Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “field optimization”

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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A consistent and conservative Phase-Field method for multiphase incompressible flows

In the present study, a consistent and conservative Phase-Field method, including both the model and scheme, is developed for multiphase flows with an arbitrary number of immiscible and incompressible fluid phases. The consistency of mass conservation and the consistency of mass and momentum transport are implemented to address the issue of physically coupling the Phase-Field equation, which locates different phases, to the hydrodynamics. These two consistency conditions, as illustrated, provide the “optimal” coupling because (i) the new momentum equation resulting from them is Galilean invariant and implies the kinetic energy conservation, regardless of the details of the Phase-Field equation, and (ii) failures of satisfying the second law of thermodynamics or the consistency of reduction of the multiphase flow model only result from the same failures of the Phase-Field equation but are not due to the new momentum equation. Physical interpretation of the consistency conditions and their formulations are first provided, and general formulations that are obtained from the consistency conditions and independent of the interpretation of the velocity are summarized. Then, the present consistent and conservative multiphase flow model is completed by selecting a reduction consistent Phase-Field equation. Several novel techniques are developed to inherit the physical properties of the multiphase flows after discretization, including the gradient-based phase selection procedure, the momentum conservative method for the surface force, and the general theorems to preserve the consistency conditions on the discrete level. Equipped with those novel techniques, a consistent and conservative scheme for the present multiphase flow model is developed and analyzed. The scheme satisfies the consistency conditions, conserves the mass and momentum, and assures the summation of the volume fractions to be unity, on the fully discrete level and for an arbitrary number of phases. All those properties are numerically validated. Finally, numerical applications demonstrate that the present model and scheme are robust and effective in studying complicated multiphase dynamics, especially for those with large-density ratios.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AC Magnetometry Using Nano-ferrofluid Cladded Multimode Interferometric Fiber Optic Sensors for Power Grid Monitoring Applications

The AC magnetic field response of the superparamagnetic nano-ferrofluid is an interplay between the Neel and Brownian relaxation processes and is generally quantified via the susceptibility measurements at high frequencies. The high frequency limit is dictated by these relaxation times which need to be shorter than the time scale of the time varying magnetic field for the nano-ferrofluid to be considered in an equilibrium state at each time instant. Even though the high frequency response of ferrofluid has been extensively investigated for frequencies up to GHz range by non-optical methods, harnessing dynamic response by optical means for AC magnetic field sensing in fiber-optic-based sensors-field remains unexplored. Instead, the incorporation of nano-ferrofluid as sensing materials has been only limited to DC magnetic field sensing, often citing their long response time as a limiting factor to AC field sensing. This work reports the finding of high frequency (up to 15 kHz) AC magnetic field sensing capability of nanomagnetic fluid as the cladding material of a fiber-optic multimode interferometry (MMI) structure optimized for the fourth self-imaging spectral response. The key parameter enabling high frequency response is the short response time (<1 ms) achieved by optimizing both the sensing structure and nano-ferrofluid solution. Focus has been imparted on 60 Hz line-frequency profiles of various current/magnetic fields to test the efficacy of these sensors in metering and monitoring current and current-induced magnetic fields in the electrical power grid systems. The magnetic field sensitivity of 240 mV/Gauss per dBm of transmitted power was achieved for 60 Hz field applied via Helmholtz coil, whereas the 60 Hz AC current sensitivity of 2.83 mV/A was measured due to magnetic field induced by current in a straight conducting wire.

42 ENGINEERING↗
DOE OSTI2021

Size- and Phase-Controlled Nanometer-Thick ß-Ga2O3 Films with Green Photoluminescence for Optoelectronic Applications

Realization and optimization of the tunable/enhanced optical properties is critical to further advance the field of optoelectronics, photonics, and nano-electronics. In this context, here, we demonstrate green-emission characteristics with a ~30 fold enhancement in selectively engineered nanocrystalline Ga2O3 with a control over size, phase and interface nanostructure. Pulsed-laser-deposited ß-phase Ga2O3 films with an average crystallite size of ~9 nm along with a highly dense, close-compact nano-columnar structure with lowest possible defect density facilitates the thirty-fold enhancement in the photoluminescence (PL) intensity in green-region. Enhanced PL-emission in the optimized, engineered nano-architecture sheds light in designing Ga2O3-materials for promising future optoelectronic/photocatalytic applications.

