Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “optimization methods”

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 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

e RPCA : Robust Principal Component Analysis for Exponential Family Distributions

Abstract Robust principal component analysis (RPCA) is a widely used method for recovering low‐rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low‐rank background is critical for process monitoring and diagnosis. However, existing RPCA methods and their extensions largely do not account for the underlying probabilistic distribution for the data matrices, which in many applications are known and can be highly non‐Gaussian. We thus propose a new method called RPCA for exponential family distributions (), which can perform the desired decomposition into low‐rank and sparse matrices when such a distribution falls within the exponential family. We present a novel alternating direction method of multiplier optimization algorithm for efficient decomposition, under either its natural or canonical parametrization. The effectiveness of is then demonstrated in two applications: the first for steel sheet defect detection and the second for crime activity monitoring in the Atlanta metropolitan area.

Zheng, Xiaojun↗
DOE OSTI2023

Scaling Optimal Allocation of Cloud Resources Using Lagrange Relaxation

The rapid growth of Cloud Computing (CC) has increased the variety of computing resources, storage, and communication services that pose significant new challenges for the efficient use of cloud resources. The cost-efficient allocation of cloud resources has become a decisive premise for the adoption of CC services. The cost-efficient selection and scheduling of these resources to meet the demands of a scientific workflow is a challenging problem that is exacerbated by the inclusion of multiple CC providers. In this paper, we present a novel strategy for the cost-efficient selection of CC resources using Lagrange relaxation. Our approach is based on preselection of resources and demand decomposition to create a series of smaller sub-problems, which allow the estimation of the best cost-structures and selection of CC service providers for a subset of the time period of the planning horizon. Decomposition of the demand is achieved through the boundary analysis of a continuous relaxation of the problem. Using the metrics defined in terms of the cost and time of completion, we demonstrate excellent performance in relation to optimal solutions. Our method reduced the computational time from hours to seconds for a representative 36-month problem and provided high-quality solutions (< 0.05% relative error). Given the importance of selecting resources and scheduling complex scientific workflows, we believe that this novel strategy will be beneficial for many researchers and users of cloud computing resources.

De La Torre, Luis↗
DOE OSTI2018

Computational Research of Electronic and Magnetic Properties of Nonmetal Doping of Graphene-Like ZnO Monolayer

In this paper, the geometrical, electronic, and magnetic properties of nonmetal (B, C, N, F, Si, P, or S) atom doping of g-ZnO monolayer supercell have been comprehensively calculated using the first-principle method. The structural optimization implies that nonmetal atom doping of g-ZnO monolayer leads to the structural distortion around the doping atoms compared with pure g-ZnO sheet. We find that g-ZnO monolayer with one O atom per supercell substituted by one B or C atom is ferromagnetic half metal, and that substituted by one N, Si, or P atom exhibits a ferromagnetic semiconductor feature, while that substituted by one F or S atom appears to be nonmagnetic semiconductor. The total magnetic moments mainly originates from the spin polarization of the doping atom (B, C, N, Si, and P), and the rest comes from the nearest Zn and O atoms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Production of high specific activity radiolanthanides for medical purposes using the UC Irvine TRIGA reactor

Radioactive lanthanides have become an important imaging, diagnostic and therapeutic tool in the medical field. The objective of our research was to investigate the feasibility of producing radioactive lanthanides with high specific activity in a small-scale research reactor using the Szilard–Chalmers method. The results indicate that the activated nuclides recoil out of the target after neutron capture and we obtain enrichment of the radionuclide compared to the bulk irradiation. These first attempts, result in enrichment factors and yields that are low but indicates a possibility of using this technique if the method is further optimized.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2018

Manufacturing of Dysprosium-Iron Alloys by Electrolysis in Fluoride-Based Electrolytes. Electrolysis in a Laboratory-Scale Cell

