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NASA NTRS2011

Prognostics of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven and Model-Based Methodologies

An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Dieattach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.

Electrolytic capacitors↗

NASA NTRS2011

Visual SLAM Using Variance Grid Maps

An algorithm denoted Gamma-SLAM performs further processing, in real time, of preprocessed digitized images acquired by a stereoscopic pair of electronic cameras aboard an off-road robotic ground vehicle to build accurate maps of the terrain and determine the location of the vehicle with respect to the maps. Part of the name of the algorithm reflects the fact that the process of building the maps and determining the location with respect to them is denoted simultaneous localization and mapping (SLAM). Most prior real-time SLAM algorithms have been limited in applicability to (1) systems equipped with scanning laser range finders as the primary sensors in (2) indoor environments (or relatively simply structured outdoor environments). The few prior vision-based SLAM algorithms have been feature-based and not suitable for real-time applications and, hence, not suitable for autonomous navigation on irregularly structured terrain. The Gamma-SLAM algorithm incorporates two key innovations: Visual odometry (in contradistinction to wheel odometry) is used to estimate the motion of the vehicle. An elevation variance map (in contradistinction to an occupancy or an elevation map) is used to represent the terrain. The Gamma-SLAM algorithm makes use of a Rao-Blackwellized particle filter (RBPF) from Bayesian estimation theory for maintaining a distribution over poses and maps. The core idea of the RBPF approach is that the SLAM problem can be factored into two parts: (1) finding the distribution over robot trajectories, and (2) finding the map conditioned on any given trajectory. The factorization involves the use of a particle filter in which each particle encodes both a possible trajectory and a map conditioned on that trajectory. The base estimate of the trajectory is derived from visual odometry, and the map conditioned on that trajectory is a Cartesian grid of elevation variances. In comparison with traditional occupancy or elevation grid maps, the grid elevation variance maps are much better for representing the structure of vegetated or rocky terrain.

Howard, Andrew B.↗

NASA NTRS2009

A Distributed Prognostic Health Management Architecture

This paper introduces a generic distributed prognostic health management (PHM) architecture with specific application to the electrical power systems domain. Current state-of-the-art PHM systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to loss of functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become unsuitable for successful deployment, and efficient distributed architectures are required. A distributed architecture though, is not effective unless there is an algorithmic framework to take advantage of its unique abilities. The health management paradigm envisaged here incorporates a heterogeneous set of system components monitored by a varied suite of sensors and a particle filtering (PF) framework that has the power and the flexibility to adapt to the different diagnostic and prognostic needs. Both the diagnostic and prognostic tasks are formulated as a particle filtering problem in order to explicitly represent and manage uncertainties; however, typically the complexity of the prognostic routine is higher than the computational power of one computational element ( CE). Individual CEs run diagnostic routines until the system variable being monitored crosses beyond a nominal threshold, upon which it coordinates with other networked CEs to run the prognostic routine in a distributed fashion. Implementation results from a network of distributed embedded devices monitoring a prototypical aircraft electrical power system are presented, where the CEs are Sun Microsystems Small Programmable Object Technology (SPOT) devices.

Bhaskar, Saha↗

NASA NTRS2013

Distributed Prognostics and Health Management with a Wireless Network Architecture

A heterogeneous set of system components monitored by a varied suite of sensors and a particle-filtering (PF) framework, with the power and the flexibility to adapt to the different diagnostic and prognostic needs, has been developed. Both the diagnostic and prognostic tasks are formulated as a particle-filtering problem in order to explicitly represent and manage uncertainties in state estimation and remaining life estimation. Current state-of-the-art prognostic health management (PHM) systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to a loss in functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become for a number of reasons somewhat ungainly for successful deployment, and efficient distributed architectures can be more beneficial. The distributed health management architecture is comprised of a network of smart sensor devices. These devices monitor the health of various subsystems or modules. They perform diagnostics operations and trigger prognostics operations based on user-defined thresholds and rules. The sensor devices, called computing elements (CEs), consist of a sensor, or set of sensors, and a communication device (i.e., a wireless transceiver beside an embedded processing element). The CE runs in either a diagnostic or prognostic operating mode. The diagnostic mode is the default mode where a CE monitors a given subsystem or component through a low-weight diagnostic algorithm. If a CE detects a critical condition during monitoring, it raises a flag. Depending on availability of resources, a networked local cluster of CEs is formed that then carries out prognostics and fault mitigation by efficient distribution of the tasks. It should be noted that the CEs are expected not to suspend their previous tasks in the prognostic mode. When the prognostics task is over, and after appropriate actions have been taken, all CEs return to their original default configuration. Wireless technology-based implementation would ensure more flexibility in terms of sensor placement. It would also allow more sensors to be deployed because the overhead related to weights of wired systems is not present. Distributed architectures are furthermore generally robust with regard to recovery from node failures.

