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At least 127 records · Page 7

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DOE OSTI2022

U-splines: Splines over unstructured meshes

U-splines are a novel approach to the construction of a spline basis for representing smooth objects in Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE). A spline is a piecewise-defined function that satisfies continuity constraints between adjacent cells in a mesh. U-splines differ from existing spline constructions, such as Non-Uniform Rational B-splines (NURBS), subdivision surfaces, T-splines, and hierarchical B-splines, in that they can accommodate local variation in cell size, polynomial degree, and smoothness simultaneously over more varied mesh configurations. Mixed cell types (e.g., triangle and quadrilateral cells in the same mesh) and T-junctions are also supported, although the continuity of interfaces with triangle and tetrahedral cells is limited in the present work. The U-spline algorithm introduces a new technique for using local null space solutions to construct basis functions for the global spline null space problem. The U-spline construction is presented for curves, surfaces, and volumes with higher dimensional generalizations possible. Lastly, a set of requirements are given to ensure that the U-spline basis is positive, forms a partition of unity, is complete, and is locally linearly independent.

42 ENGINEERING↗

DOE OSTI2018

Methods to Measure, Predict and Relate Friction, Wear and Fuel Economy

High-fidelity measurements of the coefficient of friction and the parasitic friction power of the power cylinder components have been made for the Isuzu 5.2L 4H on-highway engine. In particular, measurements of the asperity friction coefficient were made with test coupons using Argonne National Lab’s (ANL) reciprocating test rig for the ring-on-liner and skirt-on-liner component pairs. These measurements correlated well with independent measurements made by Electro-Mechanical Associates (EMA). In addition, surface roughness measurements of the Isuzu components were made using white light interferometer (WLI). The asperity friction and surface characterization are key inputs to advanced CAE simulation tools such as RINGPAK and PISDYN which are used to predict the friction power and wear rates of power cylinder components. Finally, motored friction tests were successfully performed to quantify the friction mean effective pressure (FMEP) of the power cylinder components for various oils (High viscosity 15W40, low viscosity 5W20 with friction modifier (FM) and specially blended oil containing consisting of PAO/ZDDP/MoDTC) at 25, 50, and 110°C.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2013

Progress of the Computer-Aided Engineering of Electric Drive Vehicle Batteries (CAEBAT) (Presentation)

This presentation, Progress of Computer-Aided Engineering of Electric Drive Vehicle Batteries (CAEBAT) is about simulation and computer-aided engineering (CAE) tools that are widely used to speed up the research and development cycle and reduce the number of build-and-break steps, particularly in the automotive industry. Realizing this, DOE?s Vehicle Technologies Program initiated the CAEBAT project in April 2010 to develop a suite of software tools for designing batteries.

25 ENERGY STORAGE↗

DOE OSTI2024

A Deep Learning Approach for Detection and Localization of Leaf Anomalies

The detection and localization of possible diseases in crops are usually automated by resorting to supervised deep learning approaches. In this work, we tackle these goals with unsupervised models, by applying three different types of autoencoders to a specific open-source dataset of healthy and unhealthy pepper and cherry leaf images. CAE, CVAE and VQ-VAE autoencoders are deployed to screen unlabeled images of such a dataset, and compared in terms of image reconstruction, anomaly removal, detection and localization. The vector-quantized variational architecture turns out to be the best performing one with respect to all these targets.

Calabro', Davide↗

DOE OSTI2023

GRIDS-Net: Inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning

This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. Further, an extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.

42 ENGINEERING↗

DOE OSTI2014

Utilizing rare earth elements as tracers in high TDS reservoir brines in CCS applications

