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At least 361 records · Page 20

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗

Evolution and engineering of pathways for aromatic O -demethylation in Pseudomonas putida KT2440

In this study, biological conversion of lignin from biomass offers a promising strategy for sustainable production of fuels and chemicals. However, aromatic compounds derived from lignin commonly contain methoxy groups, and O-demethylation of these substrates is often a rate-limiting reaction that influences catabolic efficiency. Several enzyme families catalyze aromatic O-demethylation, but they are rarely compared in vivo to determine an optimal biocatalytic strategy. Here, two pathways for aromatic O-demethylation were compared in Pseudomonas putida KT2440. The native Rieske non-heme iron monooxygenase (VanAB) and, separately, a heterologous tetrahydrofolate-dependent demethylase (LigM) were constitutively expressed in P. putida, and the strains were optimized via adaptive laboratory evolution (ALE) with vanillate as a model substrate. All evolved strains displayed improved growth phenotypes, with the evolved strains harboring the native VanAB pathway exhibiting growth rates ~1.8x faster than those harboring the heterologous LigM pathway. Enzyme kinetics and transcriptomics studies investigated the contribution of selected mutations toward enhanced utilization of vanillate. The VanAB-overexpressing strains contained the most impactful mutations, including those in VanB, the reductase for vanillate O-demethylase, PP_3494, a global regulator of vanillate catabolism, and fghA, involved in formaldehyde detoxification. These three mutations were combined into a single strain, which exhibited approximately 5x faster vanillate consumption than the wild-type strain in the first 8 h of cultivation. Overall, this study illuminates the details of vanillate catabolism in the context of two distinct enzymatic mechanisms, yielding a platform strain for efficient O-demethylation of lignin-related aromatic compounds to value-added products.

09 BIOMASS FUELS↗

Model-based VQ for image data archival, retrieval and distribution

An ideal image compression technique for image data archival, retrieval and distribution would be one with the asymmetrical computational requirements of Vector Quantization (VQ), but without the complications arising from VQ codebooks. Codebook generation and maintenance are stumbling blocks which have limited the use of VQ as a practical image compression algorithm. Model-based VQ (MVQ), a variant of VQ described here, has the computational properties of VQ but does not require explicit codebooks. The codebooks are internally generated using mean removed error and Human Visual System (HVS) models. The error model assumed is the Laplacian distribution with mean, lambda-computed from a sample of the input image. A Laplacian distribution with mean, lambda, is generated with uniform random number generator. These random numbers are grouped into vectors. These vectors are further conditioned to make them perceptually meaningful by filtering the DCT coefficients from each vector. The DCT coefficients are filtered by multiplying by a weight matrix that is found to be optimal for human perception. The inverse DCT is performed to produce the conditioned vectors for the codebook. The only image dependent parameter used in the generation of codebook is the mean, lambda, that is included in the coded file to repeat the codebook generation process for decoding.

