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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.

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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Large-eddy simulation study on cycle-to-cycle variation of knocking combustion in a spark-ignition engine

The cycle-to-cycle variation in the knock intensity is commonly encountered under abnormal combustion conditions. The severity of these abnormal combustion events can vary significantly, and the efficiency of engines at high loads is limited in practice by heavy knocking phenomena. Since, a thorough analysis of such recurrent but non-cyclic phenomena via experiments alone becomes highly cumbersome, in the present work, a multi-cycle large-eddy simulation study was performed to quantitatively predict cyclic variability in the combustion process and cyclic knock intensity variability in a direct injection spark-ignition engine. To account for the turbulence-chemistry interaction effects on flame propagation, the G-equation combustion model was used. Detailed chemistry was solved outside the flame front with a toluene primary reference fuel skeletal kinetic mechanism. For both the mild knock and heavy knock conditions, the numerical results were validated against experimental measurements. Based on the simulation results, a correlation analysis was performed considering combustion phasing, peak cylinder pressure and maximum amplitude of pressure oscillation. Furthermore, a detailed three-dimensional spatial analysis illustrated the evolution of auto-ignition kernel development and propagation of pressure waves during knocking combustion for three typical cycles with different knock intensities. In this process, it was found that an early occurrence of auto-ignition in the end gas was prone to high knock intensity. Although multiple auto-ignition kernels were observed in different cycles, the degree of coupling between chemical heat release and pressure waves varied, thereby leading to different maximum amplitude of pressure oscillation values.

42 ENGINEERING↗
DOE OSTI2016

3D Monte Carlo simulation of backscattered electron signal variation across pore-solid boundaries in cement-based materials

Three-dimensional (3D) Monte Carlo simulation was used to study the variation of backscattered electron (BSE) signal across pore-solid boundaries in cement-based materials in order to enhance quantitative analysis of pore structure. The effects of pore size, depth and boundary inclination angle were investigated. It is found that pores down to 1 nm can generate sufficient contrast to be detected. Visibility improves with larger pore size, smaller beam probe size and lower acceleration voltage. However, pixels in shallow pores or near pore boundaries display higher grey values (brightness) than expected due to sampling sub-surface or neighbouring solid material. Thus, cement-based materials may appear less porous or the pores appear smaller than they actually are in BSE images. Simulated BSE images were used to test the accuracy of the Overflow pore segmentation method. Results show the method is generally valid and gives low errors for pores that are 1 μm and greater.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Data-driven variational multiscale reduced order models

We propose a new data-driven reduced order model (ROM) framework that centers around the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to increase the ROM accuracy at a modest computational cost. The VMS methodology is a natural fit for the hierarchical structure of the ROM basis: In the first step, we use the ROM projection to separate the scales into three categories: (i) resolved large scales, (ii) resolved small scales, and (iii) unresolved scales. In the second step, we explicitly identify the VMS–ROM closure terms, i.e., the terms representing the interactions among the three types of scales. In the third step, we use available data to model the VMS–ROM closure terms. Thus, instead of phenomenological models used in VMS for standard numerical discretizations (e.g., eddy viscosity models), we utilize available data to construct new structural VMS–ROM closure models. Specifically, we build ROM operators (vectors, matrices, and tensors) that are closest to the true ROM closure terms evaluated with the available data. We test the new data-driven VMS–ROM in the numerical simulation of four test cases: (i) the 1D Burgers equation with viscosity coefficient v = 10 -3 ; (ii) a 2D flow past a circular cylinder at Reynolds numbers, Re = 100, Re = 500, and Re= 1000; (iii) the quasi-geostrophic equations at Reynolds number Re = 450 and Rossby number Ro = 0.0036; and (iv) a 2D flow over a backward facing step at Reynolds number Re = 1000. The numerical results show that the data-driven VMS–ROM is significantly more accurate than standard ROMs.

42 ENGINEERING↗
DOE OSTI2020

hp -VPINNs: Variational physics-informed neural networks with domain decomposition

We formulate a general framework for hp-variational physics-informed neural networks (hp-VPINNs) based on the nonlinear approximation of shallow and deep neural networks and hp-refinement via domain decomposition and projection onto the space of high-order polynomials. The trial space is the space of neural network, which is defined globally over the entire computational domain, while the test space contains piecewise polynomials. Specifically in this study, the hp-refinement corresponds to a global approximation with a local learning algorithm that can efficiently localize the network parameter optimization. Here, we demonstrate the advantages of hp-VPINNs in both accuracy and training cost for several numerical examples of function approximation and in solving differential equations.

