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At least 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Multistep inversion workflow for 3D long-offset damped elastic waves in the Fourier domain

We present a new workflow for imaging damped three-dimensional elastic wavefields in the Fourier domain. The workflow employs a multiscale imaging approach, in which offset lengths are laddered, where frequency content and damping of the data are changed cyclically. Thus, the inversion process is launched using short-offset and low-frequency data to recover the long spatial wavelength of the image at a shallow depth. Increasing frequency and offset length leads to the recovery of the fine-scale features of the model at greater depths. For the fixed offset, we employ (in the imaging process) a few discrete frequencies with a set of Laplace damping parameters. The forward problem is solved with a finite-difference frequency-domain method based on a massively parallel iterative solver. The inversion code is based upon the solution of a least squares optimisation problem and is solved using a nonlinear gradient method. It is fully parallelised for distributed memory computational platforms. Our full-waveform inversion workflow is applied to the 3D Marmousi-2 and SEG/EAGE Salt models with long-offset data. The maximum inverted frequencies are 6 Hz for the Marmousi model and 2 Hz for the SEG/EAGE Salt model. The detailed structures are imaged successfully up to the depth approximately equal to one-third of the maximum offset length at a resolution consistent with the inverted frequencies.

3D full waveform inversion↗
NASA NTRS1992

Scalable parallel communications

Coarse-grain parallelism in networking (that is, the use of multiple protocol processors running replicated software sending over several physical channels) can be used to provide gigabit communications for a single application. Since parallel network performance is highly dependent on real issues such as hardware properties (e.g., memory speeds and cache hit rates), operating system overhead (e.g., interrupt handling), and protocol performance (e.g., effect of timeouts), we have performed detailed simulations studies of both a bus-based multiprocessor workstation node (based on the Sun Galaxy MP multiprocessor) and a distributed-memory parallel computer node (based on the Touchstone DELTA) to evaluate the behavior of coarse-grain parallelism. Our results indicate: (1) coarse-grain parallelism can deliver multiple 100 Mbps with currently available hardware platforms and existing networking protocols (such as Transmission Control Protocol/Internet Protocol (TCP/IP) and parallel Fiber Distributed Data Interface (FDDI) rings); (2) scale-up is near linear in n, the number of protocol processors, and channels (for small n and up to a few hundred Mbps); and (3) since these results are based on existing hardware without specialized devices (except perhaps for some simple modifications of the FDDI boards), this is a low cost solution to providing multiple 100 Mbps on current machines. In addition, from both the performance analysis and the properties of these architectures, we conclude: (1) multiple processors providing identical services and the use of space division multiplexing for the physical channels can provide better reliability than monolithic approaches (it also provides graceful degradation and low-cost load balancing); (2) coarse-grain parallelism supports running several transport protocols in parallel to provide different types of service (for example, one TCP handles small messages for many users, other TCP's running in parallel provide high bandwidth service to a single application); and (3) coarse grain parallelism will be able to incorporate many future improvements from related work (e.g., reduced data movement, fast TCP, fine-grain parallelism) also with near linear speed-ups.

Maly, K.↗
NASA NTRS1994

Progress in Computational Aeroelasticity Using High Fidelity Flow and Structural Equations on Parallel Computers

Aeroelasticity which involves strong coupling of fluids, structures and controls is an important element in designing an aircraft. Computational aeroelasticity using low fidelity methods such as the linear aerodynamic flow equations coupled with the modal structural equations are well advanced. Though these low fidelity approaches are computationally less intensive, they are not adequate for the analysis of modern aircraft such as High Speed Civil Transport (HSCT) and Advanced Subsonic Transport (AST) which can experience complex flow/structure interactions. HSCT can experience vortex induced aeroelastic oscillations whereas AST can experience transonic buffet associated structural oscillations. Both aircraft may experience a dip in the flutter speed at the transonic regime. For accurate aeroelastic computations at these complex fluid/structure interaction situations, high fidelity equations such as the Navier-Stokes for fluids and the finite-elements for structures are needed. Computations using these high fidelity equations require large computational resources both in memory and speed. Current conventional supercomputers have reached their limitations both in memory and speed. As a result, parallel computers have evolved to overcome the limitations of conventional computers. This paper will address the transition that is taking place in computational aeroelasticity from conventional computers to parallel computers. The paper will address special techniques needed to take advantage of the architecture of new parallel computers. Results will be illustrated from computations made on iPSC/860 and IBM SP2 computer by using ENASERO code that directly couples the Euler/Navier-Stokes flow equations with high resolution finite-element structural equations.

