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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Exploratory Investigation of Disengagements and Crashes in Autonomous Vehicles Under Mixed Traffic: An Endogenous Switching Regime Framework

Autonomous Vehicles (AVs) have a large potential to improve traffic safety but also pose some critical challenges. While AVs may help reduce crashes caused by human error, they still may experience failures of technologies and sensing, as well as decision-making errors in a mixed traffic environment. A disengagement refers to an AV transitioning control from autonomous systems to the trained test driver. The safety critical nature of disengagements makes it imperative to analyze disengagements and crashes together. In this study, we analyze both crashes and disengagements from real-world AV driving in California to fill the knowledge gap regarding the relationship between disengagements and crashes in a mixed traffic environment. A nested logit model was calibrated using three different outcomes: (1) disengagement with a crash, (2) disengagement with no crash, and (3) no disengagement with a crash. Furthermore, endogenous switching regime models were also calibrated to draw distinctions between the relation of disengagements and crashes while accounting for endogeneity effects. The results show that factors related to AV systems (such as software failures) and other roadway participants increase the propensity of a disengagement without a crash. Furthermore, AVs were observed to disengage less often as the technology matured over time. Marginal effects revealed an 8% decrease. The results thus suggest that disengagements are a part of AVs' safe performance and disengagement alerts may need to be triggered in order to avoid certain failures with current technology.

42 ENGINEERING↗
NASA NTRS1989

Autonomous sensor-based dual-arm satellite grappling

Dual-arm satellite grappling involves the integration of technologies developed in the Sensing and Perception (S&P) Subsystem for object acquisition and tracking, and the Manipulator Control and Mechanization (MCM) Subsystem for dual-arm control. S&P acquires and tracks the position, orientation, velocity, and angular velocity of a slowly spinning satellite, and sends tracking data to the MCM subsystem. MCM grapples the satellite and brings it to rest, controlling the arms so that no excessive forces or torques are exerted on the satellite or arms. A 350-pound satellite mockup which can spin freely on a gimbal for several minutes, closely simulating the dynamics of a real satellite is demonstrated. The satellite mockup is fitted with a panel under which may be mounted various elements such as line replacement modules and electrical connectors that will be used to demonstrate servicing tasks once the satellite is docked. The subsystems are housed in three MicroVAX II microcomputers. The hardware of the S&P Subsystem includes CCD cameras, video digitizers, frame buffers, IMFEX (a custom pipelined video processor), a time-code generator with millisecond precision, and a MicroVAX II computer. Its software is written in Pascal and is based on a locally written vision software library. The hardware of the MCM Subsystem includes PUMA 560 robot arms, Lord force/torque sensors, two MicroVAX II computers, and unimation pneumatic parallel grippers. Its software is written in C, and is based on a robot language called RCCL. The two subsystems are described and test results on the grappling of the satellite mockup with rotational rates of up to 2 rpm are provided.

Wilcox, Brian↗
NASA NTRS2012

Comparison of Aircraft Models and Integration Schemes for Interval Management in the TRACON

Reusable models of common elements for communication, computation, decision and control in air traffic management are necessary in order to enable simulation, analysis and assurance of emergent properties, such as safety and stability, for a given operational concept. Uncertainties due to faults, such as dropped messages, along with non-linearities and sensor noise are an integral part of these models, and impact emergent system behavior. Flight control algorithms designed using a linearized version of the flight mechanics will exhibit error due to model uncertainty, and may not be stable outside a neighborhood of the given point of linearization. Moreover, the communication mechanism by which the sensed state of an aircraft is fed back to a flight control system (such as an ADS-B message) impacts the overall system behavior; both due to sensor noise as well as dropped messages (vacant samples). Additionally simulation of the flight controller system can exhibit further numerical instability, due to selection of the integration scheme and approximations made in the flight dynamics. We examine the theoretical and numerical stability of a speed controller under the Euler and Runge-Kutta schemes of integration, for the Maintain phase for a Mid-Term (2035-2045) Interval Management (IM) Operational Concept for descent and landing operations. We model uncertainties in communication due to missed ADS-B messages by vacant samples in the integration schemes, and compare the emergent behavior of the system, in terms of stability, via the boundedness of the final system state. Any bound on the errors incurred by these uncertainties will play an essential part in a composable assurance argument required for real-time, flight-deck guidance and control systems,. Thus, we believe that the creation of reusable models, which possess property guarantees, such as safety and stability, is an innovative and essential requirement to assessing the emergent properties of novel airspace concepts of operation.