42 ENGINEERING↗
DOE OSTI2015

Variational Bayesian Blind Image Deconvolution: A review

In this study, we provide a review of the recent literature on Bayesian Blind Image Deconvolution (BID) methods. We believe that two events have marked the recent history of BID: the predominance of Variational Bayes (VB) inference as a tool to solve BID problems and the increasing interest of the computer vision community in solving BID problems. VB inference in combination with recent image models like the ones based on Super Gaussian (SG) and Scale Mixture of Gaussians (SMG) representations have led to the use of very general and powerful tools to provide clear images from blurry observations. In the provided review emphasis is paid on VB inference and the use of SG and SMG models with coverage of recent advances in sampling methods. We also provide examples of current state of the art BID methods and discuss problems that very likely will mark the near future of BID.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A Prospective Comparison of the Effects of Interfractional Variations on Proton Therapy and Intensity Modulated Radiation Therapy for Prostate Cancer

Purpose: To quantify and compare the impact of interfractional setup and anatomic variations on proton therapy (PT) and intensity modulated radiation therapy (IMRT) for prostate cancer. Methods and Materials: Twenty patients with low-risk or intermediate-risk prostate cancer randomized to receive passive-scattering PT (n=10) and IMRT (n=10) were selected. For both modalities, clinical treatment plans included 50.4 Gy(RBE) to prostate and proximal seminal vesicles, and prostate-only boost to 79.2 Gy(RBE) in 1.8 Gy(RBE) per fraction. Implanted fiducials were used for prostate localization and endorectal balloons were used for immobilization. Patients in PT and IMRT arms received weekly computed tomography (CT) and cone beam CT (CBCT) scans, respectively. The planned dose was recalculated on each weekly image, scaled, and mapped onto the planning CT using deformable registration. The resulting accumulated dose distribution over the entire treatment course was compared with the planned dose using dose-volume histogram (DVH) and γ analysis. Results: The target conformity index remained acceptable after accumulation. The largest decrease in the average prostate D{sub 98} was 2.2 and 0.7 Gy for PT and IMRT, respectively. On average, the mean dose to bladder increased by 3.26 ± 7.51 Gy and 1.97 ± 6.84 Gy for PT and IMRT, respectively. These values were 0.74 ± 2.37 and 0.56 ± 1.90 for rectum. Differences between changes in DVH indices were not statistically significant between modalities. All volume indices remained within the protocol tolerances after accumulation. The average pass rate for the γ analysis, assuming tolerances of 3 mm and 3%, for clinical target volume, bladder, rectum, and whole patient for PT/IMRT were 100/100, 92.6/99, 99.2/100, and 97.2/99.4, respectively. Conclusion: The differences in target coverage and organs at risk dose deviations for PT and IMRT were not statistically significant under the guidelines of this protocol.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilation

This work studies reduced order modeling (ROM) approaches to speed up the solution of variational data assimilation problems with large scale nonlinear dynamical models. It is shown that a key requirement for a successful reduced order solution is that reduced order Karush–Kuhn–Tucker conditions accurately represent their full order counterparts. In particular, accurate reduced order approximations are needed for the forward and adjoint dynamical models, as well as for the reduced gradient. New strategies to construct reduced order based are developed for proper orthogonal decomposition (POD) ROM data assimilation using both Galerkin and Petrov–Galerkin projections. For the first time POD, tensorial POD, and discrete empirical interpolation method (DEIM) are employed to develop reduced data assimilation systems for a geophysical flow model, namely, the two dimensional shallow water equations. Numerical experiments confirm the theoretical framework for Galerkin projection. In the case of Petrov–Galerkin projection, stabilization strategies must be considered for the reduced order models. The new reduced order shallow water data assimilation system provides analyses similar to those produced by the full resolution data assimilation system in one tenth of the computational time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Variational Bayesian strategies for high-dimensional, stochastic design problems

This paper is concerned with a lesser-studied problem in the context of model-based, uncertainty quantification (UQ), that of optimization/design/control under uncertainty. The solution of such problems is hindered not only by the usual difficulties encountered in UQ tasks (e.g. the high computational cost of each forward simulation, the large number of random variables) but also by the need to solve a nonlinear optimization problem involving large numbers of design variables and potentially constraints. We propose a framework that is suitable for a class of such problems and is based on the idea of recasting them as probabilistic inference tasks. To that end, we propose a Variational Bayesian (VB) formulation and an iterative VB–Expectation-Maximization scheme that is capable of identifying a local maximum as well as a low-dimensional set of directions in the design space, along which, the objective exhibits the largest sensitivity. We demonstrate the validity of the proposed approach in the context of two numerical examples involving thousands of random and design variables. In all cases considered the cost of the computations in terms of calls to the forward model was of the order of 100 or less. The accuracy of the approximations provided is assessed by information-theoretic metrics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Analytical and variational numerical methods for unstable miscible displacement flows in porous media

The miscible displacement of one fluid by another in a porous medium has received considerable attention in subsurface, environmental and petroleum engineering applications. When a fluid of higher mobility displaces another of lower mobility, unstable patterns – referred to as viscous fingering – may arise. Their physical and mathematical study has been the object of numerous investigations over the past century. In writing this paper the objective is to present a review of these contributions with particular emphasis on variational methods. These algorithms are tailored to real field applications thanks to their advanced features: handling of general complex geometries, robustness in the presence of rough tensor coefficients, low sensitivity to mesh orientation in advection dominated scenarios, and provable convergence with fully unstructured grids.

