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Joshi, S.

Publications and source records attributed to Joshi, S..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

New Approach and Test Facility for High Field Accelerator Magnets R&D

This paper presents a new approach for developing and demonstrating high field magnet technology based on a magnet and test facility developed specifically for it. The traditional approach for carrying out high field magnet R&D has been building a new magnet to demonstrate a new design, new material or new technology. However, building a high field magnet is time consuming and expensive. To overcome this limitation, Brookhaven National Laboratory (BNL) built and successfully tested a 10 T Nb 3 Sn dipole DCC017 with large enough open or clear space (31 mm wide and 338 mm high) so that a pair of racetrack coils could be inserted into this opening without disassembling the magnet. The motivation be-hind this design was to facilitate a magnet R&D program where the new coils (with a large range in width and height accommodated) would reside in a high field region in direct contact with the existing coils (just as other magnet coils) and thus become an integral part of the magnet. We summarize the approach, the magnet, the test facility, past experiences, current and future test plans and planned up-grade. Here, the magnet facility is now available to service the needs of the wider community for testing cable and insert coils in a back-ground field of up to 10 T.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Big Data Framework with Machine Learning for D and D Applications - 19108

The nuclear industry is experiencing a steady increase in maintenance costs even though plants are maintained under high levels of safety, capability and reliability. Nuclear power plants are expected to run every unit at maximum capacity at all times, efficiently utilizing assets with minimal downtime. Surveillance and maintenance of nuclear decommissioning infrastructure provides lot challenges with respect to maintenance or decommissioning of the buildings. There is a need for a framework to analyze the huge amount of data generated by the sensors on the nuclear reactor components as well as structures, to monitor the conditions of these building over a period of time. Emerging technologies such as big data analytics have become a requirement in the nuclear industry to improve structural health monitoring and diagnostics. FIU will make use of existing mature technologies in the areas of imaging, robotics, big data, and machine learning/deep learning to assess the structural integrity of aging facilities at DOE sites. As these facilities await decommissioning, there is a need to understand the structural health of these structures. Many of these facilities were built over 50 years ago and in some cases these facilities have gone beyond the operational life expectancy. In other cases, the facilities have been placed in a state of 'cold and dark' and they are sitting unused, awaiting decommissioning. Finally, some older facilities are one-of-a-kind operational/production facilities supporting critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may be compromised, so it is imperative that adequate inspections and data collection/analysis be performed on a continuous and ongoing basis. The primary goals of the research include collecting various formats of data such as structured, unstructured and streaming data from the various sensors deployed in buildings, collect videos/pictures from various imaging sources, ingest them using a Hadoop distributed file system and process the data using Spark to perform batch processing and real time analytics. Research and development on various machine learning/deep learning algorithms will also be performed to analyze the heterogeneous data collected from nuclear decommission infrastructure. FIU will design the big data framework to ingest, store and process huge amounts of heterogeneous data collected from many sources and optimize the algorithms to provide insights into the data and predict anomalies observed when compared against baseline conditions. Various modules of this framework will include heterogeneous data sources, message broker, Hadoop distributed file system, Spark for stream and batch processing, machine learning/ deep learning, Cassandra - persistent data store and visualization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2016

LO Peg: Surface Differential Rotation, Flares, and Spot-Topographic Evolution

Using the wealth of ∼24 yr multiband data, we present an in-depth study of the star-spot cycles, surface differential rotations (SDR), optical flares, evolution of star-spot distributions, and coronal activities on the surface of young, single, main-sequence, ultrafast rotator LO Peg. From the long-term V-band photometry, we derive rotational period of LO Peg to be 0.4231 +/- 0.0001 d. Using the seasonal variations on the rotational period, the SDR pattern is investigated, and shows a solar-like pattern of SDR. A cyclic pattern with period of ∼2.7 yr appears to be present in rotational period variation. During the observations, 20 optical flares are detected with a flare frequency of ∼1 flare per two days and with flare energy of ∼10(exp 31-34) erg. The surface coverage of cool spots is found to be in the range of ∼9-26 per cent. It appears that the high- and low-latitude spots are interchanging their positions. Quasi-simultaneous observations in X-ray, UV, and optical photometric bands show a signature of an excess of X-ray and UV activities in spotted regions.

