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At least 361 records · Page 20

64x64 Analog Input Array for 3-Dimensional Neural Network Processor

In pattern recognition and classification for spatio-temporal problems, one of the most challenging tasks is to provide a good and valid solution in real-time. Because of time constraints, software-based neural network approaches may not be suitable for practical use. Hardware solutions seem to be good candidates for this class of problems. Currently, the Three Dimensional Analog Neural Network (3-DANN) is an effective approach to solving spatio-temporal problems in three-dimensional hardware.

spatio-temporal 3-DANN Three Dimensional Analog Ne↗

A new APDS approach - alarm processing based on alarm prioritization - 201

We take for granted that the advances in technology bring improvements to the conditions in which humans perform activities associated to them. Is that the case for digital alarm systems in nuclear power plants? The answer is not that simple. A review of the related literature, shows that still, the single most significant problem with alarm systems, is the large number of alarm messages presented to the operator at once. This is an issue that affects analog I and C control rooms with conventional annunciators and gets aggravated in digital I and C based control rooms. Digital alarm systems present the alarms in the form of lists in computer screens. This kind of presentation forces the operator to scroll or navigate pages to review the active alarms. Also, the alarm lists do not allow the operator to apply the pattern recognition practices acquired during training when using with fixed position alarm systems. An Alarm Processing and Diagnostic System (APDS) facilitates the alarm monitoring task by applying alarm reduction techniques based on alarm priorities. It allows the operators to focus on the most important alarms during plant upsets. A new generation of APDS should be based in an improved alarm analysis methodology. To develop such methodology, Tecnatom reviewed applicable standards and our own alarm analysis experiences. This paper documents the process and conclusions of the methodology development. (authors)

42 ENGINEERING↗

Welding Defect Classification from Simulated Ultrasonic Signals

Nondestructive testing is widely used to detect and to size up discontinuities embedded in a material. Among the several ultrasonic techniques, time of flight diffraction (TOFD) combines high speed inspection, high sizing reliability and low rate of incorrect results. However, the classification of defects through ultrasound signals acquired by the TOFD technique depends heavily on the knowledge and experience of the operator and thus, this classification is still frequently questioned. Besides, this task requires long processing time due to the large amount of data to be analyzed. Nevertheless, computational tools for pattern recognition can be employed to analyze a high amount of data with large efficiency. In the present work, simulation of ultrasound propagation in two-dimensional media containing, each one, different kinds of modeled discontinuities which mimic defects in welded joints were performed. Clustering (k-means) and classification (principal component analysis and k-nearest neighbors) algorithms were employed to associate each simulated ultrasound signal with its corresponding modeled defects. The results for each method were analyzed, discussed and compared. The results are very promising.

42 ENGINEERING↗

Both RIG-I and MDA5 detect alphavirus replication in concentration-dependent mode

Alphaviruses are a family of positive-strand RNA viruses that circulate on all continents between mosquito vectors and vertebrate hosts. Despite a significant public health threat, their biology is not sufficiently investigated, and the mechanisms of alphavirus replication and virus–host interaction are insufficiently understood. In this study, we have applied a variety of experimental systems to further understand the mechanism by which infected cells detect replicating alphaviruses. Our new data strongly suggest that activation of the antiviral response by alphavirus-infected cells is determined by the integrity of viral genes encoding proteins with nuclear functions, and by the presence of two cellular pattern recognition receptors (PRRs), RIG-I and MDA5. No type I IFN response is induced in their absence. The presence of either of these PRRs is sufficient for detecting virus replication. However, type I IFN activation in response to pathogenic alphaviruses depends on the basal levels of RIG-I or MDA5. - Highlights: • Both RIG-I and MDA5 detect alphavirus replication. • Alphavirus-induced transcriptional shutoff affects type I IFN induction. • Sensing of alphavirus replication by RIG-I and MDA5 depends on their concentrations. • High basal level of RIG-I and MDA5 allows IFN induction by pathogenic alphaviruses. • This dependence determines the discrepancy between the in vivo and in vitro data.

60 APPLIED LIFE SCIENCES↗

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, we cast the clustering problem into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We constructed an appropriate objective function whose minimum will characterize a good possibilistic partition of the data, and we derived the membership and prototype update equations from necessary conditions for minimization of our criterion function. In this paper, we show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Keller, James M.↗

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, the clustering problem was cast into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data was constructed, and the membership and prototype update equations from necessary conditions for minimization of our criterion function were derived. The ability of this approach to detect linear and quartic curves in the presence of considerable noise is shown.

