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Digital techniques for characterization of image sources, second order interpolation, sensitivity of compressed pictures to error, and simulation of mapping techniques
As nuclear technology evolves in response to increased demand for diversification and decarbonization of the energy sector, new and innovative approaches are needed to effectively identify and deter the proliferation of nuclear arms, while ensuring safe development of global nuclear energy resources. Preventing the use of nuclear material and technology for unsanctioned development of nuclear weapons has been a long-standing challenge for the International Atomic Energy Agency and signatories of the Treaty on the Non-Proliferation of Nuclear Weapons. Environmental swipe sampling has proven to be an effective technique for characterizing clandestine proliferation activities within and around known locations of nuclear facilities and sites. However, limited tools and techniques exist for detecting nuclear proliferation in unknown locations beyond the boundaries of declared nuclear fuel cycle facilities, representing a critical gap in non-proliferation safeguards. Microbiomes, defined as “characteristic communities of microorganisms” found in specific habitats with distinct physical and chemical properties, can provide valuable information about the conditions and activities occurring in the surrounding environment. Microorganisms are known to inhabit radionuclide-contaminated sites, spent nuclear fuel storage pools, and cooling systems of water-cooled nuclear reactors, where they can cause radionuclide migration and corrosion of critical structures. Microbial transformation of radionuclides is a well-established process that has been documented in numerous field and laboratory studies. These studies helped to identify key bacterial taxa and microbially-mediated processes that directly and indirectly control the transformation, mobility, and fate of radionuclides in the environment. Expanding on this work, other studies have used microbial genomics integrated with machine learning models to successfully monitor and predict the occurrence of heavy metals, radionuclides, and other process wastes in the environment, indicating the potential role of nuclear activities in shaping microbial community structure and function. Results of this previous body of work suggest fundamental geochemical-microbial interactions occurring at nuclear fuel cycle facilities could give rise to microbiomes that are characteristic of nuclear activities. These microbiomes could provide valuable information for monitoring nuclear fuel cycle facilities, planning environmental sampling campaigns, and developing biosensor technology for the detection of undisclosed fuel cycle activities and proliferation concerns.
For a period covering October 1, 1995 through August 12, 1996, the research group at CSU has conducted theoretical and experimental research on "Electrochemical Characterization of Semiconductor Materials and Structures. " The objective of this investigation was to demonstrate the applicability of electrochemical techniques for characterization of complex device structures based on InP and GaAs, Ge, InGaAs, InSb, InAs and InSb, including: (1) accurate EC-V net majority carrier concentration depth profiling, and (2) surface and bulk structural and electrical type defect densities. Our motivation for this R&D effort was as follows: "Advanced space solar cells and ThermoPhotoVoltaic (TPV) cells are fabricated using a large variety of III-V materials based on InP and GaAs for solar cells and low bandgap materials such as Ge, InGaAs, InAs and InSb for TPV applications. At the present time for complex device structures using these materials, however, there is no simple way to assess the quality of these structures prior to device fabrication. Therefore, process optimization is a very time consuming and a costly endeavor". Completion of this R&D effort would have had unquestionable benefits for space solar cell and TPV cells, since electrochemical characterization of the above cell structures, if properly designed can provide many useful structural and electrical material information virtually at any depth inside various layers and at the interfaces. This, could have been applied for step-by-step process optimization, which could have been used for fabrication of new generation high efficiency, low cost space PV and TPV cells.
