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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Tests characterizing bioprocessor hardware for analytical modeling

The tests outlined in this paper were used to characterize the hardware components of the Salad Machine, a small NASA-developed bioprocessor. The data from these tests are presented, and the methods by which this data can be integrated into system mathematical models are briefly discussed. The subsystems and physical processes discussed include the lighting system, the air loop (condensing heat exchanger and the blower), heat transfer to the surroundings, and leakage. Through this effort it was learned that in the development of a test protocol, care should be taken to order the tests such that environmental parameters, particularly humidity, require as few large adjustments as possible. Sensor calibration and installation take a substantial amount of time, which should be built into the test schedule. Two properties were particularly hard to quantify: the air flow rate and the energy from the lighting system entering into the growth volume. Flow rate can be measured using the appropriate device for the system configuration and airflow. Lighting system radiation level was measured using three methods. The results of these methods varied substantially, putting off conclusive quantification of this value.

Gustavino, S.↗
NASA NTRS2000

Applications of Fractal Analytical Techniques in the Estimation of Operational Scale

The observational scale and the resolution of remotely sensed imagery are essential considerations in the interpretation process. Many atmospheric, hydrologic, and other natural and human-influenced spatial phenomena are inherently scale dependent and are governed by different physical processes at different spatial domains. This spatial and operational heterogeneity constrains the ability to compare interpretations of phenomena and processes observed in higher spatial resolution imagery to similar interpretations obtained from lower resolution imagery. This is a particularly acute problem, since longterm global change investigations will require high spatial resolution Earth Observing System (EOS), Landsat 7, or commercial satellite data to be combined with lower resolution imagery from older sensors such as Landsat TM and MSS. Fractal analysis is a useful technique for identifying the effects of scale changes on remotely sensed imagery. The fractal dimension of an image is a non-integer value between two and three which indicates the degree of complexity in the texture and shapes depicted in the image. A true fractal surface exhibits self-similarity, a property of curves or surfaces where each part is indistinguishable from the whole, or where the form of the curve or surface is invariant with respect to scale. Theoretically, if the digital numbers of a remotely sensed image resemble an ideal fractal surface, then due to the self-similarity property, the fractal dimension of the image will not vary with scale and resolution, and the slope of the fractal dimension-resolution relationship would be zero. Most geographical phenomena, however, are not self-similar at all scales, but they can be modeled by a stochastic fractal in which the scaling properties of the image exhibit patterns that can be described by statistics such as area-perimeter ratios and autocovariances. Stochastic fractal sets relax the self-similarity assumption and measure many scales and resolutions to represent the varying form of a phenomenon as the pixel size is increased in a convolution process. We have observed that for images of homogeneous land covers, the fractal dimension varies linearly with changes in resolution or pixel size over the range of past, current, and planned space-borne sensors. This relationship differs significantly in images of agricultural, urban, and forest land covers, with urban areas retaining the same level of complexity, forested areas growing smoother, and agricultural areas growing more complex as small pixels are aggregated into larger, mixed pixels. Images of scenes having a mixture of land covers have fractal dimensions that exhibit a non-linear, complex relationship to pixel size. Measuring the fractal dimension of a difference image derived from two images of the same area obtained on different dates showed that the fractal dimension increased steadily, then exhibited a sharp decrease at increasing levels of pixel aggregation. This breakpoint of the fractal dimension/resolution plot is related to the spatial domain or operational scale of the phenomenon exhibiting the predominant visible difference between the two images (in this case, mountain snow cover). The degree to which an image departs from a theoretical ideal fractal surface provides clues as to how much information is altered or lost in the processes of rescaling and rectification. The measured fractal dimension of complex, composite land covers such as urban areas also provides a useful textural index that can assist image classification of complex scenes.

