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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Updates to a Preliminary Probabilistic Performance Assessment Model for Radiological and Chemical Contamination at the West Valley Site, New York - 20420

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States at the WNYNSC. NFS built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (640 metric tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations halted in 1972 and never restarted, leaving behind radioactive and chemical wastes. The U.S. Department of Energy (DOE) was required to complete certain waste management activities under the West Valley Demonstration Project (WVDP) Act of 1980 including decommissioning of WVDP facilities. As collaborating agencies, NYSERDA and the DOE are tasked with making decisions about decommissioning and risk reduction for the West Valley Site. Neptune and Company, Inc. (Neptune) was contracted to develop a probabilistic performance assessment (PPA) model to assist the agencies in their decision making process for decommissioning the WVDP and WNYNSC. One important tool that is needed in order to inform the decision-making process is a science-based model of the West Valley Site that evaluates potential future consequences for human health and the environment. This forms the core of the spatial domain of the West Valley PPA Model. The PPA Model, developed using the GoldSim system modeling software, is a tool intended to provide support for decision making that evaluates uncertainty, in a manner that is transparent, defensible, and robust. The PPA Model includes contaminant transport and health effects components, and is organized around geographically-grouped contaminated facilities. These include the waste disposal areas licensed by the U.S. Nuclear Regulatory Commission and the State of New York, a waste tank farm for storage of high level radioactive waste resulting from reprocessing operations, and several areas contaminated with radioactive and chemical constituents. The PPA Model evaluates contaminant transport from these sources to points of exposure across the site and into receiving surface waters and sediments. Hypothetical people and wildlife could be exposed to contamination at these locations, and the effects of these exposures are evaluated. Contaminant transport processes to be evaluated in the PPA Model include groundwater and surface water transport, contaminant translocation by plants and animals, diffusion, and erosion. The evaluation of exposures to people in this preliminary model is limited to a resident farmer scenario, and ecological assessment is performed at the level of a screening analysis. The results of these preliminary evaluations inform future model developments. PPA Model results are subjected to sensitivity analysis in order to determine those pathways and parameters that are most significant in influencing the results. This information allows analysts and decision makers to focus on those aspects of Site behavior and processes. With this information, the decision makers can drive informed, defensible decisions regarding decommissioning of the Site. This paper includes an update of the information presented at WM2019 [1]. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2007

Amazon Land Wars in the South of Para

The South of Para, located in the heart of the Brazilian Amazon, has become notorious for violent land struggle. Although land conflict has a long history in Brazil, and today impacts many parts of the country, violence is most severe and persistent here. The purpose of this article is to examine why. Specifically, we consider how a particular Amazonian place, the so-called South of Para has come to be known as Brazil's most dangerous badland. We begin by considering the predominant literature, which attributes land conflict to the frontier expansion process with intensified struggle emerging in the face of rising property values and demand for private property associated with capitalist development. From this discussion, we distill a concept of the frontier, based on notions of property rights evolution and locational rents. We then empirically test the persistence of place-based violence in the region, and assess the frontier movement through an analysis of transportation costs. The findings from the analyses indicate that the prevalent theorization of frontier violence in Amazonia does little to explain its persistent and pervasive nature in the South of Para. To fill this gap in understanding, we develop an explanation based the geographic conception of place, and we use contentious politics theory heuristically to elucidate the ways in which general processes interact with place specific history to engender a landscape of violence. In so doing, we focus on environmental, cognitive, and relational mechanisms (and implicated structures), and attempt to deploy them in an explanatory framework that allows direct observation of the accumulating layers of the region's tragic history. We end by placing our discussion within a political ecological context, and consider the implications of the Amazon Land War for the environment.