In this paper we report the result of research associated with the testing of a procedures necessary for utilizing natural occurring trace elements, specifically the Rare Earth Elements (REE) as geochemical tracers in Carbon Capture and Storage (CCS) applications. Trace elements, particularly REE may be well suited to serve as in situ tracers for monitoring geochemical conditions and the migration of CO₂-charged waters within CCS storage systems. We have been conducting studies to determine the efficacy of using REE as a tracer and characterization tool in the laboratory, at a CCS analogue site in Soda Springs, Idaho, and at a proposed CCS reservoir at the Rock Springs Uplift, Wyoming. Results from field and laboratory studies have been encouraging and show that REE may be an effective tracer in CCS systems and overlying aquifers. In recent years, a series of studies using REE as a natural groundwater tracer have been conducted successfully at various locations around the globe. Additionally, REE and other trace elements have been successfully used as in situ tracers to describe the evolution of deep sedimentary Basins. Our goal has been to establish naturally occurring REE as a useful monitoring measuring and verification (MMV) tool in CCS research because formation brine chemistry will be particularly sensitive to changes in local equilibrium caused by the addition of large volumes of CO₂. Because brine within CCS target formations will have been in chemical equilibrium with the host rocks for millions of years, the addition of large volumes of CO₂ will cause reactions in the formation that will drive changes to the brine chemistry due to the pH change caused by the formation of carbonic acid. This CO₂ driven change in formation fluid chemistry will have a major impact on water rock reaction equilibrium in the formation, which will impart a change in the REE fingerprint of the brine that can measured and be used to monitor in situ reservoir conditions. Our research has shown that the REE signature imparted to the formation fluid by the introduction of CO₂ to the formation, can be measured and tracked as part of an MMV program. Additionally, this REE fingerprint may serve as an ideal tracer for fluid migration, both within the CCS target formation, and should formation fluids migrate into overlying aquifers. However application of REE and other trace elements to CCS system is complicated by the high salt content of the brines contained within the target formations. In the United States by regulation, in order for a geologic reservoir to be considered suitable for carbon storage, it must contain formation brine with total dissolved solids (TDS) > 10,000 ppm, and in most cases formation brines have TDS well in excess of that threshold. The high salinity of these brines creates analytical problems for elemental analysis, including element interference with trace metals in Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) (i.e. element mass overlap due to oxide or plasma phenomenon). Additionally, instruments like the ICP-MS that are sensitive enough to measure trace elements down to the parts per trillion level are quickly oversaturated when water TDS exceeds much more than 1,000 ppm. Normally this problem is dealt with through dilution of the sample, bringing the water chemistry into the instruments working range. However, dilution is not an option when analyzing these formation brines for trace metals, because trace elements, specifically the REE, which occur in aqueous solutions at the parts per trillion levels. Any dilution of the sample would make REE detection impossible. Therefore, the ability to use trace metals as in situ natural tracers in high TDS brines environments requires the development of methods for pre-concentrating trace elements, while reducing the salinity and associated elemental interference such that the brines can be routinely analyzed by standard ICP-MS methods. As part of the Big Sky Carbon Sequestration Project the INL-CAES has developed a rapid, easy to use process that pre-concentrates trace metals, including REE, up to 100x while eliminating interfering ions (e.g. Ba, Cl). The process is straightforward, inexpensive, and requires little infrastructure, using only a single chromatography column with inexpensive, reusable, commercially available resins and wash chemicals. The procedure has been tested with synthetic brines (215,000 ppm or less TDS) and field water samples (up to 5,000 ppm TDS). Testing has produced data of high quality with REE capture efficiency exceeding 95%, while reducing interfering elements by > 99%.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

PART 1- techno-economic analysis of a grid scale Ground-Level Integrated Diverse Energy Storage (GLIDES) technology

In this paper, a techno-economic model / cost reduction analysis of a low-cost, dispatchable / scalable, efficient Ground-Level Integrated Diverse Energy Storage (GLIDES) system is analyzed, along with a review of existing state-of-the-art energy storage technologies. The introduced technology, GLIDES, which was invented at the Oak Ridge National Laboratory (ORNL), stores energy by compression and expansion of air using water as a liquid piston inside high-pressure reservoirs. GLIDES is introduced by combining Pumped Hydroelectric Storage (PHS) and Compressed Air Energy Storage (CAES) technologies. Here, by replacing inefficient gas compressor and turbine with higher efficiency liquid turbomachines for liquid-piston compression/expansion, GLIDES achieves higher roundtrip efficiency than other gas compression-based energy storage technologies and is a scalable energy storage system. For cost reduction purposes, various pressure reservoirs including steel vessels, carbon fiber vessels, pipe segments, and underground pressure reservoirs are analyzed in this paper. Based on the analyzed data using the models discussed in this paper, energy storage costs as low as ~$14/kWh and ~346/kWh (roundtrip efficiency (RTE) ~80%) can be achieved for a grid-scale GLIDES using depleted oil/gas reservoirs and high-pressure pipe segments respectively.