Goebel, Kai↗

NASA NTRS2011

A Model-Based Prognostics Approach Applied to Pneumatic Valves

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

Daigle, Matthew J.↗

NASA NTRS1991

Portable x-ray fluorescence spectrometer for environmental monitoring of inorganic pollutants

A portable x-ray fluorescence spectrometer has a portable sensor unit containing a battery, a high voltage power supply, an x-ray tube which produces a beam x-ray radiation directed toward a target sample, and a detector for fluorescent x-rays produced by the sample. If a silicon-lithium detector is used, the sensor unit also contains either a thermoelectric or thermochemical cooler, or a small dewar flask containing liquid nitrogen to cool the detector. A pulse height analyzer (PHA) generates a spectrum of data for each sample consisting of the number of fluorescent x-rays detected as a function of their energy level. The PHA can also store spectrum data for a number of samples in the field. A processing unit can be attached to the pulse height analyzer to upload and analyze the stored spectrum data for each sample. The processing unit provides a graphic display of the spectrum data for each sample, and provides qualitative and/or quantitative analysis of the elemental composition of the sample by comparing the peaks in the sample spectrum against known x-ray energies for various chemical elements. An optional filtration enclosure can be used to filter particles from a sample suspension, either in the form of a natural suspension or a chemically created precipitate. The sensor unit is then temporarily attached to the filtration unit to analyze the particles collected by the filter medium.

Thornton, Michael G.↗

NASA NTRS2019

Simultaneous Localization and Mapping for Satellite Rendezvous and Proximity Operations Using Random Finite Sets

Future space missions require that spacecraft have the capability to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current relative navigation filters can have difficulty in these situations, diverging due to complications with data association, high measurement uncertainty, and clutter, particularly when detailed a priori maps of the target object or spacecraft do not exist. The goal of this work is to demonstrate the feasibility of random finite set (RFS) filters for spacecraft relative navigation and pose estimation. The approach is to formulate satellite relative navigation and pose estimation as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. This work utilizes a filter developed using the framework of RFS which are well suited to multi-target SLAM operations, avoiding data association entirely. Relevant RPO scenarios with simulated flash LIDAR measurements are tested with a Probability Hypothesis Density (PHD) RFS filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer. Preliminary results show that an RFS-based filter can successfully perform SLAM in a spacecraft relative navigation scenario with no a priori map of the target. These results demonstrate the feasibility of RFS filtering for spacecraft relative navigation and motivate future studies which may expand to tracking space objects for space situational awareness, as well as relative navigation around small bodies.

Schlenker, Lauren↗

NASA NTRS2019

Simultaneous Localization and Mapping for Satellite Rendezvous and Proximity Operations using Random Finite Sets

Schlenker, Lauren↗

NASA NTRS2025

Simultaneous Localization and Mapping for Spacecraft Rendezvous and Proximity Operations Using Random Finite Sets

Future space missions require spacecraft to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current navigation filters used for RPO can have difficulty when optical sensors are used, due to complications with data association, high measurement uncertainty, and clutter. This paper provides an initial demonstration of the feasibility of using random finite set (RFS) filters for spacecraft relative navigation and pose estimation. Spacecraft relative navigation is formulated as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. Simulated flash LIDAR measurements are processed using a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer over time. Results show that an RFS-based filter can successfully perform SLAM in a spacecraft relative navigation scenario.