Guruswamy, Guru P.↗
NASA NTRS1994

Fluid/Structure Interaction Studies of Aircraft Using High Fidelity Equations on Parallel Computers

Abstract Aeroelasticity which involves strong coupling of fluids, structures and controls is an important element in designing an aircraft. Computational aeroelasticity using low fidelity methods such as the linear aerodynamic flow equations coupled with the modal structural equations are well advanced. Though these low fidelity approaches are computationally less intensive, they are not adequate for the analysis of modern aircraft such as High Speed Civil Transport (HSCT) and Advanced Subsonic Transport (AST) which can experience complex flow/structure interactions. HSCT can experience vortex induced aeroelastic oscillations whereas AST can experience transonic buffet associated structural oscillations. Both aircraft may experience a dip in the flutter speed at the transonic regime. For accurate aeroelastic computations at these complex fluid/structure interaction situations, high fidelity equations such as the Navier-Stokes for fluids and the finite-elements for structures are needed. Computations using these high fidelity equations require large computational resources both in memory and speed. Current conventional super computers have reached their limitations both in memory and speed. As a result, parallel computers have evolved to overcome the limitations of conventional computers. This paper will address the transition that is taking place in computational aeroelasticity from conventional computers to parallel computers. The paper will address special techniques needed to take advantage of the architecture of new parallel computers. Results will be illustrated from computations made on iPSC/860 and IBM SP2 computer by using ENSAERO code that directly couples the Euler/Navier-Stokes flow equations with high resolution finite-element structural equations.

Guruswamy, Guru↗
DOE OSTI2022

Dynamic Learning of Correlation Potentials for a Time-Dependent Kohn-Sham System

We develop methods to learn the correlation potential for a time-dependent Kohn-Sham (TDKS) system in one spatial dimension. We start from a low-dimensional two-electron system for which we can numerically solve the time-dependent Schr¨odinger equation; this yields electron densities suitable for training models of the correlation potential. We frame the learning problem as one of optimizing a least-squares objective subject to the constraint that the dynamics obey the TDKS equation. Applying adjoints, we develop efficient methods to compute gradients and thereby learn models of the correlation potential. Our results show that it is possible to learn values of the correlation potential such that the resulting electron densities match ground truth densities. We also show how to learn correlation potential functionals with memory, demonstrating one such model that yields reasonable results for trajectories outside the training set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Si/SiGe QuBus for single electron information-processing devices with memory and micron-scale connectivity function

The connectivity within single carrier information-processing devices requires transport and storage of single charge quanta. Single electrons have been adiabatically transported while confined to a moving quantum dot in short, all-electrical Si/SiGe shuttle device, called quantum bus (QuBus). Here we show a QuBus spanning a length of 10 μm and operated by only six simply-tunable voltage pulses. We introduce a characterization method, called shuttle-tomography, to benchmark the potential imperfections and local shuttle-fidelity of the QuBus. The fidelity of the single-electron shuttle across the full device and back (a total distance of 19 μm) is (99.7 ± 0.3) %. Using the QuBus, we position and detect up to 34 electrons and initialize a register of 34 quantum dots with arbitrarily chosen patterns of zero and single-electrons. The simple operation signals, compatibility with industry fabrication and low spin-environment-interaction in 28 Si/SiGe, promises long-range spin-conserving transport of spin qubits for quantum connectivity in quantum computing architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Microstructure and transformation behavior of Ni{sub 24.7}Ti{sub 50.3}Pd{sub 25} high temperature shape-memory alloy with Sc micro-addition

