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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Toward High-Voltage Cathodes for Zinc-Ion Batteries: Discovery Pipeline and Material Design Rules

Efficient energy storage systems are crucial to address the intermittency of renewable energy sources. As multivalent batteries, Zn-ion batteries (ZIBs), while inherently low voltage, offer a promising low-cost alternative to Li-ion batteries due to the viable use of zinc as the anode. However, to maximize the potential impact of ZIBs, rechargeable cathodes with improved Zn diffusion are needed. To better understand the chemical and structural factors influencing Zn-ion mobility within battery electrode materials, we employ a high-throughput computational screening approach to systematically evaluate candidate intercalation hosts for ZIB cathodes, expanding the chemical search space on empty intercalation hosts that do not contain Zn. We leverage a high-throughput screening funnel to identify promising cathodes in ZIBs, integrating screening criteria with density functional theory (DFT)-based calculations of Zn2+ intercalation and diffusion inside the host materials. Using these data, we identify the design principles that favor Zn-ion mobility in candidate cathode materials. Building on previous work on divalent-ion cathodes, this study broadens the chemical space for next-generation multivalent energy storage systems.

electrodes↗
DOE OSTI2015

Strong enhancement of surface diffusion by nonlinear surface acoustic waves

The phenomenon of acoustic activation of surface diffusion is investigated in a combined computational and experimental study. The ability of pulsed laser-generated surface acoustic waves (SAWs) to enhance the mobility of small atomic clusters is demonstrated by directly tracking, with fluorescence microscopy, individual Au 8 clusters moving on a (111) silicon substrate. In this work, a 19-fold increase in the effective diffusion coefficient is measured in room temperature experiments in the presence of SAWs generated by nanosecond pulse laser irradiation at a 100 Hz repetition rate. A strong enhancement of cluster mobility by SAWs is also observed in large-scale molecular dynamics simulations of surface diffusion of small atomic clusters. The analysis of the computational results demonstrates that the nonlinear sharpening of SAWs and the corresponding enrichment of the SAW spectra by high frequency harmonics which are capable of dynamic coupling to the cluster vibrations are responsible for the efficient acoustic activation of surface mobility in the simulations. The increase in the effective diffusion coefficient is proportional to the number of the SAW pulses passing through the diffusion region per unit time and a dramatic 4500-fold diffusion enhancement (corresponds to an equivalent temperature increase by ~10 3 K ) is predicted in the simulations for 15 GHz SAWs. The ability of SAWs to affect atomic-level surface processes has far-reaching implications for the design of new techniques where the acoustic energy serves as an effective substitution for thermal activation in applications where heating must be avoided or rapid switching of surface conditions is required.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Pathways to a Sustainable Aviation Ecosystem: Flight DNA: An Anonymized Aviation Data Tool and Repository

The National Renewable Energy Laboratory (NREL) has deep experience developing secure, national data repositories, which it augments with analysis, technology and market expertise, high-performance computing, and innovative data visualization. By adapting the architecture of existing mobility databases (i.e., Fleet DNA, the Transportation Secure Data Center, the National Fuel Cell Technology Evaluation Center), NREL can build a powerful aviation data clearinghouse - Flight DNA - that helps stakeholders navigate the web of pitfalls and possibilities generated by new aviation technologies.

aviation↗
DOE OSTI2021

A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem

Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet rebalancing. For this reason, operators tend to employ simplified algorithms that have been demonstrated to work well in a particular setting. To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost. In particular, by treating dispatch as part of the environment dynamics, a centralized agent can learn to intermittently direct the dispatcher to reposition free vehicles and mitigate against fleet imbalance. We formulate RL state and action spaces as distributions over a grid partitioning of the operating area, making the framework scalable and avoiding the complexities associated with multiagent RL. Numerical experiments, using real-world trip and network data, demonstrate that RL reduces waiting time by 28% to 38% for the same-day evaluation, 17% to 44% for cross-day evaluation, and 22% to 25% for cross-season evaluation compared with no rebalancing scenarios. This approach has several distinct advantages over baseline methods including: improved system cost; high degree of adaptability to the selected dispatch method; and the ability to perform scale-invariant transfer learning between problem instances with similar vehicle and request distributions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

