Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Mobile computing”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Diffusioosmotic flow reversals due to ion–ion electrostatic correlations

Existing theories of diffusioosmosis have neglected ion–ion electrostatic correlations, which are important in concentrated electrolytes. Here, we develop a mathematical model to numerically compute the diffusioosmotic mobilities of binary symmetric electrolytes across low to high concentrations in a charged parallel-plate channel. We use the modified Poisson equation to model the ion–ion electrostatic correlations and the Bikerman model to account for the finite size of ions. We report two key findings. First, ion–ion electrostatic correlations can cause a unique reversal in the direction of diffusioosmosis. Such a reversal is not captured by existing theories, occurs at ≈ 0.4 M for a monovalent electrolyte, and at a much lower concentration of ≈ 0.003 M for a divalent electrolyte in a channel with the same surface charge. This highlights that diffusioosmosis of a concentrated electrolyte can be qualitatively different from that of a dilute electrolyte, not just in its magnitude but also its direction. Second, we predict a separate diffusioosmotic flow reversal, which is not due to electrostatic correlations but the competition between the underlying chemiosmosis and electroosmosis. This reversal can be achieved by varying the magnitude of the channel surface charge without changing its sign. However, electrostatic correlations can radically change how this flow reversal depends on the channel surface charge and ion diffusivity between a concentrated and a dilute electrolyte. Furthermore, the mathematical model developed here can be used to design diffusioosmosis of dilute and concentrated electrolytes, which is central to applications such as species mixing and separation, enhanced oil recovery, and reverse electrodialysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

In Situ and Ex Situ NMR for Battery Research

A rechargeable battery stores readily convertible chemical energy to operate a variety of devices such as mobile phones, laptop computers, and electric automobiles, etc. A battery generally consists of four components, i.e., a cathode, an anode, a separator and electrolytes. The properties of these components jointly determine the safety, the lifetime, and the electrochemical performance, include but not limited to the power density and the charge, recharge time/rate associated with a battery system. Extensive amount of research is thus dedicated to understand the physical and chemical properties associated with each of the four components aimed at developing new generations of battery systems with greatly enhanced safety and electrochemical performance while at significantly reduced cost for large scale applications. Advanced characterization tools are prerequisite to fundamentally understand battery materials. Considering that some of the key electrochemical processes can only exist under in situ conditions, thus, can only be captured under a working battery conditions when electric wires are attached and current and voltage applied, in situ detection is critical. NMR, a non-invasive and atomic specific tool, is capable of detecting all phases, including crystalline, amorphous, liquid and gaseous phases simultaneously and is ideal for in situ detection on a working battery system. Ex situ NMR on the other hand can provide more detailed molecular or structural information on stable species with better spectral resolution and sensitivity. The combination of in situ and ex situ NMR, thus, offers a powerful tool for investigating the detailed electrochemistry in batteries.

Energy storage, batteries, multi nuclear NMR, 7Li,↗
DOE OSTI2022

First-principles ionized-impurity scattering and charge transport in doped materials

Scattering of carriers with ionized impurities governs charge transport in doped semiconductors. However, electron interactions with ionized impurities cannot be fully described with quantitative first-principles calculations, so their understanding relies primarily on simplified models. Here we show an ab initio approach to compute the interactions between electrons and ionized impurities or other charged defects. It includes the short- and long-range electron-defect (e-d) interactions on equal footing and allows for efficient interpolation of the e-d matrix elements. Here we combine the e-d and electron-phonon interactions in the Boltzmann transport equation to compute the carrier mobilities in doped silicon over a wide range of temperature and doping concentrations, seamlessly spanning the defect- and phonon-limited transport regimes. The individual contributions of the defect- and phonon-scattering mechanisms to the carrier relaxation times and mean-free paths are analyzed. Our method provides a powerful tool to study electronic interactions in doped materials. It broadens the scope of first-principles transport calculations, enabling studies of a wide range of doped semiconductors and oxides with application to electronics, energy, and quantum technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Identifying Genomic Islands with Deep Neural Networks

Background Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types. Results We propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools. Conclusions We used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

Computer Vision↗
DOE OSTI2012

Contribution to Fusion Materials (Semiannual Report)