Manohar, Mareboyana↗

Vibration-Based Data Used to Detect Cracks in Rotating Disks

Rotor health monitoring and online damage detection are increasingly gaining the interest of aircraft engine manufacturers. This is primarily due to the fact that there is a necessity for improved safety during operation as well as a need for lower maintenance costs. Applied techniques for the damage detection and health monitoring of rotors are essential for engine safety, reliability, and life prediction. Recently, the United States set the ambitious goal of reducing the fatal accident rate for commercial aviation by 80 percent within 10 years. In turn, NASA, in collaboration with the Federal Aviation Administration, other Federal agencies, universities, and the airline and aircraft industries, responded by developing the Aviation Safety Program. This program provides research and technology products needed to help the aerospace industry achieve their aviation safety goal. The Nondestructive Evaluation (NDE) Group of the Optical Instrumentation Technology Branch at the NASA Glenn Research Center is currently developing propulsion-system-specific technologies to detect damage prior to catastrophe under the propulsion health management task. Currently, the NDE group is assessing the feasibility of utilizing real-time vibration data to detect cracks in turbine disks. The data are obtained from radial blade-tip clearance and shaft-clearance measurements made using capacitive or eddy-current probes. The concept is based on the fact that disk cracks distort the strain field within the component. This, in turn, causes a small deformation in the disk's geometry as well as a possible change in the system's center of mass. The geometric change and the center of mass shift can be indirectly characterized by monitoring the amplitude and phase of the first harmonic (i.e., the 1 component) of the vibration data. Spin pit experiments and full-scale engine tests have been conducted while monitoring for crack growth with this detection methodology. Even so, published data are extremely limited, and the basic foundation of the methodology has not been fully studied. The NDE group is working on developing this foundation on the basis of theoretical modeling as well as experimental data by using the newly constructed subscale spin system shown in the preceding photograph. This, in turn, involved designing an optimal sub-scale disk that was meant to represent a full-scale turbine disk; conducting finite element analyses of undamaged and damaged disks to define the disk's deformation and the resulting shift in center of mass; and creating a rotordynamic model of the complete disk and shaft assembly to confirm operation beyond the first critical concerning the subscale experimental setup. The finite element analysis data, defining the center of mass shift due to disk damage, are shown. As an example, the change in the center of mass for a disk spinning at 8000 rpm with a 0.963-in. notch was 1.3 x 10(exp -4) in. The actual vibration response of an undamaged disk as well as the theoretical response of a cracked disk is shown. Experiments with cracked disks are continuing, and new approaches for analyzing the captured vibration data are being developed to better detect damage in a rotor. In addition, the subscale spin system is being used to test the durability and sensitivity of new NDE sensors that focus on detecting localized damage. This is designed to supplement the global response of the crack-detection methodology described here.

Gyekenyesi, Andrew L.↗

A Grobner Basis Solution for Lightning Ground Flash Fraction Retrieval

A Bayesian inversion method was previously introduced for retrieving the fraction of ground flashes in a set of flashes observed from a (low earth orbiting or geostationary) satellite lightning imager. The method employed a constrained mixed exponential distribution model to describe the lightning optical measurements. To obtain the optimum model parameters, a scalar function was minimized by a numerical method. In order to improve this optimization, we introduce a Grobner basis solution to obtain analytic representations of the model parameters that serve as a refined initialization scheme to the numerical optimization. Using the Grobner basis, we show that there are exactly 2 solutions involving the first 3 moments of the (exponentially distributed) data. When the mean of the ground flash optical characteristic (e.g., such as the Maximum Group Area, MGA) is larger than that for cloud flashes, then a unique solution can be obtained.

Solakiewicz, Richard↗

[MaRS Project]

The Space Exploration Division of the Safety and Mission Assurances Directorate is responsible for reducing the risk to Human Space Flight Programs by providing system safety, reliability, and risk analysis. The Risk & Reliability Analysis branch plays a part in this by utilizing Probabilistic Risk Assessment (PRA) and Reliability and Maintainability (R&M) tools to identify possible types of failure and effective solutions. A continuous effort of this branch is MaRS, or Mass and Reliability System, a tool that was the focus of this internship. Future long duration space missions will have to find a balance between the mass and reliability of their spare parts. They will be unable take spares of everything and will have to determine what is most likely to require maintenance and spares. Currently there is no database that combines mass and reliability data of low level space-grade components. MaRS aims to be the first database to do this. The data in MaRS will be based on the hardware flown on the International Space Stations (ISS). The components on the ISS have a long history and are well documented, making them the perfect source. Currently, MaRS is a functioning excel workbook database; the backend is complete and only requires optimization. MaRS has been populated with all the assemblies and their components that are used on the ISS; the failures of these components are updated regularly. This project was a continuation on the efforts of previous intern groups. Once complete, R&M engineers working on future space flight missions will be able to quickly access failure and mass data on assemblies and components, allowing them to make important decisions and tradeoffs.