Simmons, Cynthia S.↗
NASA NTRS2014

Uncertainty in Agricultural Impact Assessment

This chapter considers issues concerning uncertainty associated with modeling and its use within agricultural impact assessments. Information about uncertainty is important for those who develop assessment methods, since that information indicates the need for, and the possibility of, improvement of the methods and databases. Such information also allows one to compare alternative methods. Information about the sources of uncertainties is an aid in prioritizing further work on the impact assessment method. Uncertainty information is also necessary for those who apply assessment methods, e.g., for projecting climate change impacts on agricultural production and for stakeholders who want to use the results as part of a decision-making process (e.g., for adaptation planning). For them, uncertainty information indicates the degree of confidence they can place in the simulated results. Quantification of uncertainty also provides stakeholders with an important guideline for making decisions that are robust across the known uncertainties. Thus, uncertainty information is important for any decision based on impact assessment. Ultimately, we are interested in knowledge about uncertainty so that information can be used to achieve positive outcomes from agricultural modeling and impact assessment.

damage assessment↗
NASA NTRS2010

Heritage and Advanced Technology Systems Engineering Lessons Learned from NASA Space Missions

Use of heritage and new technology is necessary/enabling to implementing small, low cost missions, yet overruns decrease the ability to sustain future mission flight rates The majority of the cost growth drivers seen in the D&NF study were embedded early during formulation phase and later realized during the development and I&T phases Cost drivers can be avoided or significantly decreased by project management and SE emphasis on early identification of risks and realistic analyses SE processes that emphasize an assessment of technology within the mission system to identify technical issues in the design or operational use of the technology. Realistic assessment of new and heritage spacecraft technology assumptions , identification of risks and mitigation strategies. Realistic estimates of effort required to inherit existing or qualify new technology, identification of risks to estimates and develop mitigation strategies. Allocation of project reserves for risk-based mitigation strategies of each individual area of heritage or new technology. Careful tailoring of inheritance processes to ensure due diligence.

Barley, Bryan↗
NASA NTRS1998

An Assessment of the Axial and Radial Dilation of a DPIMS Tantalum Cartridge for Space Shuttle Flight Experiments

Ground-based heat treatment tests are planned on an argon gas-filled tantalum cartridge developed as pan of a Diffusion Processes in Molten Semiconductors (DPIMS) experiment conducted on NASA's Space Shuttle. The possibility that the cartridge may creep during testing and touch the furnace walls is of real concern in this program. The present paper discusses the results of calculations performed to evaluate this possibility. Deformation mechanism maps were constructed using literature data in order to identify the creep mechanism dominant under the appropriate stresses and temperatures corresponding to the test conditions. These results showed that power-law creep was dominant when the grain size of the material exceeded 55 gm but Coble creep was the important mechanism below this value of grain size. Finite element analysis was used to analyze the heat treatment tWs assuming a furnace run away condition (which is a worst case scenario) using the appropriate creep parameters corresponding to grain sizes of 1 and 100 gm. Calculations were also conducted to simulate the effect of an initial 3 tilt of the cartridge assembly, the maximum possible tilt angle. The von Mises stress and su-ain distributions were calculated assuming that the cartridge was fixed at one end as it was heated from ambient temperature to 1823 K in 1.42 h, maintained at 1823 K for 9.5 h and then further heated to an over temperature condition of 2028 K in 0.3 h. The inelastic axial and radial displacements of the cartridge walls were evaluated by resolving the von Mises strain along the corresponding directions. These calculations reveal that the maximum axial and radial displacements are expected to be about 2.9 and 0.25 mm, respectively, for both fine and coarse-grained materials at 2028 K. It was determined that these displacements occur during heat-up to temperature and creep of the cartridge is likely to be relatively insignificant irrespective of grain size. Furthermore, with a 3' tilt of the cartridge, the deflection is increased by only 0.39 gm which is negligible. Since the gap between the furnace heating elements and the cartridge is about 7.5 mm and less than the maximum radial dilation of 0.25 mm at 2028 K, it is concluded that the cartridge is unlikely to touch the furnace walls during the experiments.

