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This project will focus on diagnostics and quality control understandings of continuously produced FFI membranes [both Anion Exchange Membrane (AEM) and Proton Exchange Membrane (PEM)] and continuously produced FFI electrodes. Optimization of methods and novel quality control techniques will be developed as needed.
Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.
The goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.
Contemporary approaches to aerospace vehicle system monitoring rely heavily on thresholds that represent a compromise between providing warning early enough to avert a mishap while simultaneously minimizing false alarms. While this reliance on thresholds has been in place for decades and has permeated both cockpits and control rooms, we often find it insufficient when retrospectively analyzing data from an accident. To modernize and enhance flight test safety, we introduce new methods of monitoring for anomalous patterns of interaction rather than for thresholds exceedance. For systems with a well characterized baseline we show how the Inductive Monitoring System (IMS), utilized by NASA in the aftermath of the Columbia accident, might be implemented in real-time to provide earlier warning than currently employed techniques. For systems without such a baseline, we introduce new developments in statistical methods relating to critical slowing down, first applied in medicine and physics, which show promise for adaptation to flight test. Finally, the familiar resource constrained environment leads to a reliance on increased instrumentation that is challenging the limits of the current one-sensor, one-indicator threshold paradigm. Existing methods thus fail to accurately reflect the true complexity of a vehicle rich with interdependent interacting systems. We highlight these concepts in a brief summary of the 2001 Air Transat Flight 236 deadstick landing in the Azores. We then suggest control room and cockpit modifications to better display the information gleaned using these novel monitoring methods.
Contemporary approaches to aerospace vehicle system monitoring rely heavily on thresholds that represent a compromise between providing warning early enough to avert a mishap while simultaneously minimizing false alarms. While this reliance on thresholds has been in place for decades and has permeated both cockpits and control rooms, we often find it insufficient when retrospectively analyzing data from an accident. To modernize and enhance flight test safety, we introduce new methods of monitoring for anomalous patterns of interaction rather than for thresholds exceedance. For systems with a well characterized baseline we show how the Inductive Monitoring System (IMS), utilized by NASA in the aftermath of the Columbia accident, might be implemented in real-time to provide earlier warning than currently employed techniques. For systems without such a baseline, we introduce new developments in statistical methods relating to critical slowing down, first applied in medicine and physics, which show promise for adaptation to flight test. Finally, the familiar resource constrained environment leads to a reliance on increased instrumentation that is challenging the limits of the current "one-sensor, one-indicator" threshold paradigm. Existing methods thus fail to accurately reflect the true complexity of a vehicle rich with interdependent interacting systems. We highlight these concepts in a brief summary of the 2001 Air Transat Flight 236 deadstick landing in the Azores. We then suggest control room and cockpit modifications to better display the information gleaned using these novel monitoring methods.
A small, lightweight, and fast-response ozone sensor for various environmental applications is described. At a flow rate of 100 l/min(-1) the ozone sensor has a response time of significantly better than 0.1 s with a detection limit lower than 100 pptv. The ozone sensor was successfully tested in various environmental applications, i.e. in measuring directly the vertical ozone flux onto agricultural land utilizing the eddy correlation or covariance technique and in monitoring horizontal and vertical ozone profiles in the troposphere and stratosphere.
Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments.
This paper describes a unique vibration signature analysis technique Coherence Phase Line Enhancer (CPLE) Frequency Analysis - for roller bearing health monitoring. Defects of roller bearing (e.g. wear, foreign debris, crack in bearing supporting structure, etc.) can cause small bearing characteristic frequency shifts due to minor changes in bearing geometry. Such frequency shifts are often too small to detect by the conventional Power Spectral Density (PSD) due to its frequency bandwidth limitation. This Coherent Phase Line Enhancer technology has been evolving over the last few years and has culminated in the introduction of a new and novel frequency spectrum which is fully described in this paper. This CPLE technology uses a "key phasor" or speed probe as a preprocessor for this analysis. With the aid of this key phasor, this CPLE technology can develop a two dimensional frequency spectrum that preserves both amplitude and phase that is not normally obtained using conventional frequency analysis. This two-dimensional frequency transformation results in several newly defined spectral functions; i. e. CPLE-PSD, CPLE-Coherence and the CPLE-Frequency. This paper uses this CPLE frequency analysis to detect subtle, low level bearing related signals in the High Pressure Fuel Pump (HPFP) of the Space Shuttle Main Engine (SSME). For many rotating machinery applications, a key phasor is an essential measurement that is used in the detection of bearing related signatures. There are times however, when a key phasor is not available; i. e. during flight of any of the SSME turbopumps or on the SSME High Pressure Oxygen Turbopump (HPOTP) where no speed probe is present. In this case, the CPLE analysis approach can still be achieved using a novel Pseudo Key Phasor (PKP) technique to reconstruct a 1/Rev PKP signal directly from external vibration measurements. This paper develops this Pseudo Key Phasor technique and applies it to the SSME vibration data.
