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At least 1,117 records · Page 62

Deep Modeling: Circuit Characterization Using Theory Based Models in a Data Driven Framework

Analog computational circuits have been demonstrated to provide substantial improvements in power and speed relative to digital circuits, especially for applications requiring extreme parallelism but only modest precision. Deep machine learning is one such area and stands to benefit greatly from analog and mixed-signal implementations. However, even at modest precisions, offsets and non-linearity can degrade system performance. Furthermore, in all but the simplest systems, it is impossible to directly measure the intermediate outputs of all sub-circuits. The result is that circuit designers are unable to accurately evaluate the non-idealities of computational circuits in-situ and are therefore unable to fully utilize measurement results to improve future designs. In this paper we present a technique to use deep learning frameworks to model physical systems. Recently developed libraries like TensorFlow make it possible to use back propagation to learn parameters in the context of modeling circuit behavior. Offsets and scaling errors can be discovered even for sub-circuits that are deeply embedded in a computational system and not directly observable. The learned parameters can be used to refine simulation methods or to identify appropriate compensation strategies. We demonstrate the framework using a mixed-signal convolution operator as an example circuit.

Bolme, David S↗

Surrogate models for plasma displacement and current in 3D perturbed magnetohydrodynamic equilibria in tokamaks

Abstract A numerical database of over one thousand perturbed three-dimensional (3D) equilibria has been generated, constructed based on the MARS-F (Liu et al 2000 Phys. Plasmas 7 3681) computed plasma response to the externally applied 3D field sources in multiple tokamak devices. Perturbed 3D equilibria with the n = 1–4 ( n is the toroidal mode number) toroidal periodicity are computed. Surrogate models are created for the computed perturbed 3D equilibrium utilizing model order reduction (MOR) techniques. In particular, retaining the first few eigenstates from the singular value decomposition (SVD) of the data is found to produce reasonably accurate MOR-representations for the key perturbed quantities, such as the perturbed parallel plasma current density and the plasma radial displacement. SVD also helps to reveal the core versus edge plasma response to the applied 3D field. For the database covering the conventional aspect ratio devices, about 95% of data can be represented by the truncated SVD-series with inclusion of only the first five eigenstates, achieving a relative error (RE) below 20%. The MOR-data is further utilized to train neural networks (NNs) to enable fast reconstruction of perturbed 3D equilibria, based on the two-dimensional equilibrium input and the 3D source field. The best NN-training is achieved for the MOR-data obtained with a global SVD approach, where the full set of samples used for NN training and testing are stretched and form a large matrix which is then subject to SVD. The fully connected multi-layer perceptron, with one or two hidden layers, can be trained to predict the MOR-data with less than 10% RE. As a key insight, a better strategy is to train separate NNs for the plasma response fields with different toroidal mode numbers. It is also better to apply MOR and to subsequently train NNs separately for conventional and low aspect ratio devices, due to enhanced toroidal coupling of Fourier spectra in the plasma response in the latter case.

3D equilibrium↗

Optimizing Error-Bounded Lossy Compression for Scientific Data by Dynamic Spline Interpolation

Today's scientific simulations are producing vast volumes of data that cannot be stored and transferred efficiently because of limited storage capacity, parallel I/O bandwidth, and network bandwidth. The situation is getting worse over time because of the ever-increasing gap between relatively slow data transfer speed and fast-growing computation power in modern supercomputers. Error-bounded lossy compression is becoming one of the most critical techniques for resolving the big scientific data issue, in that it can significantly reduce the scientific data volume while guaranteeing that the reconstructed data is valid for users because of its compression-error-bounding feature. In this paper, we present a novel error-bounded lossy compressor based on a state-of-the-art prediction-based compression framework. Our solution exhibits substantially better compression quality than all of the existing error-bounded lossy compressors, with comparable compression speed. Specifically, our contribution is threefold. (1) We provide an in-depth analysis of why the best-existing prediction-based lossy compressor can only minimally improve the compression quality. (2) We propose a dynamic spline interpolation approach with a series of optimization strategies that can significantly improve the data prediction accuracy, substantially improving the compression quality in turn. (3) We perform a thorough evaluation using six real-world scientific simulation datasets across different science domains to evaluate our solution vs. all other related works. Experiments show that the compression ratio of our solution is higher than that of the second-best lossy compressor by 20%similar to 460% with the same error bound in most of the cases.