Neogi, Natasha↗
DOE OSTI2024

Calcined Polyethyleneimine-Coated Optical Fibers for Distributed pH Monitoring at High Pressures and Temperatures

In the oil and gas, CO2 sequestration, H2 subsurface storage, and geothermal energy sectors, subsurface pH measurements are critical for monitoring the geochemical conditions and estimating potential corrosion rates of wellbore systems. Real-time pH measurements in these conditions are vital for detecting and predicting corrosion deterioration of wellbore components that may jeopardize the safety and continued operation of wellbore systems. Previous tests using metal oxide-based coatings (TiO2) provided strong responses at elevated temperatures and moderate pressure stability but provided poor differentiation between acidic and alkaline solutions. Building off the pH responsiveness of the TiO2 surface and known pH sensitivity of amine-based polymers, a coating based on the secondary amine polymer polyethyleneimine (PEI) was developed. As the polymer itself is highly water soluble and easily removed by aqueous solutions, the sensor coating was treated with a high temperature (500 °C) calcination procedure in air to convert it into a more stable oxidized coating capable of withstanding hot aqueous solutions without dissolving while retaining linear pH sensitivity from pH values between 2 and 11. The sensor performance was measured using optical transmission measurements in solutions of various pHs and using optical backscatter reflectometry for distributed pH sensing demonstration in wellbore-relevant pressures (up to 1,000 psi) and temperatures (80 °C).

Shumski, Alexander↗
DOE OSTI2020

Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses -A review

Heating, ventilation and air-conditioning (HVAC) systems have been adopted to create comfortable, healthy and safe indoor environments. In the control loop, the technical feature of the human demand-oriented supply can help operate HVAC effectively. Among many technical options, real time monitoring based on feedback signals from end users has been frequently reported as a critical technology to confirm optimizing building performance. Recent studies have incorporated human thermal physiology signals and thermal comfort/discomfort status as real-time feedback signals. A series of human subject experiments used to be conducted by primarily adopting subjective questionnaire surveys in a lab-setting study, which is limited in the application for reality. With the help of advanced technologies, physiological signals have been detected, measured and processed by using multiple technical formats, such as wearable sensors. Nevertheless, they mostly require physical contacts with the skin surface in spite of the small physical dimension and compatibility with other wearable accessories, such as goggles, and intelligent bracelets. Most recently, a low cost small infrared camera has been adopted for monitoring human facial images, which could detect the facial skin temperature and blood perfusion in a contactless way. Also, according to latest pilot studies, a conventional digital camera can generate infrared images with the help of new methods, such as the Euler video magnification technology. Human thermal comfort/discomfort poses can also be detected by video methods without contacting human bodies and be analyzed by the skeleton keypoints model. Here, new sensing technologies were summarized, their cons and pros were discussed, and extended applications for the demand-oriented ventilation were also reviewed as potential development and applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Smart Scattering Scanning Near-Field Optical Microscopy

Scattering scanning near-field optical microscopy (s-SNOM) provides spectroscopic imaging from molecular to quantum materials with few nanometer deep subdiffraction limited spatial resolution. However, in its conventional implementation s-SNOM is slow to effectively acquire a series of spatio-spectral images, especially with large fields of view. This problem is further exacerbated for weak resonance contrast or when using light sources with limited spectral irradiance. Indeed, the generally limited signal-to-noise ratio prevents sampling a weak signal at the Nyquist sampling rate. Here, we demonstrate how acquisition time and sampling rate can be significantly reduced by using compressed sampling, matrix completion, and adaptive random sampling, while maintaining or even enhancing the physical or chemical image content. We use fully sampled real data sets of molecular, biological, and quantum materials as ground-truth physical data and show how deep under-sampling with a corresponding reduction of acquisition time by 1 order of magnitude or more retains the core s-SNOM image information. We demonstrate that a sampling rate of up to 6× smaller than the Nyquist criterion can be applied, which would provide a 30-fold reduction in the data required under typical experimental conditions. Furthermore, our smart s-SNOM approach is generally applicable and provides systematic full spatio-spectral s-SNOM imaging with a large field of view at high spectral resolution and reduced acquisition time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. Here, the proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Sensing propagation events and fade statistics at C-band for two over-water, line-of-sight propagation paths over a one year period