David E Gaylor↗

NASA NTRS2016

Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms

Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments

Prognostics↗

NASA NTRSDate not supplied

Testing of the ISS’s Charcoal HEPA Integrated Particle Scrubber (CHIPS) Filters to Ascertain an Efficiency Curve and Most Penetrating Particle Size

High air quality is a top priority in hermetically sealed environments, such as the International Space Station (ISS). Aerosol particulate filtration on the ISS is achieved via 21 HEPA filters. Traditional HEPA filters have an efficiency of 99.97%, but HEPA filters aboard the ISS have been found to achieve 99.99% efficiency. This may be due to the flight-grade HEPA filters chosen or, less likely, that they are combined with activated charcoal filters for siloxane removal in a combination referred to as Charcoal HEPA Integrated Particle Scrubbers (CHIPS) filters. All filters have a Most Penetrating Particle Size (MPPS), but, until now, the MPPS and efficiency curve of the CHIPS filters was not known. The efficiency curve of the CHIPS filters was ascertained at NASA Glenn Research Center through standard salt crystal aerosolization. This method was selected over aerosolized oil droplets due to a desire to preserve the filter’s function for further testing. The CHIPS filters were tested as they are oriented on the ISS (activated charcoal filter followed by a flight-grade HEPA filter), with the HEPA portion alone, and with the activated charcoal portion alone. The results of this experiment are generally useful but can be especially useful for the microbiology and Environmental Control and Life Support Systems (ECLSS). This is due to microbial contamination being a potential concern in the air revitalization systems on the ISS. Knowing the MMPS of the CHIPS filters, in conjunction with levels of airborne microbes, is useful for predicting which and how many microbes are likely to get through the HEPA filters that sit in the beginning of the ISS airstream. It may also be useful to other groups at NASA to know how many particulates are able to pass through high-efficiency filtration, and even for healthcare cleanrooms and other air purification pursuits.

A Nicolas Whitlock↗

NASA NTRS2006

An Ensemble-Based Smoother with Retrospectively Updated Weights for Highly Nonlinear Systems

Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm.

Monte Carlo↗

NASA NTRS1972

Regenerative particulate filter development

Development, design, and fabrication of a prototype filter regeneration unit for regenerating clean fluid particle filter elements by using a backflush/jet impingement technique are reported. Development tests were also conducted on a vortex particle separator designed for use in zero gravity environment. A maintainable filter was designed, fabricated and tested that allows filter element replacement without any leakage or spillage of system fluid. Also described are spacecraft fluid system design and filter maintenance techniques with respect to inflight maintenance for the space shuttle and space station.

Descamp, V. A.↗

NASA NTRS2008

Symmetric Phase-Only Filtering in Particle-Image Velocimetry

Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle-image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times. A summary of PIV and traditional PIV data processing is prerequisite to a meaningful description of SPOF PIV processing. In PIV, a pulsed laser is used to illuminate a substantially planar region of a flowing fluid in which particles are entrained. An electronic camera records digital images of the particles at two instants of time. The components of velocity of the fluid in the illuminated plane can be obtained by determining the displacements of particles between the two illumination pulses. The objective in PIV data processing is to compute the particle displacements from the digital image data. In traditional PIV data processing, to which the present innovation applies, the two images are divided into a grid of subregions and the displacements determined from cross-correlations between the corresponding sub-regions in the first and second images. The cross-correlation process begins with the calculation of the Fourier transforms (or fast Fourier transforms) of the subregion portions of the images. The Fourier transforms from the corresponding subregions are multiplied, and this product is inverse Fourier transformed, yielding the cross-correlation intensity distribution. The average displacement of the particles across a subregion results in a displacement of the correlation peak from the center of the correlation plane. The velocity is then computed from the displacement of the correlation peak and the time between the recording of the two images. The process as described thus far is performed for all the subregions. The resulting set of velocities in grid cells amounts to a velocity vector map of the flow field recorded on the image plane. In traditional PIV processing, surface flare light and bright background light give rise to a large, broad correlation peak, at the center of the correlation plane, that can overwhelm the true particle- displacement correlation peak. This has made it necessary to resort to tedious image-masking and background-subtraction procedures to recover the relatively small amplitude particle-displacement correlation peak. SPOF is a variant of phase-only filtering (POF), which, in turn, is a variant of matched spatial filtering (MSF). In MSF, one projects a first image (denoted the input image) onto a second image (denoted the filter) as part of a computation to determine how much and what part of the filter is present in the input image. MSF is equivalent to cross-correlation. In POF, the frequency-domain content of the MSF filter is modified to produce a unitamplitude (phase-only) object. POF is implemented by normalizing the Fourier transform of the filter by its magnitude. The advantage of POFs is that they yield correlation peaks that are sharper and have higher signal-to-noise ratios than those obtained through traditional MSF. In the SPOF, these benefits of POF can be extended to PIV data processing. The SPOF yields even better performance than the POF approach, which is uniquely applicable to PIV type image data. In SPOF as now applied to PIV data processing, a subregion of the first image is treated as the input image and the corresponding subregion of the second image is treated as the filter. The Fourier transforms from both the firs and second- image subregions are normalized by the square roots of their respective magnitudes. This scheme yields optimal performance because the amounts of normalization applied to the spatial-frequency contents of the input and filter scenes are just enough to enhance their high-spatial-frequency contents while reducing their spurious low-spatial-frequency content. As a result, in SPOF PIV processing, particle-displacement correlation peaks can readily be detected above spurious background peaks, without need for masking or background subtraction.

