Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Comparative framework”

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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Wind Turbine Noise Code Benchmark: A Comparison and Verification Exercise: Preprint

In a number of institutions and companies, researchers and engineers are developing modeling numerical frameworks that are used to predict the aerodynamic noise emission from a wind turbine rotor. The simulation codes vary from empirically-tuned engineering models to high-fidelity computational ones. Their common feature is the fact that they all specifically model the main aerodynamic noise mechanisms occurring at the rotating blades, namely trailing edge noise and turbulent inflow noise. Nevertheless, it can be expected that these different modeling frameworks will produce different results for a same given rotor design, and identical operating conditions. Trailing edge noise is put at the forefront of the study as it is recognized as the main audible source of noise from wind turbines. The present benchmark aims at comparing the results from the different modeling approaches, and drawing some conclusions from these comparisons. This effort, denoted as Wind Turbine Noise Code benchmark, was initiated in 2019 as a joint-activity between the IEA Wind Task 39 (Quiet Wind Turbine Technology) and Task 29 (Detailed Aerodynamics of Wind Turbines, now Task 47). In addition to the investigation of the noise emissions themselves, the rotor aerodynamic characteristics are investigated as they have a significant impact on the noise generation mechanisms. A number of test cases are defined and the aerodynamic and aeroacoustic predictions from the various models are compared. There exist some discrepancies between the different methods, but it is difficult to conclude if one methodology is better than another in order to design a wind turbine with noise as a constraint.

aeroacoustics↗
DOE OSTI2020

Metal–Organic Framework-Based Microfluidic Impedance Sensor Platform for Ultrasensitive Detection of Perfluorooctanesulfonate

The growing global concerns to public health from human exposure to perfluorooctanesulfonate (PFOS) requires rapid, sensitive, in-situ detection where current, state-of-the-art techniques are yet to adequately meet sensitivity and selectivity standards of the real-world. This work presents, for the first time, a synergistic approach for the targeted affinity-based capture of PFOS using a porous sorbent probe that enhances detection sensitivity by embedding it on a microfluidic conductive platform. This novel sorbent-containing platform functions as an electrochemical sensor to directly measure PFOS concentration through a proportional change in electrical response (increase in impedance). The extremely high surface area and pore volume of mesoporous metal-organic framework (MOF) Cr-MIL-101 is used as the probe for targeted PFOS capture based on the affinity of the chromium center towards both the fluorine tail groups as well as the sulfonate functionalities as demonstrated by spectroscopic (NMR and XPS) and microscopic (TEM) studies. Answering the need for an ultrasensitive PFOS detection technique, we are embedding these MOF capture probes inside a microfluidic channel, sandwiched between interdigitated microelectrodes (IDµE). The nanoporous geometry increases the signal to noise ratio of interdigitated microelectrodes tremendously increasing sensitivity; this in combination with the ability of the capture probes to interact with the PFOS at the molecular level and effectively transduce that response electrochemically has allowed us achieve a PFOS detection limit of 0.5 ng/L, which is unprecedented for in-situ analytical PFOS sensors and even comparable to quantification limits achieved using state-of-the-art ex situ techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Heterogeneous Growth of UiO-66-NH 2 on Oxidized Single-Walled Carbon Nanotubes to Form “Beads-on-a-String” Composites

In this work we demonstrate a facile synthesis of UiO-66-NH 2 metal-organic framework (MOF)/oxidized single-walled carbon nanotubes (ox-SWCNTs) composite at room temperature. Acetic acid (HAc) was used as a modulator to manipulate the morphology of the MOF in these composites. With zirconium oxide-cluster (Zr) to 2-aminoteraphthalate linker (ATA) 1:1.42 ratio and acetic acid modulator, we achieved predominately heterogeneous MOF growth on the sidewalls of CNTs. Understanding the growth mechanism of these composites was facilitated by conducting DFT calculations to investigate the interactions between ox-SWCNTs and the MOF precursors. The synthesized composites combine both micro-porosity of the MOF and electrical conductivity of the SWCNTs. Gas sensing tests demonstrated higher response for UiO-66-NH 2 /ox-SWCNT hybrid toward dry air saturated with dimethyl methylphosphonate (DMMP) vapor compared to oxidized single-walled carbon nanotubes (ox-SWCNTs) alone.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Fast extraction of nickel from aqueous media by using ionic metal-organic frameworks

