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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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54 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

202508 HAIIC IR Images with Attitude Data at BNF

This campaign aims to collect infrared imagery using a custom data acquisition system to improve and validate a previously developed attitude estimation algorithm, with a focus on ensuring reliable performance at higher altitudes. The system integrates a Boson+320 infrared camera, a VN-200 IMU for recording attitude data synchronized with image acquisition, and a Raspberry Pi 4B+ for onboard processing and storage. Image orientation was controlled using a Gremsy T7 gimbal. The primary dataset spans roll angles from −40° to 40° and pitch angles from −20° to 40°, sampled at 1° increments. Additional, smaller datasets were also collected at 2.5° angular intervals.

Boson+320↗
DOE OSTI2023

NGEE Arctic Rainfall Simulator Validation Data from Los Alamos National Laboratory, New Mexico, Summer 2022

Experiments evaluating the uniformity and intensity of rainfall produced by the NGEE Arctic Rainfall Simulator (NARS) were conducted at Los Alamos National Laboratory, New Mexico, over summer 2022. Petri dishes were placed in a grid within the NARS plot. Simulated rainfall was collected in each petri dish and the intensity and uniformity of the simulator was subsequently calculated. This data package contains two .csv files, one that summarizes the rainfall intensity and uniformity for each experiment, the other that contains individual petri dish water volume and intensity for each plot location and experiment. The Python scripts to control NARS are also included. The NGEE Arctic Rainfall Simulator (NARS) is a variable intensity rainfall simulator (RFS) with a frame design based on the Humphry et al. (2002) RFS and a water delivery system based on the Walnut Gulch (Paige et al., 2004) RFS. The NARS uses an aluminum frame that is fully deconstructable for transportation to field locations and a water system that enables variable rain intensity. Rain intensity control and data collection are automated using a Raspberry Pi microcomputer. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Evaluation of Galvanic Vestibular Stimulation System

Microgravity exposure results in an adaptive central reinterpretation of information from multiple sensory sources to produce a sensorimotor state appropriate for motor actions in this unique environment, but this new adaptive state is no longer appropriate for the 1-g gravitational environment on Earth. During these gravitational transitions, astronauts experience deficits in both perceptual and motor functions including impaired postural control, disruption in spatial orientation, impaired control of locomotion that include alterations in muscle activation variability, modified lower limb kinematics, alterations in head-trunk coordination as well as reduced dynamic visual acuity. Post-flight changes in postural and locomotor control might have adverse consequences if a rapid egress was required following a long-duration mission, where support personnel may not be available to aid crewmembers. The act of emergency egress includes, but is not limited to standing, walking, climbing a ladder, jumping down, monitoring displays, actuating discrete controls, operating auxiliary equipment, and communicating with Mission Control and recovery teams while maintaining spatial orientation, mobility and postural stability in order to escape safely. The average time to recover impaired postural control and functional mobility to preflight levels of performance has been shown to be approximately two weeks after long-duration spaceflight. The postflight alterations are due in part to central reinterpretation of vestibular information caused by exposure to microgravity. In this study we will use a commonly used technique of transcutaneous electrical stimulation applied across the vestibular end organs (galvanic vestibular stimulation, GVS) to disrupt vestibular function as a simulation of post-flight disturbances. The goal of this project is an engineering human-in-the-loop evaluation of a device that can degrade performance of functional tasks (e.g. to maintain upright balance) similar to what astronauts experience during transitions to new gravitational environments. Stochastic electrical stimulation can be applied to the vestibular system through electrodes placed over the mastoid process behind the ears in the binaural configuration resulting in stimulation in the mediolateral (side-to-side) plane. An additional electrode can be placed over the bony landmark of the tip of the c7 spinous process for the double monaural configuration, which will cause stimulation in the anteroposterior (forward-backward) plane. A portable constant current bipolar stimulator with subject isolation was designed and built to deliver the stimulus. The unit is powered using a 3.7 V battery pack and designed to produce currents up to 5 mA. The stimulator, controlled by a Raspberry Pi 3 computer, offers several stimulus signal generation options including a standalone mode, which uses onboard signal files stored on the flash memory card. Stochastic stimulation signals will be generated in 0-30 Hz frequency bandwidth. Stimulation amplitude can be increased incrementally to a maximum amplitude of 5.0 mA (e.g., 0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0 mA). In control trials, subjects will be experiencing vestibular stimulation with 0-mA current applied through the electrodes. The system will be evaluated at various levels of stimulation and in both the binaural and double monaural electrode configurations. One of the objectives is to identify stimulation levels producing effects most comparable to the post-flight disturbances. This is a pilot study that will set the stage for a larger, more comprehensive study that will investigate wider aspects of post-flight sensorimotor dysfunction and set sensorimotor standards for crew health.

