Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data Digitization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

ACTIVE GALACTIC NUCLEUS PAIRS FROM THE SLOAN DIGITAL SKY SURVEY. I. THE FREQUENCY ON {approx}5-100 kpc SCALES

Galaxy-galaxy mergers and close interactions have long been regarded as a viable mechanism for channeling gas toward the central supermassive black holes (SMBHs) of galaxies which are triggered as active galactic nuclei (AGNs). AGN pairs, in which the central SMBHs of a galaxy merger are both active, are expected to be common from such events. We conduct a systematic study of 1286 AGN pairs at z-bar {approx}0.1 with line-of-sight velocity offsets {Delta}v < 600 km s{sup -1} and projected separations r{sub p} < 100 h{sup -1}{sub 70} kpc, selected from the Seventh Data Release of the Sloan Digital Sky Survey (SDSS). This AGN pair sample was drawn from 138,070 AGNs optically identified based on diagnostic emission line ratios and/or line widths. The fraction of AGN pairs with 5 h{sup -1}{sub 70} kpc {approx}< r{sub p} < 100 h{sup -1}{sub 70} kpc among all spectroscopically selected AGNs at 0.02 < z < 0.16 is 3.6% after correcting for SDSS spectroscopic incompleteness; {approx}30% of these pairs show morphological tidal features in their SDSS images, and the fraction becomes {approx}> 80% for pairs with the brightest nuclei. Our sample increases the number of known AGN pairs on these scales by more than an order of magnitude. We study their AGN and host-galaxy star formation properties in a companion paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Galaxy–Galaxy Weak-lensing Measurements from SDSS. I. Image Processing and Lensing Signals

We present our image processing pipeline that corrects the systematics introduced by the point-spread function (PSF). Using this pipeline, we processed Sloan Digital Sky Survey (SDSS) DR7 imaging data in r band and generated a galaxy catalog containing the shape information. Based on our shape measurements of the galaxy images from SDSS DR7, we extract the galaxy–galaxy (GG) lensing signals around foreground spectroscopic galaxies binned in different luminosities and stellar masses. We estimated the systematics, e.g., selection bias, PSF reconstruction bias, PSF dilution bias, shear responsivity bias, and noise rectification bias, which in total is between −9.1% and 20.8% at 2 σ levels. The overall GG lensing signals we measured are in good agreement with Mandelbaum et al. The reduced χ {sup 2} between the two measurements in different luminosity bins are from 0.43 to 0.83. Larger reduced χ {sup 2} from 0.60 to 1.87 are seen for different stellar mass bins, which is mainly caused by the different stellar mass estimator. The results in this paper with higher signal-to-noise ratio are due to the larger survey area than SDSS DR4, confirming that more luminous/massive galaxies bear stronger GG lensing signals. We divide the foreground galaxies into red/blue and star-forming/quenched subsamples and measure their GG lensing signals. We find that, at a specific stellar mass/luminosity, the red/quenched galaxies have stronger GG lensing signals than their counterparts, especially at large radii. These GG lensing signals can be used to probe the galaxy–halo mass relations and their environmental dependences in the halo occupation or conditional luminosity function framework.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Fractal dimension of optical cirrus in Stripe82

ABSTRACT The geometric characteristics of dust clouds provide important information on the physical processes that structure such clouds. One of such characteristics is the 2D fractal dimension D of a cloud projected on to the sky plane. In previous studies, which were mostly based on infrared (IR) data, the fractal dimension of individual clouds was found to be in a range from 1.1 to 1.7 with a preferred value of 1.2–1.4. In this work, we use data from Stripe82 of the Sloan Digital Sky Survey to measure the fractal dimension of the cirrus clouds. This is done here for the first time for optical data with significantly better resolution as compared to IR data. To determine the fractal dimension, the perimeter-area method is employed. We also consider IR (IRAS and Herschel) counterparts of the corresponding optical fields to compare the results between the optical and IR. We find that the averaged fractal dimension across all clouds in the optical is $\langle D \rangle =1.69^{+0.05}_{-0.05}$ which is significantly larger than the fractal dimension of its IR counterparts $\langle D\rangle =1.38^{+0.07}_{-0.06}$. We examine several reasons for this discrepancy (choice of masking and minimal contour level, image and angular resolution, etc.) and find that for approximately half of our fields the different angular resolution (point spread function) of the optical and IR data can explain the difference between the corresponding fractal dimensions. For the other half of the fields, the fractal dimensions of the IR and visual data remain inconsistent, which can be associated with physical properties of the clouds, but further physical simulations are required to prove it.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Digital Twin Empowered PV Power Prediction