We examine signal fading statistics over a year period corresponding to two over-water, line-of-site, propagation links in the mid-Atlantic coast of the US. These links are comprised of a transmitter on a tower at Parramore Island, VA operating at 4.7 GHz sending simultaneous cw signals to two receiver systems located on a lighthouse and a lookout tower on Assateague Beach, VA at distances of 44 and 39 km, respectively. The receiving sites are separated by approximately 5 km. Cumulative fade distributions corresponding to yearly, monthly, and diurnal time scales were derived. Fade duration statistics correspond to sustained attenuation events were also derived. These events, which were arbitrarily defined as having fades relative to free space powers in excess of 20 dB for durations of two hours or more, are believed to be generally due to subrefraction. Analysis of synoptic weather conditions and nearby rawindsonde data during two sustained deep fading periods showed atmospheric conditions consistent with extreme subrefraction, where the refractivity-height profile had a positive lapse rate. The efficacy of employing the links as indicators of real time conditions of atmospheric propagation was also demonstrated by a telephone call-up procedure which enabled displays of time series of the fading at remote locations to be generated.

Goldhirsh, Julius↗
NASA NTRS2022

A Dynamic Landslide Hazard Monitoring Framework for the Lower Mekong Region

The Lower Mekong region is one of the most landslide-prone areas of the world. Despite the need for dynamic characterization of landslide hazard zones within the region, it is largely understudied for several reasons. Dynamic and integrated understanding of landslide processes requires landslide inventories across the region, which have not been available previously. Computational limitations also hamper regional landslide hazard assessment, including accessing and processing remotely sensed information. Finally, open-source software and modelling packages are required to address regional landslide hazard analysis. Leveraging an open-source data-driven global Landslide Hazard Assessment for Situational Awareness model framework, this study develops a region-specific dynamic landslide hazard system leveraging satellite-based Earth observation data to assess landslide hazards across the lower Mekong region. A set of landslide inventories were prepared from high-resolution optical imagery using advanced image-processing techniques. Several static and dynamic explanatory variables (i.e., rainfall, soil moisture, slope, relief, distance to roads, distance to faults, distance to rivers) were considered during the model development phase. An extreme gradient boosting decision tree model was trained for the monsoon period of 2015–2019 and the model was evaluated with independent inventory information for the 2020 monsoon period. The model performance demonstrated considerable skill using receiver operating characteristic curve statistics, with Area Under the Curve values exceeding 0.95. The model architecture was designed to use near-real-time data, and it can be implemented in a cloud computing environment (i.e., Google Cloud Platform) for the routine assessment of landslide hazards in the Lower Mekong region. This work was developed in collaboration with scientists at the Asian Disaster Preparedness Center as part of the NASA SERVIR Program’s Mekong hub. The goal of this work is to develop a suite of tools and services on accessible open-source platforms that support and enable stakeholder communities to better assess landslide hazard and exposure at local to regional scales for decision making and planning.

Nishan Kumar Biswas↗
NASA NTRS2002

The cytoskeleton and gravitropism in higher plants

The cellular and molecular mechanisms underlying the gravitropic response of plants have continued to elude plant biologists despite more than a century of research. Lately there has been increased attention on the role of the cytoskeleton in plant gravitropism, but several controversies and major gaps in our understanding of cytoskeletal involvement in gravitropism remain. A major question in the study of plant gravitropism is how the cytoskeleton mediates early sensing and signal transduction events in plants. Much has been made of the actin cytoskeleton as the cellular structure that sedimenting amyloplasts impinge upon to trigger the downstream signaling events leading to the bending response. There is also strong molecular and biochemical evidence that the transport of auxin, an important player in gravitropism, is regulated by actin. Organizational changes in microtubules during the growth response phase of gravitropism have also been well documented, but the significance of such reorientations in controlling differential cellular growth is unclear. Studies employing pharmacological approaches to dissect cytoskeletal involvement in gravitropism have led to conflicting results and therefore need to be interpreted with caution. Despite the current controversies, the revolutionary advances in molecular, biochemical, and cell biological techniques have opened up several possibilities for further research into this difficult area. The myriad proteins associated with the plant cytoskeleton that are being rapidly characterized provide a rich assortment of candidate regulators that could be targets of the gravity signal transduction chain. Cytoskeletal and ion imaging in real time combined with mutant analysis promises to provide a fresh start into this controversial area of research.