Kofman, I. S.↗
NASA NTRS2017

Toward Reliable and Energy Efficient Wireless Sensing for Space and Extreme Environments

Reliability is the critical challenge of wireless sensing in space systems operating in extreme environments. Energy efficiency is another concern for battery powered wireless sensors. Considering the physics of wireless communications, we propose an approach called Software-Defined Wireless Communications (SDC) that dynamically decide a reliable channel(s) avoiding unnecessary redundancy of channels, out of multiple distinct electromagnetic frequency bands such as radio and infrared frequencies.We validate the concept with Android and Raspberry Pi sensors and pseudo extreme experiments. SDC can be utilized in many areas beyond space applications.

Choi, Baek-Young↗
NASA NTRS2017

Design and Development of Sequential Rotary Valve

Valves are used to regulate the flow of fluids through systems. This rotary valve's main purpose is to fill, pressurize, empty, and vent three smaller tanks with the supply of one larger tank. Many different designs are being taken into consideration, which are each at different stages of development. The furthest along uses three ball valves on a common shaft to open and close their respective ports as the shaft completes one full rotation or cycle. We were tasked with advancing this design to its first test as a plastic model for flow verification, as well as sizing and ordering the necessary O-rings and fasteners. A motor will also be sized to satisfy the torque requirements, and will then be programmed using a Raspberry Pi to rotate the shaft at the calculated speed and dwelling times needed to fill each tank equally. In addition, we have also been advancing designs that use a camshaft and poppets. These are earlier on in their development, currently being sized to replicate the expected flow patterns of the rotary ball valve. Expected outcomes of this valve include bi-directionality, successful sealing under pressure, and accurate cycling.

D’Orsi, Nicholas↗
NASA NTRS2018

Enabling Experimental Evolution: Multi-Parameter Sensor System Integration into a Culture/Stressor Biofluidics System

Experimental evolution (EE) exposes microbial communities to ecological stressors, simulating dynamics up to near-extinction events. Combined with comparative sequencing and other molecular tools, such data can inform the genetic and other biological mechanisms underlying extremophile adaptation, and other observed effects. Automating this type of experiment using biofluidics can mitigate many traditional obstacles, including delays in assay results and environment adjustment and the need for many replicates. A first-generation device for automating EE procedures, the Automated Adaptive Directed Evolution Chamber (AADEC), was developed at NASA Ames. UV-C radiation was the stressor, an LED-photodiode array measured optical density, magnetic agitation and peristaltic pump systems ensured nutrient availability, and Arduino microcontrollers provided control. Escherichia coli in LB kanamycin media was used for testing and performance verification. A manual laboratory procedure with timed exposure to UV-C was performed to typify tolerance acquisition. Approximately a 106 factor increase in survival ratio was recorded over multiple iterations. Currently, a second-generation device is being developed integrating more real-time sensors: redox potential (ORP), indicating available/consumed metabolic energy; dissolved oxygen (DO), indicating aerobic/anaerobic growth; pH, indicating metabolic products; and electrical conductivity (EC), another indicator of metabolic products. The EC sensor system was constructed and calibrated in-house and matched commercial sensors in the required range. A Raspberry Pi computer automated the electrical system, allowing real-time data acquisition. The fluidics card was made of CNC-milled polycarbonate for biocompatibility. Each sensor parameter can also be used as a selection pressure alone or in combination with others to create extreme microbial environments. As a proof of concept, this work demonstrated sensor operation in one pair of growth-sensor chambers. It can be expanded to a multi-chamber system to enable inter-culture comparisons and multi-population studies. The prior Arduino system will be ported to the RPi system. Future stressors to be added include thermal, reactive oxygen species, and varying nutrient availability.

