal conditions that would otherwise be unsafe for human testing or difficult due to fatigue of a test subject. Testing has been under way in-house at JSC and analysis has been initiated to evaluate whether the technology provides sufficient performance in ensuring that the CO2 is removed sufficiently and the ventilation flow is adequate for maintaining CO2 washout in the AEMU spacesuit helmet of the crew member during an extravehicular activity. This paper will review recent CO2 washout testing and analysis activities, testing planned in-house with a spacesuit simulator, and the associated analytical work along with insights from the medical aspect on the testing.

Chullen, Cinda↗

NASA NTRSDate not supplied

Trace Contaminant Control Test Bed for Evaluation of TCC Prototypes with Vacuum Regenerable and Non-Regenerable Sorbents

A new Space Suit Exploration Portable Life Support System (xPLSS) is being designed, built, integrated, and tested into the Extra-Vehicular Mobility Unit (xEMU). The Trace Contaminant Control (TCC) system is a component in the oxygen ventilation loop of xPLSS that removes contaminants generated by the crewmembers’ metabolic processes. The primary trace contaminants that must be removed include ammonia (NH3), carbon monoxide (CO), formaldehyde (CH2O), and methyl mercaptan (CH3SH). The current state-of-the-art TCC sorbent is non-regenerable activated carbon. As a non-regenerable sorbent, its use negatively impacts the logistics for future missions. Thus, an ideal solution can be a vacuum-regenerable sorbent integrated with the xPLSS CO2/H2O removal system. To test new designs and sorbents, XploSafe has constructed a recirculating test bed that mimics the environment within the xPLSS by providing concentrations of the trace contaminant analytes generated at the operating temperature, humidity, pressure, and flow rates of the xPLSS. This system can perform regeneration or desorption by exposing the sorbent to a pressure swing from 4.3 psia to <1 torr over approximately 2 minutes. Using this test system, XploSafe evaluated various TCC prototypes constructed using rapid prototyping techniques and identified a process that allows vacuum regenerative TCC designs to be quickly examined for flow, pressure drop, and performance. By using toluene as a segregate for volatile organics, this test system was evaluated using both vacuum regenerable and non-regenerable sorbents to demonstrate the operational effectiveness of these sorbents in maintaining the concentrations of the trace contaminant analytes below the 7-day Spacecraft Maximum Allowable Concentrations.

Nicholas Materer↗

DOE OSTI2022

Development of a water source heat pump hardware-in-the-loop (HIL) testing facility for smart building applications

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while maintaining the thermal comfort of building occupants. As the pivotalpoint between building electricity consumption and indoor thermal comfort, high-efficiency electrical heatpumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on the heatpump operation andperformance under different weather, building load, and grid requests calls for investigation and verification via experimental tests with actual heat pumps integrated with real-time building and grid responses. This study presents a Water-Source Heat Pump Hardware-in-The-Loop (HIL) Test Facility developed with the capability to perform such tests. The hardware configuration for this testfacility introduces a hydronic system that emulates the conditions for the heat pump water-side, and a duct system that emulates conditions for the heat pump airside. Both data acquisition and emulator control are implemented through the National Instruments (NI) LabVIEW software running on an NI PXIplatform. The HIL mechanism based on the hardware-software integration that allows the testbed to communicate with a generic simulation environment is also discussed. Currently, the test facility setup includes a single heatpump and virtual building model in EnergyPlus coupled with an occupant behavioral model in MATLAB. Preliminary test results of the current setup demonstrate the building load emulator's ability to track the simulated gone temperature with a Root Mean Square Deviation (RSME) below 0.12°C (0.216°F). An uncertainty analysis based on sensor accuracies shows that the heat pump coefficient of performance (COP) can be measured with a relative uncertainty of 10.4% in cooling and 3.7% in heating. Apartfrom the current testing on a single heat pump, the test facility also provides the flexibility to include additional heat pumps to form a heat pump cluster, as well as coupling the heat pump with active thermal storage to provide enhanced demandflexibility.