The objectives of this work are the following: (1) The application of micro and mesoscale modeling techniques to study dislocation properties in ferritic and W-based materials; and (2) The development of computational models and tools to study damage accumulation in >1 dpa (fusion-like) conditions, both for Fe and W-based alloys. The high-temperature strength of structural ferritic alloys (ferritic/martensitic steels, ODS steels, bcc refractory alloys) hinges on the thermal stability of second phase particles and their interactions with dislocations. Irradiation damage can modify the structure and stability of both the particles and dislocations, particularly by the introduction of gas atoms, point defects and point defect clusters. The three aspects of materials strength that we are studying are: (a) Computation of dislocation mobility functions (stress-velocity relations) as a function of temperature and dislocation character. This will be done via molecular dynamics (MD) simulations of single dislocation motion under applied shear stress. This is a fundamental input to dislocation dynamics (DD) simulations and also provides fundamental insights into the high-temperature plastic behavior of ferritic materials. (b) Simulations of dislocation-obstacle interactions using MD and DD. This subtask includes simulating the effect on dislocation glide of precipitates (e.g., α' Cr precipitates), ODS particles, and irradiation induced defect clusters (e.g. voids, dislocation loops, etc.). (c) Implementation of this information (dislocation mobilities and dislocation-defect interaction rules) into DD codes that will allow us to study plasticity of single crystals Fe alloys under relevant irradiation conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Development of Methodologies for Technology Deployment for Advanced Outage Control Centers that Improve Outage Coordination, Problem Resolution and Outage Risk Management

This research effort is a part of the Light-Water Reactor Sustainability (LWRS) Program, which is a research and development (R&D) program sponsored by Department of Energy (DOE) and performed in close collaboration with industry R&D programs that provides the technical foundations for licensing and managing the long-term, safe, and economical operation of current nuclear power plants. The LWRS program serves to help the U.S. nuclear industry adopt new technologies and engineering solutions that facilitate the continued safe operation of the plants and extension of the current operating licenses. The long term viability of existing nuclear power plants in the U.S. will depend upon maintaining high capacity factors, avoiding nuclear safety issues and reducing operating costs. The slow progress in the construction on new nuclear power plants has placed in increased importance on maintaining the output of the current fleet of nuclear power plants. Recently expanded natural gas production has placed increased economic pressure on nuclear power plants due to lower cost competition. Until recently, power uprate projects had steadily increased the total output of the U.S. nuclear fleet. Errors made during power plant upgrade projects have now removed three nuclear power plants from the U.S. fleet and economic considerations have caused the permanent shutdown of a fourth plant. Additionally, several utilities have cancelled power uprate projects citing economic concerns. For the past several years net electrical generation from U.S. nuclear power plants has been declining. One of few remaining areas where significant improvements in plant capacity factors can be made is in minimizing the duration of refueling outages. Managing nuclear power plant outages is a complex and difficult task. Due to the large number of complex tasks and the uncertainty that accompanies them, outage durations routinely exceed the planned duration. The ability to complete an outage on or near schedule depends upon the performance of the outage management organization. During an outage, the outage control center (OCC) is the temporary command center for outage managers and provides several critical functions for the successful execution of the outage schedule. Essentially, the OCC functions to facilitate information inflow, assist outage management in processing information and to facilitate the dissemination of information to stakeholders. Currently, outage management activities primarily rely on telephone communication, face to face reports of status and periodic briefings in the OCC. Much of the information displayed in OCCs is static and out of date requiring an evaluation to determine if it is still valid. Several advanced communication and collaboration technologies have shown promise for facilitating the information flow into, across and out of the OCC. Additionally, advances in the areas of mobile worker technologies, computer based procedures and electronic work packages can be leveraged to improve the availability of real time status to outage managers.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Design and Implementation of a Hall Effect Sensor Array Applied to Recycling Hard Drive Magnets

Rare earths are an important resource for many electronic components and technologies. Examples abound including Neodymium magnets used in mobile devices and computer hard drives (HDDs), and a variety of renewable energy technologies (e.g., wind turbines). Approximately 21,000 metric tons of Neodymium is processed annually with less than 1% being recycled. An economic system to assist in the recycling of magnet material from post-consumer goods, such as Neodymium Iron Boron magnets commonly found in hard drives is presented. A central component of this recycling measurement system uses an array of 128 Hall Effect sensors arranged in two columns to detect the magnetic flux lines orthogonal to the HDD. Results of using the system to scan planar shaped objects such as hard drives to identify and spatially locate rare-earth magnets for removal and recycling from HDDs are presented. Applications of the sensor array in other identification and localization of magnetic components and assemblies will be presented.