Keller, James M.↗

Staphylococcal enterotoxin C2 stimulated the maturation of bone marrow derived dendritic cells via TLR-NFκB signaling pathway

Highlights: • SEC2 stimulate the maturation of BMDCs. • SEC2 induce the production of pro-inflammation cytokines in BMDCs. • SEC2 activate NF-κB signaling pathway in BMDCs. As a kind of superantigen, staphylococcal enterotoxin C2 (SEC2) is well known as a powerful immunomodulator. However, most previous studies about SEC2 focus on its T cell activating characters. But the direct effect of SEC2 on antigen-presenting cells (APCs) which are important for the T cell activation is not clearly. In this study, we investigated the effect of SEC2 on murine bone marrow-derived dendritic cells (BMDCs) which are known as the specialized professional APCs. Contrary to its effects on T cells, SEC2 could not induce proliferation or cytotoxicity to BMDCs even in high concentrations. While SEC2 could promote the mature of BMDCs with increased expression of co-stimulatory molecules on cell membrane such as CD80, CD86, and MHC II. The production of pro-inflammatory cytokines such as TNF-α, IFN-γ and IL-6 were also increased in BMDCs treated with SEC2. We also found that SEC2 enhanced the genes expression of pattern recognition receptors including toll-like receptors 2 (TLR2) and TLR4 in BMDCs, and up-regulated the key signal molecule MyD88 in both mRNA and protein levels. In addition, SEC2 also caused IκBα degradating and NFκB p65 translocating from the cytoplasm to the nucleus in BMDCs. The siRNAs for both TLR2 and TLR4, as well as NFκB specific inhibitor BAY 11–7085 could inhibit the co-stimulatory molecules expression and pro-inflammatory cytokines releasing induced by SEC2. Moreover, TLR2/4 specific siRNAs inhibited p65 and MyD88 upregulation induced by SEC2. In summary, all our results indicated that SEC2 could stimulate BMDCs maturation through TLR-NFκB signaling pathways.

60 APPLIED LIFE SCIENCES↗

Charged particle track reconstruction with SπRIT Time Projection Chamber

In this work, we present a software framework, SπRITROOT, which is capable of track reconstruction and analysis of heavy-ion collision events recorded with the SRIT time projection chamber. The track-fitting toolkit GENFIT and the vertex reconstruction toolkit RAVE are applied to a box-type detector system. A pattern recognition algorithm which performs helix track finding and handles overlapping pulses is described. The performance of the software is investigated using experimental data obtained at the Radioactive Isotope Beam Facility (RIBF) at RIKEN. This work focuses on data from 132 Sn + 124 Sn collision events with beam energy of 270 AMeV. Particle identification is established using < dE/dx >and magnetic rigidity, with pions, hydrogen isotopes, and helium isotopes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.

97 MATHEMATICS AND COMPUTING↗

Bacterial cGAS-like enzymes synthesize diverse nucleotide signals

Cyclic dinucleotides (CDNs) have central roles in bacterial homeostasis and virulence by acting as nucleotide second messengers. Bacterial CDNs also elicit immune responses during infection when they are detected by pattern-recognition receptors in animal cells. Here we perform a systematic biochemical screen for bacterial signalling nucleotides and discover a large family of cGAS/DncV-like nucleotidyltransferases (CD-NTases) that use both purine and pyrimidine nucleotides to synthesize a diverse range of CDNs. Here, a series of crystal structures establish CD-NTases as a structurally conserved family and reveal key contacts in the enzyme active-site lid that direct purine or pyrimidine selection. CD-NTase products are not restricted to CDNs and also include an unexpected class of cyclic trinucleotide compounds. Biochemical and cellular analyses of CD-NTase signalling nucleotides demonstrate that these cyclic di- and trinucleotides activate distinct host receptors and thus may modulate the interaction of both pathogens and commensal microbiota with their animal and plant hosts.