The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper proposes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical data set, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long-short-term memory model) for capturing the hidden spatial-temporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real data set is conducted to access the effectiveness of the proposed method.

14 SOLAR ENERGY↗
DOE OSTI2013

EXPLORING THE VARIABLE SKY WITH LINEAR. III. CLASSIFICATION OF PERIODIC LIGHT CURVES

We describe the construction of a highly reliable sample of ∼7000 optically faint periodic variable stars with light curves obtained by the asteroid survey LINEAR across 10,000 deg{sup 2} of the northern sky. The majority of these variables have not been cataloged yet. The sample flux limit is several magnitudes fainter than most other wide-angle surveys; the photometric errors range from ∼0.03 mag at r = 15 to ∼0.20 mag at r = 18. Light curves include on average 250 data points, collected over about a decade. Using Sloan Digital Sky Survey (SDSS) based photometric recalibration of the LINEAR data for about 25 million objects, we selected ∼200,000 most probable candidate variables with r < 17 and visually confirmed and classified ∼7000 periodic variables using phased light curves. The reliability and uniformity of visual classification across eight human classifiers was calibrated and tested using a catalog of variable stars from the SDSS Stripe 82 region and verified using an unsupervised machine learning approach. The resulting sample of periodic LINEAR variables is dominated by 3900 RR Lyrae stars and 2700 eclipsing binary stars of all subtypes and includes small fractions of relatively rare populations such as asymptotic giant branch stars and SX Phoenicis stars. We discuss the distribution of these mostly uncataloged variables in various diagrams constructed with optical-to-infrared SDSS, Two Micron All Sky Survey, and Wide-field Infrared Survey Explorer photometry, and with LINEAR light-curve features. We find that the combination of light-curve features and colors enables classification schemes much more powerful than when colors or light curves are each used separately. An interesting side result is a robust and precise quantitative description of a strong correlation between the light-curve period and color/spectral type for close and contact eclipsing binary stars (β Lyrae and W UMa): as the color-based spectral type varies from K4 to F5, the median period increases from 5.9 hr to 8.8 hr. These large samples of robustly classified variable stars will enable detailed statistical studies of the Galactic structure and physics of binary and other stars and we make these samples publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

What is Data-Intensive Science?

What is Data Intensive Science? Today we are living in a digital world, where scientists often no longer interact directly with the physical object of their research, but do so via digitally captured, reduced, calibrated, analyzed, synthesized and, at times, visualized data. Advances in experimental and computational technologies have lead to an exponential growth in the volumes, variety and complexity of this data and while the deluge is not happening everywhere in an absolute sense, it is in a relative one. Science today is data intensive. Data intensive science has the potential to transform not only how we do science, but how quickly we can translate scientific progress into complete solutions, policies, decisions and ultimately economic success. Critically, data intensive science touches some of the most important challenges we are facing. Consider a few of the grand challenges outlined by the U.S. National Academy of Engineering: make solar energy economical, provide energy from fusion, develop carbon sequestration methods, advance health informatics, engineer better medicines, secure cyberspace, and engineer the tools of scientific discovery. Arguably, meeting any of these challenges requires the collaborative effort of trans-disciplinary teams, but also significant contributions from enabling data intensive technologies. Indeed for many of them, advances in data intensive research will be the single most important factor in developing successful and timely solutions. Simple extrapolations of how we currently interact with and utilize data and knowledge are not sufficient to meet this need. Given the importance of these challenges, a new, bold vision for the role of data in science, and indeed how research will be conducted in a data intensive environment is evolving.