Hall Effect sensors↗
DOE OSTI2021

System and method for passive assay of holdup deposits in nuclear piping

Disclosed herein are a method, apparatus, and software for passive, non-destructive assay of holdup deposits in nuclear piping by in-pipe apparatus. A detector deployed within a pipe is collimated to observe radiation impinging radially inward from decay of deposits that lie on the pipe wall. A radiation detector is centered in the pipe and collimated by a pair of coaxial shielding discs disposed equidistant from the detector. This arrangement causes radiation from a truncated cylinder of pipe deposit within a field of regard to impinge on the detector, while precluding radiation emanating from pipe walls beyond the field of regard from reaching the detector. Hence, observations are unique to a known cylindrical length of pipe. The detector assembly is translated through pipes by an autonomous mobile robotic apparatus. Computer software controls the robotic apparatus, logs data, and post-processes to assay deposits.

Whittaker, William Lawrence↗
DOE OSTI2016

Resonant tunneling assisted propagation and amplification of plasmons in high electron mobility transistors

A rigorous theoretical and computational model is developed for the plasma-wave propagation in high electron mobility transistor structures with electron injection from a resonant tunneling diode at the gate. We discuss the conditions in which low-loss and sustainable plasmon modes can be supported in such structures. The developed analytical model is used to derive the dispersion relation for these plasmon-modes. A non-linear full-wave-hydrodynamic numerical solver is also developed using a finite difference time domain algorithm. The developed analytical solutions are validated via the numerical solution. We also verify previous observations that were based on a simplified transmission line model. It is shown that at high levels of negative differential conductance, plasmon amplification is indeed possible. The proposed rigorous models can enable accurate design and optimization of practical resonant tunnel diode-based plasma-wave devices for terahertz sources, mixers, and detectors, by allowing a precise representation of their coupling when integrated with other electromagnetic structures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A Scalable Multi-Modal Framework for High-Fidelity Distributed Human Mobility Simulations

The development of data-driven models for human mobility in urban settings requires access to substantial and diverse real-world data. However, existing historical data often presents challenges such as limited volume, variety, and veracity, as well as missing data and privacy preservation concerns. Also, urban mobility modeling is inherently time-variant, complex, and multi-modal, encompassing everything from individual walking and running to private road travel and large-scale public transportation. These challenges call for innovative solutions to overcome data limitations and compute needs to model mobility behaviors accurately. To address these challenges, we propose a distributed, co-simulation-based architecture DURMOSim that integrates real-world data with scalable, high-fidelity simulations, demonstrating distributed co-simulation feasibility with existing mobility models. DURMOSim underpins a modular integration that would enable using any available mobility simulators for greater extensibility and scalability in performing various urban scenarios. In this paper, we present the design, implementation, and performance evaluation of DURMOSim, highlighting its capability to model population-scale mobility patterns. Our initial results show its ability to dynamically synchronize multiple simulation models at runtime with negligible computational overhead. We believe DURMOSim could be a robust tool for advancing urban mobility research and intelligent transportation systems.