Electrolytic production of light rare earth elements and rare earth alloys with transition elements takes place in a fluoride-based electrolyte using rare earth oxides as raw material. The optimization of this method, mainly in terms of the energy efficiency and environmental impact control, is rather challenging. Anode effects, evolution of fluorine-containing compounds and side cathode reactions could largely be minimized by good control of the amount of rare earth oxide species dissolved in the fluoride-based electrolyte and their dissolution rate. The Dy{sub 2}O{sub 3} feed rate needed for stable cell operation was studied by following up the anode voltage and gas analysis. On-line analysis of the cell off-gases by FTIR showed that the electrochemical reaction for the formation of Dy-Fe alloy gives mainly CO gas and that CF{sub 4} is starting to evolve gradually at anode voltages of ca. 3.25 V. The limiting current density for the discharge of the oxide ions at the graphite anode was in the range of 0.1 to 0.18 A cm{sup −2} at dissolved Dy{sub 2}O{sub 3} contents of ca. 1 wt pct. Modeling of the laboratory cell reactor was also carried out by implementing two models, i.e., an electrical model simulating the current density distribution at the electrodes and a laminal bubbly flow model that explains the electrolyte velocity induced by gas bubble production at the anode.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Modeling and analysis of mixed convection stagnation point flow of nanofluid towards a stretching surface: OHAM and FEM approach

A theoretical study of mixed convection stagnation point flow towards a stretching surface is presented. The governing boundary layer equations are transformed into a set of highly nonlinear ordinary differential equations using suitable similarity transforms. The semi-analytical solution is obtained using optimal homotopy analysis method (OHAM) and the numerical solution is obtained via finite element method (FEM). Solutions obtained via two different approaches are in excellent agreement, which validates the accuracy of present analysis. In a special case, the present OHAM solution is also validated with the earlier available results. Effect of pertinent flow parameters on the skin friction coefficient and Nusselt number is presented in tabular form, whereas the velocity, temperature and nanoparticle distribution are presented in graphical forms. Further, a quadratic multiple regression analysis on numeric data of skin friction coefficient and Nusselt number is performed. The findings suggest that velocity slip assists the fluid motion in presence of buoyancy forces, whereas it exhibits a retarding nature on fluid motion when no buoyancy forces exist.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Coaxial color channel focus evaluation to estimate standoff height in directed energy deposition additive manufacturing

Directed energy deposition (DED) is an additive manufacturing process that is being rapidly adopted by industry and is well suited for the fabrication of complex components in a variety of metal alloys. In laser cladding systems such as DED, powder is blown in a stream to a metal substrate coincident with a laser necessary to deposit molten metal with 3D spatial control. The focus of both the laser and the powder stream are crucial, and best deposition occurs at a predetermined standoff height between the build surface and the print head. Generally, no monitoring of this distance is implemented in commercial DED systems. Due to potential over or under building, the standoff height often changes over time but tends to self-correct. However, inexpensive and minimally intrusive methods to identify optimal standoff are required to provide real-time control to maintain the optimal distance. The present work explores the quantification of the focus of the three-color channels of a coaxial camera to determine the standoff height. An experiment was performed in which a 254 mm wall is built and the standoff height, initially 5.0 mm below the optimal position, was then intentionally increased every 25.4 mm of wall length by an amount of 1.0 mm to a final position 7.0 mm above optimal. Computer vision is demonstrated to monitor the amount of focus in each color band and estimate standoff distance. Finally, a response can be calculated in under 40 ms using simple hardware and can work in most laser-based DED systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys

We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. Here we also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Recent advancements in the genetic engineering of microalgae

The development of more sustainable food, feed, and bio-products is critical to mitigating the environmental stresses facing our world today. Algae, which includes seaweeds, eukaryotic microalgae, and cyanobacteria, are a promising platform to achieving this, as they have low energy and space requirements, are safe for human and animal consumption, and can be manipulated to produce a diversity of valuable bioproducts. This review focuses on microalgae, both eukaryotic and cyanobacteria. In the past, addressing the major challenges of bringing microalgal production systems to an economically viable scale only had a relatively small genetic toolset to work with, in comparison to other microbial systems such as bacteria and yeast. Expanding the molecular tools available for genetic engineering of microalgae will lead to higher product yields, and accelerate the development of new microalgal bioproducts for commercial applications, thereby supporting the shift towards more environmentally friendly products. In this review, we highlight significant advances from recent years on the design of microalgal expression vectors, discovery of genetic regulatory elements (promoters and transcription factors), optimization of transformation methods, and development of new strain improvement techniques, all aimed at advancing microalgae to become a more efficient biomanufacturing platform. We then discuss how these tools have been applied to improving recombinant protein production, and to enhance metabolic pathway engineering.