Data Intensive Science↗
DOE OSTI2022

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the uvgriz multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first-year observation of Mephisto W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with purity = 95.4% and completeness = 96.9% for the RR Lyrae RFC and purity = 91.4% and completeness = 90.2% for the quasar RFC. In conclusion, we have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.

(galaxies:) quasars: general↗
DOE OSTI2023

Leveraging the digital thread for physics-based prediction of microstructure heterogeneity in additively manufactured parts

A major limitation of additive manufacturing (AM) processes is that local conditions of material deposition frequently lead to unintentional heterogeneities in microstructure and properties within a single component, despite nominally uniform process conditions. Up to now, there has been no way to a priori determine the distribution of these heterogeneities, requiring expensive trial-and-error approaches to fabrication, testing, and characterization. Here, a physics-based framework for creating a digital representation of the laser powder bed fusion (PBF) process is proposed to predict the variation in solidification behavior that leads to heterogeneous microstructures in an as-built part. By leveraging in situ process data stored in the part’s digital thread, the scan path and process parameters were input into a heat transfer model which predicted solidification data at the melt pool scale. A two-step unsupervised clustering algorithm was used to first cluster the local solidification conditions (12.5µm 3 voxels) and then to cluster the regional behavior on the scale of multiple scan passes and print layers (250µm 3 super-voxels). This process was used to identify regions with similar solidification characteristics for multiple locations in a Stainless Steel 316-L component. The corresponding as-built part was sectioned and characterized using electron backscatter diffraction (EBSD). Quantitative analysis of the pole figures confirmed that the predicted regions of heterogeneity in the solidification conditions corresponded with differences in the observed microstructure. In conclusion, this work shows a viable path for estimating the microstructural heterogeneity for additively manufactured parts to either limit microstructural variation throughout a part or to enable functionality-based variation of the microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

A Digitally Interfaced Analog Correlation Filter System for Object Tracking Applications

This study reports a 24×57 correlation filter system for object tracking applications. While digital interfacing of the input and output data enabled a standard and flexible way of communication with pre- and post-processing digital blocks, the multiply-accumulate (MAC) operations were performed in the analog domain to save power and area. The proposed system utilizes non-volatile floating-gate memories to store filter coefficients. The chip was fabricated in a 0.13- μm CMOS process and occupies 3.23 mm 2 of silicon area. The system dissipates 388.4 μW of power at a throughput of 11.3 kVec/s, achieving an energy efficiency of 25.2 pJ/MAC. Finally, experimental results for a custom filter designed to detect vehicles are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Recovery and Calibration of Legacy Underground Nuclear Test Seismic Data from the Leo Brady Seismic Network

The Leo Brady Seismic Network (LBSN, originally the Sandia Seismic Network) was established in 1960 by Sandia National Laboratories to monitor underground nuclear tests (UGTs) at the Nevada National Security Site (NNSS, formerly named the Nevada Test Site). The LBSN has been in various configurations throughout its existence, but it has generally been comprised of four to six stations at regional distances (~150–400 km) from the NNSS with approximately evenly spaced azimuthal coverage. Between 1962 and the end of nuclear testing in 1992, the LBSN—and a sister network operated by Lawrence Livermore National Laboratories—was the most comprehensive United States source of regional seismic data of UGTs. Approximately 75% of all UGTs performed by the United States occurred in the predigital era. At that time, LBSN data were transmitted as frequency-modulated (FM) audio over telephone lines to a central location and recorded as analog waveforms on high-fidelity magnetic audio tapes. These tapes have been in dry temperature-stable storage for decades and contain the sole record of this irreplaceable data; full waveforms of LBSN-recorded UGTs from this era were not routinely digitized or otherwise published. We have developed a process to recover and calibrate data from these tapes. First, we play back and digitize the tapes as audio. Next, we demodulate the FM “audio” into individual waveforms. We then estimate the various instrument constants through careful measurement of “weight-lift” tests performed prior to each UGT on each instrument. Finally, these coefficients allow us to scale and shape the derived instrument response of the seismographs and compute poles and zeros. Finally, the result of this process is a digital record of the recorded seismic ground motion in a modern data format, stored in a searchable database. To date, we have digitized tapes from 592 UGTs.