Yoginath, Srikanth [ORNL] (ORCID:0000000184236050)↗
DOE OSTI2024

HumoNet: A Framework for Realistic Modeling and Simulation of Human Mobility Network

Understanding, analyzing, and predicting human mobility and dynamics are valuable to solving pressing problems, developing effective plans, and prescribing timely remedies. As a computational approach, realistic human mobility simulations allow us to understand, analyze, and predict complex systems, including human societies. Accurate simulations rely on (1) the model that captures interactions and behaviors of myriad entities in our society and (2) the mapping of model instances to real-world entities. Taking this into account, this paper introduces the Human Mobility Network simulation framework (HumoNet), an integrated patterns of life (POL) simulation framework that leverages real-world data layers including transportation networks, points of interest, populations, popularity, and human trajectories. HumoNet is a data informed model in which agents are equipped with activities, locomotion, and planning capabilities. To simulate realistic kinematic maneuvers of individuals in transportation networks, HumoNet harnesses a microscopic traffic simulator that provides interaction among vehicles and traffic objects. In this paper, we describe the framework, outline our methodologies, and discuss the data processing and challenges of each data layer. Through experiments, we demonstrate that our simulations capture key features of human mobility by comparing them to the literature and real data using standard measures of human mobility (i.e., the radius of gyration, number of locations visited, level of exploration) and metrics scoring (i.e., Jensen-Shannon divergence). We envision that the synthetic data produced by HumoNet will serve as a benchmark for analyzing epidemics, deploying EV charging networks, and validating AI/ML tasks such as location prediction.

Kim, Joon-Seok↗
DOE OSTI2024

Advanced Shuttle Strategies for Parallel QCCD Architectures

Trapped ions (TIs) are at the forefront of quantum computing implementation, offering unparalleled coherence, fidelity, and connectivity. However, the scalability of TI systems is hampered by the limited capacity of individual ion traps, necessitating intricate ion shuttling for advanced computational tasks. The quantum charge-coupled device (QCCD) framework has emerged as a promising solution, facilitating ion mobility for universal quantum computation. Current QCCD architectures predominantly feature a linear topology, which is increasingly recognized as inefficient for complex quantum operations. Anticipating the shift toward more efficacious designs, this article introduces an innovative quantum scheduling strategy optimized for parallel QCCD topologies. Our strategy proposes a probabilistic formula for ion movement, alongside ingenious methods for local layer generation and layer compression, yielding a significant reduction in ion shuttle times. Through simulations, we demonstrate that our strategy not only substantially outstrips the linear model but also exhibits better performance over other parallel strategies that employ greedy algorithms. This is achieved through our nuanced resolution of complexities, such as traffic blocks and trap capacity limitations. The consequent reduction in shuttle operations leads to lower energy consumption and an enhancement in the quantum computer's fidelity, ultimately accelerating program execution times.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

A Business Case for Nuclear Plant Control Room Modernization

This paper presents a generic business case for implementation of technology that supports Control Room Modernization (CRM). The analysis presented in two forms; 1) a standalone technology upgrade, and 2) a technology upgrade that is built upon and incremental to a prior business case created for Mobile Work Packages (MWP). The business case contends that advanced communication and networking and analytical technologies will allow NPP to conduct control room operations with improved focus by reducing human factors and redundant manpower, and therefore operate with fewer errors. While some labor savings can be harvested in terms of overtime, the majority of savings are demonstrated as reduced time to take the plant off line and bring back on line in support of outages. The benefits are quantified to a rough order of magnitude that provides directional guidance to NPPs that are interested in developing a similar business case. This business case focuses on modernization of the operator control room and does not consider a complete overhaul and modernization of a plants instrument and control systems. While operators may be considering such an investment at their plants, the sizable capital investment required is not likely supported by a cost/benefit analysis alone. More likely, it is driven by obsolescence and reliability issues, and requires consideration of mechanical condition of plant systems, capital depreciation, financing, relicensing and overall viability of the plant asset over a 20-year horizon in a competitive market. Prior studies [REF] have indicated that such a modernization of plant I&C systems, alone or as part of a larger modernization effort, can yield very significant reductions in O&M costs. However, the depth of research and analysis required to develop a meaningful business case for a plant modernization effort is well beyond the scope of this study. While CRM as considered in this study can be easily integrated as part of grander plant modernization effort, it can also be considered as a stand-alone project, implemented as a supervisorial layer over and above existing systems. CRM is enabled by a suite of technologies, which are described in further detail in the body of this report. They include: • Modernized control room with interactive displays • High-bandwidth wireless networks • Mobile devices • Component identification technology • Mobile wireless video cameras • Smart or task based operator displays • Computer-Based Procedures (CBP)/Automated Mobile Work Packages (MWP) • Intelligent plant configuration • Advanced data analytics An analysis was conducted to determine how these technologies might impact outage operations at an NPP. The analysis concluded that outage management capabilities would be improved with application of these technologies, and result in annual benefits for the plant. These improved capabilities were identified and described in further detail in this report. • Reduction in need redundant manpower (verification, etc) • Improved critical path shutdown and restart duration during outages • Reduction in human factors and errors • Remote watches • Paperless outage coordination • Bulk work optimization