Raj, S.V.↗
DOE OSTI2024

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was created to offer an efficient framework for determining material properties from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enables parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM) into our image segmentation workflow. SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography and other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗
DOE OSTI2020

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

This paper introduces a surrogate modeling scheme based on Grassmannian manifold learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The method exploits subspace-structured features of each solution by projecting it onto a Grassmann manifold. This point-wise linear dimensionality reduction harnesses the structural information to assess the similarity between solutions at different points in the input parameter space. The method utilizes a solution clustering approach in order to identify regions of the parameter space over which solutions are sufficiently similarly such that they can be interpolated on the Grassmannian. In this clustering, the reduced-order solutions are partitioned into disjoint clusters on the Grassmann manifold using the eigen-structure of properly defined Grassmannian kernels and, the Karcher mean of each cluster is estimated. Then, the points in each cluster are projected onto the tangent space with origin at the corresponding Karcher mean using the exponential mapping. For each cluster, a Gaussian process regression model is trained that maps the input parameters of the system to the reduced solution points of the corresponding cluster projected onto the tangent space. Using this Gaussian process model, the full-field solution can be efficiently predicted at any new point in the parameter space. In certain cases, the solution clusters will span disjoint regions of the parameter space. In such cases, for each of the solution clusters we utilize a second, density-based spatial clustering to group their corresponding input parameter points in the Euclidean space. The proposed method is applied to two numerical examples. Here, the first is a nonlinear stochastic ordinary differential equation with uncertain initial conditions where the surrogate is used to predict the time history solution. The second involves modeling of plastic deformation in a model amorphous solid using the Shear Transformation Zone theory of plasticity, where the proposed surrogate is used to predict the full strain field of a material specimen under large shear strains.

42 ENGINEERING↗
DOE OSTI2023

LCA-based environmental sustainability assessment of hybrid additive manufacturing of a turbine blade

In this paper, a cradle-to-gate life cycle assessment (LCA) method is used to compare the overall energy consumption and environmental impact of hybrid additive manufacturing (HAM) and the traditional CNC milling process with a case study of turbine blade manufacturing. Six environmental impacts are assessed in this study: acidification potential (AP), eutrophication potential (EP), global warming potential (GWP), photochemical ozone creation potential (POCP), ozone depletion potential (ODP), and abiotic depletion potential (ADP). The results suggest that HAM can not only reduce energy consumption and material waste but also reduce the environmental impact by 53% from a life cycle perspective. Specifically, the results of GWP, AP, EP, ODP, POCP and ADP of the HAM are only 32.2%, 34.6 %, 44.7%, 27.2%, 25.6%, and 24.7 % of that in traditional CNC machining.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

satellite data↗
NASA NTRS2018

NASA NeMO-Net

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low- resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

NASA↗
DOE OSTI2024

Novel Materials R&D for Next-Generation Accelerator Target Facilities

High-Entropy Alloys and Electrospun Nanofiber materials are two novel classes of materials that can offer improved resistance to beam-induced radiation damage and thermal shock. Research to develop these new materials specifically for multi-megawatt accelerator target applications, such as beam windows and particle-production targets, has recently begun. The research program will combine in-beam experiments with complementary simulations to tailor the microstructures of these novel materials for use in next-generation accelerator target facilities. Iterative simulations to optimize the material composition, physics performance and beam-induced thermomechanical response will guide the material design and fabrication processes based on established figures of merit. Ensuing material irradiation experiments using low-energy ions and prototypic high-energy protons, followed by extensive post-irradiation material characterization, will then assess and qualify the selected novel materials. This talk will provide an overview of the novel materials development research program initiated at Fermilab through my DOE Early Career Research Program award.

43 PARTICLE ACCELERATORS↗
NASA NTRS1975

Evaluation and comparison of alternative designs for water/solid-waste processing systems for spacecraft

Promising candidate designs currently being considered for the management of spacecraft solid waste and waste-water materials were assessed. The candidate processes were: (1) the radioisotope thermal energy evaporation/incinerator process; (2) the dry incineration process; and (3) the wet oxidation process. The types of spacecraft waste materials that were included in the base-line computational input to the candidate systems were feces, urine residues, trash and waste-water concentrates. The performance characteristics and system requirements for each candidate process to handle this input and produce the specified acceptable output (i.e., potable water, a storable dry ash, and vapor phase products that can be handled by a spacecraft atmosphere control system) were estimated and compared. Recommendations are presented.