MEP Core Tools

This slide deck provides an update on the progress of the project titled "MEP Core Tools" funded by the Energy Efficient Mobility Systems program in the Vehicle Technologies Office at the Department of Energy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Proton Conducting Neuromorphic Materials and Devices

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Grain boundary plane orientation fundamental zones and structure-property relationships

Grain boundary plane orientation is a profoundly important determinant of character in polycrystalline materials that is not well understood. This work demonstrates how boundary plane orientation fundamental zones, which capture the natural crystallographic symmetries of a grain boundary, can be used to establish structure-property relationships. Using the fundamental zone representation, trends in computed energy, excess volume at the grain boundary, and temperature-dependent mobility naturally emerge and show a strong dependence on the boundary plane orientation. Analysis of common misorientation axes even suggests broader trends of grain boundary energy as a function of misorientation angle and plane orientation. Due to the strong structure-property relationships that naturally emerge from this work, boundary plane fundamental zones are expected to simplify analysis of both computational and experimental data. This standardized representation has the potential to significantly accelerate research in the topologically complex and vast five-dimensional phase space of grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2014

SU-D-BRF-07: Ultrasound and Fluoroscopy Based Intraoperative Image-Guidance System for Dynamic Dosimetry in Prostate Brachytherapy

Purpose: Prostate brachytherapy is a common treatment method for low-risk prostate cancer patients. Intraoperative treatment planning is known to improve the treatment procedure and the outcome. The current limitation of intraoperative treatment planning is the inability to localize the seeds in relation to the prostate. We developed an image-guidance system to fulfill this need to achieve intraoperative dynamic dosimetry in prostate brachytherapy. Methods: Our system is based on standard imaging equipments available in the operating room, including the transrectal ultrasound (TRUS) and the mobile C-arm. A simple fiducial is added to compute the C-arm pose. Three fluoroscopic images and an ultrasound volume of the seeds and the prostate are acquired and processed by four image processing algorithms: seed segmentation, fiducial detection with pose estimation, seed reconstruction, and seeds-to-TRUS registration. The updated seed positions allow the physician to assess the quality of implantation and dynamically adjust the treatment plan during the course of surgery to achieve improved exit dosimetry. Results: The system was tested on 10 phantoms and 37 patients. Seed segmentation resulted in a 1% false negative and 2% false positive rates. Fiducial detection with pose estimation resulted in a detection rate of 98%. Seed reconstruction had a mean reconstruction error of 0.4 mm. Seeds-to-TRUS registration had a mean registration error of 1.3 mm. The total processing time from image acquisition to registration was approximately 1 minute. Conclusion: We present an image-guidance system for intraoperative dynamic dosimetry in prostate brachytherapy. Using standard imaging equipments and a simple fiducial, our system can be easily adopted in any clinics. Robust image processing algorithms enable accurate and fast computation of the delivered dose. Especially, the system enables detection of possible hot/cold spots during the surgery, allowing the physician to address these in the operating room rather than requiring additional therapy afterwards. This research was supported in part by the National Institutes of Health/National Cancer Institute (NIH/NCI) under grant 2R44CA099374 and grant 1R01CA151395, and in part by the Department of Defense (DoD) under grant W81XWH-05-1-0407.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions

What makes you hold on to that old car? While the vast majority of household vehicles are still powered by conventional internal combustion engines, the progress of adopting emerging vehicle technologies will critically depend on how soon the existing vehicles are transacted out of the household fleet. Leveraging a nationally representative longitudinal data set, the Panel Study of Income Dynamics, this study examines how household decisions to dispose of or replace a given vehicle are: 1) influenced by the vehicle’s attributes, 2) mediated by households’ concurrent socio-demographic and economic attributes, and 3) triggered by key life cycle events. Coupled with a newly developed machine learning interpretation tool, TreeExplainer, we demonstrate an innovative use of machine learning models to augment traditional logit modeling to both generate behavioral insights and improve model performance. We find the two gradient-boosting-based methods, CatBoost and LightGBM, are the best performing machine learning models for this problem. The multinomial logistic model can achieve similar performance levels after its model specification is informed by TreeExplainer. Both machine learning and multinomial logit models suggest that while older vehicles are more likely to be disposed of or replaced than newer ones, such probability decreases as the vehicles serve the family longer. Pickup trucks and sport utility vehicles are less likely to be disposed of or replaced than cars, and leased vehicles are more likely to be transacted than owned vehicles. We find that married families, families with higher education levels, homeowners, and older families tend to keep their vehicles longer. Life events such as childbirth, residential relocation, and change of household composition and income are found to increase vehicle disposal and/or replacement. We provide additional insights on the timing of vehicle replacement or disposal, in particular, the presence of children and childbirth events are more strongly associated with vehicle replacement among younger parents.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Efficient ab initio calculations of electron-defect scattering and defect-limited carrier mobility

Electron-defect ($e - \mathrm{d}$) interactions govern charge carrier dynamics at low temperature, where they limit the carrier mobility and give rise to phenomena of broad relevance in condensed matter physics. Ab initio calculations of $e - \mathrm{d}$ interactions are still in their infancy, mainly because they require large supercells and computationally expensive workflows. Here we develop an efficient ab initio approach for computing elastic $e - \mathrm{d}$ interactions, their associated $e - \mathrm{d}$ relaxation times (RTs), and the low-temperature defect-limited carrier mobility. The method is applied to silicon with simple neutral defects, such as vacancies and interstitials. Contrary to conventional wisdom, the computed $e - \mathrm{d}$ RTs depend strongly on carrier energy and defect type, and the defect-limited mobility is temperature dependent. These results highlight the shortcomings of widely employed heuristic models of $e - \mathrm{d}$ interactions in materials. Finally, our method opens avenues for studying $e - \mathrm{d}$ scattering and low-temperature charge transport from first principles.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Mass Transfer Between Recirculation and Main Flow Zones: Is Physically Based Parameterization Possible?

Recirculation zones (RZs) are typical in many geophysical flows. These zones form near irregular solid boundaries and are separate from the main flow. Since mass can be trapped and later released locally from RZs, bulk transport can exhibit long tails—an anomalous behavior that is challenging to predict. The underlying RZ mass transfer and retention in this situation is poorly understood despite common parameterization by effective exchange coefficients in mobile-immobile (MIM) domain models. We viewed the mass transfer process using computationally resolved flow and transport fields inside two-dimensional rough fractures. RZs were delineated by a novel technique followed by quantification of mass transfer across the interface with the main flow zone. The results showed that the first-order mass transfer coefficient is a function of Reynolds number and velocity difference between the RZ and bulk flow. A distributed mobile-immobile model with the directly estimated parameters accurately reproduced bulk anomalous transport. As the distributed mobile-immobile model is not yet predictive, its development showed that mass transfer coefficients for flows involving RZs are physically meaningful, potentially predictable, and useful for elucidating local mass transfer processes within the general framework of mobile-immobile transport modeling.

58 GEOSCIENCES↗
DOE OSTI2023

Wetting of a Hydrophobic Surface: Far-IR Action Spectroscopy and Dynamics of Microhydrated Naphthalene

The interaction of water and polycyclic aromatic hydrocarbons is of fundamental importance in areas as diverse as materials science and atmospheric and interstellar chemistry. The interplay between hydrogen bonding and dipole–π interactions results in subtle dynamics that are challenging to describe from first principles. Here, we employ far-IR action vibrational spectroscopy with the infrared free-electron laser FELIX to investigate naphthalene with one to three water molecules. We observe diffuse bands associated with intermolecular vibrational modes that serve as direct probes of the loose binding of water to the naphthalene surface. These signatures are poorly reproduced by static DFT or Møller–Plesset computations. Instead, a rationalization is achieved through Born–Oppenheimer Molecular Dynamics simulations, revealing the active mobility of water over the surface, even at low temperatures. Therefore, our work provides direct insights into the wetting interactions associated with shallow potential energy surfaces while simultaneously demonstrating a solid experimental–computational framework for their investigation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