58 GEOSCIENCES↗
DOE OSTI2025

A digital twin platform for building performance monitoring and optimization: Performance simulation and case studies

Advancements in sensor technology, data analytics, affordable compute, and communication infrastructure have paved the way for Digital Twin technology in optimizing building operations and controls. This study presents the development of an open and interoperable web-based Digital Twin platform for integrating diverse data streams and facilitating effective user interactions. The platform utilizes modern technologies for the web framework and time-series data management, ensuring scalability and responsiveness. The backend supports seamless integration of diverse data sources and emulators, incorporating data from building sensors and meters, external weather Application Programming Interfaces, and advanced EnergyPlus simulation models of the building and its energy systems including the Distributed Energy Resources that are formulated in Functional Mockup Units. A simulation case study was conducted with FlexLab, a test facility on Lawrence Berkeley National Laboratory campus. The case study includes normal operations, Distributed Energy Resource integration, and power outage scenarios, to illustrate the Digital Twin’s ability to provide critical insights into energy performance and thermal resilience. The results demonstrated the platform’s potential as a decision-support tool for optimizing building energy performance and enhancing resilience against extreme weather events. Future work will focus on deploying the Digital Twin platform to a real building for field validation, extending its capabilities to cover more scenarios such as bidirectional Electric Vehicle interactions, and enhancing user engagement.

EnergyPlus↗
DOE OSTI2025

Specifications of Legacy U(Pu)Zr Metallography Data

The DOE Nuclear Energy Advanced Reactor Technologies (ART) Program has supported the creation of several databases with information describing the safety performance of fast reactors, components, and fuels. This growing collection of legacy experimental data, operating data, and analysis is available online to registered users. Metallography data is one of the most important types of PIE data being collected, organized and stored in several ART Fast Reactor Databases (https://frdb.ne.anl.gov), including the Fuels Irradiation & Physics Database (FIPD), Out-of-Pile Transient Database (OPTD), and TREAT (the Transient Reactor Test Facility) Experimental Relational Database (TREXR). These databases contain three main sets of metallography data. The first set is the metallography data from Experimental Breeder Reactor-II (EBR-II) and Fast Flux Test Facility (FFTF) irradiated fuel pins measured in the Hot Fuel Examination Facility (HFEF); the second set is the metallography data from EBR-II irradiated fuel pins measured in the Alpha-Gamma Hot Cell Facility (AGHCF); the third set is the metallography data from transient tested fuel pins (the transients tests include the out-of-pile tests and TREAT tests) measured in AGHCF. Since both the second and third sets of data were measured in AGHCF, they are governed by the same specification. The metallography data in the databases are digital images scanned from either positive or negative photos. The quality of the images, including their resolution and contrast, relies on the preserved quality of the pictures and the scanning conditions. To analyze the microstructure of a fuel pin, a series of preparatory steps must be undertaken. These include sectioning, epoxy mounting, mechanical grinding and polishing, and often etching. The resulting samples were then transferred to a secondary hot cell, or glovebox (depending the strength of radiation field) for microscopy examination. The specifications provided herein focus on the metallography examinations. The sample grinding, polishing and etching processes are also discussed. The procedures involving sectioning and epoxy mounting are out of the scope of the current specification; details can be found in the corresponding operation manuals. If more data are collected and added to the ART Fast Reactor Databases, this specification will be updated to accommodate the additional data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

The overvoltage protection module for the power supply system for the pixel detector at Belle II experiment at KEK