Emerson, Charles W.↗
DOE OSTI2019

Vibration-Based Techniques for Concrete Structural Health Monitoring. Light Water Reactor Sustainability Program report

The objectives of this ongoing research project focus on health monitoring and data analytics of concrete slabs containing reactive aggregates and thus subjected to degradation due to alkali-silica reaction (ASR). A controlled concrete slab with four pockets of reactive aggregates (pure silica, and reactive aggregates from three different quarries) was cast at Vanderbilt University and cured in representative conditions to accelerate degradation due to ASR. A set of four concrete samples were also cast and cured at the University of Alabama for ASR testing. Of these four samples, two slabs contained reactive aggregates while the other two had the non-reactive aggregate counterparts mixed throughout the samples. Four additional slabs were cast and cured at the University of Nebraska- Lincoln. Of these four samples; one contained non-reactive aggregates and no reinforcement, one contained reactive aggregates and no reinforcement, one contained reactive aggregates and rebar reinforcement in one direction, and the last contained reactive aggregates and reinforcement in two directions. Single frequency and dual frequency tests were used on these slabs to locate ASR damage within the specimens containing reactive aggregates. The vibration tests were conducted with different sensor and actuator configurations to facilitate damage detection and localization. The results from the data analysis of the dynamic testing on concrete slabs are discussed in detail in this report. Results for damage localization are dependent on multiple parameters used in the tests. This report investigates different methods of data fusion including voting, raw averaging, damage index averaging, and Bayesian fusion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like the Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.

Sreepathi, Sarat↗
NASA NTRS1985

Failure detection and accommodation in structural dynamics systems using analytic redundancy

The concept of analytic redundancy is extended to identify nonzero constant output failures in large spacecraft active control sensors and actuators, using a set of distributed sensors which do not nominally produce equivalent signals but are related through the structural dynamics of the system. The suboptimal technique uses a Sequential Probability Ratio Test on the residual sequence of a Kalman filter based on a modal structure model, to determine failure of a component based on an assumed failure hypothesis. The specific failure is identified and then the Kalman filter gains are reconfigured for the identified remaining working sensor set. Experimental data using an apparatus whose dynamics are representative of a large spacecraft show the nominal filter performance under failed and unfailed conditions.

Williams, J. P.↗
NASA NTRS2004

Evaluation of the Paratrend Multi-Analyte Sensor for Potential Utilization in Long-Duration Automated Cell Culture Monitoring

BACKGROUND: Compact and automated sensors are desired for assessing the health of cell cultures in biotechnology experiments. While several single-analyte sensors exist to measure culture health, a multi-analyte sensor would simplify the cell culture system. One such multi-analyte sensor, the Paratrend 7 manufactured by Diametrics Medical, consists of three optical fibers for measuring pH, dissolved carbon dioxide (pCO(2)), dissolved oxygen (pO(2)), and a thermocouple to measure temperature. The sensor bundle was designed for intra-vascular measurements in clinical settings, and can be used in bioreactors operated both on the ground and in NASA's Space Shuttle and International Space Station (ISS) experiments. METHODS: A Paratrend 7 sensor was placed at the outlet of a bioreactor inoculated with BHK-21 (baby hamster kidney) cells. The pH, pCO(2), pO(2), and temperature data were transferred continuously to an external computer. Cell culture medium, manually extracted from the bioreactor through a sampling port, was also assayed using a bench top blood gas analyzer (BGA). RESULTS: Two Paratrend 7 sensors were used over a single cell culture experiment (64 days). When compared to the manually obtained BGA samples, the sensor had good agreement for pH, pCO(2), and pO(2) with bias (and precision) 0.005(0.024), 8.0 mmHg (4.4 mmHg), and 11 mmHg (17 mmHg), respectively for the first two sensors. A third Paratrend sensor (operated for 141 days) had similar agreement (0.02+/-0.15 for pH, -4+/-8 mm Hg for pCO(2), and 24+/-18 mmHg for pO(2)). CONCLUSION: The resulting biases and precisions are com- parable to Paratrend sensor clinical results. Although the pO(2) differences may be acceptable for clinically relevant measurement ranges, the O(2) sensor in this bundle may not be reliable enough for the ranges of pO(2) in these cell culture studies without periodic calibration.

Validation Studies↗
DOE OSTI2023

Application of Principal Component Analysis to Electrochemical Reprocessing PM and NMAC