NiTiPd shape-memory alloys (SMAs) are potential functional materials for use as solid-state actuators in the temperature range 100–250 °C. The present study investigates the effect of 1.0 at.% Sc micro-addition to Ni{sub 24.7}Ti{sub 50.3}Pd{sub 25} alloy, Sc replacing either Ti or Ni. Results show that all the three alloys studied have stable transformation behavior on stress-free thermal cycling and hence, are suitable for cyclic actuation applications. However, the addition of Sc to NiTiPd alloy leads to decrease of transformation temperatures, the magnitude of decrease being greater for the alloy with Sc replacing Ni. The martensite finish (M{sub f}) temperature of 181 °C for the NiTiPd alloy decreased to 139 °C for Sc replacing Ti and 83 °C for Sc replacing Ni. Also, the indentation modulus of NiTiPdSc (Sc replacing Ni) alloy is found to be significantly low compared to the other alloys. Analysis indicates that the observed differences in the alloy properties are related to the solubility of Sc in the NiTiPd matrix. While the quaternary NiTiPdSc alloy, Sc replacing Ti, has a single phase microstructure, the alloy with Sc replacing Ni shows the presence of Sc-rich and TiPd-type second phases in the microstructure. TEM examination revealed that the TiPd-type phase has a distinct rod-like morphology (30–50 nm) arranged in a grid-like structure. The transformation and indentation behavior of the alloys is elucidated using thermodynamic calculations of frictional energy and an electronic structure based analysis. - Highlights: • TEM of Ni{sub 23.7}Ti{sub 50.3}Pd{sub 25}Sc{sub 1} showed distinct grid of TiPd-type phase nanorods < 50 nm. • Stress-free thermal cycling of all the three alloys showed stable transformation behavior. • Ni{sub 24.7}Ti{sub 49.3}Pd{sub 25}Sc{sub 1} and Ni{sub 23.7}Ti{sub 50.3}Pd{sub 25}Sc{sub 1} showed single and multiphase structures. • Sc micro-addition (1 at.%) to Ni{sub 24.7}Ti{sub 50.3}Pd{sub 25} alloy decreased TTs significantly. • Ni{sub 23.7}Ti{sub 50.3}Pd{sub 25}Sc{sub 1} exhibited lower modulus of 67 GPa to 85 GPa of Ni{sub 24.7}Ti{sub 50.3}Pd{sub 25}.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Comparison of the Photodesorption Activities of cis-Butene, trans-Butene and Isobutene on the Rutile TiO 2 (110) Surface

The chemical and photochemical properties of three butene molecules (cis-butene, trans-butene and isobutene) were explored on the clean rutile TiO 2 (110) surface using temperature programmed desorption (TPD) and photon simulated desorption (PSD). At the low coverage limit, trans-butene was the most strongly bound butene on the TiO 2 (110) surface, desorbing at ~ 210 K, however increased intermolecular repulsions between trans-butene molecules at higher coverage diminished its binding. Both cis-butene and isobutene saturated the first layer on TiO 2 (110) at a coverage of ~0.50 ML in a single TPD feature at 184 and 192 K, respectively. In contrast, the maximum coverage that trans-butene could achieve in its 210 K peak was ~1/3 ML, with higher coverages resulting a low temperature desorption at ~137 K. Coverages of these molecules above 0.50 ML resulted in population of second layer and multilayer states. The instability of trans-butene at a coverage of 0.5 ML on the surface was linked to the inversion center in its symmetry. In the absence of coadsorbed oxygen, the primary photochemical pathway of each butene molecule on TiO 2 (110) was photodesorption. The photoactivities of these molecules on TiO 2 (110) at an initial coverage of 0.50 ML followed the trend: isobutene > cis-butene > trans-butene. In contrast, the photoactivities of low coverages of cis-butene and trans-butene exceeded those measured at 0.50 ML. These data suggest that intermolecular interactions (repulsions) play a significant role in diminishing the photoactivities of weakly bound molecules on TiO 2 photocatalysts. Work reported here was supported by the U.S. Department of Energy, Office of Basic Energy Science, Division of Chemical Sciences, Geosciences, and Biosciences, and performed in the Williams R. Wiley Environmental Molecular Science Laboratory (EMSL), a Department of Energy user facility funded by the Office of Biological and Environmental Research. Pacific Northwest National Laboratory is a multiprogram national laboratory operated for the U.S. Department of Energy by the Battelle Memorial Institute under contract DEAC05-76RL01830.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ~ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ~ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Novel symmetry-preserving neural network model for phylogenetic inference