09 BIOMASS FUELS↗
DOE OSTI2026

Tailored Solvent Treatment for Optimized Production of Upcycled Anodes from End-Of-Life Li-Ion Batteries

Recycling processes for lithium-ion batteries typically overlook graphite because of its lower market value relative to that of transition-metal-containing cathode materials. However, graphite recovered from cycled lithium-ion batteries holds additional engineered value associated with the solid-electrolyte interphase (SEI). The SEI contributes critical electronic passivation of the graphite surface but becomes highly resistive with extended cycling, yielding poor cell performance. In this work, we apply tailored solvent treatment to end-of-life (EOL) graphite anodes to selectively remove adverse SEI components while retaining beneficially passivating species. We evaluate a series of polar protic solvents to achieve targeted removal of SEI components and control selectivity through rational variation in solvent properties. The physiochemical properties of treatment solvents correlate with both the retained SEI composition and the corresponding electrochemical performance of solvent-treated “upcycled” graphite anodes. Within the initial set of solvents evaluated, top-performing candidates show capacity and Coulombic efficiency nearly equivalent to those of an analogous pristine anode, as well as promising electrochemical performance enhancement with regard to irreversible capacity-loss metrics. This study establishes critical design principles for an optimized anode upcycling method that enhances the value of recycled graphite by retaining and upgrading the SEI.

25 ENERGY STORAGE↗
DOE OSTI2025

An interregional optimization approach for time series aggregation in continent-scale electricity system models

Modeling electric power systems with high shares of weather-dependent resources requires tradeoffs between temporal, spatial, and operational resolution. Many studies perform time series aggregation using clustering algorithms to reduce the temporal dimension, but when modeling continent-scale electricity systems that are large enough to contain multiple independent weather systems, this approach requires large numbers of representative periods to minimize errors in regional wind and solar capacity factors. Here, a new optimization-based approach for representative period selection and weighting is introduced that minimizes regional errors in average renewable capacity factors and electricity demand. The method delivers higher regional fidelity with fewer representative periods than alternative clustering methods when applied to wind, solar, and demand profiles for the contiguous United States. When representative periods are selected from multiple weather years, the optimized method reproduces regional averages with lower error than a complete 365-day time series from any single weather year. The method identifies only representative (as opposed to outlying) periods but can be combined with an iterative "stress period" identification approach to guide efficient decision-making considering both average and high-risk weather conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

A unified RANS–LES model: Computational development, accuracy and cost

Large eddy simulation (LES) is computationally extremely expensive for the investigation of wall-bounded turbulent flows at high Reynolds numbers. A way to reduce the computational cost of LES by orders of magnitude is to combine LES equations with Reynolds-averaged Navier–Stokes (RANS) equations used in the near-wall region. A large variety of such hybrid RANS–LES methods are currently in use such that there is the question of which hybrid RANS-LES method represents the optimal approach. The properties of an optimal hybrid RANS–LES model are formulated here by taking reference to fundamental properties of fluid flow equations. It is shown that unified RANS–LES models derived from an underlying stochastic turbulence model have the properties of optimal hybrid RANS–LES models. The rest of the paper is organized in two parts. First, a priori and a posteriori analyses of channel flow data are used to find the optimal computational formulation of the theoretically derived unified RANS–LES model and to show that this computational model, which is referred to as linear unified model (LUM), does also have all the properties of an optimal hybrid RANS–LES model. Second, a posteriori analyses of channel flow data are used to study the accuracy and cost features of the LUM. The following conclusions are obtained. (i) Compared to RANS, which require evidence for their predictions, the LUM has the significant advantage that the quality of predictions is relatively independent of the RANS model applied. (ii) Compared to LES, the significant advantage of the LUM is a cost reduction of high-Reynolds number simulations by a factor of 0.07Re{sup 0.46}. For coarse grids, the LUM has a significant accuracy advantage over corresponding LES. (iii) Compared to other usually applied hybrid RANS–LES models, it is shown that the LUM provides significantly improved predictions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