The impact of bone loss due to different mechanical loadings in microgravity is a major concern for astronauts upon reintroduction to gravitational forces in exploration missions to the Moon and Mars. it has been shown that astronauts not only lose bone at differing rates, with levels up to 2% per month, but each astronaut will respond to bone loss treatments differently. Pre- and post-flight imaging techniques and frozen urine samples for post-flight laboratory immunoassays To develop a novel, non-invasive, highly . sensitive, portable, intuitive, and low-powered device to measure bone resorption levels in 'real time' to provide rapid and Individualized feedback to maximize the efficacy of bone loss countermeasures 1. Collect urine specimen and analyze the level of bone resorption marker, DPD (deoxypridinoline) excreted. 2. Antibodies specific to DPD conjugated with nanoshells and mixed with specimen, the change in absorbance from agglutination is measured by an optical device. 3. The concentration of DPD is displayed and recorded on a PDA
We introduce a novel digital twin (DT) framework for the predictive maintenance of long-term physical systems. Using monitoring tire health as an application, we show how the DT framework can be used to enhance automotive safety and efficiency, and how the technical challenges can be overcome using a three-step approach. First, to manage the data complexity over a long operation span, we employ data reduction techniques to concisely represent physical tires using historical performance and usage data. Relying on these data, for fast real-time prediction, we train a transformer-based model offline on our concise dataset to predict future tire health over time, represented as remaining casing potential (RCP). Based on our architecture, our model quantifies both epistemic and aleatoric uncertainties, providing reliable confidence intervals around predicted RCP. Second, to incorporate real-time data, we update the predictive model in the DT framework, ensuring its accuracy throughout its lifespan with the aid of hybrid modeling and the use of the discrepancy function. Third, to assist decision-making in predictive maintenance, we implement a tire state decision algorithm, which strategically determines the optimal timing for tire replacement based on RCP forecasted by our transformer model. This approach ensures that our DT accurately predicts system health, continually refines its digital representation, and supports predictive maintenance decisions. Furthermore, our framework effectively embodies a physical system, leveraging big data and machine learning (ML) for predictive maintenance, model updates, and decision-making.
A novel quantitative thermal shock test of ceramics is described. The technique employs contact between a metal-cooling rod and hot disk-shaped specimen. In contrast with traditional techniques, the well-defined thermal boundary condition allows for accurate analyses of heat transfer, stress, and fracture. Uniform equibiaxial tensile stresses are induced in the center of the test specimen. Transient specimen temperature and acoustic emission are monitored continuously during the thermal stress cycle. The technique is demonstrated with soda-lime glass specimens. Experimental results are compared with theoretical predictions based on a finite-element method thermal stress analysis combined with a statistical model of fracture. Material strength parameters are determined using concentric ring flexure tests. Good agreement is found between experimental results and theoretical predictions of failure probability as a function of time and initial specimen temperature.
Five years ago we introduced a new method for measuring spacecraft chassis floating potential relative to the space plasma (absolute spacecraft potential) in low Earth orbit. The method, based on a straightforward interpretation of photoelectron spectra, shows promise for numerous applications, but has not yet been tried. In the interest of testing the method, and ultimately supplying another tool for measuring absolute spacecraft charge, we are producing a flight prototype Spacecraft Charge Monitor (SCM) with support from NASA's Small Business Innovation Research (SBIR) program. Although insight into the technique came from data collected in space over two decades ago, very little data are available. The data indicate that it may be possible to determine spacecraft floating potential to within 0.1 volt each with the SCM second under certain conditions. It is debatable that spacecraft floating potential has ever been measured with such accuracy. The compact, easily deployed SCM also offers the advantage of long-term stability in calibration. Accurate floating potential determinations from the SCM could be used to correct biases in space plasma measurements and evaluate charge mitigation and/or sensing devices. Although this paper focuses on the device's use in low Earth orbit (LEO), the device may also be able to measure spacecraft charge at higher altitudes, in the solar wind, and in orbits around other planets. The flight prototype SCM we are producing for delivery to NASA in the third quarter of 2004 will measure floating potential from 0 to -150 volts with 0.1 volt precision, weigh approximately 600-700 grams, consume approximately 2 watts, and will measure approximately 8 x 10 x 17 cm.
