Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “smoothing”

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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Adiabatic behavior of an elliptical vortex in a time-dependent external strain flow

The adiabatic behavior of two-dimensional (2D) fluid vortices subject to slowly varying, time-dependent external strain flows is studied theoretically, and experimentally using magnetized non-neutral electron plasmas. Here the E × B drift dynamics of the electrons perpendicular to the magnetic field are analogous to the motion of fluid vorticity under the 2D Euler equations describing ideal fluids. A low-dimensional elliptical vortex patch model is used along with a WKB approximation to derive a formula for the breaking of an adiabatic invariant due to the changing external flow. The invariant is interpreted as the amplitude of a perturbative oscillation about a stable fixed point corresponding to a steady elliptical vortex. Smooth, hyperbolic tangent and piecewise linear ramp functions are considered for the external strain time dependence. Standard exponential breaking is observed in the former case, whereas the latter exhibits a power-law breaking curve with periodic modulation. It is found that a driving term in the equations of motion contributes the majority of the breaking whereas the frequency variation plays a weaker, but significant role. The experimental data agree closely with the theoretical model. The most significant deviation is due to inviscid damping behavior associated with the smooth edges of the experimental vortices, which tends to reduce the amplitude of the oscillation. Finally, the results are compared and contrasted with other related experimental, numerical, and theoretical work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Measurement of the Sixth-Order Cumulant of Net-Proton Multiplicity Distributions in Au + Au Collisions at s NN = 27 , 54.4, and 200 GeV at RHIC

According to first principle Lattice QCD calculations, the transition from quark-gluon plasma to hadronic matter is a smooth crossover in the region µB ≤ Tc. As a result, higher-order cumulants and their ratios are predicted to be negative, C 6 /C 2 < 0, for example. In this paper, we report the first measurement of the midrapidity net-proton C 6 /C 2 from 27, 54.4 and 200 GeV Au+Au collisions at RHIC. The dependence on collision centrality and kinematic acceptance in (p T, y ) are analyzed. While for 27 and 54.4 GeV collisions the C 6 /C 2 values are close to zero within uncertainties, it is observed that for 200 GeV collisions, the C 6 /C 2 ratio becomes progressively negative from peripheral to central collisions. Transport model calculations without critical dynamics predict values around zero. These observations seem to favor a smooth crossover in the high energy nuclear collisions at RHIC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Cosmological Bounces Induced by a Fermion Condensate

Here, we show that it is possible for fermion condensation of the Nambu-Jona-Lasinio type to induce a non-singular bounce that smoothly connects a phase of slow contraction to a phase of expansion. A chiral condensate—a non-zero vacuum expectation value of the spinor bilinear <Ψ¯Ψ> — can form spontaneously after a slow contraction phase smooths and flattens the universe and the Ricci-curvature exceeds a critical value. In this approach, a high density of spin-aligned free fermions is not required, which avoids the problem of generating a large anisotropy and initiating chaotic mixmaster behavior during the bounce phase.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Ionic Tuning of Cobaltites at the Nanoscale

Control of materials through custom design of ionic distributions is one of the most promising approaches for developing future technologies ranging from spintronic logic and memory devices to energy storage. Perovskites have shown particular promise for ionic-devices due to their high ion mobility and sensitivity to chemical stoichiometry. In this work, we demonstrate control of ion distributions in (La,Sr)CoO3. Commensurate with control of the oxygen stoichiometry, the Co valence and saturation magnetization show a smooth continuous variation. In contrast, magnetoresistance measurements show a sharp, step-wise transition in the magnetic anisotropy and resistivity over the same range of oxygen stoichiometry. These results suggest significant phase separation, with conductive magnetic regions and oxygen-deficient, insulating, non-magnetic regions, forming percolated networks. Indeed, X-ray diffraction identifies oxygen vacancy ordering, including the emergence of Brownmillerite ordering. The unusual appearance of the Brownmillerite phase under ambient temperature is further confirmed by high resolution scanning transmission electron microscopy. This work demonstrates room-temperature ionic control of magnetism, electrical conductivity and crystalline structure, in a 40 nm film, presenting new opportunities for ionic devices that leverage multiple material properties.