Aruljothi, Arunvenkatesh↗

Remote Sensing, Uncertainty Quantification, and a Theory of Data Systems; Workshop Report

The purpose of the workshop was to invite statisticians, applied mathematicians, computer scientists, data system architects, experts in remote sensing technology, and Climate and Earth System scientists to review, discuss, and plan research on issues related to large-scale, efficient analysis of distributed data using spatial statistical methods. Our motivation in organizing this event was to catalyze interchange among experts on the fast-emerging problem of analysis of distributed data. As part of SAMSI's 2017-2018 Program on Mathematical and Statistical Methods for Climate and the Earth System, a Working Group on Remote Sensing was established to address statistical and mathematical research problems in the analysis of remote sensing data. The Working Group has five subgroups: 1) Spatial Retrieval Methodology (the so-called \Spatial-X" subgroup); 2) Spatial Analysis for Hyperspectral Data (the so-called \Spatial-Y" subgroup); 3) Emulators for Complex Forward Models; 4) Optimization for Remote Sensing Retrievals; and 5) Theory of Data Systems (ToDS). The ToDS subgroup spent the first half of this academic year formulating a framework in which to consider the joint problem of a) optimizing statistical methods for environments where data are distributed and too large to move to a central location, and b) the design of data system infrastructures within which to implement those statistical methods. To x ideas, the Workshop focused on spatial statistical methods. To date there are many new spatial statistical methods designed with massive data sets in mind, in the literature. However, very few have been implemented for remote sensing data, and none have been implemented in operational settings like those used by NASA and NOAA. A major impediment to their use in these cases is that the data are not only massive, but are stored in different physical locations. These data must be brought together in some way in order to estimate spatial covariance functions, but moving data to a central location for analysis is tedious at best and impossible at worst. Some remote data reduction is almost certainly necessary, but how much? What are the consequences for inference? The fundamental issue underlying these questions is how to navigate the trade-space between costs and uncertainty in the estimates or inferences that are ultimately produced.

Braverman, Amy↗

Remote Sensing of Lineage Functional Types for Modeling and Monitoring Biodiversity

Hyperspectral remote sensing has the potential to continuously scale plant function and plant diversity information from landscape to global extents. Numerous studies have indicated that VSWIR (400-2500 nm) reflectance properties of vegetation capture evolutionarily conserved biochemical, structural, and other functional attributes of plant species. Spectral properties conserved in plants provide the opportunity to both 1) aggregate species into lineages with improved classification accuracy and 2) link those lineages directly to plant traits. Full realization of this goal will enable parameterization of Land Surface Models (LSMs) with remotely sensed information, e.g., canopy nitrogen, and better representations of biodiversity and functional diversity in biogeographic studies. In this study, we use hyperspectral AVIRIS data from the 2013 HyspIRI campaign over the Southern Sierra Nevada, California flight box to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from roughly 1372 surveys and a phylogeny representing 1361 species. We aggregate species into lineages ranging from species level groups down to similar number of Plant Functional Types as often used in LSMs. We assessed the ability of Random Forest and Partial Least Squares Discriminant Analysis to discriminate across these different phylogenetic scales and determine the optimal number of lineages to classify. Although there are some temporal and spatial differences in our training data, our best approaches achieved moderate classification accuracy (Kappa > 0.65). Given an optimal number of lineages, we explored approaches to improve classifications including machine learning and unmixing approaches. This work suggests that lineage-based methods may be a promising way to leverage the huge amounts of data that will come from high resolution and high return interval hyperspectral data planned for the Surface Biology and Geology mission with sparsely sampled existing ground-based ecological data.

Hyperspectral↗

Wireless Channel Characterization in the Airport Surface Environment

Given the anticipated increase in air traffic in the coming years, modernization of the National Airspace System (NAS) is a necessity. Part of this modernization effort will include updating current communication, navigation, and surveillance (CNS) systems to deal with the increased traffic as well as developing advanced CNS technologies for the systems. An example of such technology is the integrated CNS (ICNS) network being developed by the Advanced CNS Architecture and Systems Technology (ACAST) group for use in the airport surface environment. The ICNS network would be used to convey voice/data between users in a secure and reliable manner. The current surface system only supports voice and does so through an obsolete physical infrastructure. The old system is vulnerable to outages and costly to maintain. The proposed ICNS network will include a wireless radio link. To ensure optimal performance, a thorough and accurate characterization of the channel across which the link would operate is necessary. The channel is the path the signal takes from the transmitter to the receiver and is prone to various forms of interference. Channel characterization involves a combination of analysis, simulation, and measurement. My work this summer was divided into four tasks. The first task required compiling and reviewing reference material that dealt with the characterization and modeling of aeronautical channels. The second task involved developing a systematic approach that could be used to group airports into classes, e.g. small airfields, medium airports, large open airports, large cluttered airports, etc. The third task consisted of implementing computer simulations of existing channel models. The fourth task entailed measuring possible interference sources in the airport surface environment via a spectrum analyzer.