In this report, data from an electrorefiner (ER) for nuclear fuel reprocessing is evaluated for process monitoring (PM) conclusions. This data comes from tests performed at the Idaho National Laboratory in 2022. Multivariate approaches utilizing methods of Principal Component Analysis (PCA) is applied. This is based off established work in process monitoring for fault detection in industrial facilities. This report will discuss the background, methods, and results of the application and some of the conclusions and applications that can be drawn from them. PCA is applied to two different electrorefiner (ER) operations that occurred at Idaho National Laboratory between August and October 2022. The first operation occurred with little incident while the second had several noted faults in the equipment in operational logs. The data from the first run was used to train the data for “normal” operations and applied to both sets of data to determine when operations were in an “off-normal” condition and identify where the fault occurs through PCA. PCA was able to identify off-normal events and identify the cause for off-normal operations. These identified off-normal events matched with the events and their causes in the operational logs. However, small amounts of variance in the data led to false detection of “off-normal” events. Thus, careful selection of training data and a-posteriori conclusions based off operator assessments will both be required for application of PCA to PM applications. This work demonstrated that multivariate approaches and latent variables are applicable to pyroprocessing PM applications and can be further expanded in future work as quality variables such as salt concentration from sensors and sampling become available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Data Repository for Multi-Objective Urban Observational Strategies: A risk-based framework for expanding flood sensor networks.

These data support the manuscript "Multi-Objective Urban Observational Strategies: A risk-based framework for expanding flood sensor networks." These data are generated to allow water managers to reason about optimal locations to expand a flood observation system from multiple perspectives, specifically focusing on flood hazards, and population exposure to flooding. The data included are a) a shapefile of individual sensor locations b) a shapefile of river reach catchments, c) raster of FEMA flood likelihood layers d) shapefile of population locations and population socioeconomic characteristics. The code is written in R and includes all files necessary to generate the figures for the associated manuscript. Interactive maps of the final calculated maps of hazard, vulnerability, exposure, and risk are also included as html files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Air Conditioning Systems Fault Detection and Diagnosis-Based Sensing and Data-Driven Approaches

The air conditioning (AC) system is the primary building end-use contributor to the peak demand for energy. The energy consumed by this system has grown as fast as it has in the last few decades, not only in the residential section but also in the industry and transport sectors. Therefore, to combat energy crises, urgent actions on energy efficiency should be taken to support energy security. Consequently, the faults in AC system components increase energy consumption due to the degradation of the system’s performance and the losses in the energy conversion procedure. In this work, AC system fault detection and diagnosis (FDD) methods are investigated to propose analytic tools to identify faults and provide solutions to those problems. The analysis of existing work shows that data-driven approaches are more accurate for both soft and hard fault detection and diagnosis in AC systems. Therefore, the proposed methods are not accurate for simultaneous fault detection, while in some works, authors tested the method with several faults separately without investigating scenarios that combine more than one fault. Moreover, this study shows that integrating data-driven approaches requires deploying an optimal sensing and measurement architecture that can detect a maximum number of faults with minimally deployed sensors. The new sensing, information, and communication technologies are discussed for their integration in AC system monitoring in order to optimize system operation and detect faults.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Field characterization of elastic properties across a fault zone reactivated by fluid injection

In this paper, we studied the elastic properties of a fault zone intersecting the Opalinus Clay formation at 300 m depth in the Mont Terri Underground Research Laboratory (Switzerland). Four controlled water injection experiments were performed in borehole straddle intervals set at successive locations across the fault zone. A three-component displacement sensor, which allowed capturing the borehole wall movements during injection, was used to estimate the elastic properties of representative locations across the fault zone, from the host rock to the damage zone to the fault core. Young's moduli were estimated by both an analytical approach and numerical finite difference modeling. Results show a decrease in Young's modulus from the host rock to the damage zone by a factor of 5 and from the damage zone to the fault core by a factor of 2. In the host rock, our results are in reasonable agreement with laboratory data showing a strong elastic anisotropy characterized by the direction of the plane of isotropy parallel to the laminar structure of the shale formation. In the fault zone, strong rotations of the direction of anisotropy can be observed. Finally, the plane of isotropy can be oriented either parallel to bedding (when few discontinuities are present), parallel to the direction of the main fracture family intersecting the zone, and possibly oriented parallel or perpendicular to the fractures critically oriented for shear reactivation (when repeated past rupture along this plane has created a zone).

58 GEOSCIENCES↗
NASA NTRS1992

An expert system/ion trap mass spectrometry approach for life support systems monitoring

Efforts to develop sensor and control system technology to monitor air quality for life support have resulted in the development and preliminary testing of a concept based on expert systems and ion trap mass spectrometry (ITMS). An ITMS instrument provides the capability to identify and quantitate a large number of suspected contaminants at trace levels through the use of a variety of multidimensional experiments. An expert system provides specialized knowledge for control, analysis, and decision making. The system is intended for real-time, on-line, autonomous monitoring of air quality. The key characteristics of the system, performance data and analytical capabilities of the ITMS instrument, the design and operation of the expert system, and results from preliminary testing of the system for trace contaminant monitoring are described.