25 ENERGY STORAGE↗

DOE OSTI2021

Assessment of Prognostic Value of High-Sensitivity Cardiac Troponin T for Early Prediction of Chemoradiation Therapy-Induced Cardiotoxicity in Patients with Non-Small Cell Lung Cancer: A Secondary Analysis of a Prospective Randomized Trial

Cardiotoxicities induced by cancer therapy can negatively affect quality of life and survival. We investigated whether high-sensitivity cardiac troponin T (hs-cTnT) levels could serve as biomarker for early detection of cardiac adverse events (CAEs) after chemoradiation therapy (CRT) for non-small cell lung cancer (NSCLC).

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

High-order shock-fitted detonation propagation in high explosives

In this paper, a highly accurate numerical shock and material interface fitting scheme composed of fifth-order spatial and third- or fifth-order temporal discretizations is applied to the two-dimensional reactive Euler equations in both slab and axisymmetric geometries. High rates of convergence are not typically possible with shock-capturing methods as the Taylor series analysis breaks down in the vicinity of discontinuities. Furthermore, for typical high explosive (HE) simulations, the effects of material interfaces at the charge boundary can also cause significant computational errors. Fitting a computational boundary to both the shock front and material interface (i.e. streamline) alleviates the computational errors associated with captured shocks and thus opens up the possibility of high rates of convergence for multi-dimensional shock and detonation flows. Several verification tests, including a Sedov blast wave, a Zel'dovich–von Neumann–Döring (ZND) detonation wave, and Taylor–Maccoll supersonic flow over a cone, are utilized to demonstrate high rates of convergence to nontrivial shock and reaction flows. Comparisons to previously published shock-capturing multi-dimensional detonations in a polytropic fluid with a constant adiabatic exponent (PF-CAE) are made, demonstrating significantly lower computational error for the present shock and material interface fitting method. For an error on the order of 10 m / s , which is similar to that observed in experiments, shock-fitting offers a computational savings on the order of 1000. In addition, the behavior of the detonation phase speed is examined for several slab widths to evaluate the detonation performance of PBX 9501 while utilizing the Wescott–Stewart–Davis (WSD) model, which is commonly used in HE modeling. It is found that the thickness effect curve resulting from this equation of state and reaction model using published values is dramatically more steep than observed in recent experiments. Utilizing the present fitting strategy, in conjunction with a nonlinear optimizer, a new set of reaction rate parameters improves the correlation of the model to experimental results. In conclusion, this new model is tested against two dimensional slabs as a validation test.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2023

QuadConv: Quadrature-based convolutions with applications to non-uniform PDE data compression

We present a new convolution layer for deep learning architectures which we call QuadConv — an approximation to continuous convolution via quadrature. Our operator is developed explicitly for use on non-uniform, mesh-based data, and accomplishes this by learning a continuous kernel that can be sampled at arbitrary locations. Moreover, the construction of our operator admits an efficient implementation which we detail and construct. As an experimental validation of our operator, we consider the task of compressing partial differential equation (PDE) simulation data from fixed meshes. Here, we show that QuadConv can match the performance of standard discrete convolutions on uniform grid data by comparing a QuadConv autoencoder (QCAE) to a standard convolutional autoencoder (CAE). Further, we show that the QCAE can maintain this accuracy even on non-uniform data. In both cases, QuadConv also outperforms alternative unstructured convolution methods such as graph convolution.