High thermal stability and excellent dielectric properties of a novel X8R-type CdCu3Ti4O12 ceramics through a sol-gel technique

Highlights: • CdCu{sub 3}Ti{sub 4}O{sub 12} (CdCTO) ceramics were successfully synthesized via the sol-gel method, and the optimized processing parameters were obtained. • The outstanding dielectric properties: ε{sub r} ≈ 1.63 × 10{sup 4} and tan δ = 0.035 (at 10 kHz) could be obtained in CdCTO ceramics sintered at 1000 °C. • The CdCTO-SG ceramics obtained an excellent thermal stability. - Abstract: CdCu{sub 3}Ti{sub 4}O{sub 12} (CdCTO) ceramics were systematically synthesized via the sol-gel technique. The optimal CH{sub 3}COOH and Ti(OC{sub 4}H{sub 9}){sub 4} concentrations in the synthesis solution and processing temperature were found to be 5.0 vol%, 0.50 mol/L and 60 °C, respectively. These materials exhibited high densities and fine-grained microstructures along with outstanding dielectric properties, including an ε{sub r} of approximately 1.63 × 10{sup 4} and a tan δ of 0.035 at 10 kHz. Over the temperature range from −55 to 150 °C, the temperature coefficients of the dielectric constant at 1 and 10 kHz were −13.9% to 1.20% and −9.10% to 3.92%, respectively, indicating that these ceramics possess excellent thermal stability. These properties also meet the requirements for X8R applications. Data acquired from complex impedance spectroscopy and electrical modulus assessments demonstrates that the superior dielectric properties of these CdCTO ceramics can be attributed to the internal barrier layer capacitor theory (IBLC).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Multiwalled carbon nanotubes in titania based nanocomposite as trap for photoexcitons for enhanced photocatalytic hydrogen production under solar light irradiation

Highlights: • Facile synthesis of FCNTs-TiO{sub 2} composite through wet impregnation method. • Parameters were optimized for enhanced photocatalytic hydrogen production under sunlight irradiation. • At optimal conditions nanocomposite showed 8 folds higher hydrogen efficiency than pristine TiO{sub 2}. • Functionalized carbon nanotubes acts as cocatalyst in this study. - Abstract: The impact of functionalized multiwalled carbon nanotubes in titania based nanocomposite for photocatalytic hydrogen production was investigated. Founctionalization experiments were carried out to remove amorphous carbon atttached to carbon nanotubes by strong acid treatment followed by calcination at desired temperature and time intervals. Materials characterization revealed that anatase TiO{sub 2} nanoparticles randomly dispersed onto the walls of one dimensional multiwalled carbon nanotubes with surface hydroxyl and carbonyl groups besides significant change in optical absorption. Photocatalytic activity was performed in aqueous glycerol solution under solar light and volume of hydrogen production was quantified using gas chromatography. Highest hydrogen production rate of 13,107 μmol·h{sup −1}·g{sup −1} of catalyst was recorded under optimized conditions due to cocatalyst role of functionalized carbon nanotubes. Here, photo electrons were trapped by carbon nanotubes for continuous hydrogen production through reduction reaction.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Investigation of the temperature-dependent failure processes in PVD Cr-coated ZIRLO nuclear fuel cladding using in situ X-ray micro-tomography imaging

In this work, an accident tolerant fuel cladding system with Cr coating deposited using physical vapour deposition (PVD) method onto commercial Optimized ZIRLO™ was studied. The cladding tubes were machined into C-rings and loaded to failure under compression with real-time synchrotron micro-X-ray computed tomography (XCT) imaging at room temperature (RT), 345 °C, 650 °C and 950 °C in argon (Ar) atmosphere. The mechanical behaviour and failure processes were found strongly temperature-dependent where the Cr coating showed brittle fracture at RT and 345 °C, ductile fracture at 650 °C and a reversion to brittle fracture at 950 °C. Nanoindentation measurements and scanning electron microscopy (SEM) imaging were conducted on the materials after high temperature testing. It was found that recrystallisation of the Cr coating occurred at elevated temperatures, which significantly affected its local properties hence the failure behaviour at different temperatures. This work represents the first in situ 3D XCT observation of progressive failure processes in PVD Cr-coated ZIRLO claddings up to 950 °C providing critical insights into its brittle-to-ductile transition (BDTT) behaviour and subsequent ductile-to-brittle reversion with the increase in temperature. Moreover, results are compared with other PVD Cr-coated Zircaloy materials from open literature; the influences of the coating microstructure and local properties on the failure stress/strain and fracture processes are discussed.