Ga2O3, Nanocrystalline Films, Pulsed-Laser Deposit↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs: Preprint

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and allocations and to develop a training data set for ML. This process included simulating the historical injection and production since 1979 and prediction of future performance through 2040. ML networks were created and trained using TensorFlow based on multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN) architectures. These networks took as input selected flow rates, injection temperatures, and historical field operation data and produced estimates of future production temperatures. This approach was first successfully tested on a simplified single fracture doublet system, followed by the application to the BHS reservoir. Using an initial BHS dataset with 37 simulated scenarios, the trained and validated network predicted the production temperature for 6 production wells with the mean absolute percentage error of less than 8%. In a complementary analysis effort, the principal component analysis applied to 13 BHS geological parameters revealed that vertical fracture permeability shows the strongest correlation with fault density and fault intersection density. A new BHS reservoir model was developed considering the fault intersection density as proxy for permeability. This new reservoir model helps to explore under-exploited zones in the reservoir. A data gathering plan to obtain additional subsurface data was developed; it includes temperature surveying for three idle injection wells, at which the reservoir simulations indicate high bottom-hole temperatures. The collected data assist with calibrating the reservoir model and may lead to converting these wells to producers to access under-exploited zones in the reservoir. Data gathering activities are planned for the first quarter of 2021.

40 EE - Geothermal Technologies Office (EE-4G)↗
NASA NTRS2014

Initial Field Deployment Results of Green PCB Removal from Sediment Systems (GPRSS)

The goal of this task order was to complete optimization and development of the Green PCB Remediation from Sediment Systems(GPRSSs) technology, culminating in the production of functioning demonstration test units which would be deployed at a suitable demonstration location. This location would be selected in conjunction with Toxicological & Ecological Associates who have entered into a SAA with NASA to partner with and further develop this technology. The GPRSSs technology was initially developed under ESC Task Order 83 with the purpose of providing a green remediation technology capable of in-situ removal and remediation of polychlorinated biphenyls (PCBs) from contaminated sediments. The core concept of the technology, a polymeric blanket capable of absorbing PCBs when in contact with contaminated sediments was then transitioned to Task Order 165 where the primary objective was to fully design and optimize a functioning test unit capable of testing the theoretical and laboratory scale concepts in a real world situation. Results from both task orders are included in this report for completeness, although Task Order 165 focused on the blanket design and the small scale field demonstration in which is currently still ongoing in Altavista, VA.

Deployment↗
DOE OSTI2024

Level-set topology optimization with PDE generated conformal meshes

This paper presents a level-set topology optimization approach that uses conformal meshes for the analysis of the displacement field. The structure’s boundary is represented by the iso-contour of a level-set field discretized on a fixed background design mesh. The conformal mesh is updated for each design iteration via a PDE based mesh morphing process that identifies the set of facets in the background mesh that are homeomorphic to the boundary and relaxes the homeomorphic mesh to conform to the structure’s boundary and ensure high element quality. The conformal mesh allows for a more accurate computation of the response versus density and some level-set based methods which interpolate material properties using the volume fraction. Numerical examples illustrate the proposed approach by optimizing linear-elastic two- and three-dimensional structures, wherein insight into the performance of the mesh morphing process is provided. The examples also highlight the scalability of the approach.

42 ENGINEERING↗
DOE OSTI2021

Automated CT registration, segmentation, and quantification (AutoCT) v1.0

Processing and analyzing brain imaging is crucial in both scientific development and clinical field. In this software package, we build a pipeline that integrates automatic registration, segmentation, and quantitative analysis for subjects' CT scans. Leveraging diffeomorphic transformations, we enable optimized forward and inverse mappings between an image and the reference. Furthermore, we extract localized features from deformation field based on an online template process, which advances statistical learning downstream. The created templates, atlas as well as our methods provide the brain imaging community tools for AI implementations.