Zhao, Kai↗

Application configuration selection for energy-efficient execution on multicore systems

Balanced performance and energy consumption are incorporated in the design of modern computer systems. Several runtime factors, such as concurrency levels, thread mapping strategies, and dynamic voltage and frequency scaling (DVFS) should be considered in order to achieve optimal energy efficiency fora workload. Selecting appropriate run-time factors, however, is one of the most challenging tasks because the run-time factors are architecture-specific and workload-specific. And while most existing works concentrate on either static analysis of the workload or run-time prediction results, we present a hybrid two-step method that utilizes concurrency levels and DVFS settings to achieve the energy efficiency configuration for a worldoad. The experimental results based on a Xeon E5620 server with NPB and PARSEC benchmark suites show that the model is able to predict the energy efficient configuration accurately. On average, an additional 10% EDP (Energy Delay Product) saving is obtained by using run-time DVFS for the entire system. An off-line optimal solution is used to compare with the proposed scheme. Finally, the experimental results show that the average extra EDP saved by the optimal solution is within 5% on selective parallel benchmarks.

97 MATHEMATICS AND COMPUTING↗

Meteoroid Impact Detection for Exploration of Asteroids (MIDEA)

Asteroids contain a wealth of resources including water and precious metals that can be extracted. These resources could be applied to in-space manufacture of products that depend less on material launched from Earth's surface. The Meteoroid Impact Detection for Exploration of Asteroids (MIDEA) concept addresses the challenge of characterizing an asteroid surface using a small satellite with a constellation of free-flying plasma sensors to assess the asteroid’s viability for in situ resource utilization (ISRU). The plasma sensors detect ions ejected from the surface of an asteroid by meteoroid impacts, enabling the surface composition to be inferred. The objective of this NIAC Phase I study was to demonstrate feasibility of the MIDEA architecture in the context of proximity operations around an asteroid target and to develop the design of an orbital geometry and attitude control strategy for the ultralight plasma sensors. This was undertaken through a simulation framework to identify and characterize a favorable orbit for the MIDEA sensor constellation, and developing a sensor geometry that is consistent with maintaining the pointing requirements necessary to operate with sufficient power generation. Our study showed that a polar orbit aligned along the asteroid terminator provided sufficient stability for the sensors in the low gravitational environment under the influence of substantial solar radiation pressure. Reflector vanes using controlled reflectivity devices implemented with liquid crystal technology are capable of maintaining the sensor attitude so that it consistently points its solar panels in the sun direction and the sensor electrode at the asteroid surface. Finally, the reduction in meteoroid impact detection due to visibility constraints from the proposed orbit does not substantially extend the expected mission duration. These results indicate that the MIDEA concept can be achievable using a 10–20 kg spacecraft, which would be able to characterize the surface composition of an asteroid within 30–50 days of proximity operations. This architecture, implemented in parallel to multiple asteroid targets, would enable widespread exploration of near-Earth asteroids at low cost.