We use high-resolution spectral emission line data obtained by the SERTS instrument during three rocket flights to demonstrate a new approach for constraining electron densities of solar active region plasma.We apply differential emission measure (DEM) forward-fitting techniques to characterize the multithermal solar plasma producing the observed EUV spectra, with constraints on the high-temperature plasma from the Yohkoh Soft X-ray Telescope. In this iterative process, we compare line intensities predicted by an input source distribution to observed line intensities for multiple iron ion species, and search a broad range of densities to optimize chi-square simultaneously for the many available density-sensitive lines. This produces a density weighted by the DEM, which appears to be useful for characterizing the bulk of the emitting plasma over a significant range of temperature. This "DEM-weighted density" technique is complementary to the use of density-sensitive line ratios and less affected by uncertainties in atomic data and ionization fraction for any specific line. Once the DEM shape and the DEM-weighted density have been established from the iron lines, the relative elemental abundances can be determined for other lines in the spectrum. We have also identified spectral lines in the SERTS wavelength range that may be problematic
Additive manufacturing (AM) is a rapidly developing technology that allows industries to automate and simplify the production of highly complicated items. Recently, there are concerns that AM techniques have the potential to be utilized in the fields of nuclear weapons and nuclear enrichment technology. Presently, there are very few international or domestic export controls that apply to AM's role in the nuclear industry, constituting an unmanaged proliferation pathway. Pre-existing export controls focus on general concepts and processes and do not consider specific nuances of various techniques that are categorized as additive manufacturing. To introduce legislation and controls that will be effective in monitoring proliferation pathways, one must investigate and characterize AM techniques and their nuclear applications. This paper categorizes and ranks 33 AM techniques based on their potential impact on the nuclear fuel cycle and the development of nuclear weapons. Through this method of characterization and categorization, export controls would address specific AM nuclear proliferation risks without disrupting the entire industry and fuel cycle. Additionally, legislation employing this method would identify loopholes in export controls using a holistic approach to managing and monitoring proliferation pathways.
The measured performance characteristics of a rectenna array are reviewed and compared to the performance of a single element. It is shown that the performance may be extrapolated from the individual element to that of the collection of elements. Techniques for current and voltage combining are demonstrated. The array performance as a function of various operating parameters is characterized and techniques for overvoltage protection and automatic fault clearing in the array are demonstrated. A method for detecting failed elements also exists. Instrumentation for deriving performance effectiveness is described. Measured harmonic radiation patterns and fundamental frequency scattered patterns for a low level illumination rectenna array are presented.
The measured performance characteristics of a rectenna array are reviewed and compared to the performance of a single element. It is shown that the performance may be extrapolated from the individual element to that of the collection of elements. Techniques for current and voltage combining were demonstrated. The array performance as a function of various operating parameters is characterized and techniques for overvoltage protection and automatic fault clearing in the array demonstrated. A method for detecting failed elements also exists. Instrumentation for deriving performance effectiveness is described. Measured harmonic radiation patterns and fundamental frequency scattered patterns for a low level illumination rectenna array are presented.
This research effort focused on the use of eddy current techniques for characterizing flaws in graphite-based filament-wound cylindrical structures. A major emphasis was on incorporating artificial intelligence techniques into the signal analysis portion of the inspection process. Developing an eddy current scanning system using a commercial robot for inspecting graphite structures (and others) has been a goal in the overall concept and is essential for the final implementation for expert system interpretation. Manual scans, as performed in the preliminary work here, do not provide sufficiently reproducible eddy current signatures to be easily built into a real time expert system. The expert systems approach to eddy current signal analysis requires that a suitable knowledge base exist in which correct decisions as to the nature of the flaw can be performed. In eddy current or any other expert systems used to analyze signals in real time in a production environment, it is important to simplify computational procedures as much as possible. For that reason, we have chosen to use the measured resistance and reactance values for the preliminary aspects of this work. A simple computation, such as phase angle of the signal, is certainly within the real time processing capability of the computer system. In the work described here, there is a balance between physical measurements and finite element calculations of those measurements. The goal is to evolve into the most cost effective procedures for maintaining the correctness of the knowledge base.
Graphite pebble composite structures based on TRistructural-ISOtropic (TRISO) particles are being developed as core nuclear fuels in advanced power reactors, promising safe operation at increased temperatures. Ensuring the structural integrity of these nuclear fuels requires comprehensive and accurate non-destructive evaluation (NDE) techniques to characterize defects and damage in the pebbles. However, traditional acoustic evaluation methods face limitations in defect characterization due to the highly attenuative, and geometrically and compositionally complex nature of these structures. This study proposes an improved acoustic NDE technique for accurate detection and classification of anticipated relevant defects and damage in graphite pebbles using high-resolution acoustic signals and optimized transmit-receive sensor networks. The proposed approach utilizes a triangular three-sensor network as the base unit, comprising three transmit-receive sensors. The sensor separation distance, as well as acoustic excitation center frequency, pulse-width, and bandwidth are optimized to enhance spatial resolution and improve signal-to-noise ratio, enabling effective characterization of the smallest size and widest range of defects in pebbles. Furthermore, the use of the triangular sensor configuration instead of a more conventional transmit-receive sensor pair expands the inspection region from a one-dimensional linear path to a two-dimensional area, increasing spatial coverage. To mitigate challenges associated with processing of complex acoustic signals arising from high-frequency, high-bandwidth excitation in these structures, a machine-learning-based signal processing algorithm is integrated with the sensor network. In the machine-learning-based algorithm, multi-domain features are extracted from the acoustic signals to capture intricate signal characteristics, significantly improving defect identification and classification compared to traditional approaches. The proposed acoustic NDE technique offers considerable promise for practical and reliable defect/damage diagnostics of advanced nuclear pebble fuels.
