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Results for “DEPTH”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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In depth analysis of the combined HERA data in the dipole models with and without saturation
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Comment on “In-depth Plasma-Wave Heating of Dense Plasma Irradiated by Short Laser Pulses”
Sherlock et al. have reported on the heating of solid density targets by collisional damping of wakefields that are driven by relativistic electron bunches generated in relativistic laser matter interaction. Analyzing collisional particle-in-cell simulations they calculate the fast electron current j f inside the plasma by adding contributions from electrons with energies greater than E cut = 50 keV; time-integrating the specific resistive energy deposition η j 2 f they arrive at a temperature profile and compare the result to the one 'measured' in their simulation, defined as the energy of particles with E < 30 keV; the discrepancy is due to collisional damping of wake fields (CDW). Here, we disagree with their metric of fast current, which leads to false conclusions about CDW heating being a volumetric, rather than surface effect.
Simulation of open quantum systems via low-depth convex unitary evolutions
Simulating physical systems on quantum devices is one of the most promising applications of quantum technology. Current quantum approaches to simulating open quantum systems are still practically challenging on NISQ-era devices, because they typically require ancilla qubits and extensive controlled sequences. In this work, we propose a hybrid quantum-classical approach for simulating a class of open system dynamics called random-unitary channels. These channels naturally decompose into a series of convex unitary evolutions, which can then be efficiently sampled and run as independent circuits. The method does not require deep ancilla frameworks and thus can be implemented with lower noise costs. We implement simulations of open quantum systems up to dozens of qubits and with large channel ranks. Published by the American Physical Society 2024
Quantum Coding with Low-Depth Random Circuits
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Correlation-Informed Permutation of Qubits for Reducing Ansatz Depth in the Variational Quantum Eigensolver
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Deterministic Constant-Depth Preparation of the AKLT State on a Quantum Processor Using Fusion Measurements
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Depth-dependent atomic valence determination by synchrotron techniques
The properties of many materials can be strongly affected by the atomic valence of the contained individual elements, which may vary at surfaces and other interfaces. These variations can have a critical impact on material performance in applications. In this work, a non-destructive method for the determination of layer-by-layer atomic valence as a function of material thickness is presented for La 0.7 Sr 0.3 MnO 3 (LSMO) thin films. The method utilizes a combination of bulk- and surface-sensitive X-ray absorption spectroscopy (XAS) detection modes; here, the modes are fluorescence yield and surface-sensitive total electron yield. The weighted-average Mn atomic valence as measured from the two modes are simultaneously fitted using a model for the layer-by-layer variation of valence based on theoretical model Hamiltonian calculations. Using this model, the Mn valence profile in LSMO thin film is extracted and the valence within each layer is determined to within an uncertainty of a few percent. The approach presented here could be used to study the layer-dependent valence in other systems or extended to different properties of materials such as magnetism.
In situ depth-resolved synchrotron radiation X-ray spectroscopy study of radiation-induced Au deposition
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In-Depth Optimization with the OpenACC-to-FPGA Framework on an Arria 10 FPGA
The reconfigurable computing paradigm that uses field programmable gate arrays (FPGAs) has received renewed interest in the high-performance computing field due to FPGAs' unique combination of performance and energy efficiency. However, difficulties in programming and optimizing FPGAs have prevented them from being widely accepted as general-purpose computing devices. In accelerator-based heterogeneous computing, portability across diverse heterogeneous devices is also an important issue, but the unique architectural features in FPGAs make this difficult to achieve. To address these issues, a directive-based, high-level FPGA programming and optimization framework was previously developed. In this work, developed optimizations were combined holistically using the directive-based approach to show that each individual benchmark requires a unique set of optimizations to maximize performance. The relationships between FPGA resource usages and runtime performance were also explored.
Design, Optimization, and In-Depth Understanding of Front and Rear Junction Screen-Printed Double-Side Passivated Contacts Solar Cells
In this work, detailed numerical modeling is performed for front junction (FJ) and rear junction (RJ) n-type Si solar cells with screen-printed double-side poly-Si based tunnel oxide passivated contacts (TOPCon). Fundamental understanding of both structures is crucial to explore factors that limit the efficiency potential of the two designs. By investigating several key parameters such as front poly sheet resistance and thickness, bulk material properties, and carrier transport in our simulation model, we determine and explain why RJ cells outperform FJ cells. Our findings reveal that FJ suffers from present technological limitations of p-poly based passivated contacts, namely, i) large recombination observed in textured p-TOPCon emitter and ii) low solid solubility and hole mobility in boron-doped poly-Si resulting in very high sheet resistance of front p-poly layers which must be made thin to reduce absorption losses. In contrast, RJ desensitized the cell efficiency to front sheet resistance allowing application of ultra-thin front n-poly layers and is therefore ideally suited for double-side TOPCon cells. Lastly, we have developed a roadmap for efficiency projections of commercial-type screen-printed RJ and FJ topologies reaching ~24% and >22.5% efficiencies respectively, quantifying and explaining the impact of technological innovations on the performance of each design.
Short-Depth Circuits for Dicke State Preparation
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Ocean Current Segmentation at Different Depths and Correlation with Temperature in a MPAS-Ocean Simulation
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Full- and Reduced-Order Observers for Image-Based Depth Estimation Using Concurrent Learning
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Fisher Information Analysis of Depth-of-Interaction Estimation in Double-Sided Strip Detectors
Abstract not provided
In‐depth comparative analysis of Illumina ® MiSeq run metrics: Development of a wet‐lab quality assessment tool
Abstract Whole genome sequencing of bacterial isolates has become a daily task in many laboratories, generating incredible amounts of data. However, data acquisition is not an end in itself; the goal is to acquire high‐quality data useful for understanding genetic relationships. Having a method that could rapidly determine which of the many available run metrics are the most important indicators of overall run quality and having a way to monitor these during a given sequencing run would be extremely helpful to this effect. Therefore, we compared various run metrics across 486 MiSeq runs, from five different machines. By performing a statistical analysis using principal components analysis and a K ‐means clustering algorithm of the metrics, we were able to validate metric comparisons among instruments, allowing for the development of a predictive algorithm, which permits one to observe whether a given MiSeq run has performed adequately. This algorithm is available in an Excel spreadsheet: that is, MiSeq Instrument & Run (In‐Run) Forecast. Our tool can help verify that the quantity/quality of the generated sequencing data consistently meets or exceeds recommended manufacturer expectations. Patterns of deviation from those expectations can be used to assess potential run problems and plan preventative maintenance, which can save valuable time and funding resources.
Drought legacies are dependent on water table depth, wood anatomy and drought timing across the eastern US
Abstract Severe droughts can impart long‐lasting legacies on forest ecosystems through lagged effects that hinder tree recovery and suppress whole‐forest carbon uptake. However, the local climatic and edaphic factors that interact to affect drought legacies in temperate forests remain unknown. Here, we pair a dataset of 143 tree ring chronologies across the mesic forests of the eastern US with historical climate and local soil properties. We found legacy effects to be widespread, the magnitude of which increased markedly in diffuse porous species, sites with deep water tables, and in response to late‐season droughts (August–September). Using an ensemble of downscaled climate projections, we additionally show that our sites are projected to drastically increase in water deficit and drought frequency by the end of the century, potentially increasing the size of legacy effects by up to 65% and acting as a significant process shaping forest composition, carbon uptake and mortality.