59 BASIC BIOLOGICAL SCIENCES↗

The Center for Momentum Transport and Flow Organization in Plasmas - Final Scientific Report

Overview of University of Colorado Efforts: The University of Colorado group has focused on two primary fronts during the grant period: development of a variety of multi-point diagnostic and/or imaging analysis techniques, and momentum-transport related experiments on a variety of devices (NSTX at PPPL, CSDX at UCSD, LAPD at UCLA, DIII-D at GA). Experimental work has taken advantage of several diagnostic instruments, including fast-framing cameras for imaging of electron density fluctuations (either directly or using injected gas puffs), ECEI for imaging of electron temperature fluctuations, and multi-tipped Langmuir and magnetic probes for corroborating measurements of Reynolds and Maxwell stresses. Mode Characterization in CSDX: We have performed a series of experiments at the CSDX linear device at UCSD, in collaboration with Center PI G. Tynan's group. The experiments included a detailed study of velocity estimation techniques, including direct comparisons between Langmuir probes and image-based velocimetry from fast-framing camera data. We used the camera data in a second set of studies to identify the spatial and spectral structure of coherent modes, which illuminates wave behavior to a level of detail previously unavailable, and enables direct comparison of dispersion curves to theoretical estimates. In another CSDX study, similar techniques were used to demonstrate a controlled transition from nonlinearly coupled discrete eigenmodes to fully developed broadband turbulence. The axial magnetic field was varied from 40-240 mT, which drove the transition. At low magnetic fields, the plasma is dominated by drift waves. As the magnetic field is increased, a strong potential gradient at the edge introduces an ExB shear-driven instability. At the transition, another mode with signatures of a rotation-induced Rayleigh–Taylor instability appears at the central plasma region. Concurrently, large axial velocities were found in the plasma core. For larger magnetic fields, all of the instabilities co-exist, leading to rich plasma dynamics and fully developed broadband turbulence. Edge-Turbulence and Flow Experiments in NSTX: A series of Gas Puff Imaging (GPI) observations on NSTX revealed a quasi-periodic oscillation in the plasma edge preceding the L-H transition in a limited set of neutral beam heated plasmas. These ~3 kHz flow oscillations exhibit both long wavelength and long correlation lengths, suggesting they are zonal-flow-like. The flow oscillations are strongly correlated with modulations of the level of edge turbulence, thus the system appears to undergo a predator--prey-type limit-cycle preceding the L-H transition. However, a clear trigger for the L-H transition was not observed. Reynolds stress profiles were obtained directly from image velocimetry for L-mode periods ELM-Precursor Studies in NSTX: A separate study based on NSTX-GPI data captured the two-dimensional evolution of edge-localized mode (ELM) precursors. Precursor events were observed preceding ELMs and ELM-induced H–L back-transitions in radio-frequency heated H-mode plasmas, and the growth of the precursor mode through the ELM filamentation was imaged in the plane perpendicular to the local B-field. Strong edge intensity modulations appeared to propagate in the electron diamagnetic direction while steadily drifting radially outwards. Intensity fluctuations were observed at frequencies around 20 kHz and wavenumbers of 0.05-0.2 cm -1 . Upon growing to a trigger point, precursor fluctuations were seen to form filamentary structures and move into the scrape-off layer (SOL) explosively with radial velocities peaking at 8 km/s. Once in the SOL, filaments reverse their propagation direction and travel in the ion diamagnetic direction. Edge intensity fluctuations were strongly correlated with magnetic signals from Mirnov coils, and toroidally distributed coils estimated toroidal mode numbers of n=5-10. Quantitatively similar precursors have been observed in ohmic H-mode plasmas as well, though significantly fewer events are seen in the ohmic cases and none were observed in the near-threshold NBI H-modes studied. Development of Velocimetry and Image Analysis Techniques: Along with the experiments listed above, the Colorado group has continued to explore various velocimetry techniques and their range of validity. We have developed a “linear optical flow” code, which calculates smooth velocity maps while accurately assessing local regions of high curl. This is critical for separating spatial scales of velocity behavior, and thus transport. This code has recently come on-line, and we are currently using it to revisit a number of older datasets. Additionally, we have worked on developing pattern-recognition techniques for imaging diagnostics, based on established digital image compression algorithms. This has the potential to open the analysis of turbulent plasma behavior beyond the well-trodden Fourier and wavelet approaches. Finally, we have extended several of these image-analysis routines to multiple other diagnostic sets, including GPI datasets in NSTX and ECEI imaging on DIII-D. In one study, correlation maps were used to measure the full 2-D mode structure of drift-wave level structures using ECEI for the first time. This enabled direct comparison to gyrokinetic simulations using the GEM code, from which is was determined that TEM modes were being measured in DIII-D.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Neutrino event selection in the MicroBooNE liquid argon time projection chamber using Wire-Cell 3D imaging, clustering, and charge-light matching

