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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Advanced Sensor Platform to Evaluate Manloads For Exploration Suit Architectures

Space suit manloads are defined as the outer bounds of force that the human occupant of a suit is able to exert onto the suit during motion. They are defined on a suit-component basis as a unit of maximum force that the suit component in question must withstand without failure. Existing legacy manloads requirements are specific to the suit architecture of the EMU and were developed in an iterative fashion; however, future exploration needs dictate a new suit architecture with bearings, load paths, and entry capability not previously used in any flight suit. No capability currently exists to easily evaluate manloads imparted by a suited occupant, which would be required to develop requirements for a flight-rated design. However, sensor technology has now progressed to the point where an easily-deployable, repeatable and flexible manloads measuring technique could be developed leveraging recent advances in sensor technology. INNOVATION: This development positively impacts schedule, cost and safety risk associated with new suit exploration architectures. For a final flight design, a comprehensive and accurate man loads requirements set must be communicated to the contractor; failing that, a suit design which does not meet necessary manloads limits is prone to failure during testing or worse, during an EVA, which could cause catastrophic failure of the pressure garment posing risk to the crew. This work facilitates a viable means of developing manloads requirements using a range of human sizes & strengths. OUTCOME / RESULTS: Performed sensor market research. Highlighted three viable options (primary, secondary, and flexible packaging option). Designed/fabricated custom bracket to evaluate primary option on a single suit axial. Manned suited manload testing completed and general approach verified.

McFarland, Shane↗
DOE OSTI2026

IPC-Fusion (Infrastructure Perception and Control (IPC): Multisensor Data Fusion Software) [SWR-25-153]

As part of the National Laboratory of the Rockies' (NLR’s) Infrastructure Perception and Control Laboratory, the IPC-Fusion toolkit provides a probabilistic, scalable, multi-sensor fusion framework that integrates (late-stage fusion) heterogeneous object detection data from traffic sensors to enable robust, real-time tracking of roadway occupants. The algorithmic design of the toolkit is motivated by the need for creating a digital twin of traffic at the edge in a scalable and affordable manner. The software operates by combining object-level measurements (such as position and velocity) from a suite of sensors (such as radar, lidar, camera) using Kalman filtering and probabilistic data association techniques to overcome individual sensor limitations and achieve superior tracking performance in complex traffic zones. The framework addresses key challenges including heterogeneous measurement uncertainties, asynchronous data streams, varying spatiotemporal data resolutions, robust data association, and adaptive object lifecycle management. Validated on real-world traffic intersection data including vehicles and pedestrians, IPC-Fusion demonstrates enhanced tracking reliability across scenarios involving occlusions, sensor failures, and varying traffic densities, supporting the broader IPC initiative's goal of transforming transportation infrastructure through advanced perception capabilities for intelligent transportation systems, traffic safety applications, and autonomous vehicle support.

Sandhu, Rimple [National Laboratory of the Rockies↗
DOE OSTI2020

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

This report summarizes a comprehensive literature review for the selected sensor suites used in building controls and fault detection and diagnostics (FDD), which is a Q2 deliverable of the Sensor Impact Evaluation and Verification project. The project’s overarching goal is to develop a framework for investigating the impact of sensor deployment and configuration for building energy optimization, fault detection and diagnosis (FDD), occupants’ thermal comfort, and potential grid efficiency. While the first phase of the literature review performed in Q1 reviewed the current methods of various sensor selection and placements and their impact, this report focuses on high impactful potential use cases for the selected sensor suites in building control and FDD. The use cases identified in this literature review will be further investigated and be included in the final sensor impact evaluation framework. For the review, the multi-laboratory team reviewed 241 research papers, technical reports, and books to identify existing technologies, the potential impact on energy savings and thermal comfort, and future research opportunities for the selected use cases in building controls and FDD. Along with the literature review, the team conducted a series of interviews with experts in sensors and controls area to identify the state-of-art sensor technologies, and the most impactful use cases in controls and FDD areas. The interview results will also be incorporated with the findings from the literature review in the next quarter, and the final use cases to be evaluated will be selected.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A guideline to document occupant behavior models for advanced building controls

The availability of computational power, and a wealth of data from sensors have boosted the development of model-based predictive control for smart and effective control of advanced buildings in the last decade. More recently occupant-behavior models have been developed for including people in the building control loops. However, while important objectives of scientific research are reproducibility and replicability of results, not all information is available from published documents. Therefore, the aim of this paper is to propose a guideline for a thorough and standardized occupant-behavior model documentation. For that purpose, the literature screening for the existing occupant behavior models in building control was conducted, and the occupant behavior modeling processes were studied to extract practices and gaps for each of the following phases: problem statement, data collection, and preprocessing, model development, model evaluation, and model implementation. Here, the literature screening pointed out that the current state-of-the-art on model documentation shows little unification, which poses a particular burden for the model application and replication in field studies. In addition to the standardized model documentation, this work presented a model-evaluation schema that enabled benchmarking of different models in field settings as well as the recommendations on how OB models are integrated with the building system.

