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At least 343 records · Page 19

Digitally Calibrated TR Modules Enabling Real-Time Beamforming SweepSAR Architectures for DESDynI-Class Radar Instruments

Real-time digital beamforming, combined with lightweight, large aperture reflectors, enable SweepSAR architectures such as that of the proposed DESDynI [Deformation, Ecosystem Structure, and Dynamics of Ice] SAR [Synthetic Aperture Radar] Instrument (or DSI). SweepSAR promises significant increases in instrument capability for solid earth and biomass remote sensing, while reducing mission mass and cost. This new instrument concept requires new methods for calibrating the multiple channels, which must be combined on-board, in real-time. We are developing new methods for digitally calibrating digital beamforming arrays to reduce development time, risk and cost of precision calibrated TR modules for array architectures by accurately tracking modules' characteristics through closed-loop Digital Calibration, thus tracking systematic changes regardless of temperature

SweepSar↗

Chalcogenide Glass-Capped Fiber-Optic Sensor for Real-Time Temperature Monitoring in Extreme Environments

We demonstrate a novel chalcogenide glass (ChG)-capped optical fiber temperature sensor capable of operating within harsh environment. The sensor architecture utilizes the heat-induced phase change (amorphous-to-crystalline) property of ChGs, which rapidly (80–100 ns) changes the optical properties of the material. The sensor response to temperature variation around the phase change of the ChG cap at the tip of the fiber provides abrupt changes in the reflected power intensity. This temperature is indicative of the temperature at the sensing node. We present the sensing performance of six different compositions of ChGs and a method to interpret the temperature profile between 440 °C and 600 °C in real-time using an array structure. The unique radiation-hardness property of ChGs makes the devices compatible with high-temperature and high-radiation environments, such as monitoring the cladding temperature of Light Water (LWR) or Sodium-cooled Fast (SFR) reactors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Flood Monitoring and Crop Damage Assessment

In recent years, the occurrence and impact of inland and coastal flood events have become more frequent and damaging, especially within agricultural fields, due to the global climate change and consistent sea level rise. Monitoring and measuring the magnitude of flood events in a timely manner and assessing the subsequent crop damages accurately are precursors in minimizing detrimental consequences that could potentially lead to a global food security crisis. Traditional gauge-based measurements with sophisticated hydrological models are capable of monitoring flood events precisely but limited within the smaller spatial extent, time-consuming, and costly. In recent decades, advancement in airborne- and satellite-based remote sensing technologies offering products at a daily global spatial extent with various spectral resolution helps address the shortcomings of the traditional in situ approaches in flood monitoring. Furthermore, the methods such as classification and band ratioing using remote sensing products are simple and effective in assessing flood-induced agricultural damages. The combination of remote sensing products and geographic information systems along with the current development in web mapping, users now can get near real-time flood monitoring and crop damage assessments, albeit dependent upon the quality of available data. A case study to quantify the impact of the 2011 Missouri Mississippi River flooding on the surrounding cornfield was performed through a regression model. The model was trained using historical daily NDVI and corn yield across Nebraska and Missouri, and the overall accuracy in estimating corn yield was about 90%. The method implemented in this localized case study could be extended at a larger geographical scale.

Flood monitoring↗

Integrating fiber optic sensors into metallic components for sensing in harsh environments

The integration of fiber optic sensors into high-temperature materials is critical for real-time monitoring and autonomous operation of engineering systems. This study demonstrated a spark plasma sintering (SPS)-assisted embedding process for integrating sapphire fiber optic sensors into stainless steel components during part fabrication. Optical fibers were encapsulated in stainless steel 316L powders which were sintered at different fabrication conditions using SPS to investigate the effects of sintering parameters on the embedment. Measurements of optical transmittance, combined with microstructural analysis (X-ray computed tomography and scanning electron microscopy) and mechanical testing (tensile and microhardness), were conducted to examine the fiber functionality, fiber–matrix bonding quality, and properties of the sintered materials. Here, the results show that under suitable fabrication conditions, intact optical fibers can be encapsulated in highly-densified (>98 % relative density) stainless steel components. These conditions also led to a good bond at the fiber–matrix interface with micron-sized material interdiffusion across the interface. The sintering parameters were observed to affect fiber optical attenuation, where high temperature, pressure, and hold time during SPS enhanced fiber–matrix bonding and adversely affected optical transmission. Tensile testing confirmed the superior tensile strength and ductility of the matrix fabricated by SPS. Furthermore, the materials exhibited limited strength reduction (~70 MPa) upon the integration of fibers. This study demonstrates the effectiveness of SPS for fiber-material integration for high-temperature applications.