An accurate and efficient event reconstruction is required to realize the full scientific capability of liquid argon time projection chambers (LArTPCs). The current and future neutrino experiments that rely on massive LArTPCs create a need for new ideas and reconstruction approaches. Wire-Cell, proposed in recent years, is a novel tomographic event reconstruction method for LArTPCs. The Wire-Cell 3D imaging approach capitalizes on charge, sparsity, time, and geometry information to reconstruct a topology-agnostic 3D image of the ionization electrons prior to pattern recognition. A second novel method, the many-to-many charge-light matching, then pairs the TPC charge activity to the detected scintillation light signal, thus enabling a powerful rejection of cosmic-ray muons in the MicroBooNE detector. A robust processing of the scintillation light signal and an appropriate clustering of the reconstructed 3D image are fundamental to this technique. In this paper, we describe the principles and algorithms of these techniques and their successful application in the MicroBooNE experiment. A quantitative evaluation of the performance of these techniques is presented. Using these techniques, a 95% efficient pre-selection of neutrino charged-current events is achieved with a 30-fold reduction of non-beam-coincident cosmic-ray muons, and about 80% of the selected neutrino charged-current events are reconstructed with at least 70% completeness and 80% purity.

3D imaging↗

TU-F-CAMPUS-T-05: A Cloud-Based Monte Carlo Dose Calculation for Electron Cutout Factors

Purpose: For electron cutouts of smaller sizes, it is necessary to verify electron cutout factors due to perturbations in electron scattering. Often, this requires a physical measurement using a small ion chamber, diode, or film. The purpose of this study is to develop a fast Monte Carlo based dose calculation framework that requires only a smart phone photograph of the cutout and specification of the SSD and energy to determine the electron cutout factor, with the ultimate goal of making this cloud-based calculation widely available to the medical physics community. Methods: The algorithm uses a pattern recognition technique to identify the corners of the cutout in the photograph as shown in Figure 1. It then corrects for variations in perspective, scaling, and translation of the photograph introduced by the user’s positioning of the camera. Blob detection is used to identify the portions of the cutout which comprise the aperture and the portions which are cutout material. This information is then used define physical densities of the voxels used in the Monte Carlo dose calculation algorithm as shown in Figure 2, and select a particle source from a pre-computed library of phase-spaces scored above the cutout. The electron cutout factor is obtained by taking a ratio of the maximum dose delivered with the cutout in place to the dose delivered under calibration/reference conditions. Results: The algorithm has been shown to successfully identify all necessary features of the electron cutout to perform the calculation. Subsequent testing will be performed to compare the Monte Carlo results with a physical measurement. Conclusion: A simple, cloud-based method of calculating electron cutout factors could eliminate the need for physical measurements and substantially reduce the time required to properly assure accurate dose delivery.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

MicroRNA-29a mitigation of toll-like receptor 2 and 4 signaling and alleviation of obstructive jaundice-induced fibrosis in mice

Highlights: • miR-29a overexpression in cholestatic mice obstructed TLR2 and TLR4 signaling in liver tissues. • miR-29a overexpression in cholestatic mice decreased IL-1β, MCP-1, TGF-β, TNF-α, as well as HMGB1, and BRD4 expression. • Overexpression of miR-29a downregulated α-SMA, TLR2, TLR4 and BRD4 expression in HSCs. Cholestasis and hepatitis can cause continuous liver damage that may ultimately result in liver fibrosis. In a previous study, we demonstrated that microRNA-29a (miR-29a) protects against liver fibrosis. Toll-like receptor 2 (TLR2) and TLR4 are pattern recognition receptors of bacterial lipoprotein and lipopolysaccharide, both of which participate in activating hepatic stellate cells and liver fibrosis. The purpose of this study is to characterize the biological influence of miR-29a on TLR2 and TLR4 signaling in livers injured with bile duct ligation (BDL). We performed BDL on both miR-29a transgenic mice (miR-29aTg) and wild-type mice to induce cholestatic liver injury. Primary HSCs were transfected with a miR-29a mimic and inhibitor. In the wild-type mice, the BDL demonstrated significant α-smooth muscle actin fibrotic matrix formation and hepatic high mobility group box-1 expression. However, in the miR-29aTg mice, these factors were significantly reduced. Furthermore, miR-29a overexpression reduced the BDL exaggeration of TLR2, TLR4, MyD88, bromodomain-containing protein 4 (BRD4), phospho-p65 as well as proinflammatory cytokines, IL-1β, MCP-1, TGF-β, and TNF-α. In vitro, miR-29a mimic transfection reduced α-SMA, BRD4,TLR2, and TLR4 expressions in HSCs. This study provides new molecular insight into the ability of miR-29a to inhibit TLR2 and TLR4 signaling, which thus slows the progression of cholestatic liver deterioration.