Here in this paper we expand the technique of characterizing nonlinear optics from off-energy closed orbits (NOECO) to cover harmonic sextupoles in storage rings. The existing NOECO technique has been successfully used to correct the chromatic sextupole errors on the MAX-IV machine (Olsson et al., 2020), however, it does not account for harmonic sextupoles, which are still widely used on many other machines. By generating vertical dispersion with chromatic skew quadrupoles, a measurable chromatic function dependence on harmonic sextupoles can be observed on hybrid dispersive orbits. Proof of concept of this technique was accomplished by simulations and beam measurements on the National Synchrotron Light Source II (NSLS-II) storage ring.
Here, we outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self-organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure-function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.
In this paper we expand the technique of characterizing nonlinear optics from off-energy closed orbits (NOECO) to cover harmonic sextupoles in storage rings. The existing NOECO technique has been successfully used to correct the chromatic sextupole errors on the MAX-IV machine [Olsson et al., Phys. Rev. Accel. Beams 23, 102803], however, it does not account for harmonic sextupoles, which are still widely used on many other machines. By generating vertical dispersion with chromatic skew quadrupoles, a measurable chromatic function dependence on harmonic sextupoles can be observed on hybrid dispersive orbits. Proof of concept of this technique was accomplished by simulations and beam measurements on the National Synchrotron Light Source II (NSLS-II) storage ring.
Scientists within NASA#s Applied Research & Technology Project Office (formerly the Applied Sciences Directorate) have developed a well-characterized remote sensing Verification & Validation (V&V) site at the John C. Stennis Space Center (SSC). This site enables the in-flight characterization of satellite and airborne high spatial resolution remote sensing systems and their products. The smaller scale of the newer high resolution remote sensing systems allows scientists to characterize geometric, spatial, and radiometric data properties using a single V&V site. The targets and techniques used to characterize data from these newer systems can differ significantly from the techniques used to characterize data from the earlier, coarser spatial resolution systems. Scientists have used the SSC V&V site to characterize thermal infrared systems. Enhancements are being considered to characterize active lidar systems. SSC employs geodetic targets, edge targets, radiometric tarps, atmospheric monitoring equipment, and thermal calibration ponds to characterize remote sensing data products. Similar techniques are used to characterize moderate spatial resolution sensing systems at selected nearby locations. The SSC Instrument Validation Lab is a key component of the V&V capability and is used to calibrate field instrumentation and to provide National Institute of Standards and Technology traceability. This poster presents a description of the SSC characterization capabilities and examples of calibration data.
The growing application of composite materials in commercial aircraft manufacturing has significantly increased the risk of aircraft damage from lightning strikes. Composite aircraft designs require new mitigation strategies and engineering practices to maintain the same level of safety and protection as achieved by conductive aluminum skinned aircraft. Researchers working under the NASA Aviation Safety Program s Integrated Vehicle Health Management (IVHM) Project are investigating lightning damage on composite materials to support the development of new mitigation, diagnosis & prognosis techniques to overcome the increased challenges associated with lightning protection on composite aircraft. This paper provides an overview of the electrical characterizations being performed to support IVHM lightning damage diagnosis research on composite materials at the NASA Langley Research Center.