36 MATERIALS SCIENCE↗

Making Sense of Plant Health

Ciencia, Inc. created a new device, known as a Portable Photosynthesis Analyzer, or Phase Fluorometer, that provides real-time data about the photochemical efficiency of phytoplankton and other plant forms. The commercial version of this technology is used for photosynthesis research and offers major benefits to the field of life science. This new instrument is the first portable instrument of its kind. Through a license agreement with Ciencia, Oriel Instruments, of Stratford, Connecticut, manufactures and markets the commercial version of the instrument under the name LifeSense.TMLifeSense is a 70 MHz single-frequency fluorometer that offers unrivaled capabilities for fluorescence lifetime sensing and analysis. LifeSense provides information about all varieties of photosynthetic systems. Photosynthesis research contributes important health assessments about the plant, be it phytoplankton or a higher form of plant life. With its unique sensing capabilities, LifeSense furnishes data regarding the yield of a plant's photochemistry, as well as its levels of photosynthetic activity. The user can then gain an extremely accurate estimate of the plant's chlorophyll biomass, primary production rates, and a general overview of the plant's physiological condition.

Source record↗

Remote sensing of chlorophyll and temperature in marine and fresh waters.

An airborne differential radiometer was demonstrated to be a sensitive, real-time detector of surface chlorophyll content in water bodies. The instrument continuously measures the difference in radiance between two wavelength bands, one centered near the maximum of the blue chlorophyll a absorption region and the other at a reference wavelength outside this region. Flights were made over fresh water lakes, marine waters, and an estuary, and the results were compared with 'ground truth' measurements of chlorophyll concentration. A correlation between output signal of the differential radiometer and the chlorophyll concentration was obtained. Examples of flight data are illustrated. Simultaneous airborne measurements of chlorophyll content and water temperature revealed that variations in chlorophyll are often associated with changes in temperature. Thus, simultaneous sensing of chlorophyll and temperature provides useful information for studies of marine food production, water pollution, and physical processes such as upwelling.

Arvesen, J. C.↗

Microbial potentiometric sensor technology for real-time detecting and monitoring of toxic metals in aquatic matrices

Considering that toxic metals can affect metabolic processes in microorganisms adversely, it can be hypothesized that these metals in water matrices would induce a decrease in metabolic activity of the biofilm microorganisms populating the surface of a sensing electrode, which could be registered as a change in the open-circuit potential (OCP) generated by the biofilm microorganisms. The goal of this study was to test this hypothesis and demonstrate the underlying principle that microbial potentiometric sensor (MPS) technology could be used for long-term and real-time monitoring and detection of rapid changes in metal concentrations in realistic aquatic environments. To address the goal, four objective were addressed: (1) a batch reactor with three graphite-based MPS electrodes was fabricated; (2) a set of single-ion solutions and one multiple ion solution were prepared reflecting realistic concentrations of metals found in electroplating wastewaters; (3) the responses of the MPS to the simultaneous presence of multiple toxic metal ions in a single solution were measured; and (4) the changes of the MPS signals to the presence of individual metal ion solutions were examined. While the hypothesis was validated, the study also revealed that the MPS was sufficiently sensitive to not only detect, but also quantify, toxic metal ion concentrations in aqueous solutions. The coefficients of determination, which were R 2 > 0.995, and responsiveness of <1 μmol/L for some toxic metal cations, strongly support the performance of MPS technology in the echelons of expensive analytical tools capable detecting and measuring trace elements.The magnitude of the MPS response was toxic metal specific. When the molar concertation normalizes the inhibition portion of the signal area, the assessed sensitivity order was: Se>Cd>Pb>Ag>Ni> Zn. The study provides valuable information for enforcement agents, environmental professionals, and wastewater treatment operators, so toxic metal pollution and its detrimental impacts can be prevented and mitigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Integrating Overhead Camera Imagery with Reinforcement Learning to Improve Fuel Economy Through Adaptive Traffic Control