60 APPLIED LIFE SCIENCES↗

Activation of TLR3 and its adaptor TICAM-1 increases miR-21 levels in extracellular vesicles released from human cells

Highlights: • Activation of TLR3 increases miR-21 levels in extracellular vesicles. • TLR3 requires TICAM-1 adaptor to increase miR-21 levels in extracellular vesicles. • YBX-1 is involved in TLR3-mediated increase of miR-21 levels. Pattern-recognition receptors (PRRs) recognizes viral RNAs and trigger the innate immune responses. Toll-like receptor 3 (TLR3), a PRR, recognizes viral double-stranded RNA (dsRNA) in endolysosomes, whereas cytoplasmic dsRNA is sensed by another PRR, MDA5. TLR3 and MDA5 utilize TICAM-1 and MAVS, respectively, to trigger the signal for inducing innate immune responses. Extracellular vesicles (EVs) include the exosomes and microvesicles; an accumulating body of evidence has shown that EVs delivers functional RNA, such as microRNAs (miRNAs), to other cells and thus mediate intercellular communications. Therefore, EVs carrying miRNAs affect innate immune responses in macrophages and dendritic cells. However, the mechanism underlying the regulation of miRNA levels in EVs remains unclear. To elucidate the mechanism, we sought to reveal the pathway that control miRNA expression levels in EVs. Here, we found that TLR3 stimulation increased miR-21 levels in EVs released from various types of human cells. Ectopic expression of the TLR3 adaptor, TICAM-1, increased miR-21 levels in EVs but not intracellular miR-21 levels, suggesting that TICAM-1 augmented sorting of miR-21 to EVs. In contrast, the MDA5 adaptor, MAVS, did not increase miR-21 levels in EVs. The siRNA for TICAM-1 reduced EV miR-21 levels after stimulation of TLR3. Collectively, our data indicate a novel role of the TLR3-TICAM-1 pathway in controlling miR-21 levels in EVs.

60 APPLIED LIFE SCIENCES↗

Biophysical characterization of Ca{sup 2+}-binding of S100A5 and Ca{sup 2+}-induced interaction with RAGE

S100A5 is a calcium-binding protein of S100 family, which represents a major ligand to the receptor for advanced glycation end product (RAGE), a pattern recognition receptor engaged in diverse pathological processes. Here we have characterized calcium binding of S100A5 and the complex formation between S100A5 and RAGE using calorimetry and NMR spectroscopy. S100A5 binds to calcium ions in a sequential manner with the equilibrium dissociation constants (K{sub D}) of 1.3 μM and 3.5 μM, which corresponds to the calcium-binding at the C-terminal and N-terminal EF-hands. Upon calcium binding, S100A5 interacts with the V domain of RAGE (RAGE-v) to form a heterotrimer (K{sub D} ∼5.9 μM) that is distinct among the S100 family proteins. Chemical shift perturbation data from NMR titration experiments indicates that S100A5 employs the periphery of the dimer interface to interact with RAGE-v. Distinct binding mode and stoichiometry of RAGE against different S100 family proteins could be important to modulate diverse RAGE signaling. - Highlights: • Thermodynamic of sequential Ca{sup 2+}-binding to S100A5 is determined quantitatively. • S100A5 binds to RAGE-v to form a distinct dynamic heterotrimer with K{sub D} ∼5.9 μM. • RAGE-v binds to the rim of the S100A5 dimer interface via hydrophobic interaction. • Different binding modes of S100 proteins to RAGE may modulate RAGE signaling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Operating displays - new concept: high performance displays - 118