With the advancement of high numerical aperture extreme ultraviolet (EUV) lithography, the new platform will enable chipmakers to achieve critical dimensions of 8 nm. However, resist materials face significant challenges in delivering increased sensitivity while managing rising stochastic variations. We aim to develop comprehensive techniques to characterize the chemical profile of latent images, stored in EUV resists after exposure and postexposure baking, which is essential for understanding the origin of stochastic effects. Infrared photo-induced force microscopy (IR PiFM) is a bimodal atomic force microscopy technique combined with an infrared light source, allowing for simultaneous sub-5 nm topographic and chemical characterization within a localized environment. Critical-dimension resonant soft X-ray scatterometry (CD-RSoXS) provides statistical data that reveal structural and chemical information for comparative analysis. For the first time, IR PiFM has been used to chemically map the latent images of EUV resists (after exposure and postexposure baking) at a record high resolution of 12 nm half-pitch, enabling nondestructive analysis of patterns prior to development. Furthermore, CD-RSoXS offers direct experimental observation and comparison of exposed, postexposure baked, and developed patterns, which align with the IR PiFM results. We demonstrate that the IR PiFM technique offers valuable insights into both high spatial resolution and local chemical information simultaneously. In addition, CD-RSoXS provides statistical results that support our main findings. This hybrid metrology approach leverages a multifaceted dataset by combining the most reliable information from each source, which is essential for a comprehensive understanding of the stochastic effects in EUV lithography processes.
Significant noise suppression for magnetohydrodynamics (MHD) mode characterization in the spatial and spectral domain is achieved by processing two-dimensional (2D) electron cyclotron emission imaging (ECEI) data with a one-dimensional (1D) ECEI technique using a short time window ($1\;{\text{ms}}$). The technique is applied to detect toroidal Alfven eigenmodes (TAEs) in the temporal spectrum and fit their radial envelope using the data from the DIII-D tokamak W-band 2D ECEI system. Using the data length (time window) of only 1 ${\text{ms}}$, the 1D ECEI can clearly detect the TAEs (~100 ${\text{kHz}}$) on the spectrum, while similar spectrum quality requires ~10 ${\text{ms}}$ data length with the cross power spectrum between two midplane ECEI channels. The 1D ECEI technique also effectively avoids biased fitting when resolving the fine structure of the TAE's radial envelope. The radially spatial resolution of 1D ECEI is constrained by the finite ECE radiation volume of the ECEI receiver. Here, with forward radiation modeling, we find the DIII-D ECEI system can sensitively measure the even parity MHD activities, for which the mode width is >15 mm, and tearing modes (odd parity MHD activities), for which the island full width is >30 mm.
This paper demonstrates how an analytical and experimental method can be used to rapidly define the 45 additive manufacturing settings for a new alloy where the process parameters were previously unknown. A nickel-based superalloy, Haynes 282, was chosen for the analysis. An experimental matrix of focused processing parameters was predicted with a dimensionless number and 100 samples were printed using the Selective Laser Melting technique. High-throughput measurements validated the predicted process conditions needed to achieve desired density and hardness. The whole process was completed in 16 hours. The new technique was confirmed with analytical processing maps adopted by the metal additive manufacturing community. With the predicted set of process parameters, microstructural characterizations and tensile testing were used to confirm the predictions. The resultant as-fabricated microstructures have refined length scales of both microsegregation and secondary phase distributions. As a result, the mechanical properties were comparable within the predicted processing window and exhibited high strength and high ductility.
The use of eddy current techniques for characterizing flaws in graphite-based filament-wound cylindrical structures is described. A major emphasis was also placed upon incorporating artificial intelligence techniques into the signal analysis portion of the inspection process. Developing an eddy current scanning system using a commercial robot for inspecting graphite structures (and others) was a goal in the overall concept and is essential for the final implementation for the expert systems interpretation. Manual scans, as performed in the preliminary work here, do not provide sufficiently reproducible eddy current signatures to be easily built into a real time expert system. The expert systems approach to eddy current signal analysis requires that a suitable knowledge base exist in which correct decisions as to the nature of a flaw can be performed. A robotic workcell using eddy current transducers for the inspection of carbon filament materials with improved sensitivity was developed. Improved coupling efficiencies achieved with the E-probes and horseshoe probes are exceptional for graphite fibers. The eddy current supervisory system and expert system was partially developed on a MacIvory system. Continued utilization of finite element models for predetermining eddy current signals was shown to be useful in this work, both for understanding how electromagnetic fields interact with graphite fibers, and also for use in determining how to develop the knowledge base. Sufficient data was taken to indicate that the E-probe and the horseshoe probe can be useful eddy current transducers for inspecting graphite fiber components. The lacking component at this time is a large enough probe to have sensitivity in both the far and near field of a thick graphite epoxy component.