In this paper the overvoltage protection modules (OVP) for the power supply (PS) system of the Belle II pixel detector (PXD) are described. The aim of the OVP is to protect the detector and associated electronics against overvoltage conditions. Most critical in the system are voltages supplying the front-end ASICs. The PXD detector consists of the DEPFET sensor modules with integrated chips like the Drain Current Digitizer, the Switcher and the Data Handling Processor. These chips, implemented in modern sub-micron technologies, are quite vulnerable to variations in the supply voltages. The PXD will be placed in the Belle II experiment as close as possible to the interaction point, where access during experiment is very limited or even impossible, thus the PS and OVP systems exploit the remote-sensing method. Overvoltage conditions are due to failures of the PS itself, wrong setting of the output voltages or transient voltages coming out of hard noisy environment of the experiment. The OVP modules are parts of the PS modules. For powering the PXD 40 PS modules are placed 15 m outside the Belle II spectrometer. Each one is equipped with the OVP board. All voltages (22) are grouped in 4 domains: Analog, Digital, Steering and Gate which have independent grounds. The OVP boards are designed from integrated circuits from Linear Technology. All configurations were simulated with the Spice program. The control electronics is designed in a Xilinx CPLD. Two types of integrated circuits were used. LT4356 surge stopper protects loads from high voltage transients. The output voltages are limited to a safe value and also protect loads against over current faults. For less critical voltages, the LTC2912 voltage monitors are used that detect under-voltage and overvoltage events. It has to be noted that the OVP system is working independently of any other protection of the PS system, which increases its overall reliability. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Summary of Methodology for Mitigating Risks Associated with Licensing and Qualifying AM Nuclear Materials

The US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program focuses on accelerating the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. Laser powder bed fusion (LPBF) is one of the most popular additive manufacturing (AM) processes for fabricating components with intrinsically complex geometries. LPBF was extensively explored for nuclear applications under the previous Transformational Challenge Reactor program. Additionally, Oak Ridge National Laboratory developed and licensed the Peregrine software and larger digital platform that couples machine learning and in situ data collection during AM to detect anomalies and any evolved defects. The digital platform will be critical to (1) the qualification of AM components for nuclear applications that link location-specific data to macroscopic properties and (2) predict final component performance. Current in situ process monitoring tools are valuable for observing the formation of stochastic flaws, but additional data are needed to predict the resulting microstructures and associated material performance. Rapid cooling rates and large thermal gradients have caused large heterogeneities in the microstructure, which cause anisotropy in mechanical performance. The AMMT program is evaluating the best approaches for addressing these heterogeneities and their effect on component performance using a combination of multiscale modeling, enhanced in situ process monitoring, and high throughput experimental testing. This report summarizes strategies for mitigating the risks associated with qualifying AM components, including developing new sensing capabilities for in situ process monitoring and characterizing melt pool solidification and residual stresses to inform multiscale modeling efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Quantum computing without quantum computers: Database search and data processing using classical wave superposition

Quantum computers are proven to be more efficient at solving a specific class of problems compared to traditional digital computers. Superposition of states and quantum entanglement are the two key ingredients that make quantum computing so powerful. However, not all quantum algorithms require quantum entanglement (e.g., search through an unsorted database). Is it possible to utilize classical wave superposition to speed up database searching as much as by using quantum computers? There were several attempts to mimic quantum computers using classical waves. It was concluded that the use of classical wave superposition comes with the cost of an exponential increase in resources. In this work, we consider the feasibility of building classical wave-based devices able to provide fundamental speedup over digital counterparts without the exponential overhead. We present experimental data on database searching through a magnetic database using spin wave superposition. The results demonstrate the same speedup as expected for quantum computers. Also, we present examples of numerical modeling demonstrating classical wave interference for period finding. This approach may not compete with quantum computers with efficiency but outperform classical digital computers. We argue that classical wave-based devices can perform some of the quantum algorithms with the same efficiency as quantum computers as long as quantum entanglement is not required.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