thin films, magnetism, xmcd, pnr, STEM, defects↗
DOE OSTI2025

Amplification of laser imprint in the presence of strong, externally imposed, target-normal magnetic fields

Here, an experiment was performed on the OMEGA EP Laser System to investigate the effects of magnetic fields on laser imprint. A 30 µ⁢m thick CH target was driven by a single beam delivering 3.0 kJ of UV energy in a 5 ns square pulse without smoothing by spectral dispersion. The Rayleigh-Taylor amplification of this beam's imprint on the target surface was monitored using face-on gated x-ray radiography from a Gd backlighter. Magnetic fields of up to 45 T were applied normal to the target surface. Analysis of the resulting radiographs shows a 60 ± 13% increase in the spectrally resolved surface perturbation amplitudes, consistent at all times and for all unsaturated frequencies. This consistency indicates that the increase in perturbation amplitudes was due to a change in the initial amplitudes rather than Rayleigh-Taylor growth. This is supported by the trajectory of individual modes and the inferred time-averaged perturbation growth rates. Laser imprint is therefore inferred to have increased due to strong magnetic fields that remain in the conduction zone of the target, suppressing off-axis electron motion and limiting the effects of thermal smoothing.

direct drive↗
DOE OSTI2013

Model morphing and sequence assignment after molecular replacement

A procedure for model building is described that combines morphing a model to match a density map, trimming the morphed model and aligning the model to a sequence. A procedure termed ‘morphing’ for improving a model after it has been placed in the crystallographic cell by molecular replacement has recently been developed. Morphing consists of applying a smooth deformation to a model to make it match an electron-density map more closely. Morphing does not change the identities of the residues in the chain, only their coordinates. Consequently, if the true structure differs from the working model by containing different residues, these differences cannot be corrected by morphing. Here, a procedure that helps to address this limitation is described. The goal of the procedure is to obtain a relatively complete model that has accurate main-chain atomic positions and residues that are correctly assigned to the sequence. Residues in a morphed model that do not match the electron-density map are removed. Each segment of the resulting trimmed morphed model is then assigned to the sequence of the molecule using information about the connectivity of the chains from the working model and from connections that can be identified from the electron-density map. The procedure was tested by application to a recently determined structure at a resolution of 3.2 Å and was found to increase the number of correctly identified residues in this structure from the 88 obtained using phenix.resolve sequence assignment alone (Terwilliger, 2003 ▶) to 247 of a possible 359. Additionally, the procedure was tested by application to a series of templates with sequence identities to a target structure ranging between 7 and 36%. The mean fraction of correctly identified residues in these cases was increased from 33% using phenix.resolve sequence assignment to 47% using the current procedure. The procedure is simple to apply and is available in the Phenix software package.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Synthetic Reference-based Transition Optimal Control of Inverter for Surge-Current-Free Rapid Interface to Grid

This article addresses the problem of the transition between objective functions in optimal control for power inverters when switching between standalone and grid-connected modes. The work proposes the use of a synthetic reference during the transition between the corresponding objective functions. Moreover, the authors aim to show that a transition function can be imposed and controlled externally from the main optimal control routine. In the end, the simulation results demonstrate the importance of properly applying the proposed strategy for a smooth transition between the operating modes with minimal surge current. These results contrast with the strategies found in the literature where the smooth transition comes with the price of a few grid cycles to sync.