Building control↗
DOE OSTI2020

Innovations in Sensors and Controls for Building Energy Management: Research and Development Opportunities Report for Emerging Technologies

Sensors, actuators, and controllers, which collectively serve as the backbone of cyberphysical systems for building energy management, are one of the core technical areas of investment for achieving the U.S. Department of Energy (DOE) Building Technologies Office's (BTO's) goals for energy affordability in the national building stock - both commercial and residential. In fact, an aggregated annual energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms. energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Manipulating polymer composition to create low-cost, high-fidelity sensors for indoor CO2 monitoring

Abstract Carbon dioxide (CO 2 ) has been linked to many deleterious health effects, and it has also been used as a proxy for building occupancy measurements. These applications have created a need for low-cost and low-power CO 2 sensors that can be seamlessly incorporated into existing buildings. We report a resonant mass sensor coated with a solution-processable polymer blend of poly(ethylene oxide) (PEO) and poly(ethyleneimine) (PEI) for the detection of CO 2 across multiple use conditions. Controlling the polymer blend composition and nanostructure enabled better transport of the analyte gas into the sensing layer, which allowed for significantly enhanced CO 2 sensing relative to the state of the art. Moreover, the hydrophilic nature of PEO resulted in water uptake, which provided for higher sensing sensitivity at elevated humidity conditions. Therefore, this key integration of materials and resonant sensor platform could be a potential solution in the future for CO 2 monitoring in smart infrastructure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.

Xu, Shichao↗
DOE OSTI2022

Secure mmWave Spectrum Sharing with Autonomous Beam Scheduling for 5G and Beyond

Spectrum Sharing (SS) has seen a renewed set of initiatives in 5G with the availability of shared and unlicensed spectrum bands that can be used by multiple cellular service providers and private cellular networks. Beam based transmission, instead of the traditional sector based transmission in conjunction with the spectrum agility of the 5G New Radio (NR) has brought new opportunities to optimized sharing of spectrum. Currently in the U.S., a centralized Spectrum Access Server (SAS) is used to co-ordinate spectrum sharing among networks sharing the same spectrum band. However, SAS becomes a focal point for security attacks and a performance bottleneck. In addition, SAS relies on an Environmental Sensor Network (ESN), separate from the 5G network. Without trusted spectral occupancy information, false reporting of spectrum sensing data can create sub-optimal and unfair spectrum usage. This paper summarizes our recent research findings in using a decentralized scheme for multiple networks to securely share spectrum with autonomous beam scheduling : 1) A new stochastic network framework based on Lyapunov Optimization approach is developed to optimize scheduling at the base stations; 2) Game theoretic (GT) approach is used to formulate the distributed scheduler; 3) Another distributed scheduler with Q-learning is presented that utilizes the Reinforcement Learning (RL) approach; 4) The performance and convergence rate of these distributed solutions to use shared and unlicensed spectrum are compared with existing solutions. Conditions under which the performance of these schedulers approach the theoretical upper bound, which is the performance possible with no interference among the operators sharing the spectrum, are presented; 5) The ability of a base station to use its own user equipment as sensors, for optimal spectrum sharing with base stations in other operator networks, is demonstrated to be an effective approach.

5G↗
DOE OSTI2025

Assessing Indoor versus Outdoor PM 2.5 Concentrations during the 2025 Los Angeles Fires Using the PurpleAir Sensor Network

In January 2025, a series of fast-moving wildland-urban-interface (WUI) fires swept through the Los Angeles (LA) metropolitan area, causing severe air pollution. While the impacts of WUI fires on outdoor air quality have been extensively studied, indoor exposure remains less understood, despite most people sheltering indoors during WUI fires. Here, this study investigates the spatial and temporal patterns of indoor and outdoor PM 2.5 concentrations across the South Coast Air Basin, with a focus on LA County during the LA fires. Using high-resolution data from co-located indoor and outdoor PurpleAir (PA) sensors, we analyze hourly PM 2.5 levels and indoor/outdoor ratios. Outdoor PM 2.5 concentrations spiked sharply during the fires, reaching unhealthy levels exceeding 130 μg/m 3 , compared to the mean concentration (12 μg/m 3 ) during non-fire hours. Indoor concentrations also increased, though to a lesser extent, peaking around 60 μg/m 3 compared to a mean of 7 μg/m 3 during non-fire hours. This reflects the partial shielding that indoor environments provide from outdoor air pollution. The mean (0.42) and median (0.29) indoor/outdoor PM 2.5 ratios during LA fire hours were lower than the mean (0.93) and median (0.66) ratios during non-fire hours. Indoor/outdoor PM 2.5 ratios across sensors showed a wide distribution, reflecting differences in building characteristics and occupant behavior, such as indoor activities and the use of air purifiers. These findings emphasize the need for guidance and interventions to reduce indoor PM 2.5 exposure and protect public health during extreme WUI fire events.