Calfa, Caleb↗

DOE OSTI2024

Learning-Based Building Flexibility Estimation and Control to Improve Microgrid Economics and Resilience: Preprint

This paper proposes a learning-based building flexibility estimation and control framework to improve system economics and resilience. A data-driven building load flexibility model consisting of weather forecasting and estimating load consumption is proposed to quantify building heating, ventilation, and air conditioning (HVAC) load flexibility. A reinforcement learning-based microgrid controller is proposed to dispatch distributed generators, distributed energy resources, and build HVAC loads while taking flexibility information as one of the inputs. Simulation analysis is conducted on the model of a real microgrid in California. The effectiveness of the proposed learning-based building flexibility estimation and control in reducing microgrid energy costs and improving the sustainability of critical loads is demonstrated.

building load flexibility↗

NASA NTRS2010

International Space Station Crew Quarters Ventilation and Acoustic Design Implementation

The International Space Station (ISS) United States Operational Segment has four permanent rack sized ISS Crew Quarters (CQs) providing a private crew member space. The CQs use Node 2 cabin air for ventilation/thermal cooling, as opposed to conditioned ducted air-from the ISS Common Cabin Air Assembly (CCAA) or the ISS fluid cooling loop. Consequently, CQ can only increase the air flow rate to reduce the temperature delta between the cabin and the CQ interior. However, increasing airflow causes increased acoustic noise so efficient airflow distribution is an important design parameter. The CQ utilized a two fan push-pull configuration to ensure fresh air at the crew member's head position and reduce acoustic exposure. The CQ ventilation ducts are conduits to the louder Node 2 cabin aisle way which required significant acoustic mitigation controls. The CQ interior needs to be below noise criteria curve 40 (NC-40). The design implementation of the CQ ventilation system and acoustic mitigation are very inter-related and require consideration of crew comfort balanced with use of interior habitable volume, accommodation of fan failures, and possible crew uses that impact ventilation and acoustic performance. Each CQ required 13% of its total volume and approximately 6% of its total mass to reduce acoustic noise. This paper illustrates the types of model analysis, assumptions, vehicle interactions, and trade-offs required for CQ ventilation and acoustics. Additionally, on-orbit ventilation system performance and initial crew feedback is presented. This approach is applicable to any private enclosed space that the crew will occupy.

Broyan, James L., Jr.↗

DOE OSTI2021

Human-in-the-loop Sensing and Control for Commercial Building Energy Efficiency and Occupant Comfort

Most of the existing heating, ventilation and air conditioning (HVAC) systems in commercial buildings operate in a conservative manner by assuming maximum occupancy in each room during pre-specified periods of the week, leading to significant energy being wasted as rooms are over-conditioned compared to the actual requirements of the occupants. Though critical, our understanding of occupancy patterns and thermal comfort needs of the occupants in commercial buildings is lacking and it is well known that both of these quantities are stochastic and time-varying, thus requiring sensing solutions to estimate them. This project had the goal of designing, implementing and evaluating a hardware and software solution to ameliorate this challenge. In particular, a depth camera (one whose pixels reveal distance from the camera as opposed to color values) placed on doorways is used to detect entrance and exit events from thermal zones in the building, and thereby estimate their occupancy levels. This information is then fed to a novel control algorithm that can, through interactions with the HVAC system, learn how to provide control inputs that maximize comfort and minimize energy waste. The resulting system represents a significant improvement over existing controllers for commercial HVAC systems and allowed us to improve our understanding of the design of future human-in-the-loop control solutions. For this solution to be feasible, the project had target metrics for its performance and cost. In particular, entrance and exit events for occupants moving about the building would need to be detected with an accuracy higher than 97%; and the resulting control inputs derived from this information would need to lead to approximately 10% energy savings compared to a schedule-based controller. Furthermore, regarding the final hardware design, the project had a target bill of materials (BOM) cost for the sensing solution of less than US$200 per unit while using less than 25W of power on average. All of these target metrics were met or exceeded by our final proposed solution. We performed evaluations by deploying the system in over 20 rooms of different types across 6 commercial buildings in Pittsburgh, PA over the course of three years, and performing targeted controlled experiments to test its performance along the different metrics. The human-in-the-loop control solutions (both hardware and software) developed through this project are expected to lead to significant improvements in the comfort and energy efficiency of HVAC systems used in commercial buildings. The insights we developed through the project pave the way to HVAC systems that can condition interior spaces according to their real-time utilization and the thermal comfort needs of the occupants, thereby reducing energy use. They also open up a new learning-based way of configuring HVAC controllers without having to manually fine-tune them for each building. These innovations can significantly increase the adoption of novel control solutions by the industry and thereby save resources and reduce costs of operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Ventilation and Indoor Air Quality in Recently Constructed U.S. Homes: Measured Data From Select Southeastern States