Abstract Motivation Scientists world-wide are putting together massive efforts to understand how the biodiversity that we see on Earth evolved from single-cell organisms at the origin of life and this diversification process is represented through the Tree of Life. Low sampling rates and high heterogeneity in the rate of evolution across sites and lineages produce a phenomenon denoted “long branch attraction” (LBA) in which long nonsister lineages are estimated to be sisters regardless of their true evolutionary relationship. LBA has been a pervasive problem in phylogenetic inference affecting different types of methodologies from distance-based to likelihood-based. Results Here, we present a novel neural network model that outperforms standard phylogenetic methods and other neural network implementations under LBA settings. Furthermore, unlike existing neural network models in phylogenetics, our model naturally accounts for the tree isomorphisms via permutation invariant functions which ultimately result in lower memory and allows the seamless extension to larger trees. Availability and implementation We implement our novel theory on an open-source publicly available GitHub repository: https://github.com/crsl4/nn-phylogenetics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

HEU Metal Delayed Critical Experiments with 10 to 19 Inch Thick Graphite Reflectors

Approximately 100 graphite-reflected highly enriched uranium (HEU, 93.14 wt % 235 U) metal annular and cylindrical critical experiments were performed in the early 1960s at the Oak Ridge Critical Experiments Facility (ORCEF). This report presents details from experiment logbooks, experimental data sheets and the author's memory for 44 HEU metal (93.14 wt % 235 U) critical assemblies with graphite reflectors varying from 10 to 19 in. thick, outside diameters varying from 7 to 15 in., inside diameters varying from 7 to 13 in. and critical HEU metal masses varying from 20.4 to 69.0 kg. The data from the 44 experiments described in this report are acceptable for use as criticality safety benchmark experiments for the International Criticality Safety Evaluation Program (ICSBEP) once the uncertainty analysis on the measured k eff is completed. Based on previous ICSBEP benchmarks with this HEU metal at ORCEF, the uncertainties in the measured k eff are expected to be as low as ±0.0004. Preparation of this report is part of an effort at Oak Ridge National Laboratory (ORNL) to document more than 15 undocumented series of critical and subcritical experiments enumerated in Critical and Subcritical NEA Benchmark Possibilities for Measurements at ORCEF and Other US DOE Facilities (Mihalzo, ORNL/TM-2019/1188, 2019) and performed by ORNL at ORCEF and other US Department of Energy critical experiments facilities. More than 500 operational days of critical facility time were used, not including setup and dismantlement time. This documentation for a part of one series of graphite reflected highly enriched uranium metal critical experiments, that used 50 operational days of ORCEF time, was performed using funding received from the DOE Office of Nuclear Energy’s Nuclear Energy University Programs at the University of Tennessee Nuclear Engineering Department. This documentation was also supported by the Nuclear Criticality, Radiation Transport, and Safety programs at ORNL.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2011

MOSFET Switching Circuit Protects Shape Memory Alloy Actuators

A small-footprint, full surface-mount-component printed circuit board employs MOSFET (metal-oxide-semiconductor field-effect transistor) power switches to switch high currents from any input power supply from 3 to 30 V. High-force shape memory alloy (SMA) actuators generally require high current (up to 9 A at 28 V) to actuate. SMA wires (the driving element of the actuators) can be quickly overheated if power is not removed at the end of stroke, which can damage the wires. The new analog driver prevents overheating of the SMA wires in an actuator by momentarily removing power when the end limit switch is closed, thereby allowing complex control schemes to be adopted without concern for overheating. Either an integral pushbutton or microprocessor-controlled gate or control line inputs switch current to the actuator until the end switch line goes from logic high to logic low state. Power is then momentarily removed (switched off by the MOSFET). The analog driver is suited to use with nearly any SMA actuator.