Overhead cameras have been deployed for some time as a sensor for traffic control, with many advantages over conventional inductive loop sensors in terms of robustness, ease of installation, and simplicity with respect to maintenance. We explore the utility of such imagery (in particular, fisheye lens cameras) for intelligent traffic control in real-time using reinforcement learning, with a goal of improving fuel economy for the overall intersection and grid of intersections. We summarize our initial work in data collection and approach to ascertain the ability to visually sense vehicular fuel consumption using real-world data. We then describe our deep reinforcement learning topology to test adaptive control informed by the camera capabilities. Next, we tested the proof-of-concept through a variety of simulation scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator, which included different estimates on vehicle classifications, different traffic distributions, and different traffic densities. Six different control policies were tested ranging from simple timing, to heuristic visual-based methods, to actual policies learned via the reinforcement learning approach. Our results demonstrate the feasibility of the approach for controlling traffic lights for better fuel efficiency based on visual vehicle estimates from commercial, fisheye lens cameras.

Karnowski, Thomas↗

Building a Real-Time Flood Prediction Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the local government of Howard County, Maryland, to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a statistical model capable of hindcasting the two severe flash flood events that devastated Ellicott City and transitioned to a ‘Long Short-Term Memory’ based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using Nash-Sutcliffe Efficiency. The final product, the Sequentially Trained Real-time EstimAted Model (STREAM) predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

NASA DEVELOP↗

Real-Time Flight Envelope Monitoring System

The objective of this effort was to show that real-time aircraft control-surface hinge-moment information could be used to provide a robust and reliable prediction of vehicle performance and control authority degradation. For a given airfoil section with a control surface -- be it a wing with an aileron, rudder, or elevator -- the control-surface hinge moment is sensitive to the aerodynamic characteristics of the section. As a result, changes in the aerodynamics of the section due to angle-of-attack or environmental effects such as icing, heavy rain, surface contaminants, bird strikes, or battle damage will affect the control surface hinge moment. These changes include both the magnitude of the hinge moment and its sign in a time-averaged sense, and the variation of the hinge moment with time. The current program attempts to take the real-time hinge moment information from the aircraft control surfaces and develop a system to predict aircraft envelope boundaries across a range of conditions, alerting the flight crew to reductions in aircraft controllability and flight boundaries.

Kerho, Michael↗

Digitally Calibrated TR Modules Enabling Real-Time Beamforming SweepSAR Architectures