Yuan, Guanjie↗
DOE OSTI2018

X-ray Emission Spectroscopy as an in Situ Diagnostic Tool for X-ray Crystallography of Metalloproteins Using an X-ray Free-Electron Laser

Serial femtosecond crystallography (SFX) using the ultrashort X-ray pulses from a X-ray free-electron laser (XFEL) provides a new way of collecting structural data at room temperature that allows for following the reaction in real time after initiation. XFEL experiments are conducted in a shot-by-shot mode as the sample is destroyed and replenished after each X-ray pulse, and therefore, monitoring and controlling the data quality by using in situ diagnostic tools is critical. To study metalloenzymes, we developed the use of simultaneous collection of X-ray diffraction of crystals along with X-ray emission spectroscopy (XES) data that is used as a diagnostic tool for crystallography, by monitoring the chemical state of the metal catalytic center. We have optimized data analysis methods and sample delivery techniques for fast and active feedback to ensure the quality of each batch of samples and the turnover of the catalytic reaction caused by reaction triggering methods. Here, we describe this active in situ feedback system using Photosystem II as an example that catalyzes the oxidation of H2O to O2at the Mn4CaO5active site. We used the first moments of the Mn Kβ1,3emission spectra, which are sensitive to the oxidation state of Mn, as the primary diagnostics. This approach is applicable to different metalloproteins to determine the integrity of samples and follow changes in the chemical states of the reaction that can be initiated by light or activated by substrates and offers a metric for determining the diffraction images that are used for the final data sets.

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

Comparing the Expense and Accuracy of Methods to Simulate Atomic Vibrations in Rubrene

Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder in metal–organic frameworks. Currently, there are several methods for predicting these vibrations using simulations, but the accuracy–efficiency tradeoffs have not been examined in depth. In this work, rubrene is used as a model system to predict atomic vibrational properties using six different simulation methods: density functional theory, density functional tight binding, density functional tight binding with a Chebyshev polynomial-based correction, a trained machine learning model, a pretrained machine learning model called ANI-1, and a classical forcefield model. The accuracy of each method is evaluated by comparison to the experimental inelastic neutron scattering spectrum. All methods discussed here show some accuracy across a wide energy region, though the Chebyshev-corrected tight-binding method showed the optimal combination of high accuracy with low expense. We then offer broad simulation guidelines to yield efficient, accurate results for inelastic neutron scattering spectrum prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Toward a QUBO-Based Density Matrix Electronic Structure Method

Density matrix electronic structure theory is used in many quantum chemistry methods to “alleviate” the computational cost that arises from directly using wave functions. Although density matrix based methods are computationally more efficient than wave function based methods, significant computational effort is involved. Because the Schrödinger equation needs to be solved as an eigenvalue problem, the time-to-solution scales cubically with the system size in mean-field type approaches such as Hartree–Fock and density functional theory and is solved as many times in order to reach charge or field self-consistency. We hereby propose and study a method to compute the density matrix by using a quadratic unconstrained binary optimization (QUBO) solver. This method could be useful to solve the problem with quantum computers and, more specifically, quantum annealers. Our proposed approach is based on a direct construction of the density matrix using a QUBO eigensolver. We explore the main parameters of the algorithm focusing on precision and efficiency. We show that, while direct construction of the density matrix using a QUBO formulation is possible, the efficiency and precision have room for improvement. Moreover, calculations performed with quantum annealing on D-Wave’s new Advantage quantum computer are compared with results obtained with classical simulated annealing, further highlighting some problems of the proposed method. Finally, we also suggest alternative methods that could lead to a more efficient QUBO-based density matrix construction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Refine your search to explore more results.