Little data is available that quantifies IAQ or associates contaminant levels with air exchange in U.S. homes. To address this gap, the U.S. Department of Energy (DOE) Building America Program conducted a study to characterize IAQ in U.S. homes constructed since 2013, along with presence, functionality, and occupant use of control measures. Specific objectives of this study effort included: Measuring time-integrated concentrations and temporal profiles of established contaminants of concern; monitoring the use of ventilation equipment; and tracking activities that impact air pollutant emissions and removal processes in typical homes in various climate zones; Characterizing the prevalence, type, and installed performance of mechanical ventilation equipment in new homes; exploring regional variations in system designs and performance; Investigating associations between contaminant levels and the presence of control measures including whole-house mechanical ventilation (WHMV). The study engaged research teams to collect data at a regional level. This report introduces the study protocol and presents and discusses high-level results obtained from Florida Solar Energy Center (FSEC) data collection efforts in the southeastern U.S. states of Florida, Georgia, and South Carolina.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1994

Cabin fuselage structural design with engine installation and control system

Design requirements for the cabin, cabin system, flight controls, engine installation, and wing-fuselage interface that provide adequate interior volume for occupant seating, cabin ingress and egress, and safety are presented. The fuselage structure must be sufficient to meet the loadings specified in the appropriate sections of Federal Aviation Regulation Part 23. The critical structure must provide a safe life of 10(exp 6) load cycles and 10,000 operational mission cycles. The cabin seating and controls must provide adjustment to account for various pilot physiques and to aid in maintenance and operation of the aircraft. Seats and doors shall not bind or lockup under normal operation. Cabin systems such as heating and ventilation, electrical, lighting, intercom, and avionics must be included in the design. The control system will consist of ailerons, elevator, and rudders. The system must provide required deflections with a combination of push rods, bell cranks, pulleys, and linkages. The system will be free from slack and provide smooth operation without binding. Environmental considerations include variations in temperature and atmospheric pressure, protection against sand, dust, rain, humidity, ice, snow, salt/fog atmosphere, wind and gusts, and shock and vibration. The following design goals were set to meet the requirements of the statement of work: safety, performance, manufacturing and cost. To prevent the engine from penetrating the passenger area in the event of a crash was the primary safety concern. Weight and the fuselage aerodynamics were the primary performance concerns. Commonality and ease of manufacturing were major considerations to reduce cost.

Balakrishnan, Tanapaal↗

DOE OSTI2025

Multi-Agent Hierarchical Deep Reinforcement Learning for HVAC Control With Flexible DERs

As electricity consumption in commercial and residential buildings continues to rise, reducing energy costs presents an increasing challenge. Heating, ventilating, and air-conditioning (HVAC) systems, which typically account for 40%-50% of a building's energy use, are prime targets for energy savings. Intelligent control of HVAC temperature through the exploitation of HVAC load flexibility brings significant potential to reduce energy consumption and electricity expenses. The nonlinear models of HVAC systems challenge traditional control methods, while the uncertainty introduced by HVAC load flexibility complicates distributed energy resource (DER) management using conventional optimal dispatch techniques. In response to these challenges, we propose a hierarchical multi-agent deep reinforcement learning (DRL) approach. The lower-level agents focus on balancing comfort and energy conservation, while the upper-level DRL agents optimize the use of DERs to reduce peak demand based on the control outcomes of the HVAC by the lower-level agents. Here, in the upper-level agents, we incorporate a multi-agent structure based on ensemble learning, which acts based on historical and current data without relying on precise load forecasting to address the delayed rewarding issue in DRL. This allows for the effective reduction of energy costs. The proposed method is tested using a real-world microgrid comprising 413 buildings in Southern California, and the results demonstrate that our approach can significantly reduce overall electricity bills while ensuring the comfort of consumers and residents.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1982

Health and safety evaluation of a modified tunnel borer design for application to single entry coal mine development

A health and safety analysis of a single entry coal tunnel borer system is given. The results of the health analysis indicated that while the tunnel borer design offered improvements in dust control through the use of water sprays, a higher face ventilation rule, and the application of spalling rather than the conventional grinding process, it interjected an additional mutagen and toxic compound into the environment through the use of shotcrete. The tunnel borer system easily conformed with the prescribed fatality limit, but exceeded the required limits for disabling and overall injuries. It also exhibited projected disabling and overall injury rates considerably higher than existing continuous mining injury rates. Consequently, the tunnel borer system was not considered an advanced system.

Zimmerman, W. F.↗