2025 Los Angeles Fires↗
NASA NTRS2006

The Flostation - an Immersive Cyberspace System

A flostation is a computer-controlled apparatus that, along with one or more computer(s) and other computer-controlled equipment, is part of an immersive cyberspace system. The system is said to be immersive in two senses of the word: (1) It supports the body in a modified form neutral posture experienced in zero gravity and (2) it is equipped with computer-controlled display equipment that helps to give the occupant of the chair a feeling of immersion in an environment that the system is designed to simulate. Neutral immersion was conceived during the Gemini program as a means of training astronauts for working in a zerogravity environment. Current derivatives include neutral-buoyancy tanks and the KC-135 airplane, each of which mimics the effects of zero gravity. While these have performed well in simulating the shorter-duration flights typical of the space program to date, a training device that can take astronauts to the next level will be needed for simulating longer-duration flights such as that of the International Space Station. The flostation is expected to satisfy this need. The flostation could also be adapted and replicated for use in commercial ventures ranging from home entertainment to medical treatment. The use of neutral immersion in the flostation enables the occupant to recline in an optimal posture of rest and meditation. This posture, combines savasana (known to practitioners of yoga) and a modified form of the neutral posture assumed by astronauts in outer space. As the occupant relaxes, awareness of the physical body is reduced. The neutral body posture, which can be maintained for hours without discomfort, is extended to the eyes, ears, and hands. The occupant can be surrounded with a full-field-of-view visual display and nearphone sound, and can be stimulated with full-body vibration and motion cueing. Once fully immersed, the occupant can use neutral hand controllers (that is, hand-posture sensors) to control various aspects of the simulated environment.

Park, Brian↗
DOE OSTI2022

Extending the Brick schema to represent metadata of occupants

Here, energy-related behaviors of occupants constitute a key factor influencing building performance; accordingly, the measured occupant data can support the objective assessment of the indoor environment and energy performance of buildings, which can inform building design and operational decisions. Existing data schemas focus on metadata of sensors, meters, physical equipment, and IoT devices in buildings; however, they are limited in representing the metadata of occupant data, including occupants' presence in spaces, movement between spaces, interactions with building systems or IoT devices, and preference of indoor environmental needs. To address this gap, an extension to the widely adopted metadata schema, Brick, is proposed to represent the contextual, behavioral, and demographic information of occupants. The proposed extension includes four parts: (1) a new “Occupant” class to represent occupants' demography and energy related behavioral patterns, (2) new subclasses under the Equipment class to represent envelope system and personal thermal comfort devices, (3) new subclasses under the Point class to represent occupant sensing and status, and (4) new auxiliary properties for occupant interactable equipment to represent the level of controllability for each piece of equipment by occupants. The extension is implemented in the Brick schema and has been tested using multiple occupant datasets from the ASHRAE Global Occupant Database. The extension enables Brick schema to capture diverse types of occupant sensing data and their metadata for FAIR data research and applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1978

Carbon Monoxide Sensor

The fuel cell is a system which employs an electrochemical process to convert gases- J such as hydrogen and oxygen directly into electricity. Under NASA sponsorship, GE's Aircraft Equipment Division developed fuel cells to supply electrical power for the Gemini and Biosatellite spacecraft of the sixties and is currently working on advanced fuel cell development. This long-term effort has resulted in a series of spinoff applications using the same general technology for a variety of purposes, among them the recently marketed Dosimeter. The Dosimeter is designed to help users meet safety requirements for industrial atmospheres, as specified by the Occupational Safety and Health Administration and other regulatory agencies. The compact, pocket-sized sensor measures personnel exposure to carbon monoxide and provides both a visual and an audible alarm if the concentration of the gas exceeds present levels. The Dosimeter offers substantial improvement in measuring accuracy over earlier warning indicators.