Asteroids↗

Precise time and time interval applications to electric power systems

There are many applications of precise time and time interval (frequency) in operating modern electric power systems. Many generators and customer loads are operated in parallel. The reliable transfer of electrical power to the consumer partly depends on measuring power system frequency consistently in many locations. The internal oscillators in the widely dispersed frequency measuring units must be syntonized. Elaborate protection and control systems guard the high voltage equipment from short and open circuits. For the highest reliability of electric service, engineers need to study all control system operations. Precise timekeeping networks aid in the analysis of power system operations by synchronizing the clocks on recording instruments. Utility engineers want to reproduce events that caused loss of service to customers. Precise timekeeping networks can synchronize protective relay test-sets. For dependable electrical service, all generators and large motors must remain close to speed synchronism. The stable response of a power system to perturbations is critical to continuity of electrical service. Research shows that measurement of the power system state vector can aid in the monitoring and control of system stability. If power system operators know that a lightning storm is approaching a critical transmission line or transformer, they can modify operating strategies. Knowledge of the location of a short circuit fault can speed the re-energizing of a transmission line. One fault location technique requires clocks synchronized to one microsecond. Current research seeks to find out if one microsecond timekeeping can aid and improve power system control and operation.

Robert E. Wilson↗

Validation of astronaut psychological select-in criteria

An optional astronaut selection strategy would select-in individuals on the basis of personality attributes associated with superior performance. Method: A test battery, the Astronaut Personal Characteristics Inventory (ASTROPCI) was developed which assesses positive and negative components of achievement, motivation, and interpersonal orientations and skills. The battery was administered to one hundred three astronaut candidates and sixty-six current U.S. Shuttle astronauts. To determine performance, a series of conceptual areas related to space flight performance were defined. Astronauts rated their peers on each of these dimensions. Ratings were obtained on all eighty-four current astronauts (excluding those selected in 1990). In addition to peer ratings, supervisor assessments of the same dimensions were obtained for each astronaut. Results: Cluster and factor analysis techniques were employed to isolate subgroups of astronauts. Those astronauts with both high achievement needs and interpersonal skills were most often rated among the top five by their peers and least often rated among the lowest five. A number of scales discriminated between astronauts rated high and low on one or more performance dimensions. Conclusions: The results parallel findings from the personality assessment of individuals in other demanding professions, including aircraft pilots and research scientists, suggesting that personality factors are significant determinants to performance in the space environment.

Rose, R. M.↗

Persistent Viral Reservoirs in Lymphoid Tissues in SIV-Infected Rhesus Macaques of Chinese-Origin on Suppressive Antiretroviral Therapy

Understanding HIV latent reservoirs in tissues is essential for the development of new strategies targeting these sites for eradication. Here, we assessed the size of latent reservoirs and the source of residual viruses in multiple lymphoid tissues of SIV-infected and fully suppressed rhesus macaques of Chinese-origin (cRMs). Eight cRMs were infected with SIVmac251 and treated with tenofovir and emtricitabine daily for 24 weeks initiated 4 weeks post-infection. Four of the eight animals reached sustained full viral suppression with undetectable viremia. The levels of cell-associated SIV DNA varied in peripheral blood mononuclear cells (PBMCs) and multiple lymphoid tissues, but with higher levels in the mesenteric lymph nodes (MesLNs). The levels of cell-associated SIV RNA also varied in different tissues. The higher frequency of viral RNA detection in the MesLNs was also observed by in situ hybridization. Consistently, the infection unit per million cells (IUPM) in the MesLNs was higher than in PBMCs and other tested lymphoid tissues by quantitative viral outgrowth assay (QVOA). Furthermore, env gp120 from tissue SIV RNA was amplified by single genome amplification. Phylogenetic analysis revealed diverse variants from tissues parallel to the viral inoculum in all viral suppressed animals. These results demonstrate that the latency and viral reservoirs in the lymphoid tissues still exist in aviremic macaques under full suppressive therapy. Moreover, the size of viral latent reservoirs differs in various lymphoid tissues with a relatively larger size in the MesLNs.