Neville, Joshua T.↗

Optimize Use of Space Research and Technology for Medical Devices

systems, and cutting-edge component technologies to conduct a wide range of scientific observations and measurements. These technologies are also considered for practical applications that benefit society in remarkable ways. At NASA Goddard, the technology transfer initiative promotes matching technologies from Earth and space science needs to targeted industry sectors. This requires clear knowledge of industry needs and priorities and social demands. The process entails matching mature technologies where there are known innovation challenges and good opportunities for matching technology needs. This requires creative thinking and takes commitment of time and resources. Additionally, we also look at applications for known hot industry or societal needs. Doing so has given us occasion to host discussions with representatives from industry, academia, government organizations, and societal special interest groups about the application of NASA Goddard technologies for devices used in medical monitoring and detection tools. As a result, partnerships have been established. Innovation transpired when new products were enabled because of NASA Goddard research and technology programs.

Optimize↗

Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields

Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surface. Furthermore, these regions can be difficult to identify and isolate from each other or may not align well with human intuition, making it challenging to systematically remove bias in the dataset. While traditional clustering and pruning (down-sampling) approaches can be useful for this, they can often lead to information loss or a failure to properly identify distinct regions of the potential energy surface due to difficulties associated with the high dimensionality of atomic descriptors. In this work, we introduce the Multi-kernel Edge Attention-based Graph Autoencoder (MEAGraph) model, an unsupervised approach for analyzing atomic datasets. MEAGraph combines multiple linear kernel transformations with attention-based message passing to capture geometric sensitivity and enable effective dataset pruning without relying on labels or extensive training. Demonstrated applications on niobium, tantalum, and iron datasets show that MEAGraph efficiently groups similar atomic environments, allowing for the use of basic pruning techniques for removing sampling bias. This approach provides an effective method for representation learning and clustering that can be used for data analysis, outlier detection, and dataset optimization.

Materials science↗

Estimation and detection of signals in multiplicative noise

A class of detection-estimation problems on matrix Lie groups is defined in which the observation noise is multiplicative in nature. By examining the differential versions of the hypotheses, which are bilinear in nature, it is possible to derive the relevant likelihood ratio formula and the associated optimal estimation equations for the signal given the observations and the assumption that the signal is present. These estimation equations are of interest in their own right, in that they represent a finite dimensional optimal solution to a nonlinear estimation problem and can be viewed as consisting of a Kalman-Bucy filter along with the on-line computation of the solution of the associated Riccati equation, which is driven by the observations. The usefulness of these results is illustrated via an example concerning the detection of an actuator failure in a rigid body rotational control system.

Willsky, A. S.↗

Program to study optimal protocol for cardiovascular and muscular efficiency

Training programs necessary for the development of optimal strength during prolonged manned space flight were examined, and exercises performed on the Super Mini Gym Skylab 2 were compared with similar exercises on the Universal Gym and calisthenics. Cardiopulmonary gains were found negligible but all training groups exhibited good gains in strength.

Olree, H. D.↗

Analysis procedures and subjective flight results of a simulator validation and cue fidelity experiment

A joint experiment to investigate simulator validation and cue fidelity was conducted by the Dryden Flight Research Facility of NASA Ames Research Center (Ames-Dryden) and NASA Langley Research Center. The primary objective was to validate the use of a closed-loop pilot-vehicle mathematical model as an analytical tool for optimizing the tradeoff between simulator fidelity requirements and simulator cost. The validation process includes comparing model predictions with simulation and flight test results to evaluate various hypotheses for differences in motion and visual cues and information transfer. A group of five pilots flew air-to-air tracking maneuvers in the Langley differential maneuvering simulator and visual motion simulator and in an F-14 aircraft at Ames-Dryden. The simulators used motion and visual cueing devices including a g-seat, a helmet loader, wide field-of-view horizon, and a motion base platform.