Source record↗
DOE OSTI2023

Development of ASTM International D8405—Standard Test Method for Evaluating PM 2.5 Sensors or Sensor Systems Used in Indoor Applications

Sensors and sensor systems for monitoring fine particles with aerodynamic diameters smaller than 2.5 µm can provide real-time feedback on indoor air quality and thus can help guide actions to manage indoor air pollutant concentrations. Standardized verification of the performance and accuracy of sensors and sensor systems is crucial for predicting the efficacy of such monitoring. A new ASTM International standard test method (ASTM D8405) was created for this need and is the most exacting laboratory protocol published to date for evaluating indoor air quality sensors and sensor systems measuring particles smaller than 2.5 µm in diameter. ASTM D8405 subjects sensors and sensor systems to five test phases: (1) an initial particle concentration ramp; (2) exposure to various temperature and humidity conditions; (3) exposure to interfering particles; (4) temperature cycling; and (5) a final particle concentration ramp to assess drift. This paper discusses the development of the standard test method, key aspects of the testing process, example evaluation results, and a comparison of this standard test method against peer evaluation protocols.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Dialogue Between Lighting and HVAC Systems: Improving building system integration

Lighting systems have long been capable of sensing when someone enters or exits a room and using that knowledge to turn lights on or off. More recently, connected lighting systems with sensors integrated into every luminaire have become broadly available, facilitating highly granular occupancy detection. Similarly, HVAC systems have long been able to use an understanding of building occupancy to adjust temperature setpoints and reduce energy use without significant impacts to occupant comfort. Energy codes (e.g., ASHRAE/IES Standard 90.1, IECC, Title 24) now require “occupied standby HVAC control,” whereby systems adjust both temperature and ventilation setpoints in zones that are determined to be unoccupied during normal occupancy hours. Here, this article discusses current issues that stymie the integration of Lighting and HVAC systems, and DOE activities focused on addressing them.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1989

A tesselated probabilistic representation for spatial robot perception and navigation

The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations.

Elfes, Alberto↗
DOE OSTI2020

Review: Sensor Impact on Building Controls and Automatic Fault Detection and Diagnosis (AFDD)

This report summarizes a literature review as a Q1 deliverable of the “Sensor Impact Evaluation and Verification” project. This project’s overarching goal is to develop a framework for investigating the impact of sensor deployment and configuration for building energy optimization, fault detection and diagnosis (FDD), occupants’ thermal comfort, and potential grid efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2019

Safe2Ditch Steer-To-Clear Development and Flight Testing

This paper describes a series of small unmanned aerial system (sUAS) flights performed at NASA Langley Research Center in April and May of 2019 to test a newly added Steer-to-Clear feature for the Safe2Ditch (S2D) prototype system. S2D is an autonomous crash management system for sUAS. Its function is to detect the onset of an emergency for an autonomous vehicle, and to enable that vehicle in distress to execute safe landings to avoid injuring people on the ground or damaging property. Flight tests were conducted at the City Environment Range for Testing Autonomous Integrated Navigation (CERTAIN) range at NASA Langley. Prior testing of S2D focused on rerouting to an alternate ditch site when an occupant was detected in the primary ditch site. For Steer-to-Clear testing, S2D was limited to a single ditch site option to force engagement of the Steer-to-Clear mode. The implementation of Steer-to-Clear for the flight prototype used a simple method to divide the target ditch site into four quadrants. An RC car was driven in circles in one quadrant to simulate an occupant in that ditch site. A simple implementation of Steer-to- Clear was programmed to land in the opposite quadrant to maximize distance to the occupant’s quadrant. A successful mission was tallied when this occurred. Out of nineteen flights, thirteen resulted in successful missions. Data logs from the flight vehicle and the RC car indicated that unsuccessful missions were due to geolocation error between the actual location of the RC car and the derived location of it by the Vision Assisted Landing component of S2D on the flight vehicle. Video data indicated that while the Vision Assisted Landing component reliably identified the location of the ditch site occupant in the image frame, the conversion of the occupant’s location to earth coordinates was sometimes adversely impacted by errors in sensor data needed to perform the transformation. Logged sensor data was analyzed to attempt to identify the primary error sources and their impact on the geolocation accuracy. Three trends were observed in the data evaluation phase. In one trend, errors in geolocation were relatively large at the flight vehicle’s cruise altitude, but reduced as the vehicle descended. This was the expected behavior and was attributed to sensor errors of the inertial measurement unit (IMU). The second trend showed distinct sinusoidal error for the entire descent that did not always reduce with altitude. The third trend showed high scatter in the data, which did not correlate well with altitude. Possible sources of observed error and compensation techniques are discussed.

Petty, Bryan J.↗