Well tests with sinusoidal variation in pumping rate are appealing because of the ability to filter competing signals and they also can be set up with no net removal of water, which is an advantage at contaminated sites. Current methods of interpreting sinusoidal-rate well tests analyze the phase lag and amplitude of pressure signals in the aquifer. This approach is restricted by the availability of monitoring wells. An alternative approach could be to measure the stain in the vadose zone instead of pressure in the aquifer. The goal of this research is to evaluate the feasibility of using signals from in-situ sensors in the vadose zone to characterize sinusoidal-rate well tests. Well tests with a variety of periods and amplitudes were performed using a well completed in an unconfined saprolite aquifer with a water table at 9m depth at our test site near Clemson, South Carolina. Vertical strain was measured every meter between 1 to 7 m using a distributed optical fiber sensing technique called coherence length gated microwave photonics interferometry (CMPI). A novel packaging system was designed to couple the optical fiber to the saprolite. The results show that strain measured in the vadose zone during sinusoidal-rate pumping varies periodically with amplitudes from 0.1 to 3 microstrain, depending on the amplitude of the sinusoidal pumping rate. Hydraulic heads at similar radial distances were also periodic with changing amplitudes ranging up to ±0.2 meters. The strain in the vadose zone responds to pressure in the underlying aquifer—the strain is tensile when the pressure is falling, and compressive when the pressure is rising. The phase lag between the pumping rate and the strain was 3 min with a pumping period of 15 minutes, and this gives a hydraulic diffusivity of 0.06 m2/s. This is the same diffusivity obtained by analyzing a constant rate pumping test, and it is slightly larger than the diffusivity (0.044 m2/s) obtained by analyzing the phase lag of the pressure in a nearby monitoring well. Strain measurements in the vadose zone could be made with much less cost and effort than installing a deep monitoring well. These results suggest that strain measurements in the vadose zone could be used to supplement monitoring wells, thereby increasing the resolution, and reducing costs of analyzing well tests.
The ability to accurately identify and isolate failures in the electrical power system (EPS) is critical to ensure the reliability of spacecraft. This paper proposes a novel solution to the problem of fault detection and diagnosis in direct current (DC) electric power systems for spacecraft. Autonomous operation becomes essential during deep space missions that lack the ability to monitor and control the spacecraft from ground locations. The current state of EPS fault supervision is insufficient to guarantee highly reliable operation. To solve this issue, a combination of model-based and knowledge-based techniques are used in a hierarchical framework to improve the diagnostic performance of the system. Noise, disturbances, and modeling errors are considered in the design of the fault detection system. Practical considerations related to spacecraft flight hardware and software are accounted for in the system design for flight applications. To assess the functionality of the design, a wide array of failures are simulated in a series of experiments. The experiments showed that the technique improved the capability of the autonomous system by increasing the number of fault types diagnosed. The significance of this study is to provide a framework capable of advanced diagnostics of an EPS with little to no interaction from human operators.
Forest stand height (FSH), or average canopy height, serves as an important indicator for forest monitoring. The information provided about above ground biomass for greenhouse gas emissions reporting, and estimating carbon storage is relevant for reporting for Reducing Emissions from Deforestation and Forest Degradation (REDD+). A novel forest height estimation method utilizing a fusion of backscatter and Interferometric Synthetic Aperture Radar (InSAR) data from JAXA’s Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) are applied to a use case in Savannakhet, Lao. Compared with LiDAR, the estimated height from the fusion method had an RMSE of 4.90 m and an R2 of 0.26. These results are comparable to previous studies using SAR estimation techniques. Despite limitations of data quality and quantity, the Savannakhet, Lao use case demonstrates the applicability of these techniques utilizing L-band SAR data for estimating FSH in tropical forests and can be used as a springboard for use of L-band data from the future NASA-ISRO SAR (NISAR) mission.