Palmer, Peter T.↗
NASA NTRS1995

Silicon Micromachined Sensor for Broadband Vibration Analysis

The development of a family of silicon based integrated vibration sensors capable of sensing mechanical resonances over a broad range of frequencies with minimal signal processing requirements is presented. Two basic general embodiments of the concept were designed and fabricated. The first design was structured around an array of cantilever beams and fabricated using the ARPA sponsored multi-user MEMS processing system (MUMPS) process at the Microelectronics Center of North Carolina (MCNC). As part of the design process for this first sensor, a comprehensive finite elements analysis of the resonant modes and stress distribution was performed using PATRAN. The dependence of strain distribution and resonant frequency response as a function of Young's modulus in the Poly-Si structural material was studied. Analytical models were also studied. In-house experimental characterization using optical interferometry techniques were performed under controlled low pressure conditions. A second design, intended to operate in a non-resonant mode and capable of broadband frequency response, was proposed and developed around the concept of a cantilever beam integrated with a feedback control loop to produce a null mode vibration sensor. A proprietary process was used to integrat a metal-oxide semiconductor (MOS) sensing device, with actuators and a cantilever beam, as part of a compatible process. Both devices, once incorporated as part of multifunction data acquisition and telemetry systems will constitute a useful system for NASA launch vibration monitoring operations. Satellite and other space structures can benefit from the sensor for mechanical condition monitoring functions.

Gutierrez, Adolfo↗
DOE OSTI2017

Chemical Tool Peer Review Summary.

Chemical tracers are commonly used to characterize fracture networks and to determine the connectivity between the injection and production wells. Currently, most tracer experiments involve injecting the tracer at the injection well, manually collecting liquid samples at the wellhead of the production well, and sending the samples off for laboratory analysis. While this method provides accurate tracer concentration data, it does not provide information regarding the location of the fractures conducting the tracer between wellbores. The goal of this project is to develop chemical sensors and design a prototype tool to help understand the fracture properties of a geothermal reservoir by monitoring tracer concentrations along the depth of the well. The sensors will be able to detect certain species of the ionic tracers (mainly iodide) and pH in-situ during the tracer experiment. The proposed high-temperature (HT) tool will house the chemical sensors as well as a standard logging sensor package of pressure, temperature, and flow sensors in order to provide additional information on the state of the geothermal reservoir. The sensors and the tool will be able to survive extended deployments at temperatures up to 225 °C and high pressures to provide real-time temporal and spatial feedback of tracer concentration. Data collected from this tool will allow for the real-time identification of the fractures conducting chemical tracers between wellbores along with the pH of the reservoir fluid at various depths.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Sensitive detection of formaldehyde using an interband cascade laser near 3.6 μm

Here, we report the development of a formaldehyde (H 2 CO) trace gas sensor using a continuous wave (CW), thermoelectrically-cooled (TEC), distributed-feedback interband cascade laser (DFB-ICL) at 3.6 μm. Wavelength modulation spectroscopy was used to detect the second harmonic spectra of a strong H 2 CO absorption feature centered at 2778.5 cm -1 (3599 nm) in its ν 1 fundamental vibrational band. A compact and novel multipass cell (7.6-cm physical length and 32-ml sampling volume) was implemented to achieve an effective optical path length of 3.75 m. A minimum detection limit of 6 parts per billion (ppb) at an optimum gas pressure of 200 Torr was achieved with a 1-s data acquisition time. An Allan-Werle deviation analysis was performed to investigate the long-term stability of the sensor system and a 1.5 ppb minimum detectable concentration could be achieved by averaging up to 140 s. Absorption interference eeffects from atmospheric H 2 O (2%) and CH 4 (5 ppm) were also analyzed in this work and proved to be insignificant for the current sensor configuration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Incubating advances in integrated photonics with emerging sensing and computational capabilities

As photonic technologies grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities for research communities. Applications include data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities continuously growing. Herein, we review state-of-the-art integrated photonic on-chip sensors that operate in the visible to mid-infrared wavelength region on various material platforms. Among the different materials, architectures, and technologies leading the way for on-chip sensors, we discuss the optical sensing principles that are commonly applied to biochemical and gas sensing. Our focus is on passive optical waveguides, including dispersion-engineered metamaterial-based structures, which are essential for enhancing the interaction between light and analytes in chip-scale sensors. We harness a diverse array of cutting-edge sensing technologies, heralding a revolutionary on-chip sensing paradigm. Our arsenal includes refractive-index-based sensing, plasmonics, and spectroscopy, which forge an unparalleled foundation for innovation and precision. Furthermore, we include a brief discussion of recent trends and computational concepts, incorporating Artificial Intelligence & Machine Learning (AI/ML) and deep learning approaches over the past few years to improve the qualitative and quantitative analysis of sensor measurements.