X-ray computed tomography-based porosity analysis: Algorithms and application for porous woody biomass

The properties of lignocellulosic biomass, such as bulk physical, thermal, and mechanical properties, as well as the mobility of enzymes or catalysts, are largely affected by porosity, pore structures, and pore size distribution. While X-ray computed tomography (CT) has been introduced to effectively produce 3D volumetric images of material microstructure, a quantitative porosity analysis from the 3D images has never been done. This work introduces a first-of-its-kind X-ray CT-based quantitative porosity analysis method and applies the developed toolkit to characterize the internal porosity distribution of loblolly pine. A sample loblolly pine chip was scanned by a nano-resolution X-ray CT system and digitally reconstructed after a sequence of image processing operations. Here, a comprehensive porosity analysis was then performed to quantify the envelope porosity, the spatial distribution of local porosity, and the directional porosity. Solutions to the various challenges in image processing, porosity calculation, and large data handling are provided.

42 ENGINEERING↗
DOE OSTI2025

FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations

In edge computing, users' service profiles are migrated between edge servers due to user mobility. Reinforcement Learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE's boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. Here, we extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures.

Edge computing↗
DOE OSTI2017

Enabling Real-Time Volume Rendering of Functional Magnetic Resonance Imaging on an iOS Device

Powerful non-invasive imaging technologies like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI) are used daily by medical professionals to diagnose and treat patients. While 2D slice viewers have long been the standard, many tools allowing 3D representations of digital medical data are now available. The newest imaging advancement, functional MRI (fMRI) technology, has changed medical imaging from viewing static to dynamic physiology (4D) over time, particularly to study brain activity. Add this to the rapid adoption of mobile devices for everyday work and the need to visualize fMRI data on tablets or smartphones arises. However, there are few mobile tools available to visualize 3D MRI data, let alone 4D fMRI data. Building volume rendering tools on mobile devices to visualize 3D and 4D medical data is challenging given the limited computational power of the devices. This paper describes research that explored the feasibility of performing real-time 3D and 4D volume raycasting on a tablet device. The prototype application was tested on a 9.7” iPad Pro using two different fMRI datasets of brain activity. The results show that mobile raycasting is able to achieve between 20 and 40 frames per second for traditional 3D datasets, depending on the sampling interval, and up to 9 frames per second for 4D data. While the prototype application did not always achieve true real-time interaction, these results clearly demonstrated that visualizing 3D and 4D digital medical data is feasible with a properly constructed software framework.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Technical Characterization and Benefit Evaluation of 5G-Enabled Grid Data Transport and Applications

This report summarizes the Year 1 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research Program. 5G is a breakthrough technology that enables a fully mobile and connected society, and a 5G-enabled digital continuum will be one of the critical foundations for a clean energy economy and grid modernization. In collaboration with PNNL’s Advanced Wireless Communication team and Center for Advanced Technology Evaluation team, the project team has been evaluating the system performance of 5G testbeds in the PNNL 5G Innovation Studio, and has formulated a co-simulation test case of power system transmission, distribution, and communication (T&D&C) networks considering 5G technology and high penetration of distributed energy resources. The methodology developed in the 5G Energy FRAME project can be customized to fit different future grid scenarios to evaluate multiple (dynamic) configurations (computing, sensing, communication, environment) for different stakeholders. In summary, our main technical highlights in project Year 1 are as follows: 1) Technical characterization of 5G standalone architectures, 2) Formulation of co-simulation test case of T&D&C networks embedded with 5G, 3) Initial benefit evaluation of 5G communication platform for grid use cases, and 4) Additional extended discussions on edge computing, artificial intelligence and machine learning, and high-performance computing and cloud computing adoptions. In addition, a collection of system performance data is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications

24 POWER TRANSMISSION AND DISTRIBUTION↗