Essiari, Abdelilah↗
DOE OSTI2021

Automated CT registration, segmentation, and quantification (AutoCT) v1.1

Processing and analyzing brain imaging is crucial in both scientific development and clinical field. In this software package, we build a pipeline that integrates automatic registration, segmentation, and quantitative analysis for subjects' CT scans. Leveraging diffeomorphic transofrmations, we enable optimized forward and inverse mappings between an image and the reference. Furthermore, we extract localized features from deformation field based on an online template process, which advances statistical learning downstream. The created templates, atlas as well as our methods provide the brain imaging community tools for AI implementations

Bai, Zhe↗
DOE OSTI2021

Calibration of elastoplastic constitutive model parameters from full-field data with automatic differentiation-based sensitivities

Here, we present a framework for calibration of parameters in elastoplastic constitutive models that is based on the use of automatic differentiation (AD). The model calibration problem is posed as a partial differential equation-constrained optimization problem where a finite element (FE) model of the coupled equilibrium equation and constitutive model evolution equations serves as the constraint. The objective function quantifies the mismatch between the displacement predicted by the FE model and full-field digital image correlation data, and the optimization problem is solved using gradient-based optimization algorithms. Forward and adjoint sensitivities are used to compute the gradient at considerably less cost than its calculation from finite difference approximations. Through the use of AD, we need only to write the constraints in terms of AD objects, where all of the derivatives required for the forward and inverse problems are obtained by appropriately seeding and evaluating these quantities. We present three numerical examples that verify the correctness of the gradient, demonstrate the AD approach's parallel computation capabilities via application to a large-scale FE model, and highlight the formulation's ease of extensibility to other classes of constitutive models.

42 ENGINEERING↗
DOE OSTI2022

Predictive Tools for Customizing Heat Treatment of Additively Manufactured Aerospace Components

Laser-bed powder fusion (LBPF) additive manufacturing is increasingly being used to produce components of complex geometries using the Ni-base superalloy Inconel 718. The composition and the microstructure of the alloy are currently well optimized for wrought components made using conventional manufacturing processes such as rolling, forging, extrusion, etc. The attractive mechanical properties of the alloy result from the underlying austenitic matrix with fine equiaxed grains, and a high density and uniform distribution of the precipitation hardening phase, γ". Heat treatment steps such as homogenization, solutioning and aging are well documented for the wrought alloy. However, when the same wrought alloy compositions are used for the additive manufacturing (AM) processes, the asprocessed microstructure is significantly different, because of the different thermal history associated with LBPF, including rapid solidification and multiple temperature excursions that lead to multiple re-melting and reheating in the solid state. Rapid solidification introduces potential non-equilibrium effects at the moving solid-liquid interfaces that impact the extent of solute segregation, as well as the morphology of the dendritic grains that form. In order to recover the target mechanical properties, AM components have to undergo post-process heat treatments. However, such heat treatments have to be custom designed for the AM process and the component geometry because of the expected vast differences in the microstructure at various locations of a component with complex geometry. The homogenization and precipitation steps should be optimized for the component so that target mechanical properties can be obtained throughout the part. The objective of this research is to utilize High Performance Computing in phase field simulations of microstructure evolution during post-processing of AM components. The physics-based modeling will be beneficial in reducing the experimental effort required for heat treatment process selection, optimization, and certification, thus leading to a significant reduction in energy consumption for AM and post-processing heat treatment. The optimization study will help identify heat treatments steps that are critical for development of a final desired microstructure with the minimum energy input. This combined with shortening of the production cycle (time-to-market) by reducing the number of failed parts (property targets), and reduction in the number of iterations for process optimization, will enable 30-40% savings in the energy costs. Phase field simulations of the degree of homogenization and the effect of local matrix composition on the nucleation and growth of competing precipitating phases were performed using the Microstructure Evolution Using Massively Parallel Phase Field Simulations code developed in-house at the Oak Ridge National Laboratory. The simulations were able to successfully capture the kinetics of nucleation and growth, and morphologies of various precipitating phases as a function of local matrix compositions and composition gradients characteristic of local microstructures arising from location-dependent variations in the thermal conditions. Future work will involve extending the simulations to a length scale consisting of multiple dendrites, so that the effect of homogenization on the coarsening of the dendrites can be simulated and used as an additional input to the optimization of the heat treatment process.