Carr, Peter C.↗

Purification, Crystallization, and Preliminary X-ray Analysis of Native Canavalin

The protein canavalin is a 7S vicilin, from the Jack Bean, Canavalis ensfomis. Canavalin is described as a seed storage protein, an energy source for a developing seed, as no other known activity or function has been found. The protein was first isolated and crystallized by Sumner and Howell (J. Biol. Chem. 113, 607-610, 1936). Canavalin spontaneously crystallizes after proteolytic cleavage at neutral pH, which removes residues 1-46, 224-245, and 325-330 and produces peptides of approximately 25, 13, and 12 kDa. Preliminary gel filtration experiments indicated the presence of nucleic acid with the uncleaved protein. We developed a dual column procedure, ion exchange followed by hydroxy apatite chromatography, that effectively removes the nucleic acid and yields an essentially pure uncut canavalin preparation with an OD 280/260 ratio of approximately 1.9-2.0. Standard crystallization screens using this material gave a number of positive results having a common requirement for alcohols and Mg(2+) ion, with crystals typically appearing within a day or less. Optimization experiments to date have shown that we can obtain crystals from pH 6.5 to pH 8.2, using MPD from 5 to 20% and 0.05 to 0.2M Mg(2+) (sulfate or acetate). The crystals are of space group P2(sub 1)2(sub 1)2(sub 1), unit cell dimensions, and a complete data set to 1.5 Angstroms, resolution has now been collected at a synchrotron source. Most importantly, the crystals are not twinned, a persistent problem with the most commonly obtained rhombohedral form of proteolytically cleaved canavalin.

Pusey, Marc↗

Incorporation of Electrical Systems Models Into an Existing Thermodynamic Cycle Code

Integration of entire system includes: Fuel cells, motors, propulsors, thermal/power management, compressors, etc. Use of existing, pre-developed NPSS capabilities includes: 1) Optimization tools; 2) Gas turbine models for hybrid systems; 3) Increased interplay between subsystems; 4) Off-design modeling capabilities; 5) Altitude effects; and 6) Existing transient modeling architecture. Other factors inclde: 1) Easier transfer between users and groups of users; 2) General aerospace industry acceptance and familiarity; and 3) Flexible analysis tool that can also be used for ground power applications.