SweepSAR, a novel radar architecture that depends on a DBF (digital beamforming) array, requires calibration accuracies that are order(s) of magnitude greater than is possible with traditional techniques, such as a priori characterization of TR (transmit/receive) modules in thermal vacuum chambers, or simple loop-back of the calibration signal. The advantages of a SweepSAR architecture are so great that it is worth applying significant resources to calibration efforts. Due to the nature of the DBF, each channel contains a digitizer and very powerful digital processor. Each channel can independently digitize (with the digitizer) and analyze (with the processor) its channel's unique calibration signal, and extract the relevant calibration parameters, namely channel gain and channel phase delay commonly referred to as the gain (or amplitude) and phase of the channel. Using the processor, each channel's gain and phase can theoretically be estimated with arbitrary precision through averaging a sufficiently large number of samples. Systematic errors and the changing gain and phase of the channels, typically due to temperature drifts, limits how long the averaging can occur, which limits the precision of the calibration estimate. However, results indicate that calibration knowledge of both the transmit and receive chains of each TR module can be improved by one or two orders of magnitude. Due to the digital nature of the receiver data, the channel's gain and phase may be corrected by a similar amount, while the transmit chain can only be corrected in a traditional manner. To implement Sweep SAR, the order of magnitude improvement in the knowledge of the channel's gain and phase is needed, and the control of the receiver to a similar level is required. Inherent to the DBF array is the individual digitization of each of the array's receiver channels. Current systems typically combine all of the analog signals in the array into one or two analog channels, which are then digitized and processed. All signal conditioning performed prior to digitization is done using analog hardware (which is less precise than digital signal conditioning and dependent on temperature). The DBF digitizes every signal prior to combining, and can therefore analyze and correct received signals, as well as analyze signals that are being transmitted through analog hardware (by sampling a copy and digitizing). Each channel of a DBF also has a powerful processor. With this combination, one is able to digitize, analyze, and correct each channel prior to its being combined. A unique factor is the ability to digitize and analyze (in real time) each of the array's channels independently, allowing one to achieve unprecedented knowledge of each channel's performance (gain and phase), and since the combining is done digitally, each receive channel can be corrected prior to combining. This enables an unprecedented level of accuracy and control through onboard processing. SweepSAR promises significant increases in instrument capability for solid earth and biomass remote sensing, while reducing mission mass and cost. This new instrument concept requires new methods for calibrating the multiple channels, which must be combined onboard, in real time. New methods are being developed for digitally calibrating digital beam-forming arrays to reduce development time, risk, and cost of precision calibrated TR modules for array architectures by accurately tracking modules' characteristics through closed-loop digital calibration, thus tracking systematic changes regardless of temperature.-

Hoffman, James P.↗

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗

Monitoring Damage Accumulation in Ceramic Matrix Composites Using Electrical Resistivity

The electric resistance of woven SiC fiber reinforced SiC matrix composites were measured under tensile loading conditions. The results show that the electrical resistance is closely related to damage and that real-time information about the damage state can be obtained through monitoring of the resistance. Such self-sensing capability provides the possibility of on-board/in-situ damage detection and accurate life prediction for high-temperature ceramic matrix composites. Woven silicon carbide fiber-reinforced silicon carbide (SiC/SiC) ceramic matrix composites (CMC) possess unique properties such as high thermal conductivity, excellent creep resistance, improved toughness, and good environmental stability (oxidation resistance), making them particularly suitable for hot structure applications. In specific, CMCs could be applied to hot section components of gas turbines [1], aerojet engines [2], thermal protection systems [3], and hot control surfaces [4]. The benefits of implementing these materials include reduced cooling air requirements, lower weight, simpler component design, longer service life, and higher thrust [5]. It has been identified in NASA High Speed Research (HSR) program that the SiC/SiC CMC has the most promise for high temperature, high oxidation applications [6]. One of the critical issues in the successful application of CMCs is on-board or insitu assessment of the damage state and an accurate prediction of the remaining service life of a particular component. This is of great concern, since most CMC components envisioned for aerospace applications will be exposed to harsh environments and play a key role in the vehicle s safety. On-line health monitoring can enable prediction of remaining life; thus resulting in improved safety and reliability of structural components. Monitoring can also allow for appropriate corrections to be made in real time, therefore leading to the prevention of catastrophic failures. Most conventional nondestructive evaluation (NDE) techniques such as ultrasonic C-scan, x-ray, thermography, and eddy current are limited since they require structural components of complex geometry to be taken out of service for a substantial length of time for post-damage inspection and assessment. Furthermore, the typical NDE techniques are useful for identifying large interlaminar flaws, but insensitive to CMC materials flaws developed perpendicular to the surface under tensile creep conditions. There are techniques such as piezoelectric sensor [7,8], and optical fiber [9,10] that could be used for on-line health monitoring of CMC structures. However, these systems involve attaching an external sensor or putting special fibers in CMC composites, which would be problematic at high temperature applications.

Smith, Craig E.↗

Astrobee: Five years of Completed, Current, and Future Research on the International Space Station using Free Flying Robots.