Compression↗

DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗

DOE OSTI2022

Carbon acquisition ecological strategies to connect soil microbial biodiversity and carbon cycling

Soil carbon feedbacks to global change are uncertain, and the biological processes that govern soil organic matter decomposition are not resolved in current ecosystem models. Though it is recognized that microbial biodiversity influences decomposition rates, incorporating this relationship into ecosystem models is challenging because microbial communities are prohibitively diverse. It is likely necessary to distill microbial biodiversity by focusing on functional groups or ecological strategies. The ecological strategies that currently dominate the microbial ecology literature derive from macroecological theory, have clear weaknesses, and have had limited success when applied to predict soil carbon dynamics. Here, we present a new framework for soil microorganisms: Carbon Acquisition Ecological Strategies (CAES), and we outline a path toward incorporating microbial biodiversity into ecosystem models using this framework to enhance predictions of soil carbon feedbacks to global change. Because a microorganism's diet is central to its ecological niche and likely to covary with other ecologically significant traits, we posit that carbon acquisition may serve as a tractable foundation for developing ecological strategies. Further, we describe four candidate ecological strategies for soil microorganisms: 1° decomposers that assimilate complex plant polymers, 2° decomposers that assimilate microbial necromass, passive consumers that assimilate dissolved organic carbon, and predatory microbes that assimilate live microbial biomass. These strategies are directly linked to soil carbon pools currently represented in ecosystem models and may provide a foundation for greater integration of microbial community dynamics into ecosystem models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Full-wave modeling of RF waves in fusion plasmas with finite element method: Progress in past decades and its future role

This paper reviews the progress in computing radio frequency (RF) wave fields in fusion plasmas, specifically focusing on simulations utilizing the finite element method (FEM) over the past few decades. Computing RF wave fields in fusion plasmas presents unique challenges due to large simulation domains, complex antenna structures, non-local dielectric properties, and wide ranges of spatial scales. It highlights key developments and outlines future directions, primarily addressing waves in the ion cyclotron (IC) to lower hybrid (LH) frequency range. We begin with briefly revisiting earlier developments before the widespread availability of modern computer-aided engineering (CAE) software based on FEM. This historical perspective illuminates early progress and the physics difficulties that motivated ongoing work within the community. Modern wave simulations for RF antennas based on FEM are characterized by the use of detailed 3D antenna model geometry generated from engineering CAD software and localized wave dielectric model. Significant advancements have also been made in improving physics models to include phenomena such as RF sheath rectification and wave scattering. FEM-based RF simulations have also been applied to compute wave propagation in the core region, where the inclusion of non-local dielectric response is crucial. This is a challenging goal, and several promising approaches have been proposed in this area. Additionally, RF simulation development initiatives based on open-source libraries have gained popularity, demonstrating scalability and flexibility in extending physics models. This paper will discuss the advantages and disadvantages of using such a publicly available FEM library.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Simulative design and process optimization of the two-stage stretch-blow molding process

The total production costs of PET bottles are significantly affected by the costs of raw material. Approximately 70 % of the total costs are spent for the raw material. Therefore, stretch-blow molding industry intends to reduce the total production costs by an optimized material efficiency. However, there is often a trade-off between an optimized material efficiency and required product properties. Due to a multitude of complex boundary conditions, the design process of new stretch-blow molded products is still a challenging task and is often based on empirical knowledge. Application of current CAE-tools supports the design process by reducing development time and costs. This paper describes an approach to determine optimized preform geometry and corresponding process parameters iteratively. The wall thickness distribution and the local stretch ratios of the blown bottle are calculated in a three-dimensional process simulation. Thereby, the wall thickness distribution is correlated with an objective function and preform geometry as well as process parameters are varied by an optimization algorithm. Taking into account the correlation between material usage, process history and resulting product properties, integrative coupled simulation steps, e.g. structural analyses or barrier simulations, are performed. The approach is applied on a 0.5 liter PET bottle of Krones AG, Neutraubling, Germany. The investigations point out that the design process can be supported by applying this simulative optimization approach. In an optimization study the total bottle weight is reduced from 18.5 g to 15.5 g. The validation of the computed results is in progress.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Thermal Stability of Silica for Application in Thermal Energy Storage