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Synchrophasor Data Anomaly Detection on Grid Edge by 5G Communication and Adjacent Compute

The fifth-generation mobile communication (5G) technology offers the opportunities to enhance the grid real-time monitoring. The 5G-enabled phasor measurement units (PMUs) features flexible positioning and cost-effective long-term maintenance, without constraints of fixing wire. This paper is the first to demonstrate the applicability of 5G in PMU communication, and the experiment was carried out at Verizon non-standalone testbed at Pacific Northwest National Laboratory (PNNL) Advanced Wireless Communication lab. The performance of 5G-enabled PMU communication setup is reviewed and discussed in this paper, and the paper presents a real-time dynamic linear model (DML) based synchrophasor data anomaly detection application. Last but not least, the practicability of implementing 5G for wide-area protection strategies is explored and discussed by analyzing the experimental results.

5G, Synchrophasor data, machine learning, anomaly ↗
DOE OSTI2019

ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries

High throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and envi-ronmental samples will revolutionize our understanding of the role of these intriguing and chemically diverse molecules in biological systems. Despite recent technological advances, a metabolomics study still results in the detection of a dispropor-tionate number of features than cannot be confidently assigned to a chemical structure. This inadequacy is driven by the sin-gle most significant limitation in metabolomics: the reliance on reference libraries constructed through the analysis of au-thentic reference chemicals and the limited commercial availability of these compounds. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e. conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The ap-proach employs state-of-the-art first-principles simulation, distinguished by use of molecular dynamics, quantum chemistry, and ion mobility calculations to generate predictions, all without training data. Importantly, optimization of ISiCLE includ-ed a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational effi-ciency by over two orders of magnitude. Calculated CCS values were validated against 1,983 experimental CCS values and compared to previously reported CCS calculation approaches. Average CCS calculation absolute error is 3.2% with standard deviation 3.1%. An online database is introduced for sharing both calculated and experimental CCS values (metabolom-ics.pnnl.gov), initially including a CCS library with over 0.8M entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including the identification of an environmental degradation product, the separation of molecular isomers, and the decoding of complex blinded mixtures of exposure chemicals. This work represents a promising method to address the limitations of small molecule identification, and offers alternatives to standards-based chemical identification amenable to high-performance computation.

metabolomics, standards-free, collision cross sect↗
DOE OSTI2020

A Customizable Metric to Provide a Comprehensive Picture of the Mobility Potential of a Location

Accessibility in a geo-spatial context refers to the ease of reaching a variety of opportunities from a given location. Accessibility theories (and resulting metrics) have traditionally focused on quantifying access to specific opportunities (such as jobs), or focused on specific modes (such as car, bike, etc.). Such approaches often fall short of providing a comprehensive picture of the true accessibility potential of a location as a combination of multiple modes to multiple types of destinations. Addressing this drawback, a novel metric labeled the ‘Mobility Energy Productivity (MEP) Metric’ was developed at the National Renewable Energy Laboratory to quantify the mobility potential of a location to connect people to goods, services, and employment using a variety of modes, while accounting for time, energy, and affordability. The MEP metric has been integrated with advance travel behavior models to compute the changes in mobility potential for various future scenarios, such as introduction of automated vehicles, and/or electric vehicles—but does so at the aggregate, or average-citizen level. The MEP in its initial iteration is not customized to the particular socio-economic contingents, or even to an individual whose modal availability or pattern of trip making may substantially differ from the average. Addressing this gap, this research effort extends the MEP framework from a static state to a more tailored and dynamic state, one in which an individual, or group can customize the metric for their unique characteristics, such as modes, activity patterns, and time-of-day preferences. The extended MEP metric framework can now be integrated to assess the customized mobility energy productivity of an individual.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