After five years on the International Space Station (ISS), the Astrobee Research Facility, has completed over 160 Test Sessions logging over 1200 hours of operations. Managed by the NASA ISS Program OZ office and supported by NASA Ames Research Center (ARC) in California, the Astrobee Team currently maintains two identical free-flying Astrobee robots and a Docking Station for research on the ISS. As a technology demonstration platform, the Astrobee Robots are available for Guest Scientists to use for a spectrum of research capabilities. Using ambient air on the ISS, propelled by battery-operated fans, Astrobee is designed to autonomously operate throughout most of the USOS (US Orbital Segment), with the objective of minimizing the need for astronaut support. Astrobee carries a suite of six cameras, a two degree-of-freedom (DOF) arm with a gripper that can grasp ISS handrails and other objects, and three payload bays that provide power and data for guest science hardware. Astrobee can autonomously execute hours-long flight plans or be tele-operated from the ground. While the Astrobee Team continues to improve mapping and autonomous flight capabilities, one of the main goals of Astrobee Robots is to provide research opportunities for Guest Scientists. The Astrobee Robot Software (ARS) makes extensive use of the open-source Robot Operating System (ROS). The ARS can be used interchangeably with an Astrobee Simulator or as Astrobee’s onboard software. ARS features include autonomous docking and perching, real-time teleoperations from the ground, plan based autonomous tasks, multi Astrobee communication, among other capabilities. Through simulation software and ground testing laboratories, the Astrobee Team is available to support Guest Scientists during development and testing and lead real-time ISS operations. The Astrobee Team and Guest Scientists have completed research including Astrobatics maneuvers, RFID and sound sensing capabilities, Gecko materials studies, student Robotics Programming Challenges, and Free Flyer formation flight investigations. Current science with the Astrobee Robots is investigating new docking capabilities through software only research as well as testing new docking hardware installed on the Astrobees. The Astrobee Team and other researchers at NASA Ames continue to explore robotics applications for future NASA missions such as Gateway and potential experiments involving human-robot interactions. Continued advanced mapping resolution capabilities, and high-resolution panoramic imagery also remains areas of research. Exciting in development research involves docking for rendezvous proximity operation (CLINGERS), multi resolution 3D scanning (MRS), space debris removal in microgravity (REACCH). This presentation will mainly focus on completed research over the past year and current science being performed on the Astrobees. This presentation will also focus on how a Guest Scientist/Researcher progresses from conception to running their science on the Astrobees on the ISS, as well as discuss the Astrobee Facility resources available for supporting ground testing and real-time ISS operations.

Astrobee↗

Fluorescence-Enabled Self-Reporting for Redox Flow Batteries

Monitoring battery health is challenging. Self-reporting enables the rapid health assessment of redox flow batteries (RFBs) and provides insight into degradation mechanisms of electrochemically active molecules (redoxmers). In this study, we introduce fluorescence as an orthogonal property to probe this chemistry in situ. An established anolyte redoxmer, 2,1,3-benzothiadiazole, is rendered an efficient blue-green fluorophore through minimalistic derivatization. One of the derivatives is electrochemically reversible with a long lifetime and cycling stability in the charged state. Spectroscopic measurements on this new redoxmer reveal strong effects of the electrolyte cation on the fluorescence that are useful for probing the solvent microenvironment. Using this probe, we demonstrate proof-of-concept in situ crossover sensing and characterize the effects of electrolyte composition on this crossover. In this way, real-time tracing of redoxmers in a flow cell is demonstrated, paving the way to include still more self-reporting functions into the redoxmers.

25 ENERGY STORAGE↗

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗

Payload Operations

The objective of this paper is to provide the future ISS scientist and/or engineer a sense of what ISS payload operations are expected to be. This paper uses a real-time operations scenario to convey this message. The real-time operations scenario begins at the initiation of payload operations and runs through post run experiment analysis. In developing this scenario, it is assumed that the ISS payload operations flight and ground capabilities are fully available for use by the payload user community. Emphasis is placed on telescience operations whose main objective is to enable researchers to utilize experiment hardware onboard the International Space Station as if it were located in their terrestrial laboratory. An overview of the Payload Operations Integration Center (POIC) systems and user ground system options is included to provide an understanding of the systems and interfaces users will utilize to perform payload operations. Detailed information regarding POIC capabilities can be found in the POIC Capabilities Document, SSP 50304.

Cissom, R. D.↗