36 MATERIALS SCIENCE↗
NASA NTRS2000

Stress Recovery and Error Estimation for 3-D Shell Structures

The C1-continuous stress fields obtained from finite element analyses are in general lower- order accurate than are the corresponding displacement fields. Much effort has focussed on increasing their accuracy and/or their continuity, both for improved stress prediction and especially error estimation. A previous project developed a penalized, discrete least squares variational procedure that increases the accuracy and continuity of the stress field. The variational problem is solved by a post-processing, 'finite-element-type' analysis to recover a smooth, more accurate, C1-continuous stress field given the 'raw' finite element stresses. This analysis has been named the SEA/PDLS. The recovered stress field can be used in a posteriori error estimators, such as the Zienkiewicz-Zhu error estimator or equilibrium error estimators. The procedure was well-developed for the two-dimensional (plane) case involving low-order finite elements. It has been demonstrated that, if optimal finite element stresses are used for the post-processing, the recovered stress field is globally superconvergent. Extension of this work to three dimensional solids is straightforward. Attachment: Stress recovery and error estimation for shell structure (abstract only). A 4-node, shear-deformable flat shell element developed via explicit Kirchhoff constraints (abstract only). A novel four-node quadrilateral smoothing element for stress enhancement and error estimation (abstract only).

Riggs, H. R.↗
DOE OSTI2022

Iterative wavefront optimization of ultrafast laser beams carrying orbital angular momentum

Structured intense laser beams offer degrees of freedom that are highly attractive for high-field science applications. However, the performance of high-power laser beams in these applications is often hindered by deviations from the desired spatiotemporal profile. This study reports the wavefront optimization of ultrafast Laguerre-Gaussian beams through the synergy of adaptive optics and genetic algorithm-guided feedback. The results indicate that the intensity fluctuations along the perimeter of the target ring-shaped profile can be reduced up to ~15%. Furthermore, the radius of the ring beam profile can be tailored to a certain extent by establishing threshold fitting criteria. The versatility of this approach is experimentally demonstrated in conjunction with different focusing geometries.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

High Efficiency 6-Junction Solar Cells for the Global and Direct Spectra

We show 6-junction inverted metamorphic solar cells with high efficiencies under both the global and direct spectrum, and discuss improvements to device components. High voltage AlGaInP subcells are demonstrated on GaAs substrates miscut 2° towards (111)B by using Sb surfactant to reduce atomic ordering. This miscut enables high voltage and low dislocation density GaInAs subcells by using atomically-ordered GaInP-based graded buffers. One-sun efficiencies of 39.2 ± 1.3% under the global spectrum and 39.4 ± 1.1% direct spectrum have been demonstrated by using these high voltage subcell components. For high efficiency under the concentrated direct spectrum, low resistance is also necessary, which requires a challenging and nonintuitive optimization of tunnel junctions and heterobarriers. Increasing the thickness of a (Al)GaInAs spacer layer between the back surface fields (BSF) and tunnel junctions (TJ) of latticemismatched subcells reduces nonlinear resistance, which implies a detrimental interaction between the BSF and TJ. Concentrator devices with optimized spacer layers show reduced effective resistance and maintain fill factor > 75% at 1100 suns. Device efficiencies under the concentrated direct spectrum peak at 47.1 ± 3.2% at 143 suns.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.

Bayesian optimal experimental design↗
DOE OSTI2021

Visualizing Intrinsic 3D-Strain Distribution in Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging and Transmission Electron Microscopy with Respect to Piezotronic Applications

Novel devices ranging from bio magnetic field sensors to energy harvesting nano machines utilize the piezotronic effect. For optimal function, understanding the interaction of electrical and strain phenomena within the semiconductor crystal is necessary. Here, studies of a model piezotronic system are presented, consisting of a ZnO microrod coated by a thin layer of gold, which forms a Schottky contact with the piezoelectric ZnO material. Coherent X-ray diffraction imaging (CXDI) and transmission electron microscopy (TEM) are used to visualize the structure and strain distribution, showing that the ZnO microrod exhibits strains of multiple origins in the bulk and at the interface. Strain values of -6 × 10 -4 have been measured by CXDI at the ZnO/Au interface. The origin is shown to be a combination of an interface strain, possibly caused by the Schottky contact formation, and distinct, localized electrical fields inside the crystal which are assigned to electron depletion and screening in a bent ZnO/Au piezotronic rod. These findings will contribute to sensor development and to a better understanding of piezotronic applications.