As global demand for stainless steel rapidly increases, supply for minerals such as nickel, manganese, and chromium become more critical. To shift mineral peak production and delay scarcity of alloying minerals, recycling rate of the mineral needs to increase. Development of simple and cost-effective techniques for critical mineral production and recovery from industrial wastewater will lead to enhance the current recycling rate of the mineral and reduce potential hazards of industrial wastewater bearing high concentrations of minerals. Here, in this study, ionic metal-organic framework (iMOF)-based sorbents are successfully designed for critical metal ion extraction from aqueous solution. The iMOFs are highly stable in aqueous solution over wide pH ranges, indicating that they are suitable for use in industrial wastewater with harsh chemical environments. The sorption capacity of iMOFs towards Ni reaches 34.1 mg/g with a removal efficiency of >99.9%. Notably, complete extraction of Ni takes place within 5 min, which is much faster compared to other sorbents. Moreover, the iMOFs can simultaneously coextract multiple minerals, proving their effectiveness as general sorbents for critical mineral recovery.

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

Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system

New ways to integrate energy systems to maximize efficiency are being sought to meet carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts are a leading solution that couples a nuclear power plant with renewable energy, hydrogen generation plants, and energy storage systems, such that thermal and electrical power are dispatchable to fulfill grid-flexibility requirements while also producing hydrogen and maximizing revenue. Here, this paper introduces a deep reinforcement learning (DRL)-based framework to address the complex decision-making tasks for NR-IES. The objective is to maximize revenue by generating and selling hydrogen and electricity simultaneously according to their time-varying prices while keeping the energy flow in the subsystems in balance. A Python-based simulator for a NR-IES concept has been developed to integrate with OpenAI Gym and Ray/RLlib to enable an efficient and flexible computational framework for DRL research and development. Three state-of-the-art DRL algorithms have been investigated, including two-delayed deep deterministic policy gradient (TD3), soft-actor critic (SAC), proximal policy optimization (PPO), to illustrate DRL’s superiority for controlling NR-IES by comparing it with a conventional control approach, particle swarm optimization (PSO). In this effort, PPO has shown more-stable performance and also better generalization capability than SAC and TD3. Comparisons with PSO have demonstrated that, on average, PPO can achieve 13.9% more mean episode returns from the training process and 29.4% more mean episode returns from the testing process when different hydrogen-production targets are applied.

08 HYDROGEN↗
DOE OSTI2022

Graph neural networks for efficient learning of mechanical properties of polycrystals

Herein we present graph neural networks (GNNs) as an efficient and accurate machine learning approach to predict mechanical properties of polycrystalline materials. Here, a GNN was developed based on graph representation of polycrystals incorporating only fundamental features of grains including their crystallographic orientation, size, and grain neighbor connectivity information. We tested our method on modeling stiffness and yield strength of α -Ti microstructures, varying in their crystallographic texture. We find the GNN predicts both properties with high accuracy with mean relative errors of ~1% for unseen microstructures from a given set of textures and < 2% for microstructures of unseen texture, even when presented with limited training data. This accuracy is comparable to methods that require high-resolution three-dimensional (3D) microstructure data, such as 3D convolutional neural networks (3D-CNNs) and models that depend on the computation of spatial statistics. The present results show that graph-based deep learning is a promising framework for property prediction, especially considering the high cost associated with obtaining high-resolution 3D microstructure data and the general scarcity of experimental materials datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bayesian prior construction for uncertainty quantification in first-principles statistical mechanics