Jain, Sourabh (ORCID:0000000279923275)↗
DOE OSTI2025

EVALUATION OF OPERATION TEMPERATURES UNDER NATURAL CONVECTION HELIUM FLOW IN A CONFINED CAVITY

The Spallation Neutron Source (SNS) is a high-power accelerator-based pulsed neutron source led by Oak Ridge National Laboratory (ORNL) to achieve high fluxes of neutrons for scientific experiments. Active and passive cooling of the systems and parts forming the SNS have been considered to warrant the safe operation of the facility. The diverse cooling systems make use of conjugated heat transfer mechanisms to provide a stable operation temperature for all components in the machine. Thermal power deposited into stainless-steel piping lines due to particle radiation may reach values of up to 1.2 W/cc in the regions located closer to the center of the lower IRP. These energy deposition levels, in not actively cooled components, such as the transfer-line-outer-vacuum-layer may increase the temperature of the component beyond design requirement limitations. The evaluation of the operation temperatures for the former components relies in the assumption that a low-pressure helium atmosphere provides enough heat removal capacity based on natural convection phenomena. In this work, the evaluation of steady state temperatures in components such as the CMS transfer lines has been evaluated using computational fluid dynamics (CFD), analytical correlations and experimental measurements. The companion experiments were conducted in a closed helium system at pressures varying from 1.1 to 1.5 bar. A copper rod was affixed horizontally between viewing windows and heated at constant power, and measurements were made of both the rod temperature and ambient temperature via a system fiberoptic distributed temperature sensors and RTDs. It was found that the measured heat transfer coefficients agree well with the predictions of Churchill and Chu correlations across the range of cases considered. Additionally, the ambient helium volume above the rod was imaged via background oriented schlieren (BOS), and these data was used to determine the line-averaged density gradients in this region. These gradients were compared to simulation data to validate the predictions of natural convection simulations.

Dominguez-Ontiveros, Elvis [ORNL] (ORCID:000000018↗
NASA NTRS1994

Highly transparent and rugged sensor for velocity determinations of cosmic dust particles

In order to understand the evolution of interplanetary dust, numerous dust particles have been collected and analyzed. An analysis of the composition often provides information on the particle's origin. So does its origin. Composition and orbit data complement each other and should be determined together. If the last orbit of a particle can be determined, its orbital history can often be calculated backward in time and associated with its parent body. To determine the last orbit, the velocity needs to be measured before the particle is collected. The precision required in determining the velocity components relative to the spacecraft should be 1 percent or better. A sensor for naturally charged cosmic dust particles is discussed. Two models of the sensor were tested, one with a free-falling steel ball and the other with particles accelerated to high speed. Analytic expressions of the sensor signals are presented and compared with the test results. The errors in speed and angle were estimated to be about 0.3 percent and 0.2 degrees respectively.

Auer, Siegfried↗
DOE OSTI2021

Low-cost sensors and analytics for a digital factory

ORNL Manufacturing Demonstration Facility and Perisense worked on a low-cost solution for machining process monitoring to enable a digital factory. Enabling machine connectivity for digital factors is expensive for many small to medium manufacturers. The hypothesis for the research project is that a lowcost sensor suite, which can be retrofitted to a legacy machine tool or a new machine tool, can effectively provide real-time, in-situ sensing for manufacturing processes, which can enable improvements in process performance and efficiency. The Perisense sensor node was installed on the Haas TM1 machine with a current sensor and an accelerometer. Cutting tests were performed at different process parameters and sensor data was recorded. Results show a correlation of the current sensor to the spindle load. The accelerometer, mounted on the machine base, showed a correlation to the spindle speed as well as the spindle load. The sensor data can be used to monitor the machining process and provide insights on the machine spindle load utilization.

42 ENGINEERING↗