Freeh, Josh↗

Nuclear Thermal Rocket (NTR) Development Risk Communication

There are clear advantages of development of a Nuclear Thermal Rocket (NTR) for a crewed mission to Mars. NTR for in-space propulsion enables more ambitious space missions by providing high thrust at high specific impulse (approximately 900 sec) that is 2 times the best theoretical performance possible for chemical rockets. Missions can be optimized for maximum payload capability to take more payload with reduced total mass to orbit; saving cost on reduction of the number of launch vehicles needed. Or missions can be optimized to minimize trip time significantly to reduce the deep space radiation exposure to the crew. NTR propulsion technology is a game changer for space exploration. However, "NUCLEAR" is a word that is feared and vilified by some groups and the hostility towards development of any nuclear systems can meet great opposition by the public as well as from national leaders and people in authority. Communication of nuclear safety will be critical to the success of the development of the NTR. Why is there a fear of nuclear? A bomb that can level a city is a scary weapon. The first and only times the Nuclear Bomb was used in a war was on Hiroshima and Nagasaki during World War 2. The "Little Boy" atomic bomb was dropped on Hiroshima on August 6, 1945 and the "Fat Man" on Nagasaki 3 days later on August 9th. Within the first 4 months of bombings, 90- 166 thousand people died in Hiroshima and 60-80 thousand died in Nagasaki. It is important to note for comparison that over 500 thousand people died and 5 million made homeless due to strategic bombing (approximately 150 thousand tons) of Japanese cities and war assets with conventional non-nuclear weapons between 1942- 1945. A major bombing campaign of "firebombing" of Tokyo called "Operation Meetinghouse" on March 9 and 10 consisting of 334 B-29's dropped approximately1,700 tons of bombs around 16 square mile area and over 100 thousand people have been estimated to have died. The declaration of death is very clear for conventional weapons and then the declaration of death due to radiation becomes vague and unclear. This may have been due to people mis-understanding the dangers and effects of radiation when assessing the damage and harm to people initially, but it is also become insidious when expressing opposition to nuclear energy. A nuclear radiation accident can be scary due to the power involved and the fear of radiation release. The International Atomic Energy Agency defines a nuclear and radiation accident a "an event that has led to significant consequences to people, the environment or the facility." There have been 3 commercial nuclear reactor accidents (Chernobyl, Three Mile Island, and Fukushima) that stand out to the public and much of the information about the result and impact to workers, environment, and public can be misleading. Often information is presented without clear correlation with radiation and other pertinent information is left out presenting a very scary situation to affect the emotions of the reader. A very boring but "critically acclaimed" movie was made in 1979 called "The China Syndrome" starring Jane Fonda and Jack Lemmon. The film was released on March 16, 1979, 12 days before the 3-Mile Island nuclear accident in Pennsylvania. The basis from the movie was from a few nuclear plant incidents and in particular, the Brown's Ferry Alabama Power Plant fire. In one scene from the movie, a physicist Dr. Elliott Lowell played by Donald Hotton states that a China Syndrome event would make "an area the size of Pennsylvania" permanently uninhabitable. Real serious nuclear incidents like Chernobyl and Fukushima are often sited to make people fear the consequences of using nuclear power. However, the consequences are at best poorly communicated and at worst fictitiously inflated to instigate social unrest against nuclear power. There is an article being circulated on Facebook with a title "28 Signs that the (US) West Coast is being absolutely fried with nuclear radiation from Fukushima" which focus on mis-information and fear mongering. Nuclear power and NTR are powerful resources that can open many doors for future prosperity and capability. With great power comes great responsibility. Radiation and its effects need to be better understood, quantified, and communicated. A human mission to mars has its own risks of deep space radiation and is considered a considerable risk at 400 milli-Sieverts per year in deep space and 245 milli-Sieverts per year on the surface of Mars as measured by the Mars Curiosity mission. Although these quantities of ionizing radiation are within the astronaut career limit, it exceeds the yearly average amounts of ionizing radiation. Astronaut crews have experienced these levels of radiation before, but for durations shorter than a year, and a mission to Mars could possibly be 3 years in length. There is also evidence that people can comfortably handle higher levels of ionizing radiation where the radiation occurs naturally like Ramsar, Iran when people can experience 270 milli-Sieverts per year. A risk posture that the development, test, and flight of an NTR will meet opposition from groups who oppose nuclear energy must be likely and the impact can be sever to the effort. Active risk mitigation must be taken for an NTR full-scale development project. The NTR design must take into account safety for transport and off nominal conditions. Nuclear fuel element must consider containment of fission products and Low Enriched Uranium (LEU) that may meet less opposition should be considered for safety and security reasons. Even though testing was conducted on Rover/NERVA safely and successfully in the 60's with exhaust sent heavenward in to open air, modern testing of NTR must consider full containment and no release of ionizing radiation to the public and must meet the current requirement of no more than 0.1 milli-Sieverts per year to the public. 0.1 milli-Sieverts is equivalent to eating one banana or a 20 hour plane flight. Good communication with the public and regulatory agencies will be essential to show that all effort is applied toward protection to the public and astronauts. The inspiring endeavor to put humans on Mars to study the planet, search for life, and learn more about this Solar System will be full of risks but it will be worth it. NTR will be worth the development effort if it allows humans to explore our Solar System.

Kim, Tony↗

Normalized Point Source Sensitivity for Off-Axis Optical Performance Evaluation of the Thirty Meter Telescope

The Normalized Point Source Sensitivity (PSSN) has previously been defined and analyzed as an On-Axis seeing-limited telescope performance metric. In this paper, we expand the scope of the PSSN definition to include Off-Axis field of view (FoV) points and apply this generalized metric for performance evaluation of the Thirty Meter Telescope (TMT). We first propose various possible choices for the PSSN definition and select one as our baseline. We show that our baseline metric has useful properties including the multiplicative feature even when considering Off-Axis FoV points, which has proven to be useful for optimizing the telescope error budget. Various TMT optical errors are considered for the performance evaluation including segment alignment and phasing, segment surface figures, temperature, and gravity, whose On-Axis PSSN values have previously been published by our group.

optical performance metric↗