Review↗
NASA NTRS1992

Microgravity accelerometer characterization on Columbia STS-32 mission

The Honeywell In-Space Accelerometer (HISA) is a three-axis microgravity accelerometer instrument package recently developed by Honeywell Systems and Research Center (SRC) to monitor oscillatory and transient accelerations onboard spacecraft and spaceborne structures. The HISA was designed to be co-located with materials and life sciences experiments to record real-time accelerometer event data, sampling time, and temperature. The HISA was originally developed to monitor the microgravity disturbances associated with a polymer morphology experiment developed by 3M Company in Minneapolis, Minnesota. The HISA was first flight tested with the 3M experiment on the Space Shuttle Atlantis STS-34 in October 1989. The HISA was successfully flown on a second shuttle mission (Columbia STS-32 in January 1990) in support of the NASA JSC-sponsored Microgravity Disturbances Experiment (MDE), which focused on the effects of microgravity disturbances on the growth of high-quality Indium crystals. The primary objective of the STS-32 MDE experiment was to investigate the effects of crew-induced gravity disturbances on the microstructure (crystal defects and uniformity of impurity distribution) of float-zone-grown crystals. The float-zone technique involves establishing a suspended molten zone between two cylindrical samples a pure, single-crystal sample and an impure, polycrystalline sample. Microgravity disturbances due to crew treadmill activity and orbiter maneuvering system thruster firings were sensed and recorded by the HISA to understand their effects on the stability of the float zone. The principle of operation of the HISA, the flight configuration of the HISA supporting the MDE experiment, and the characterization of STS-32 treadmill disturbance data are summarized.

Schoess, Jeff↗
NASA NTRS2014

Applications of Satellite Remote Sensing for Response to and Recovery from Meteorological Disasters

Numerous on‐orbit satellites provide a wide range of spatial, spectral, and temporal resolutions supporting the use of their resulting imagery in assessments of disasters that are meteorological in nature. This presentation will provide an overview of recent use of Earth remote sensing by NASA's Short‐term Prediction Research and Transition (SPoRT) Center in response to disaster activities in 2012 and 2013, along with case studies supporting ongoing research and development. The SPoRT Center, with support from NASA's Applied Sciences Program, has explored a variety of new applications of Earth‐observing sensors to support disaster response. In May 2013, the SPoRT Center developed unique power outage composites representing the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF‐5 tornado that occurred on May 20. Subsequent ASTER, MODIS, Landsat‐7 and Landsat‐8 imagery help to identify the damaged areas. Higher resolution imagery of Moore, Oklahoma were provided by commercial satellites and the recently available International Space Station (ISS) SERVIR Environmental Research and Visualization System (ISERV) instrument. New techniques are being explored by the SPoRT team in order to better identify damage visible in high resolution imagery, and to monitor ongoing recovery for Moore, Oklahoma. This presentation will provide an overview of near real‐time data products developed for dissemination to SPoRT's partners in NOAA's National Weather Service, through collaboration with the USGS and other federal agencies. Specifically, it will focus on integration of various data sets within the NOAA National Weather Service Damage Assessment Toolkit, which allows meteorologists in the field to consult available satellite imagery while performing their damage assessment.

Molthan, Andrew I.↗
NASA NTRS2014

Applications of Satellite Remote Sensing for Response to and Recovery from Meteorological Disasters

Numerous on‐orbit satellites provide a wide range of spatial, spectral, and temporal resolutions supporting the use of their resulting imagery in assessments of disasters that are meteorological in nature. This presentation will provide an overview of recent use of Earth remote sensing by NASA's Short‐term Prediction Research and Transition (SPoRT) Center in response to disaster activities in 2012 and 2013, along with case studies supporting ongoing research and development. The SPoRT Center, with support from NASA's Applied Sciences Program, has explored a variety of new applications of Earth‐observing sensors to support disaster response. In May 2013, the SPoRT Center developed unique power outage composites representing the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF‐5 tornado that occurred on May 20. Subsequent ASTER, MODIS, Landsat‐7 and Landsat‐8 imagery help to identify the damaged areas. Higher resolution imagery of Moore, Oklahoma were provided by commercial satellites and the recently available International Space Station (ISS) SERVIR Environmental Research and Visualization System (ISERV) instrument. New techniques are being explored by the SPoRT team in order to better identify damage visible in high resolution imagery, and to monitor ongoing recovery for Moore, Oklahoma. This presentation will provide an overview of near real‐time data products developed for dissemination to SPoRT's partners in NOAA's National Weather Service, through collaboration with the USGS and other federal agencies. Specifically, it will focus on integration of various data sets within the NOAA National Weather Service Damage Assessment Toolkit, which allows meteorologists in the field to consult available satellite imagery while performing their damage assessment.