First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into downstream calculations, in addition to any uncertainty inherent to DFT calculations. Bayesian frameworks address this by quantifying uncertainty and incorporating expert knowledge through priors. However, constructing effective priors remains challenging. This work introduces and describes practical strategies for building Bayesian cluster expansions, focusing on basis truncation, hyperparameter selection, and ground state replication. We analyze multiple basis truncation schemes, compare cross-validation to the evidence-approximation for hyperparameter optimization, and provide methods to find and enforce ground-state-preserving models through priors. Additionally, we compare the uncertainties between different approximations to DFT (LDA, PBE, SCAN) against the uncertainty introduced with the use of cluster expansion surrogate models. These approaches are demonstrated on the BCC Li x Mg 1-x and Li x Al 1-x alloys, which are both of interest for solid-state Li batteries. Our results provide guidelines for constructing and utilizing Bayesian cluster expansions, thereby improving the transparency of materials modeling. Furthermore, the approaches and insights developed in this work can be transferred to a wide range of cluster expansion surrogate models, including the atomic cluster expansion and related machine-learned interatomic potential architectures.

Alloy theory↗
DOE OSTI2025

Community civic capacities for meaningful engagement in siting infrastructure for the energy transition

To address the driving forces of climate change and to ensure society has reliable and plentiful energy, considerable amounts of new energy infrastructure will need to be built in scores of communities over the near future. Democratic societies give communities considerable authority, influence, and autonomy on land-use decisions and regulatory policy making. Involving community members and stakeholders in decision making about facility siting and hosting is vital to minimize local opposition. But while there is much written about how to engage communities successfully, there is comparatively little attention given to understanding the civic capacities communities need to be able to participate. This paper reviews literatures on civic capacity and presents a new taxonomy based on six categories: leadership, knowledge, resources, civic engagement, social capital, and culture. It then proposes a systems framework to convey how capacities are developed and employed in collaborative decision making processes about siting and hosting energy facilities. Project sponsors, regulators, stakeholder groups, and communities can use these insights to better prepare and empower communities to participate as equal partners in conversations about energy facility siting.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Radial burnup characteristics of spent fuel pellets by zirconium analysis

Resonance ionization mass spectrometry is a powerful tool for in-situ measurements of elemental ratios in micrometer-sized spent fuel samples. By measuring the concentration of the fission product Zr we provide a framework for determining fuel burnup while simultaneously considering the 239 Pu fission fraction. Here, the analyzed spent fuel samples were taken from different fuels and distances from the fuel pellets’ edge. Results show a burnup enhancement at the edge of a fuel pellet of 180–220% compared to the center of the pellet. By comparing the concentration of the anthropogenic 93 Zr to the naturally occurring Zr isotopes, any influence of non-fissiogenic Zr or disturbing neutron capture reactions can be ruled out. This established 93 Zr as a new tool to measure fuel burnup.

Burnup↗
DOE OSTI2026

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗
DOE OSTI2021

Th IV –Desferrioxamine: characterization of a fluorescent bacterial probe

Diversifying our ability to guard against emerging pathogenic threats is essential for keeping pace with global health challenges, including those presented by drug-resistant bacteria. Some modern diagnostic and therapeutic innovations to address this challenge focus on targeting methods that exploit bacterial nutrient sequestration pathways, such as the desferrioxamine (DFO) siderophore used by Staphylococcus aureus (S. aureus) to sequester Fe III . Building on recent studies that have shown DFO to be a versatile vehicle for chemical delivery, we show proof-of-principle that the Fe III sequestration pathway can be used to deliver a potential radiotherapeutic. Our approach replaces the FeIII nutrient sequestered by H 4 DFO + with Th IV and made use of a common fluorophore, FITC, which we covalently bonded to DFO to provide a combinatorial probe for simultaneous chelation paired with imaging and spectroscopy, H 3 DFO_FITC. Combining insight provided from FITC-based imaging with characterization by NMR spectroscopy, we demonstrated that the fluorescent DFO_FITC conjugate retained the Th IV chelation properties of native H4DFO+. Fluorescence microscopy with both [Th(DFO_FITC)] and [Fe(DFO_FITC)] complexes showed similar uptake by S. aureus and increased intercellular accumulation as compared to the FITC and unchelated H 3 DFO_FITC controls. Collectively, these results demonstrate the potential for the newly developed H 3 DFO_FITC conjugate to be used as a targeting vector and bacterial imaging probe for S. aureus. The results presented within provide a framework to expand H 4 DFO + and H 3 DFO_FITC to relevant radiotherapeutics (like 227Th).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil

Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve because of the high degrees of freedom as well as the coupled nature of the conservation laws. While these issues pose a bottleneck for the use of such models in aerospace design, computational costs can be significantly minimized by constructing special, structure-preserving surrogate models called reduced-order models. In this work, we propose a machine learning method to construct reduced-order models via deep neural networks and we demonstrate its ability to preserve accuracy with a significantly lower computational cost. In addition, our machine learning methodology is physics-informed and constrained through the utilization of an interpretable encoding by way of proper orthogonal decomposition. Application to the inviscid transonic flow past the RAE2822 airfoil under varying freestream Mach numbers and angles of attack, as well as airfoil shape parameters with a deforming mesh, shows that the proposed approach adapts to high-dimensional parameter variation well. Notably, the proposed framework precludes the knowledge of numerical operators utilized in the data generation phase, thereby demonstrating its potential utility in the fast exploration of design space for diverse engineering applications. Comparison against a projection-based nonintrusive model order reduction method demonstrates that the proposed approach produces comparable accuracy and yet is orders of magnitude computationally cheap to evaluate, despite being agnostic to the physics of the problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fermi–Löwdin orbital self-interaction correction of adsorption energies on transition metal ions

Density functional theory (DFT)-based descriptions of the adsorption of small molecules on transition metal ions are prone to self-interaction errors. Here, we show that such errors lead to a large over-estimation of adsorption energies of small molecules on Cu + , Zn + , Zn 2+ , and Mn + in local spin density approximation (LSDA) and Perdew, Burke, Ernzerhof (PBE) generalized gradient approximation calculations compared to reference values computed using the coupled-cluster with single, doubles, and perturbative triple excitations method. These errors are significantly reduced by removing self-interaction using the Perdew–Zunger self-interaction correction (PZ-SIC) in the Fermi–Löwdin Orbital (FLO) SIC framework. In the case of FLO-PBE, typical errors are reduced to less than 0.1 eV. Furthermore, analysis of the results using DFT energies evaluated on self-interaction-corrected densities [DFT(@FLO)] indicates that the density-driven contributions to the FLO-DFT adsorption energy corrections are roughly the same size in DFT = LSDA and PBE, but the total corrections due to removing self-interaction are larger in LSDA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

New physics particles mixing with mesons: Production in the fragmentation chain

A class of extensions to the Standard Model adds hypothetical long-lived particles (LLPs) that have mass- or kinetic-mixing with neutral mesons, such as pions or rho mesons. The mixing can contribute significantly to the production of LLPs at proton accelerator experiments, and no consistent description of these production modes exists in the literature. In this paper, we develop a framework for studying different LLPs—dark photons, vector mediators coupled to the baryon current, and axionlike particles with different coupling patterns. In particular, we implement the production mechanisms in PYTHIA 8, study how the overall flux and kinematic distributions depend on the LLP’s mass, and compare various subprocesses where the mixing contributes—proton bremsstrahlung, meson decay, and production in the fragmentation chain. We find that our new description of LLP production predicts an integrated flux that differs from current approaches by one to two orders of magnitude, and highlight the unavoidable theoretical uncertainties coming from poor knowledge of the properties of heavy mesons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Enabling HPC Scientific Workflows for Serverless

The convergence of edge computing, big data analytics, and AI with traditional scientific calculations is increasingly being adopted in HPC workflows. Workflow management systems are crucial for managing and orchestrating these complex computational tasks. However, it is difficult to identify patterns within the growing population of HPC workflows. Serverless has emerged as a novel computing paradigm, offering dynamic resource allocation, quick response time, fine-grained resource management and auto-scaling. In this paper, we propose a framework to enable HPC scientific workflows on serverless. Our approach integrates a widely used traditional HPC workflow generator with an HPC serverless workflow management system to create benchmark suites of scientific workflows with diverse characteristics. These workflows can be executed on different serverless platforms. We comprehensively compare executing workflows on traditional local containers and serverless computing platforms. Our results show that serverless can reduce CPU and memory usage respectively by 78.11% and 73.92% without compromising performance.