Roig Greidanus, Mateo D.↗
DOE OSTI2024

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem’s wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

Alnajjar, Anees↗
DOE OSTI2020

Magneto-Structural Combined Dimensional and Topology Optimization of Interior Permanent Magnet Synchronous Machine Rotors

A magneto-structural combined dimensional and topology optimization approach for interior permanent magnet synchronous machine (IPMSM) rotor design is proposed using the Solid Isotropic Material Penalization (SIMP) density-based topology optimization method. This method optimizes the location and dimensions of the permanent magnets (PMs) embedded in the rotor, while simultaneously optimizing the lamination flux barriers using topology optimization.To allow for the location and dimensions of the PMs to change while retaining the same number of mesh elements and connectivity, the mesh must be deformed. Mesh deformation is accomplished using a Laplace smoothing technique. To increase the range of PM displacement and size changes, the non-PM areas of the rotor design domain are divided into sub-regions along whose boundaries the mesh nodes are redistributed also using a Laplace smoothing algorithm. Three examples of IPMSM rotor optimizations are presented to demonstrate the proposed approach.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Incorporating Residential Smart Electric Vehicle Charging in Home Energy Management Systems

Electric vehicles (EVs) are expected to drastically increase residential electricity consumption and could provide a significant source of flexible demand. Aggregating smart EV charge controllers with other smart home devices through a home energy management system can lead to more optimal outcomes that benefit homeowners, utilities, and grid operators. Control strategies should consider occupant convenience by accounting for the need for fully charged EVs near the EV departure time. In this paper, we develop an EV charging framework that accounts for occupant convenience using OCHRE, a residential energy model, and foresee, a home energy management system. We simulate a community with high EV penetration and show that integrated, smart EV charging reduces peak demand and smooths night-time energy consumption. Simulation results show that the proposed control strategy nearly eliminates peak period EV charging and reduces the daily peak demand from EVs by 23%.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
DOE OSTI2014

Compiler generation and autotuning of communication-avoiding operators for geometric multigrid

This paper describes a compiler approach to introducing communication-avoiding optimizations in geometric multigrid (GMG), one of the most popular methods for solving partial differential equations. Communication-avoiding optimizations reduce vertical communication through the memory hierarchy and horizontal communication across processes or threads, usually at the expense of introducing redundant computation. We focus on applying these optimizations to the smooth operator, which successively reduces the error and accounts for the largest fraction of the GMG execution time. Our compiler technology applies both novel and known transformations to derive an implementation comparable to manually-tuned code. To make the approach portable, an underlying autotuning system explores the tradeoff between reduced communication and increased computation, as well as tradeoffs in threading schemes, to automatically identify the best implementation for a particular architecture and at each computation phase. Results show that we are able to quadruple the performance of the smooth operation on the finest grids while attaining performance within 94% of manually-tuned code. Overall we improve the overall multigrid solve time by 2.5× without sacrificing programer productivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Estimation of RTT and Loss Rate of Wide-Area Connections Using MPI Measurements

Scientific computations are expected to be increasingly distributed across wide-area networks, and the Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. These computations should account for certain network parameters to ensure an effective execution, for example, by avoiding highly congested and long connections. The execution times of MPI basic operations reflect the connection parameters, including the Round Trip Time (RTT) and loss rate. We describe five machine leaning methods to estimate the connection RTT and loss rate using execution times of MPI basic operations. We utilize execution time measurements of MPI Sendrecv operations collected over emulated 10 Gbps connections with 0-366 ms round-trip times, wherein the longest connection spans the globe, under up to 20% periodic losses. These methods provide disparate, namely, linear and non-linear, and smooth and non-smooth, estimates of RTT and loss rate. Our results show that accurate estimates can be generated at low loss rates but they become inaccurate at loss rates 10% and higher. Overall, these results constitute a case study of the strengths and limitations of machine learning methods in inferring network-level parameters using application-level measurements.

Rao, Nageswara S.↗
DOE OSTI2023

An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data

This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using row-resolution (i.e., 15 minutes) smart meter data. We first demonstrate that the electricity consumption profiles on mild-temperature days can be used to approximate the base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed from the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and verify the proposed methods. Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers.