Govinda Raj, Chinmayee↗
NASA NTRSDate not supplied

Automating the Study of Microbial Adaptation Dynamics on and off the ISS

The International Space Station (ISS) not only serves as a unique environment for humans, but also the microorganisms that join alongside. Many microbes present on the spacecraft arrive via humans, and as they interact with different surfaces they begin to inhabit those locations. Much like how human health has shown to be impacted by these extreme environments, microbial viability and response to stress also changes. Experimental evolution (EE) can aid in studying how microbes’ growth and activity changes within the ISS environments by applying controlled stressors to microbial cultures and monitoring their response over generations. EE studies are commonly done manually in laboratories, but, with multiple environmental variables to measure and adjust, it becomes highly labor-intensive, prone to human error, and challenging to scale. A multipurpose automated EE system named the AADEC has been developed to address these problems. This system integrates multiple sensors into a single fluidic chamber using UV-C flux, temperature, and media composition as stressors. AADEC contains five sensors: oxidation-reduction potential, electrical conductivity, pH, dissolved oxygen, and optical density. On their own, each is able to provide certain information on growth rate or metabolism; together, they show in detail how stressors affect life. AADEC studies can be conducted on Earth and repeated aboard the ISS to see how behavior changes when exposed to space mission stressors such as microgravity and radiation. AADEC’s auxiliary systems include peristaltic pumps for media exchange, magnetic rods for agitation, and a Raspberry Pi microprocessor to monitor, store, and adjust stressor levels real-time. This allows researchers to gather information within rapid generations, data and accuracy which is challenging to achieve through manual studies. With further miniaturization and automation, such as a more robust single-piece fluidics card, AADEC has the potential to be developed as a spacecraft payload. Support: NASA Ames CIF Award

Automating↗
NASA NTRSDate not supplied

Enabling a Voice Management System for Space Applications, Design and Software Development

Sustainable missions, beyond low Earth orbit, will require autonomous capabilities in order to achieve NASA’s Artemis program objectives. Correspondingly, the crew must have a means to efficiently interact with these autonomous systems; this can be facilitated via voice and speech communications. Voice-based controls enable the user to access autonomous systems hands-free/eyes-free, allowing the user to better focus on critical tasks. The goal of this project was to explore the knowledge and technology needed to successfully design effective voice interfaces for autonomous systems. The main objective was to understand how a crew member, through voice interaction, could most efficiently and intuitively communicate with a notional autonomous vehicle system manager. This project leveraged prior research conducted by the University of Michigan’s Bioastronautics and Life Support System (BLiSS) team as part of a NASA Moon to Mars eXploration Systems and Habitation (M2M X-Hab) 2020 Academic Innovation Challenge. The X-Hab 2020 work from the BliSS Team resulted in an intuitive graphical user interface/user experience that was built on an Internet of Things (IOT) platform. The Voice User Interface (VUI) design for the M2M X-Hab 2021 project leveraged this technology and incorporated a voice-based assistant and NASA’s Platform for Autonomous Systems (NPAS) software. This required technologies to convert voice to text, conduct semantic interpretations, and convert responses from the autonomous system to text and to speech; additionally, the background noise environment of spacecraft was assessed, and a relatable personality for the autonomous system to facilitate human-like conversations was created. This work’s success was largely due to the diverse team that included expertise in Space Systems Engineering, Human Computer Interaction, Aerospace Engineering, Computer Science, Biomedical Engineering, and Applied Physics. The differing perspectives fostered elaborate discussions, resulting in the conception of three main interactions: (1) User-System, (2) NPAS-System, and (3) Environment-System. The system developed, i.e. the VUI, had to be unique, efficient, and intuitive; thus, the team crafted a personality for the system to enable human-like conversation. User surveys sent to students and young professionals were used to help determine these personality traits by capturing perspectives and expectations of the “Artemis Generation Astronauts”. To further simulate human-like conversations, the system had to be able to quickly interpret user speech and be able to integrate with NASA’s NPAS system for quick and reliable information transfer. Results of this research include (1) a working prototype user interface, that is compatible with NASA’s NPAS system; (2) software that demonstrates the ability to interpret user requests and respond appropriately; (3) the capability to implement fully expanded conversations between user and system using intuitive communication in four request categories; and (4) software and hardware recommendations that optimize the system’s ability to operate, i.e. be heard, in a noisy environment. The technologies chosen for this project’s demonstrations included the following: Raspberry Pi, RASA, Mozilla Deep Speech, Coqui, RTX Voice and Adobe XD. This work has laid the foundation for the development of VUI’s used for autonomy, and is intended to provide guidance for future VUI development.