Wemet, Mark P.↗

NASA NTRS2012

A Comparison of Filter-based Approaches for Model-based Prognostics

Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the stateparameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.

prognostics↗

NASA NTRS2008

Lunar Dust Effects on Spacesuit Systems: Insights from the Apollo Spacesuits

Systems and components of selected Apollo A7L/A7LB flight-article spacesuits that were worn on the lunar surface have been studied to determine the degree to which they suffered contamination, abrasion and wear or loss of function due to effects from lunar soil particles. Filter materials from the lithium hydroxide (LiOH) canisters from the Apollo Command Module were also studied to determine the amount and type of any lunar dust particles they may have captured from the spacecraft atmosphere. The specific spacesuit study materials include the outermost soft fabric layers on Apollo 12 and 17 integrated thermal micrometeorite garment assemblies and outermost fabrics on Apollo 17 extravehicular pressure gloves. In addition, the degree of surface wear in the sealed wrist rotation bearing from Apollo 16 extravehicular and intravehicular pressure gloves was evaluated and compared. Scanning electron microscope examination of the Apollo 12 T-164 woven TeflonO fabric confirms the presence of lunar soil particles and the ability of these particles to cause separation and fraying of the Teflon fibers. Optical imaging, chemical analysis and particle sampling applied to the outer fabric of the Apollo 17 spacesuit has identified Ti as a potentially useful chemical marker for comparing the amount of lunar soil retained on different areas of the spacesuit outer fabric. High-yield particle sampling from the Apollo 17 fabric surfaces using adhesive tape found 80% of particles on the fabric are lunar soil particles averaging 10.5 m in diameter, with the rest being intrinsic fabric materials or environmental contaminants. Analysis of the mineralogical composition of the lunar particles found that on a grain-count basis the particle population is dominated by plagioclase feldspar and various types of glassy particles derived mostly from soil agglutinates, with a subordinate amount of pyroxene. On a grain size basis, however, the pyroxene grains are generally a factor of 2 larger than glass and plagioclase, so conversion of the data to a modal (volume %) basis results in pyroxene becoming the modally dominant particle type with glass and plagioclase significantly less abundant. When comparisons are made to the modal composition of lunar soil at the Apollo 17 landing site, the results suggest that pyroxene particles have overall better retention on the spacesuit outer fabric compared to plagioclase and especially glass. Scanning electron microscopy revealed no measureable difference in the amount of wear and abrasion in the wrist rotation bearing of an Apollo 16 pressure glove worn only in the spacecraft and one worn only for extravehicular activity on the lunar surface. The results suggest either that the bearing prevented entry of lunar dust, or that dust was not sufficiently abrasive to damage the bearing, or both.

Christoffersen, Roy↗

NASA NTRS2004

Filtering in Hybrid Dynamic Bayesian Networks

We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2 - T i e Slice DBN (2T-DBN) from [Koller & Lerner, 20001 to model fault detection in a watertank system. In [Koller & Lerner, 20001 a generic Particle Filter (PF) is used for inference. We extend the experiment and perform approximate inference using The Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Furthermore, we combine these techniques in a 'non-strict' Rao-Blackwellisation framework and apply it to the watertank system. We show that UKF and UKF in a PF framework outperfom the generic PF, EKF and EKF in a PF framework with respect to accuracy and robustness in terms of estimation RMSE. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. We also show that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the water[ank simulation. Theory and implementation is based on the theory presented.