60 APPLIED LIFE SCIENCES↗

Social network structure and the spread of complex contagions from a population genetics perspective

Ideas, behaviors, and opinions spread through social networks. If the probability of spreading to a new individual is a non-linear function of the fraction of the individuals’ affected neighbors, such a spreading process becomes a “complex contagion”. This non-linearity does not typically appear with physically spreading infections, but instead can emerge when the concept that is spreading is subject to game theoretical considerations (e.g. for choices of strategy or behavior) or psychological effects such as social reinforcement and other forms of peer influence (e.g. for ideas, preferences, or opinions). Here we study how the stochastic dynamics of such complex contagions are affected by the underlying network structure. Motivated by simulations of complex contagions on real social networks, we present a framework for analyzing the statistics of contagions with arbitrary non-linear adoption probabilities based on the mathematical tools of population genetics. The central idea is to use an effective lower-dimensional diffusion process to approximate the statistics of the contagion. This leads to a tradeoff between the effects of ”selection” (microscopic tendencies for an idea to spread or die out), random drift, and network structure. Our framework illustrates intuitively several key properties of complex contagions: stronger community structure and network sparsity can significantly enhance the spread, while broad degree distributions dampen the effect of selection compared to random drift. Finally, we show that some structural features can exhibit critical values that demarcate regimes where global contagions become possible for networks of arbitrary size. Our results draw parallels between the competition of genes in a population and memes in a world of minds and ideas. Our tools provide insight into the spread of information, behaviors, and ideas via social influence, and highlight the role of macroscopic network structure in determining their fate.

59 BASIC BIOLOGICAL SCIENCES↗

Shattered pellet injection simulations with NIMROD

Optimal strategies for disruption mitigation benefit from the understanding of details both spatially and temporally. Beyond the assessment of the efficacy of a particular proposed Disruption Mitigation System (DMS), ITER's longevity will require accounting of both mitigated and unmitigated disruptions. Accurate models and validated simulations that detail multiple ITER scenarios with mitigated and unmitigated disruptions are essential for accurate estimates of load damage. The primary candidate for ITER's DMS is Shattered Pellet Injection (SPI); its efficacy must be evaluated within the next several years. To perform critical time dependent 3-D nonlinear simulations, we have developed a particle based SPI model in the NIMROD code coupled to its modified single fluid equations with impurity and radiation [Izzo, Nucl. Fusion 46(5), 541 (2006)]. SPI validation simulations of the thermal quench and comparisons to DIII-D impurity scan experiments [Shiraki et al., Phys. Plasmas 23(6), 062516 (2016)] are presented. We also present an initial ITER Q = 10 pure neon SPI simulation and compare it with the DIII-D SPI simulations. NIMROD SPI simulations demonstrate that the ablating fragment drives strong parallel flows that transport the impurities and governs the thermal quench. Analysis of SPI simulations shows that the mixed deuterium/neon SPI results in a more benign thermal quench due to the enhanced transport caused by the additional deuterium. These results suggest that an optimal pellet mixture exists for the SPI system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Fabrication of nanoporous membranes for tuning microbial interactions and biochemical reactions

Here we describe how new strategies for combining conventional photo- and soft- lithographic techniques with high-resolution patterning and etching strategies are needed in order to produce multi-scale fluidic platforms that address the full range of functional scales seen in complex biological and chemical systems. The smallest resolution required for an application often dictates the fabrication method used. Micromachining and micro-powder blasting yield higher throughput, but lack the resolution needed to fully address biological and chemical systems at the cellular and molecular scales. In contrast, techniques such as electron beam lithography or nanoimprinting allow nanoscale resolution, but are traditionally considered costly and slow. Other techniques such as photolithography or soft lithography have characteristics between these extremes. Combining these techniques to fabricate multi-scale or hybrid fluidics allows fundamental biological and chemical questions can be answered. In this study, a combination of photolithography and electron beam lithography are used to produce two multi-scale fluidic devices that incorporate porous membranes into complex fluidic networks to control the flow of energy, information, and materials in chemical form. In the first device, materials and energy were used to support chemical reactions. A nanoporous membrane fabricated with e-beam lithography separates two parallel, serpentine channels. Photolithography was used to write microfluidic channels around the membrane. The pores were written at 150nm and reduced in size with silicon dioxide deposition from plasma enhanced chemical vapor deposition (PECVD) and atomic layer deposition (ALD). Using this method, the molecular weight cutoff (MWCO) of the membrane can be adapted to the system of interest. In the second approach, photolithography was used to fabricate 200nm thin pores. The pores confined microbes and allowed energy replenishment from a media perfusion channel. The same device can be used for study of intercellular communication via the secretion and uptake of signal molecules. Pore size was tested with 750nm fluorescent polystyrene beads and fluorescein dye. The 200nm PDMS pores were shown to be robust enough to hold 750nm beads while under pressure, but allow fluorescein to diffuse across the barrier. Further testing showed that extended culture of bacteria within the chambers was possible. Finally, these two examples show how lithographically defined porous membranes can be adapted to two unique situations and used to tune the flow of chemical energy, materials, and information within a microfluidic network.