Tara Vega↗
NASA NTRSDate not supplied

On-board Limb-based Shape Modeling for Small Body Navigation

Missions to small bodies within our solar system are becoming more frequent. Generally, shape models of the target body are required to perform proximity operations as demonstrated by the OSIRIS-REx, Hayabusa2, and Rosetta missions. However, these missions required image downlinking to create high-resolution models on the ground. In some missions, especially small-sats, there may be data downlink data constraints, resulting in the inability to provide the large number of images needed for high-resolution shape models. A solution to this is the ability to generate shape models on-board during the approach to the target or initial proximity surveying. Current work implements a limb-based shape model routine that is able to be executed on a Raspberry Pi 1, which is similar to the processing capability to the flight computer on OSIRIS-REx, and that does not require low phase angle geometries. Initial shape model results generated from Bennu approach (Nov 2-3, 2018) show that the limb-based shape model agrees well with a 75-cm SPC shape model generated after Preliminary Survey; Results are: min difference -7.047m, max difference 17.347m, mean 1.122m, and RMS 3.219m. Further scenarios are presented herein.

Limb-based↗
NASA NTRS2022

Design of a Low-Cost, Submersible, Digital Holographic Microscope for in Situ Microbial Imaging

The methodologies for studying marine microbiology typically consist of utilizing instrumentation within a laboratory. This typically requires extracting a sample from its place of origin prior to examination, which may be days after collection. Oftentimes, the solution is to bring the lab to the ocean, which may be costly and provide further limitations for a sterile and stable laboratory environment. Here we present a low-cost, submersible, digital holographic microscope (DHM) designed to image marine microorganisms (such as bacteria and plankton) in their natural underwater environment. Our instrument eliminates the need to transport samples and allows for instantaneous data collection of microbes in-situ. The DHM achieves sub-micron spatial resolution and is paired with artificial intelligence for the detection and tracking of specimens to reduce the overall collected data. This instrument also aims to reduce the cost of manufacturing and field expenses relative to marine microbiological research. “Off the shelf” components were selected in the design process of this instrument which allows us to achieve precise results without sacrificing data quality. The DHM itself costs under one thousand dollars and features a low-cost high-resolution camera, the Arducam MT9J001. Included in the design were five main subsystems: optical, mechanical, electrical, power, and machine learning. Our on board computer and artificial intelligence consist of a Raspberry Pi 4 (8 Gb) and Google Coral USB Tensorflow accelerator. Instrument testing has successfully proven our abilities of data acquisition for at least two hours in depths of at least forty meters below sea level. Additionally, our artificial intelligence system is currently capable of tracking up to ten areas of interest in a fraction of a second with over ninety percent confidence via the neural net driven by the tensor cores on the Google Coral. Furthermore, we have demonstrated the versatility of our instrument by mounting it on an ocean-going ROV, the BlueRov2 by BlueRobotics. Our tests on the BlueRov2 exemplified the cost-effective nature of a submersible and reusable instrument that can be implemented in moderate environments and on most vessels.

Wallace, James Kent↗
NASA NTRSDate not supplied

Microbial Vessel for Impedance Spectroscopy and Electrochemistry (Mvise): an Extensible, Interoperable Data Acquisition Platform for Liquid Culture Studies in Space Biology Research