Thermal energy storage (TES) systems have enabled concentrating solar power (CSP) to remain competitive in the modern energy mix by providing economical load shifting grid services and firming up intermittent solar resource. Free from siting constraints, TES also shows promise as an economical alternative to traditional pumped-storage hydropower (PSH) and compressed air energy storage (CAES). As potential thermal energy storage media, some solid particles demonstrate stability over wide temperature ranges which allows for increased sensible energy storage density and is essential in achieving low-cost storage. Silica sand, in the form of a-quartz, is one such candidate. This work presents a brief review of relevant silica thermophysical properties and further investigates the thermal stability of silica particles as a candidate TES media by subjecting them to two different thermal tests: (1) a 500-hour thermal treatment at 1200 degrees C under varied atmospheres; and (2) cycling 25, 50, and 100 times between 300 degrees C and 1200 degrees C. For both tests, particle stability is examined by means of pre- and post-treatment Mie scattering. An additional XRD analysis is conducted for the 500-hour treatment in air. Results indicate limited changes in both particle distribution and crystallographic structure which is promising for the application as solid particle media for thermal energy storage.

concentrating solar power↗

DOE OSTI2024

Eigenmode twist by energy flux during fast-ion driven instabilities

Twisting of the spatial structure of eigenmodes by energy fluxes generated due to the spatial channeling (SC)—the fast-ion energy and momentum transfer across the magnetic field by destabilized modes—is considered. It is revealed that there is a correlation between the direction of the radial wave energy flux, orientation of the mode twist (MT), and the direction of mode rotation. The mode twist parameter is introduced and relations connecting it with the energy flux are established. It is shown the energy flux transforms zeros of the radial profile of the mode amplitude into minima (i.e., zeros disappear). It is found that the radial group velocity and phase velocity of reversed shear Alfvén eigenmodes (RSAEs) have opposite directions. These findings can be used for diagnostics of the energy fluxes during fast-ion driven instabilities and understanding whether the SC degrades or improves plasma performance. Specific calculations are carried out for fast magnetoacoustic modes, FMMs (known also as compressional Alfvén eigenmodes, CAEs), and Alfvén eigenmodes, AEs. A DIII-D experiment where RSAEs were observed is considered. It is concluded that an outward SC took place in this experiment. Peculiarities of various modes are discussed, which may explain why twisting of AEs, but not of FMMs, was observed experimentally in the DIII-D and NSTX tokamaks.

Kolesnichenko, Ya. I. (ORCID:0000000323219681)↗

DOE OSTI2015

Suppression of energetic particle driven instabilities with HHFW heating

In plasmas in the National Spherical Torus Experiment (NSTX) [Ono et al., Nucl. Fusion 40 (2000) 557] heated with neutral beams, the beam ions typically excite Energetic Particle Modes (EPMs or fishbones), and Toroidal, Global or Compressional Alfvén Eigenmodes (TAE, GAE, CAE). These modes can redistribute the energetic beam ions, altering the beam driven current profile and the plasma heating profile, or they may affect electron thermal transport or cause losses of the beam ions. In this paper we present experimental results where these instabilities, driven by the super-thermal beam ions, are suppressed with the application of High Harmonic Fast Wave heating.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Application of simulations and theory of sub-cyclotron frequency modes to DIII-D

Theoretical and numerical study of Alfvén eigenmodes (AEs) in the sub-cyclotron frequency range explains the observed frequency scaling with beam parameters in experiments across different devices. Global Alfvén (GAEs) and compressional Alfvén eigenmodes (CAEs) are frequently excited during neutral beam injection (NBI) in the National Spherical Torus Experiment (NSTX/NSTX-U), as well as other beam-heated devices such as MAST and DIII-D. These modes are driven unstable through the Doppler shifted cyclotron resonance with the NBI ions and can be excited in ITER due to super-Alfvénic velocities and anisotropy of the fast ion distribution. Numerical models and theory for sub-cyclotron frequency modes, previously developed and used to study the excitation of GAEs/CAEs in the NSTX(-U), have been successfully applied to explain the DIII-D observations. Simulations for DIII-D demonstrate that the modes with ω/ω ci ≈ 0.6, previously mis-identified as compressional Alfvén eigenmodes, have shear polarization and should be identified as the GAEs. Simulation results match the observed frequencies and estimated toroidal mode numbers in DIII-D experiments. Good agreement is found between scaling of the experimentally observed GAE frequencies with NBI parameters for NSTX, NSTX-U and DIII-D and scaling predicted by the theory and simulations. Here, the DIII-D simulation results also demonstrate that GAEs can be excited for larger aspect-ratio devices and smaller (sub- Alfvénic) beam injection velocities, therefore they can be unstable in ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