36 MATERIALS SCIENCE↗

Theoretical assessments of CO 2 activation and hydrogenation pathways on transition-metal surfaces

Carbon dioxide (CO 2 ) hydrogenation on transition-metal active sites offers a promising carbon utilization route toward mitigating greenhouse gas emissions. C 1 products are often formed in parallel during CO 2 hydrogenation, prompting investigations into the intrinsic properties of transition metals that drive activity and product selectivity. Here, in this work, close-packed surfaces of a selection of transition-metal catalysts (Ni, Co, Rh, Ru, Pd, and Pt) were studied with density functional theory (DFT) calculations to understand their fundamental reactivities for CO 2 transformation reactions. Results indicate that CO 2 conversion proceeds through CO* formation and hydrogenation to form C 1 products (* denotes an adsorbed species). Ni, Co, Rh, and Ru favor CO/CH 4 formation, while Pd and Pt favor CO/CH 3 OH formation. The ability of a metal to dissociate C-O bonds drives selectivity between CH 4 and CH 3 OH, while competition between CO* desorption and surface hydrogenation describes CO selectivities. The C-O bond dissociation steps often impose the highest barrier along CH 4 formation reaction profiles, suggesting their kinetic relevance for CH 4 formation rates. The provided DFT-derived data sets detail a comprehensive reaction network of elementary steps relevant to C 1 chemistries, ultimately offering a benchmark for insights into design strategies for materials that exploit transition-metal active sites in carbon capture or utilization processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Practical Framework for Simulating Time-Resolved Spectroscopy Based on a Real-Time Dyson Expansion

Time-resolved spectroscopy is a powerful tool for probing electron dynamics in molecules and solids, revealing transient phenomena on subfemtosecond time scales. The interpretation of experimental results is often enhanced by parallel numerical studies, which can provide insight and validation for experimental hypotheses. However, developing a theoretical framework for simulating time-resolved spectra remains a significant challenge. The most suitable approach involves the many-body nonequilibrium Green's function formalism, which accounts for crucial dynamical many-body correlations during time evolution. While these dynamical correlations are essential for observing emergent behavior in time-resolved spectra, they also render the formalism prohibitively expensive for large-scale simulations. Substantial effort has been devoted to reducing this computational cost─through approximations and numerical techniques─while preserving the key dynamical correlations. The ultimate goal is to enable first-principles simulations of time-dependent systems ranging from small molecules to large, periodic, multidimensional solids. Here, in this perspective, we outline key challenges in developing practical simulations for time-resolved spectroscopy, with a particular focus on Green's function methodologies. We highlight a recent advancement toward a scalable framework: the real-time Dyson expansion (RT-DE) [Phys. Rev. Lett. 2024, 133, 226902]. We introduce the theoretical foundation of RT-DE and discuss strategies for improving scalability, which have already enabled simulations of system sizes beyond the reach of previous fully dynamical approaches. We conclude with an outlook on future directions for extending RT-DE to first-principles studies of dynamically correlated, nonequilibrium systems.