Andrei da silva, Anderson↗
DOE OSTI2023

Inclusion of $K\Lambda $ electroproduction data in a coupled channel analysis

Exclusive electroproduction reactions provide an access to the structure of excited baryons. To extract electroproduction multipoles encoding this information, the Julich-Bonn-Washington (JBW) analysis framework is extended to the analysis of differential cross sections in KΛ electroproduction. Here this update enlarges the scope of previous coupled-channel analyses of pions and eta mesons, with photoproduction reactions as boundary condition in all analyzed electroproduction reactions. Polarization observables are predicted and compared to recent CLAS data. The comparison shows the relevance of these data to pin down baryon properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Performance of three-dimensional printed nasopharyngeal swabs for COVID-19 testing

At the start of the COVID-19 pandemic, the US faced nationwide shortages of nasopharyngeal swabs due to both overwhelmed supply chains and an increase in demand. To address this shortfall, multiple 3D printed swabs were ultimately produced and sold for COVID-19 testing. In this work, we present a framework for mechanical and functional bench-testing of nasopharyngeal swabs using standard and widely available material testing equipment. Using this framework, we offer a comprehensive, quantitative comparison of the 3D printed swabs to benchmark their performance against traditional flocked swabs. The test protocols were designed to emulate the clinical use of the nasopharyngeal swabs and to evaluate potential failure modes. Overall, the 3D printed swabs performed comparably to, or outperformed, the traditional swabs in all mechanical tests. While traditional swabs outperformed some of the new 3D printed swabs in terms of sample uptake and retention, similar amounts of RNA were recovered from both 3D printed and traditional swabs.

3D printing↗
DOE OSTI2024

AEOLIAN INTERDUNE FACIES OF THE NAVAJO SANDSTONE, UTAH

During the Early Jurassic an extensive desert environment covered a large area of the Western Interior of the USA. The Navajo Sandstone erg is thought to be one of the largest dunefields preserved in the rock record, estimated to have covered well over 500,000 km2, though the preserved extent is less due to erosion. The Navajo Sandstone outcrops extensively in eastern and southern Utah, forming spectacular domes and arches, including those in Arches National Park. Navajo Sandstone aeolian dunes consist of stacked, tabular sandstone bodies, displaying steeply dipping crossbedding, and are on average 15 m thick, with high porosity and permeability. In addition to these extensive paleodunes, interdune deposits are also documented, consisting of lensoid sandstone or pale grey limestone, of approximately 1-7 m thick. This research forms part of a broader investigation into the Navajo Sandstone in Utah, focusing on its potential as a CO2 reservoir. The internal stratigraphy of the Navajo Sandstone can be complex, with multiple types of aeolian surfaces present including discontinuous inter-dune contacts, and inclined bedding planes associated with large climbing dunes. Interdune deposits may be associated with more laterally extensive stratigraphic surfaces, highlighting their importance in the internal stratigraphic framework of the Navajo Sandstone erg. Here we interpret facies and depositional processes of aeolian interdune deposits of the Navajo Sandstone throughout Utah, using well log, outcrop, and thin sections. Interdune deposits range in type from short lived, small ponds of predominantly reworked aeolian sediment, to comparatively long-lived oases consisting of lakes with extensive ecosystems. This project aims to increase our understanding of an important potential CO2 reservoir, and enhance understanding of stratigraphic complexity within one of the world's most significant aeolian systems.

Mahon, Elizabeth (ORCID:0000000252692454)↗