Karmakar, Subhajeet↗
DOE OSTI2015

Design and testing of the first 2D Prototype Vertically Integrated Pattern Recognition Associative Memory

An associative memory-based track finding approach has been proposed for a Level 1 tracking trigger to cope with increasing luminosities at the LHC. The associative memory uses a massively parallel architecture to tackle the intrinsically complex combinatorics of track finding algorithms, thus avoiding the typical power law dependence of execution time on occupancy and solving the pattern recognition in times roughly proportional to the number of hits. This is of crucial importance given the large occupancies typical of hadronic collisions. The design of an associative memory system capable of dealing with the complexity of HL-LHC collisions and with the short latency required by Level 1 triggering poses significant, as yet unsolved, technical challenges. For this reason, an aggressive R&D program has been launched at Fermilab to advance state of-the-art associative memory technology, the so called VIPRAM (Vertically Integrated Pattern Recognition Associative Memory) project. The VIPRAM leverages emerging 3D vertical integration technology to build faster and denser Associative Memory devices. The first step is to implement in conventional VLSI the associative memory building blocks that can be used in 3D stacking, in other words, the building blocks are laid out as if it is a 3D design. In this paper, we report on the first successful implementation of a 2D VIPRAM demonstrator chip (protoVIPRAM00). The results show that these building blocks are ready for 3D stacking.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Dynamic characteristics of undoped and p-doped Fabry-Perot InAs/InP quantum dash based ridge waveguide lasers for access/metro networks

In this paper, we report the characteristics of InAs/InP quantum dashes (QDash) based lasers emitting around 1.55 μm. An unprecedented high modal gain of ∼100 cm{sup −1} is obtained for an optimized active structure by stacking 12 QDash layers. Directly modulated lasers allowed achieving a modulation bandwidth of ∼10 GHz and a Henry factor around 5. Thanks to p-type doping, the Henry factor value is reduced down to 2.7 while the modulation bandwidth still amounts to ∼10 GHz. This shows that doping of the active region is important to improve the dynamic characteristics of QDash lasers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Investigating the molecule-substrate interaction of prototypic tetrapyrrole compounds: Adsorption and self-metalation of porphine on Cu(111)

We report on the adsorption and self-metalation of a prototypic tetrapyrrole compound, the free-base porphine (2H-P), on the Cu(111) surface. Our multitechnique study combines scanning tunneling microscopy (STM) results with near-edge X-ray absorption fine-structure (NEXAFS) and X-ray photoelectron spectroscopy (XPS) data whose interpretation is supported by density functional theory calculations. In the first layer in contact with the copper substrate the molecules adsorb coplanar with the surface as shown by angle-resolved NEXAFS measurements. The quenching of the first resonance in the magic angle spectra of both carbon and nitrogen regions indicates a substantial electron transfer from the substrate to the LUMO of the molecule. The stepwise annealing of a bilayer of 2H-P molecules sequentially transforms the XP and NEXAFS signatures of the nitrogen regions into those indicative of the coordinated nitrogen species of the metalated copper porphine (Cu-P), i.e., we observe a temperature-induced self-metalation of the system. Pre- and post-metalation species are clearly discriminable by STM, corroborating the spectroscopic results. Similar to the free-base porphine, the Cu-P adsorbs flat in the first layer without distortion of the macrocycle. Additionally, the electron transfer from the copper surface to the molecule is preserved upon metalation. This behavior contrasts the self-metalation of tetraphenylporphyrin (2H-TPP) on Cu(111), where both the molecular conformation and the interaction with the substrate are strongly affected by the metalation process.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Ultrafast switching in wetting properties of TiO{sub 2}/YSZ/Si(001) epitaxial heterostructures induced by laser irradiation

We have demonstrated dark hydrophilicity of single crystalline rutile TiO{sub 2}(100) thin films, in which rapid switching from a hydrophobic to a hydrophilic surface was achieved using nanosecond excimer laser irradiation. The TiO{sub 2}/YSZ/Si(001) single crystalline heterostructures were grown by pulsed laser deposition and were subsequently irradiated by a single pulse of a KrF excimer laser at several energies. The wettability of water on the surfaces of the samples was evaluated. The samples were hydrophobic prior to laser annealing and turned hydrophilic after laser annealing. Superhydrophilic surfaces were obtained at higher laser energy densities (e.g., 0.32 J.cm{sup -2}). The stoichiometries of the surface regions of the samples before and after laser annealing were examined using XPS. The results revealed the formation of oxygen vacancies on the surface, which are surmised to be responsible for the observed superhydrophilic behavior. According to the AFM images, surface smoothening was greater in films that were annealed at higher laser energy densities. The samples exhibited hydrophobic behavior after being placed in ambient atmosphere. The origin of laser induced wetting behavior was qualitatively understood to stem from an increase of point defects near the surface, which lowered the film/water interfacial energy. This type of rapid hydrophobic/hydrophilic switching may be used to facilitate fabrication of electronic and photonic devices with novel properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