Gummin, Mark A.↗
DOE OSTI2015

Impact of plasma jet vacuum ultraviolet radiation on reactive oxygen species generation in bio-relevant liquids

Plasma medicine utilizes the combined interaction of plasma produced reactive components. These are reactive atoms, molecules, ions, metastable species, and radiation. Here, ultraviolet (UV, 100–400 nm) and, in particular, vacuum ultraviolet (VUV, 10–200 nm) radiation generated by an atmospheric pressure argon plasma jet were investigated regarding plasma emission, absorption in a humidified atmosphere and in solutions relevant for plasma medicine. The energy absorption was obtained for simple solutions like distilled water (dH{sub 2}O) or ultrapure water and sodium chloride (NaCl) solution as well as for more complex ones, for example, Rosewell Park Memorial Institute (RPMI 1640) cell culture media. As moderate stable reactive oxygen species, hydrogen peroxide (H{sub 2}O{sub 2}) was studied. Highly reactive oxygen radicals, namely, superoxide anion (O{sub 2}{sup •−}) and hydroxyl radicals ({sup •}OH), were investigated by the use of electron paramagnetic resonance spectroscopy. All species amounts were detected for three different treatment cases: Plasma jet generated VUV and UV radiation, plasma jet generated UV radiation without VUV part, and complete plasma jet including all reactive components additionally to VUV and UV radiation. It was found that a considerable amount of radicals are generated by the plasma generated photoemission. From the experiments, estimation on the low hazard potential of plasma generated VUV radiation is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Experimental study of the processes accompanying argon breakdown in a long discharge tube at a reduced pressure

Results are presented from experimental studies of the breakdown stage of a low-pressure discharge (1 and 5 Torr) in a glass tube the length of which (75 cm) is much larger than its diameter (2.8 cm). Breakdowns occurred under the action of positive voltage pulses with an amplitude of up to 9.4 kV and a characteristic rise time of 2–50 μs. The discharge current in the steady-state mode was 10–120 mA. The electrode voltage, discharge current, and radiation from the discharge gap were detected simultaneously. The dynamic breakdown voltage was measured, the prebreakdown ionization wave was recorded, and its velocity was determined. The dependence of the discharge parameters on the time interval between voltage pulses (the socalled “memory effect”) was analyzed. The memory effect manifests itself in a decrease or an increase in the breakdown voltage and a substantial decrease in its statistical scatter. The time interval between pulses in this case can reach 0.5 s. The effect of illumination of the discharge tube with a light source on the breakdown was studied. It is found that the irradiation of the anode region of the tube by radiation with wavelengths of ≤500 nm substantially reduces the dynamic breakdown voltage. Qualitative explanations of the obtained results are offered.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

An Efficient Multicore Implementation of a Novel HSS-Structured Multifrontal Solver Using Randomized Sampling

Here, we present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factoriz ation leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite. The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK - STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2022

Can Machine Learning Reveal Precursors of Reversals of the Geomagnetic Axial Dipole Field?