The White House Office of Science and Technology Policy (OSTP) has declared 2023 to be the Year of Open Science following an initiative to democratize scientific knowledge. Simultaneously, new sensor technologies have broadened the experimental space available to bioastronautics research. With these open-science goals and technological advances in mind, we have designed and constructed a data acquisition platform for high-precision, real-time monitoring of liquid culture systems. The vessel rig is fitted with six Atlas Scientific probes (micro pH, electrical conductivity, dissolved oxygen, oxidation-reduction potential, liquid temperature, air CO2) and a custom optical density probe similar to the one on BioSentinel’s BioSensor payload. A custom dielectric spectroscopy probe is also planned. The structure of the vessel is resin 3-D printed on a hobbyist-level machine, reducing the production cost and iteration time by over 60% each while increasing extensibility. Data acquisition and storage is controlled with a standalone C state machine-based program running on a Raspberry Pi 3 Model B. When not running headless, an additional program automatically generates and updates plots for live data visualization. Validation of the rig as a data collection system was performed with a yeast liquid culture experiment. While the vessel rig is currently used for standalone experiments, it can also be used as the base perception unit in a self-driving laboratory (SDL). SDLs are high-throughput data collection systems that employ automation and artificial intelligence to conduct and manage routine experiments. Here, we envision an SDL driven by several vessel rigs in which an automated script compares key results, informing the design of future experiments. A vessel rig SDL would streamline many operations, including 1) strain selection for the Lunar Explorer Instrument for space biology Applications (LEIA) investigation and 2) the study of bioregenerative life support systems (BLSS). Ultimately, the datasets that can now be acquired will provide crucial information for accelerating bioastronautics application development in the era of commercial space.

Stephen Lantin↗
DOE OSTI2020

Scalable, Physical Effects Measurable Microgrid for Cyber Resilience Analysis (SPEMMCRA)

The ability to advance state of the art automated protections for industrial control systems (ICS) has as a precursor in the ability to understand the tradeoff space. That is, to enable a cyber feedback loop in a control system environment you must first consider both the security mitigation available, the benefits and the impacts to the control system functionality when the mitigation is used. More damaging impacts could be precipitated that the mitigation was intended to rectify. This paper details networked ICS that controls a simulation of the frequency response represented with the swing equation. The microgrid loads and base generation can be balanced through the control of an emulated battery and power inverter. The simulated plant, which is implemented in Raspberry Pi computers, provides an inexpensive platform to realize the physical effects of cyber attacks to show the tradeoffs of available mitigatoins. This network design can include a commercial ICS controller to introduce real world implementation of feedback controls, and provides a scalable, physical effects measurable Microgrid for cyber resilience analysis (SPEMMCRA).

42 ENGINEERING↗
DOE OSTI2024

Implementation of a Time-domain Cosmic-ray-muon Tagger for the NEXUS Low-background Cryogenic Facility

Quantifying the effects of radiation on the operation of qubits both as quantum information systems as well as particle detectors has emerged as a pressing issue in quantum science in recent years. We present an overview of the design, operation, and deployment of a 90-$\mathrm{in}^2$ three-panel muon detector for use in the NEXUS experimental facility at Fermilab to temporally isolate correlated errors in qubits and determine if they possess an astrophysical origin. Constructed with three scintillator-attached PMTs read out with NIM modules in a triply-coincident logic scheme, we measure a surface-level muon luminosity of 9.7425 muons per second---consistent with an average surface-level cosmic-ray flux of approximately one muon per square centimeter per minute. Integrating the NIM modules with an MCC 128 DAQ HAT and Raspberry Pi, a Python script records exactly when a muon struck the detector and writes a timestamp to a log file for follow-up cross referencing. This experiment will broadly contribute to further studies aimed at understanding the source and mitigation of information loss in qubits.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Novel technology of non-contact real-time radiation damage sensors for high power targets.

This report summarizes the contributions of an intern participating in the Community College Internship (CCI) program at Fermilab, focusing on the development of a novel, non-contact, real-time radiation damage sensor technology. The core objective is to create a reliable sensor capable of measuring radiation-induced degradation on high-power targets without physical contact. The experiment involves using a Class 3B supercontinuum laser directed toward a single material sample placed within a vacuum test chamber. The laser beam reflects off the sample's surface, with changes in reflectivity, indicative of radiation damage, measured by a spectrometer positioned at the chamber’s output port. The intern’s primary responsibilities included designing an interlock system to ensure laser operational safety, developing a camera-based monitoring system using Raspberry Pi devices, and creating structural supports using 3D modeling and printing techniques. Components for the interlock and camera systems were successfully designed and ordered, with preliminary 3D models printed and refined through iterative testing. Challenges encountered in the 3D printing process, such as fragile initial prototypes and difficult support removal, were overcome by adjusting printer settings and incorporating design enhancements like chamfered edges. Future activities, pending component delivery, involve installing and configuring the interlock and camera systems, as well as further improving the structural supports. Overall, the internship significantly enhanced the intern’s technical proficiency in hardware design, software integration, and advanced 3D printing, contributing directly to Fermilab’s operational safety standards and experimental effectiveness in high-energy physics research.