Reeves, Cian C. [Univ. of California, Santa Barbar↗

Radiative Heat Transfer Calculation on 16384 GPUs Using a Reverse Monte Carlo Ray Tracing Approach with Adaptive Mesh Refinement

Modeling thermal radiation is computationally challenging in parallel due to its all-to-all physical and resulting computational connectivity, and is also the dominant mode of heat transfer in practical applications such as next-generation clean coal boilers, being modeled by the Uintah framework. However, a direct all-to-all treatment of radiation is prohibitively expensive on large computers systems whether homogeneous or heterogeneous. DOE Titan and the planned DOE Summit and Sierra machines are examples of current and emerging GPU-based heterogeneous systems where the increased processing capability of GPUs over CPUs exacerbates this problem. These systems require that computational frameworks like Uintah leverage an arbitrary number of on-node GPUs, while simultaneously utilizing thousands of GPUs within a single simulation. We show that radiative heat transfer problems can be made to scale within Uintah on heterogeneous systems through a combination of reverse Monte Carlo ray tracing (RMCRT) techniques combined with AMR, to reduce the amount of global communication. In particular, significant Uintah infrastructure changes, including a novel lock and contention-free, thread-scalable data structure for managing MPI communication requests and improved memory allocation strategies were necessary to achieve excellent strong scaling results to 16384 GPUs on Titan.

Computer Science↗

Quantitative spatiotemporal mapping of thermal runaway propagation rates in lithium-ion cells using cross-correlated Gabor filtering

Abuse testing of lithium-ion batteries is widely performed in order to develop new safety standards and strategies. However, testing methodologies are not standardised across the research community, especially with failure mechanisms being inherently difficult to reproduce. High-speed X-ray radiography is proven to be a valuable tool to capture events occurring during cell failure, but the observations made remain largely qualitative. We have therefore developed a robust image processing toolbox that can quantify, for the first time, the rate of propagation of battery failure mechanisms revealed by high-speed X-ray radiography. Using Gabor filter, the toolbox selectively tracks the electrode structure at the onset of failure. This facilitated the estimation of the displacement of electrodes undergoing abuse via nail penetration, and also the tracking of objects, such as the nail, as it propagates through a cell. Further, by cross-correlating the Gabor signals, we have produced practical, illustrative spatiotemporal maps of the failure events. From these, we can quantify the propagation rates of electrode displacement prior to the onset of thermal runaway. The highest recorded acceleration (≈514 mm s -2 ) was when a nail penetrated a cell radially (perpendicular to the electrodes) as opposed to axially (parallel to the electrodes). The initiation of thermal runaway was also resolved in combination with electrode displacement, which occurred at a lower acceleration (≈108 mm s -2 ). Our assistive toolbox can also be used to study other types of failure mechanisms, extracting otherwise unattainable kinetic data. Ultimately, this tool can be used to not only validate existing theoretical mechanical models, but also standardise battery failure testing procedures.

25 ENERGY STORAGE↗

LiNbO{sub 3}: A photovoltaic substrate for massive parallel manipulation and patterning of nano-objects

The application of evanescent photovoltaic (PV) fields, generated by visible illumination of Fe:LiNbO{sub 3} substrates, for parallel massive trapping and manipulation of micro- and nano-objects is critically reviewed. The technique has been often referred to as photovoltaic or photorefractive tweezers. The main advantage of the new method is that the involved electrophoretic and/or dielectrophoretic forces do not require any electrodes and large scale manipulation of nano-objects can be easily achieved using the patterning capabilities of light. The paper describes the experimental techniques for particle trapping and the main reported experimental results obtained with a variety of micro- and nano-particles (dielectric and conductive) and different illumination configurations (single beam, holographic geometry, and spatial light modulator projection). The report also pays attention to the physical basis of the method, namely, the coupling of the evanescent photorefractive fields to the dielectric response of the nano-particles. The role of a number of physical parameters such as the contrast and spatial periodicities of the illumination pattern or the particle deposition method is discussed. Moreover, the main properties of the obtained particle patterns in relation to potential applications are summarized, and first demonstrations reviewed. Finally, the PV method is discussed in comparison to other patterning strategies, such as those based on the pyroelectric response and the electric fields associated to domain poling of ferroelectric materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