It is well known that the axial dipole part of Earth’s magnetic field reverses polarity, so that the magnetic North Pole becomes the South Pole and vice versa. The timing of reversals is well documented for the past 160 Myr, but the conditions that lead to a reversal are still not well understood. It is not known if there are reliable ‘precursors’ of reversals (events that indicate that a reversal is upcoming) or what they might be. We investigate if machine learning (ML) techniques can reliably identify precursors of reversals based on time-series of the axial magnetic dipole field. The basic idea is to train a classifier using segments of time-series of the axial magnetic dipole. This training step requires modification of standard ML techniques to account for the fact that we are interested in rare events—a reversal is unusual, while a non-reversing field is the norm. Without our tweak, the ML classifiers lead to useless predictions. Perhaps even more importantly, the usable observational record is limited to 0–2 Ma and contains only five reversals, necessitating that we determine if the data are even sufficient to reliably train and validate an ML algorithm. To answer these questions we use several ML classifiers (linear/non-linear support vector machines and long short-term memory networks), invoke a hierarchy of numerical models (from simplified models to 3-D geodynamo simulations), and two palaeomagnetic reconstructions (PADM2M and Sint-2000). The performance of the ML classifiers varies across the models and the observational record and we provide evidence that this is not an artefact of the numerics, but rather reflects how ‘predictable’ a model or observational record is. Studying models of Earth’s magnetic field via ML classifiers thus can help with identifying shortcomings or advantages of the various models. For Earth’s magnetic field, we conclude that the ability of ML to identify precursors of reversals is limited, largely due to the small amount and low frequency resolution of data, which makes training and subsequent validation nearly impossible. Put simply: the ML techniques we tried are not currently capable of reliably identifying an axial dipole moment (ADM) precursor for geomagnetic reversals. This does not necessarily imply that such a precursor does not exist, and improvements in temporal resolution and length of ADM records may well offer better prospects in the future.

Kyle Gwirtz↗
DOE OSTI2025

Neural correspondence to spectrum of environmental uncertainty in multiple-cue probability judgment system with time delay

Despite state-of-the-art technologies like artificial intelligence, human judgment is critically essential in cooperative systems, such as the multi-agent system (MAS), which collect information among agents based on multiple-cue judgment. Human agents can prevent impaired situational awareness of automated agents by confirming situations under environmental uncertainty. System error caused by uncertainty can result in an unreliable system environment, and this environment affects the human agent, resulting in non-optimal decision-making in MAS. Thus, it is necessary to know how human behavior is changed to capture system reliability under uncertainty. Another issue affecting MAS is time delay, which can delay agent information transfer, resulting in low performance and instability. However, it is difficult to find studies on the influence of time delay on human agents. This study is about understanding the human decision-making process under a specific system reliability environment by uncertainty with time delay. We used concepts of expected and unexpected uncertainty to implement reliability of the system usage environment with three types of time delay conditions: no time delay, regular time delay, and irregular time delay conditions. We used electroencephalogram (EEG) for human cognitive neural mechanisms in multiple-cue judgment systems to understand human decision-making. In the reliability of system usage environment, the unreliable system environment significantly creates less memory load by less utilization of system rules for decision-making. In terms of time delay, delayed information delivery does not significantly affect memory load for decision-making.

cognitive process↗
DOE OSTI2022

Feasibility Study of Millimeter Wave Radars For Safeguards Applications

Containment and surveillance are fundamental measures in nuclear safeguards. Techniques such as video surveillance and laser curtain for containment provide effective monitoring in areas where maintaining continuity of knowledge is required. These systems, however, can be susceptible to loss of monitoring capabilities under certain environmental conditions such as poor visibility (i.e. low light conditions, smoke, fog, etc.) or extended power loss past the duration that the backup power system is designed for. Brookhaven National Laboratory has been investigating the feasibility of millimeter waves (mmWave) as a new perimeter seal in which radio frequency waves in the range of 60-64 GHz are used to detect and monitor objects of interest. Signals in this frequency range are not susceptible to environmental conditions. For proof-of-concept tests, mmWave sensors from Texas Instruments (TI), specificallyIWR6843, are used in a test bed at BNL's Waste Management facility to simulate the operations at nuclear facilities. The unique design of TI mmWave sensors requires less memory and power consumption compared to counterpart systems. These devices are capable of exporting 3D point-cloud data, which is visualized graphically and compared to videos recorded at the same time to validate the performance of the mmWave sensor. A set of experiments were planned to test the feasibility of the mmWave in this application, including monitoring static containers in a storage area and detecting intrusions at the boundaries of the area. In addition, the experiments also identify potential blind spots relative to sensor position and utilize multiple operating sensors simultaneously to reduce or eliminate such blind spots. The optimal positioning of multiple sensors was determined for the experimental room configuration. In this paper, we will discuss the details of this novel perimeter sealing concept and present the test results.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