Molthan, Andrew L.↗
NASA NTRSDate not supplied

Novel Hourly-Resolved Global Cloud Property Composite from Operational Satellites Imagers

Numerous applications in satellite remote sensing of surface properties, atmospheric state, composition, and radiation require accurate knowledge of the location and characteristics of clouds. Operational satellite imager radiances are valuable for cloud detection and for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. This paper describes a new global high-resolution dataset of cloud properties made available for community use that is constructed from analyses of a constellation of meteorological satellite imagers. Data taken from Meteosat-8/9 and -11, Himawari-8/9, GOES-16, and -17/-18, Aqua, Terra, Suomi-NPP and NOAA-20 are processed and composited on a 3-km grid to provide hourly global coverage. A historical multi-year dataset is available to serve various needs including modeling challenges related to cloud evaluations and parameterizations. Near real-time data products are also currently available between 60N and 60S. Efforts are underway to operationalize polar orbiting satellite cloud detection methods for low-latency applications over polar regions. The cloud detection and retrieval algorithms have been developed over many years to support NASA weather and climate programs such as the Clouds and Earth’s Radiant Energy System (CERES). To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. A brief description of the methods highlighting the unique aspects of the SatCORPS data products will be presented along with information on their status and availability.

SatCORPS↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗
NASA NTRS2010

SPOT Program

A Spacecraft Position Optimal Tracking (SPOT) program was developed to process Global Positioning System (GPS) data, sent via telemetry from a spacecraft, to generate accurate navigation estimates of the vehicle position and velocity (state vector) using a Kalman filter. This program uses the GPS onboard receiver measurements to sequentially calculate the vehicle state vectors and provide this information to ground flight controllers. It is the first real-time ground-based shuttle navigation application using onboard sensors. The program is compact, portable, self-contained, and can run on a variety of UNIX or Linux computers. The program has a modular objec-toriented design that supports application-specific plugins such as data corruption remediation pre-processing and remote graphics display. The Kalman filter is extensible to additional sensor types or force models. The Kalman filter design is also strong against data dropouts because it uses physical models from state and covariance propagation in the absence of data. The design of this program separates the functionalities of SPOT into six different executable processes. This allows for the individual processes to be connected in an a la carte manner, making the feature set and executable complexity of SPOT adaptable to the needs of the user. Also, these processes need not be executed on the same workstation. This allows for communications between SPOT processes executing on the same Local Area Network (LAN). Thus, SPOT can be executed in a distributed sense with the capability for a team of flight controllers to efficiently share the same trajectory information currently being computed by the program. SPOT is used in the Mission Control Center (MCC) for Space Shuttle Program (SSP) and International Space Station Program (ISSP) operations, and can also be used as a post -flight analysis tool. It is primarily used for situational awareness, and for contingency situations.

Smith, Jason T.↗
DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗
NASA NTRS1992

Gravity and the geoid in the Nepal Himalaya

Materials within the Himalaya are rising due to convergence between India and Asia. If the rate of erosion is comparable to the rate of uplift the mean surface elevation will remain constant. Any slight imbalance in these two processes will lead to growth or attrition of the Himalaya. The process of uplift of materials within the Himalaya coupled with surface erosion is similar to the advance of a glacier into a region of melting. If the melting rate exceeds the rate of downhill motion of the glacier then the terminus of the glacier will receed up-valley despite the downhill motion of the bulk of the glacier. Thus although buried rocks, minerals and surface control points in the Himalaya are undoubtably rising, the growth or collapse of the Himalaya depends on the erosion rate which is invisible to geodetic measurements. Erosion rates are currently estimated from suspended sediment loads in rivers in the Himalaya. These typically underestimate the real erosion rate since bed-load is not measured during times of heavy flood, and it is difficult to integrate widely varying suspended load measurements over many years. An alternative way to measure erosion rate is to measure the rate of change of gravity in a region of uplift. If a control point moves vertically it should be accompanied by a reduction in gravity as the point moves away from the Earth's center of mass. There is a difference in the change of gravity between uplift with and without erosion corresponding to the difference between the free-air gradient and the gradient in the acceleration due to gravity caused by a corresponding thickness of rock. Essentially gravity should change precisely in accord with a change in elevation of the point in a free-air gradient if erosion equals uplift rate. We were funded by NASA to undertake a measurement of absolute gravity simultaneously with measurements of GPS height within the Himalaya. Since both absolute gravity and time are known in an absolute sense to 1 part in 10(exp 10) it is possible to estimate gravity with a precision of 0.1 mu gal. Known systematic errors reduce the measurement to an absolute uncertainty of 6 mu gal. The free air gradient at the point of measurement is typically about 3 mu gals/cm. At Simikot where our experiment was conducted we determined a vertical gravity gradient of 4.4 mu gals/cm.

Bilham, Roger↗