Ovarian estrogen impacts the normal homeostatic and metabolic processes of all tissues and organ systems within the body: particularly, but not limited to canonical space-flight impacted systems: bone, muscle, immune, wound repair, and cardiovascular. Effects of space flight on the ovarian estrogen production are therefore critical to our understanding of all space flight experiments using female mice, the current paradigm being used on the International Space Station (ISS). Recently, we demonstrated that vaginal wall histology could be used to determine the stage of the estrous cycle in female mice at the time of sacrifice in space. Moreover, this robust technique was completed following two post-flight freezethaw procedures of the carcasses (RR1 experiment). Thus, this technique represents a viable mechanism to determine the estrous cycle status of the female at the time of sacrifice and can be completed in a manner that does not impact primary experimental objectives. We propose that vaginal wall histology become a standard procedure completed on all mice sacrificed in space and that the individual estrous status of each animal be shared with all investigators. While evidence of estrous cyclicity was present in long-term (33 day) RR1 mice, fertility of female mice exposed to weightlessness remains unknown. In preparation for an upcoming funded NASA flight investigating the effects of long duration spaceflight on female fertility, we have refined our experimental design to minimize crew flight time and to accommodate the duration of Dragon capsule berth. These refinements maintain all our proposed primary and secondary experimental objectives. Briefly, in order to evaluate fertility, we will super ovulate mice using standard procedures (PMSG hCG), followed by collection of reproductive tract after follicular stimulation alone (PMSG) or following ovulation (hCG). Ovarian folliculogenesis and ovulation rate will be determined in fixed tissues following return in order to determine fertility. Ovarian and uterine tissues will also be evaluated by hormonal and gene expression profiling using quantitative approaches (radioimmunoassays, western blots, digital droplet PCR). Comparisons will be made to contemporary vivarium and Rodent Research Hardware Transporter and Habitat housed animals maintained on earth. Supported by NNX15AB48G to JST.
Abstract Nitrogen recovery from wastewater represents a sustainable route to recycle reactive nitrogen (Nr). It can reduce the demand of producing Nr from the energy‐extensive Haber‐Bosch process and lower the risk of causing eutrophication simultaneously. In this aspect, source‐separated fresh urine is an ideal source for nitrogen recovery given its ubiquity and high nitrogen contents. However, current techniques for nitrogen recovery from fresh urine require high energy input and are of low efficiencies because the recovery target, urea, is a challenge to separate. In this work, we developed a novel fresh urine nitrogen recovery treatment process based on modular functionalized metal–organic frameworks (MOFs). Specifically, we employed three distinct modification methods to MOF‐808 and developed robust functional materials for urea hydrolysis, ammonium adsorption, and ammonia monitoring. By integrating these functional materials into our newly developed nitrogen recovery treatment process, we achieved an average of 75 % total nitrogen reduction and 45 % nitrogen recovery with a 30‐minute treatment of synthetic fresh urine. The nitrogen recovery process developed in this work can serve as a sustainable and efficient nutrient management that is suitable for decentralized wastewater treatment. This work also provides a new perspective of implementing versatile advanced materials for water and wastewater treatment.
Nitrogen recovery from wastewater represents a sustainable route to recycle reactive nitrogen (Nr). It can reduce the demand of producing Nr from the energy-extensive Haber-Bosch process and lower the risk of causing eutrophication simultaneously. In this aspect, source-separated fresh urine is an ideal source for nitrogen recovery given its ubiquity and high nitrogen contents. However, current techniques for nitrogen recovery from fresh urine require high energy input and are of low efficiencies because the recovery target, urea, is a challenge to separate. In this work, we developed a novel fresh urine nitrogen recovery treatment process based on modular functionalized metal–organic frameworks (MOFs). Specifically, we employed three distinct modification methods to MOF-808 and developed robust functional materials for urea hydrolysis, ammonium adsorption, and ammonia monitoring. By integrating these functional materials into our newly developed nitrogen recovery treatment process, we achieved an average of 75 % total nitrogen reduction and 45 % nitrogen recovery with a 30-minute treatment of synthetic fresh urine. The nitrogen recovery process developed in this work can serve as a sustainable and efficient nutrient management that is suitable for decentralized wastewater treatment. This work also provides a new perspective of implementing versatile advanced materials for water and wastewater treatment.