Pumarino Meza, Rafael [Unlisted; Fermilab]↗
DOE OSTI2026

A New Era for Modern Bubble Chamber Technology: Cooling and Real-Time Control for the MAMBA Neutrino Bubble Chamber∗

Future neutrino experiments such as DUNE will be limited less by statistics than by how well neutrino--nucleus interactions are understood, and the cleanest way to improve that understanding is to measure interactions with light nuclei such as hydrogen or deuterium. The detector best suited to the job, the bubble chamber, has not been built for a neutrino beam in about fifty years. MAMBA (Modern Adaptive Modular Bubble chamber Archetype) is a small prototype at Fermilab intended to bring the technology back with modern cryogenics and automation, cycling continuously at 1~Hz. This paper summarizes my work on two of its subsystems during a summer internship. A new solid copper thermal link brought the coldhead to 21.3~K in a commissioning cooldown, near the 20~K operating target. An Industrial Shields Raspberry Pi programmable logic controller (PLC) running OpenPLC was characterized at a median round-trip response of 0.64~ms over 20,000 trials, with 0.66\% of trials exceeding 1~ms. Both results support continuous cycling.

Williams, Nicholas [Fermilab; DuPage Coll.]↗
DOE OSTI2020

Methods for In Situ Electroluminescence Imaging of Photovoltaic Modules Under Varying Environmental Conditions

Electroluminescence (EL) imaging is a powerful tool used to identify defects in photovoltaic solar cells. Typically, this type of characterization is performed in the dark using a current injection that equals short-circuit current measured at standard test conditions (STC). Restricting imaging to such a temperature range limits the information obtained about the module and cells. However, it is not trivial to develop a tool that would allow for EL imaging to be performed under a wider range of temperatures. Here we demonstrate an in situ EL imaging capability developed within an environmental chamber that allows for control of sample temperatures between –40 and 90 °C. Additionally, we demonstrate EL imaging of 4-cell mini-modules (MiMo) under front-side mechanical loading. A Raspberry Pi-connected camera with short-pass filter removed is used for EL imaging. The camera is low-cost with a small form-factor, appropriate for use in a harsh, enclosed environment. The camera is installed within a thermally isolating housing mounted within the environmental chamber. Three example cases are given for MiMos that exhibit various forms of degradation including solder fatigue and cell cracking. It is shown that by measuring at conditions above and below STC, different behaviors may be identified. In some cases, restricting characterization to STC may lead to a failure to detect damage existing in the sample.

14 SOLAR ENERGY↗
DOE OSTI2024

IoT-Based Comfort Control and Fault Diagnostics System for Energy-Efficient Homes

This project studies an Internet of Things (IoT)-based comfort control and fault diagnostics system (referred as iComfort in this report) for energy-efficient homes. The system delivers an occupant-comfort-oriented thermal environment adaptive to fault scenarios and achieves HVAC energy savings in a cost-effective and straightforward way. This smart iComfort home system consists of the following key features. 1) Cost-effectiveness and scalability of the entire hardware and software system: The system includes low-cost temperature, humidity, and airflow sensors, and a Raspberry Pi-based local hub that interfaces with the cloud and IoT-enabled devices. The cost is low, not only for sensors, but also the costs associated with sensor installation, system setup and commissioning, data communication and storage, and data analytics (e.g., the development of automated fault detection and diagnosis (AFDD), as well as adaptive control strategies that are both computationally efficient and practical to implement). 2) Energy performance and user satisfaction: The system delivers user satisfaction and energy savings. This includes a) ease of use, b) optimal occupant thermal comfort, and c) accurate system feedback (e.g., low false alarm of AFDD strategies). 3) Favorable demonstrated prototype performance: The prototype tested at the Pacific Northwest National Laboratory (PNNL) Lab Homes demonstrates the accuracy of fault detections and diagnoses and shows thermal comfort improvement and energy savings through adaptive and optimal HVAC operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks

Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the “edge” of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.

58 GEOSCIENCES↗