36 MATERIALS SCIENCE↗
NASA NTRS2021

Effect of High-K Dielectric Layer on 1/f Noise Behavior in Graphene Field-Effect Transistors

We report the 1/f noise characteristics at low-frequency in graphene field-effect transistors that utilized a high- dielectric tantalum oxide encapsulated layer (a few nanometers thick) placed by atomic layer deposition on Si3N4. A low-noise level of ~ 2.2  10-10 Hz-1 has been obtained at f = 10 Hz. The origin and physical mechanism of the noise can be interpreted by the McWhorter context, where fluctuations in the carrier number contribute dominantly to the low-frequency noise. Optimizing fabrication processes reduced the number of charged impurities in the graphene field-effect transistors. The study has provided insights into the underlying physical mechanisms of the noise at low-frequency for reducing the noise in graphene-based devices.

Yifei Wang↗
DOE OSTI2025

Spin State Modulation via Magnetic Fields in Fe Single Atom Catalysts for High-Performance Aqueous Zinc–Sulfur Batteries

Aqueous zinc–sulfur battery has garnered significant attention as a high-energy, low-cost, and safe energy storage system. However, the multielectron transfer kinetics of sulfur cathodes are relatively slow, presenting challenges such as limited sulfur utilization and lower discharge voltage, which significantly hinder their practical applications. Here, in this study, we explored a comprehensive design approach for high-performance, long-cycle aqueous zinc–sulfur batteries. The simultaneous introduction of ZnI 2 and Fe single atoms (Fe-SAs) as catalytically active agents decouples the redox reactions, effectively facilitating ZnS oxidation and S reduction separately. The application of an external magnetic field regulates the spin state of Fe-SAs, further enhancing their catalytic activity and electron transfer capability. Electrochemical tests demonstrate that the S@Fe-NC HS/ZnI 2 cathode assembled under a magnetic field exhibits excellent rate performance, achieving an impressive specific capacity of 1399 mAh g –1 at a high current density of 5 A g –1 and good cycling stability over 300 cycles, representing the highest reported high-current discharge capacity to date. This study provides a comprehensive design framework for optimizing zinc–sulfur (Zn–S) battery performance and elucidates the influence of magnetic field-induced spin state modulation on catalytic behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of AlGaN‐Based Deep‐Ultraviolet Avalanche Photodetectors—Toward Their Intrinsic Characteristics

Al 0.6 Ga 0.4 N deep‐UV p–i–n avalanche photodiodes (APDs) are demonstrated grown by metalorganic chemical vapor deposition (MOCVD) on a (0001) c‐plane aluminum nitride (AlN) bulk substrate and fabricated both with and without an ion implantation process for mesa‐edge electric‐field termination. Three design considerations are taken into account: the optical absorption of the AlN bulk substrate, the photosensitivity of the APD, and the breakdown electric field. The MOCVD growth conditions of the AlGaN APD layers are optimized. Then, a detailed description of the seven device fabrication steps of the APDs is provided, which includes nitrogen ion implantation. The APDs fabricated by the ion implant process exhibit a dark‐current density under low reverse bias ≈1 × 10 −9 A cm −2 . This is one order of magnitude lower than the APD fabricated without ion implantation from the same wafer. The breakdown voltage of the APD is ≈−140 V. The calculated optical gain of the ion‐implanted APD beyond avalanche breakdown is ≈5.2 × 10 5 (current limited) and the device has an average zero‐bias photoresponse of ≈68 mA W −1 at a wavelength of 250 nm.

Jeong, Hoon [Department of Electronic Engineering ↗