Andersen, Morten Nonboe↗

NASA NTRS2014

Current Ground Test Options for Nuclear Thermal Propulsion (NTP)

About 20 different NTP engines/ reactors were tested from 1959 to 1972 as part of the Rover and Nuclear Engine for Rocket Vehicle Application (NERVA) program. Most were tested in open air at test cell A or test cell C, at the Nevada Test Site (NTS). Even after serious engine breakdowns of the reactor (e.g., Phoebus 1A), the test cells were cleaned up for other engine tests. The engine test stand (ETS) was made for high altitude (approximately 1 psia) testing of an NTP engine with a flight configuration, but still had the exhaust released to open air. The Rover/NERVA program became aware of new environmental regulations which would prohibit the release of any significant quantity of radioactive particulates and noble gases into the open air. The nuclear furnace (NF-1) was the last reactor tested before the program was cancelled in 1973, but successfully demonstrated a scrubber concept on how to filter the NTP exhaust. The NF-1 was demonstrated in the summer of 1972. The NF-1 used a 44MW reactor and operated each run for approximately 90 minutes. The system cooled the hot hydrogen exhaust from the engine with a water spray before entering a particle filter. The exhaust then passed through a series of heat exchangers and water separators to help remove water from the exhaust and further reduce the exhaust temperatures. The exhaust was next prepared for the charcoal trap by passing through a dryer and effluent cooler to bring exhaust temperatures close to liquid nitrogen. At those low temperatures, most of the noble gases (e.g., Xe and Kr made from fission products) get captured in the charcoal trap. The filtered hydrogen is finally passed through a flare stack and released to the air. The concept was overall successful but did show a La plating on some surfaces and had multiple recommendations for improvement. The most recent detailed study on the NTP scrubber concept was performed by the ARES Corporation in 2006. The concept is based on a 50,000 lbf thrust engine (approximately 1 GW) with a maximum burn time of 1 hour. The concept utilized lessons learned from NF-1. The strategy breaks down the exhaust into parallel paths to allow flexibility with engine size and mass flow of exhaust. Similar to NF-1, the exhaust is slowed down, cooled, filtered of particulates, filtered of noble gases, and then the clean hydrogen is flared to open air. Another concept proposed by Steve Howe (currently Director of the Center for Space Nuclear Research) to simplify the NTP exhaust filtering is to run the hydrogen exhaust into boreholes underground to filter the exhaust. The two borehole site locations proposed are at the NTS and at the Idaho National Laboratory (INL). At NTS, the boreholes are 8' diameter and 1200' deep. The permeability of hydrogen through the soil and its buoyancy will allow it to rise up through the soil and allow the filtering of noble gases and radioactive particulates. The exhaust needs to be cooled to 600C before entering the borehole to avoid soil glazing. Preliminary analysis shows a small buildup of back pressure with time which depends on permeability. Noble gases entering the borehole walls deep can take a long time before reaching the surface. Other factors affecting permeability include borehole pressure, water saturation, and turbulence. Also, a possible need to pump out contaminated water collected at the bottom of the borehole. At INL, the borehole concept is slightly different. The underground borehole has openings to the soil at special depths which have impermeable interbeds above the water table and below the surface to allow the exhaust to travel horizontal between the impermeable layers. Preliminary results indicate better permeability than at NTS. The last option is total containment of the exhaust during the test run. The concept involves slowing down the flow to subsonic in a water cooled diffuser. The hydrogen is burned off in an oxygen rich afterburner with the only products being steam, oxygen, and some noble gases. A heat exchanger and water spray pulls heat from the steam and lowers the temperature for condensation. The optimum ratio between the two is being investigated, with a goal to minimize the total volume of the water hold tanks. A water tank farm collects the contaminated water. The amount of water produced from burning the hydrogen is approximately 100,000 gallons (not including cooling water) for a 25k lbf engine operating for 50 minutes. Residual gases (e.g., oxygen and some noble gases) can be captured at cryogenic levels with a liquid nitrogen cooled dewar. After a few weeks post-test, the radiation levels can drop to more favorable levels before slowly draining each capture tank and using existing filters. With today's environmental regulations, the NTP exhaust is filtered to meet 10 mrem/year exposure to the general public (at a DOE site) or 100 mrem/year (via NRC when tested elsewhere), when natural background radiation exposure to the general public is 300- 600 mrem per year. The current society feels more comfortable with filtering even lower to as low as reasonably achievable (ALARA).

Gerrish, Harold P., Jr.↗