Kim, Hyeonjin↗
DOE OSTI2019

Evaluating the Environmental Impacts of Connected and Automated Vehicles: Potential Shortcomings of a Binned-Based Emissions Model

In addition to providing safety and mobility benefits, Connected and Automated Vehicles (CAVs) have the potential to reduce fuel consumption and emissions. As new CAV applications are developed, it is valuable to estimate these potential environmental benefits, typically using vehicle activity data and emissions models. To date, most researchers in the U.S. have used the MOVES vehicle emissions model, developed and maintained by the U.S. Environmental Protection Agency (EPA). However, because MOVES uses a binning approach, it is likely underestimating the true energy and emissions savings that occur when CAV applications smooth traffic flow. To illustrate this problem, we measure and model the fuel consumption and CO 2 emissions for a real-world CAV application: Eco-Approach and Departure (EAD) at signalized intersections. Real-world measurements are compared to a MOVES-based estimate, as well as to an estimate provided by the physical-based Comprehensive Modal Emissions Model (CMEM). Results show that MOVES consistently underestimates the energy and emissions benefits of the CAV application, primarily since the bin sizes in MOVES are too large to catch the nuances of traffic smoothing. On the other hand, CMEM provided a more accurate energy and emissions estimate, primarily since it uses analytical functions to model emissions and does not suffer from the same binning problem.

42 ENGINEERING↗
DOE OSTI2022

Adaptive Urban Traffic Signal Control for Multiple Intersections: An LQR Approach

Traffic congestion leads to severe problems especially in urban traffic networks. It increases the chance of accidents, energy waste, and social costs. In order to address these problems, an adaptive linear quadratic regulator (LQR) approach is developed for traffic signal control at multiple intersections in an urban area. The proposed method controls the green time of the traffic signals to reduce traffic congestion and smooth traffic flow. Real-world data from vision-based traffic sensors are used to build the traffic network model, which mimics the real-world traffic behavior. In addition, the proposed control utilizes recursive least square parameter estimation, which is capable of tracking dynamic changes in traffic conditions. Simulation of Urban MObility (SUMO) is used to analyze the efficacy of the proposed method. Results of the simulation show that the proposed method outperforms pretimed control in various aspects.

adaptive LQR control↗
DOE OSTI2017

Data Challenges in Estimating the Capacity Value of Solar Photovoltaics

We examine the robustness of solar capacity-value estimates to three important data issues. The first is the sensitivity to using hourly averaged as opposed to subhourly solar-insolation data. The second is the sensitivity to errors in recording and interpreting load data. The third is the sensitivity to using modeled as opposed to measured solar-insolation data. We demonstrate that capacity-value estimates of solar are sensitive to all three of these factors, with potentially large errors in the capacity-value estimate in a particular year. If multiple years of data are available, the biases introduced by using hourly averaged solar-insolation can be smoothed out. Multiple years of data will not necessarily address the other data-related issues that we examine. Our analysis calls into question the accuracy of a number of solar capacity-value estimates relying exclusively on modeled solar-insolation data that are reported in the literature (including our own previous works). Our analysis also suggests that multiple years' historical data should be used for remunerating solar generators for their capacity value in organized wholesale electricity markets.

14 SOLAR ENERGY↗
DOE OSTI2021

Solar Transposition Modeling via Deep Neural Networks With Sky Images

This article presents a notable advance toward the development of a new method of increasing the single-axis tracking photovoltaic (PV) system power output by improving the determination and near-term prediction of the optimum module tilt angle. The tilt angle of the plane receiving the greatest total irradiance changes with Sun position and atmospheric conditions including cloud formation and movement, aerosols, and particulate loading, as well as varying albedo within a module's field of view. In this article, we present a multi-input convolutional neural network that can create a profile of plane-of-array irradiance versus surface tilt angle over a full 180° arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs an N -dimensional vector, where N is the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. Furthermore, these profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.

42 ENGINEERING↗