Spurlock, J. M.↗
NASA NTRS1990

Risk analysis and management

Present software development accomplishments are indicative of the emerging interest in and increasing efforts to provide risk assessment backbone tools in the manned spacecraft engineering community. There are indications that similar efforts are underway in the chemical processes industry and are probably being planned for other high risk ground base environments. It appears that complex flight systems intended for extended manned planetary exploration will drive this technology.

Smith, H. E.↗
NASA NTRS1990

Cryogenic hydrogen-induced air-liquefaction technologies

Extensive use of a special advanced airbreathing propulsion archives data base, as well as direct contacts with individuals who were active in the field in previous years, a technical assessment of cryogenic hydrogen induced air liquefaction, as a prospective onboard aerospace vehicle process, was performed and documented in 1986. The resulting assessment report is summarized. Technical findings relating the status of air liquefaction technology are presented both as a singular technical area, and also as that of a cluster of collateral technical areas including: Compact lightweight cryogenic heat exchangers; Heat exchanger atmospheric constituents fouling alleviation; Para/ortho hydrogen shift conversion catalysts; Hydrogen turbine expanders, cryogenic air compressors and liquid air pumps; Hydrogen recycling using slush hydrogen as heat sinks; Liquid hydrogen/liquid air rocket type combustion devices; Air Collection and Enrichment System (ACES); and Technically related engine concepts.

Escher, William J. D.↗
NASA NTRS1993

Relating ground truth collection to model sensitivity

The importance of collecting high quality ground truth before a SAR mission over a forested area is two fold. First, the ground truth is used in the analysis and interpretation of the measured backscattering properties; second, it helps to justify the use of a scattering model to fit the measurements. Unfortunately, ground truth is often collected based on visual assessment of what is perceived to be important without regard to the mission itself. Sites are selected based on brief surveys of large areas, and the ground truth is collected by a process of selecting and grouping different scatterers. After the fact, it may turn out that some of the relevant parameters are missing. A three-layer canopy model based on the radiative transfer equations is used to determine, before hand, the relevant parameters to be collected. Detailed analysis of the contribution to scattering and attenuation of various forest components is carried out. The goal is to identify the forest parameters which most influence the backscattering as a function of frequency (P-, L-, and C-bands) and incident angle. The influence on backscattering and attenuation of branch diameters, lengths, angular distribution, and permittivity; trunk diameters, lengths, and permittivity; and needle sizes, their angular distribution, and permittivity are studied in order to maximize the efficiency of the ground truth collection efforts. Preliminary results indicate that while a scatterer may not contribute to the total backscattering, its contribution to attenuation may be significant depending on the frequency.

Amar, Faouzi↗
NASA NTRS1996

Using Design Capability Indices to Satisfy Ranged Sets of Design Requirements

For robust design it is desirable to allow the design requirements to vary within a certain range rather than setting point targets. This is particularly important during the early stages of design when little is known about the system and its requirements. Toward this end, design capability indices are developed in this paper to assess the capability of a family of designs, represented by a range of top-level design specifications, to satisfy a ranged set of design requirements. Design capability indices are based on process capability indices from statistical process control and provide a single objective, alternate approach to the use of Taguchi's signal-to- noise ratio which is often used for robust design. Successful implementation of design capability indices ensures that a family of designs conforms to a given ranged set of design requirements. To demonstrate an application and the usefulness of design capability indices, the design of a solar powered irrigation system is presented. Our focus in this paper is on the development and implementation of design capability indices as an alternate approach to the use of the signal-to-noise ratio and not on the results of the example problem, per se.

Chen, Wei↗
NASA NTRS2016

Cross Section Calculations and Comparison to Experiment

Understanding fragmentation of galactic cosmic ray nuclei in collisions within spacecraft structures and human tissues is an important element in assessing biological risk to crew members from this radiation source. Over the past four decades, various models have been developed to describe these important processes. Some models invoke semi-classical concepts based upon geometric descriptions of collisions between spherical nuclei.

Townsend, L. W.↗