New control rooms in Nuclear Power Plants using digital Distributed Control System (DCS) operated with software displays, are providing thousands of signals to the operating crew. When an event happens, the Flood of information provided to operators could obscure the initiator event increasing the operator response time, work load and complicating the selection of the actions to be performed. There are some initiatives that are trying to minimize the 'avalanche effect' as the alarm engineering filtering and prioritization or diagnostic displays. Likewise, new control rooms are lost the availability to know the plant status just with a quick glance to the panel indicators. Regarding Small Modular Reactors, where in some cases four operators should monitor and control twelve reactors, these problems are even more significant. The challenge is to find a solution, based on current digital systems, that supply information to operators in order to prevent events and take early actions to minimize or avoid any unexpected incident. In order to minimize human errors, Tecnatom has developed a specific set of displays, called 'High Performance Displays' (HPD) based on objectives, work environment limitation, and non-anticipated events. These displays are being design following the HFE standards: applying the NUREG-0711 elements to identify the information that they have to content and the NUREG-0700 to design the interface. The function of these displays is monitoring the plant situation in a way that the operators are able to recognize any potential event at a simple glance but controlling thousands of signals through pattern recognition, symmetries, regular polygons and colors. The objective of the HPD is to provide an integrated function minimizing the tunnel effect and reduce the workload during the diagnosis phase. Moreover, HPD enhance operators to take the right actions to anticipate the response to any event, even avoiding it, if possible This paper analyzes how HPD's have been design as well as presents the results of a preliminary validation of their capabilities. The preliminary validation is a reduce validation in a full scope simulator running two representative scenarios of abnormal and emergency operation. The analysis is focused on the first stage of the event, i.e. the detection phase, HPD's have been installed in a generic full scope simulator of a nuclear power plant, digital controlled. Both scenarios have been performed by two different operator crews. The first operator shift has worked with the conventional human machine interface, that is, displays similar to the ones currently installed in existing nuclear power plant with digital control. The second crew, reproduce the same scenarios but with HPD available. The studio collects preliminary data in order to compare results, draw lesson learned from the experience to improve HPD design, and find solutions in terms of reducing human errors, human workload, and increase the availability of the plant. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A Study of Physics-Informed Deep Learning for System Fluid Dynamics Closures

The sub-grid-scale (SGS) physics models, or so-called closure relations (CR), are essential in thermal-fluid modeling and simulation codes, ranging from large-eddy simulation to system-level thermo-fluid dynamic problems. While details vary, all thermo-fluid codes solve coupled mass-momentum-energy conservation partial differential equations (PDE). Traditionally, the analytical form of closure models has to be known, and then we calibrate the model using data from a set of applicable experiments. Characteristically, it takes long effort to gain insights and mechanistic understanding needed for developing closure models. Such a model is a compact form of data. The long time required for new model development limits the pace of model application while dealing with new geometries, new coolants or system conditions, particularly in newly designed systems. Alternative to mechanistic and semi-analytical models, it is possible to discover the pattern of closure relations by applying the modern machine learning methods to the body of relevant experimental data. Deep learning (DL) is a relatively new branch in the machine learning family, and a major breakthrough has been achieved since Hinton introduces a fast algorithm to efficiently train a feed-forward neural network (NN) with multilayer perceptrons. Generally, any NN with more than two layers is considered as deep learning, and it contains a large number of hyper-parameters to achieve pattern recognition or regression for complex datasets. Hornik shows that multilayer NN is compatible with universal approximation theorem. There is no constraint for a multilayer NN to capture the properties of any measurable information. This capability makes DL attractive for closure models in a system thermal-hydraulic code. As system geometry and/or operating conditions change, new physical experiments (increasingly sophisticated) are conducted to support evaluation and revision of closure models. Such a so-called data-driven modeling relaxes the structural form of system codes to directly assimilate data instead of hard-wiring the model to pre-calibrated correlations. The objective of this research is to enable cost-effective development of a DL-based closure that reflects the data. The previous work on applying machine learning techniques to fluid dynamics focused on statistical learning with basic NN from direct numerical simulations (DNS) dataset. In the present work, we show that DL-based closure models can cause the fluid calculation to diverge if multilayer NN is used. To address the issue, we investigate conditions by which the DL-based closure models work compatibly, stably, and effectively with PDE-constrained forward prediction problems. Optimally meeting the compatibility conditions constitutes a physics-informed deep learning (PIDL) strategy. We refer to this process as PDE-constrained machine learning or PDE-ML. To develop the technical basis for PDE-ML using DL-based fluid dynamics closure, numerical experiments are performed for manufactured problems of increasing complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