42 ENGINEERING↗
DOE OSTI2024

Convergence of variational Monte Carlo simulation and scale-invariant pre-training

We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. Here, we study both the energy minimization phase and the supervised pre-training phase that is commonly used prior to energy minimization. For the energy minimization phase, the standard algorithm is scale-invariant by design, and we provide a proof of convergence for this algorithm without modifications. The pre-training stage typically does not feature such scale-invariance. We propose using a scale-invariant loss for the pretraining phase and demonstrate empirically that it leads to faster pre-training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A variational mimetic finite difference method for elliptic interface problems on non-matching polytopal meshes with geometric interface inconsistencies

A new variational mimetic finite difference method for elliptic interface problems with perfect and imperfect thermal contacts on non-matching polytopal meshes with geometric interface inconsistencies is developed and analyzed theoretically and numerically. The method is defined on multiple non-matching submeshes with gaps and overlaps along their interfaces. The discrete equations are derived from a minimization problem for the augmented Dirichlet functional. For a perfect thermal contact, the functional uses a modified mimetic gradient with extended stencil which couples unknowns from both sides of an interface, as well as penalty terms to enforce weak continuity of temperature across the interface. The method leads to a symmetric positive definite matrix for any scaling of the penalty terms. For an imperfect thermal contact, the Dirichlet functional is supplemented with a quadratic jump term along the interface related to the interface thermal resistance. We prove that the method conserves the total heat flux across each interface. In conclusion, the obtained results are verified with numerical experiments showing convergence in the discrete L 2 and L ∞ norms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Predicting river turbidity in Pine Island Bayou using machine learning techniques coupled with variational mode decomposition

Elevated turbidity levels pose significant public health risks by facilitating the transport of harmful pollutants, including metals, organic compounds, and pathogenic microorganisms into the surface water. These conditions create serious challenges for public recreational water use and drinking water treatment, leading to economic losses and health risks. This study utilizes water monitoring data in Pine Island Bayou, Texas, and develops a Sequence-to-Sequence (S2S) model to predict turbidity using Attention-based Gated Recurrent Units with Encoder-Decoder (AT-GRU-ED) and Long Short-Term Memory (LSTM), coupled with Variational Mode Decomposition (VMD). Compared to the model without VMD, the model demonstrates satisfactory 72-hour turbidity prediction performance, achieving MAEs of 2.60 and 3.29 NTU (reductions of 53% and 58%), RMSEs of 21.08 and 31.49 NTU (reductions of 82% and 80%), and R² values of 0.96 and 0.84 on the validation and test sets, respectively. Feature importance analysis reveals that water temperature is the dominant factor influencing seasonal turbidity patterns, while real-time hourly rainfall significantly contributes to short-term variability. Turbidity typically peaks within 48 hours after rainfall events due to lagged effects from surface runoff and upstream flow. Findings suggest suspending recreational water use and water supply pumping for three days after heavy rainfall can benefit public health and improve water treatment processes. Discharges above 100 m3/s are found to accelerate sediment dilution and transport, reducing turbidity levels more quickly after the peak. In conclusion, the proposed model demonstrates reliable 72-hour turbidity prediction, supporting decision-making for water treatment plant operations and providing early warning for public recreational water use.

Deep learning↗
DOE OSTI2011

Lattice variations of Ti-6Al-4V alloy with hydrogen content

Effect of hydrogen content on the lattice parameter of Ti-6Al-4V alloy has been investigated by X-ray diffraction. The experimental results show that the solution of hydrogen in the Ti-6Al-4V alloy affects significantly on the lattice parameters of {alpha}, {beta} and {delta} phases, especially the {beta} phase. Furthermore, the critical hydrogen content of {delta} hydride formation for Ti-6Al-4V alloy is 0.385 wt.%. When the hydrogen content is lower than the critical hydrogen content, the {delta} hydride cannot precipitate and the lattice parameter ({alpha}) of {beta} phase linearly increases with the increasing of hydrogen content. When the hydrogen content is higher than the critical hydrogen content, the {delta} hydride precipitates and the lattice parameter ({alpha}) of {beta} phase varies inconspicuously with hydrogen content. In addition, the effects of lattice variations and {delta} hydride formation on microstructure are discussed. The {alpha}/{beta} interfaces of lamellar transformed {beta} phase become fuzzy with the increasing of hydrogen content because of the lattice expansion of {beta} phase. Compared with that of the Ti-6Al-4V alloy at low hydrogen content ({<=} 0.385 wt.%), the contrasts of primary {alpha} phase and transformed {beta} phase of Ti-6Al-4V alloy at high hydrogen content ({>=} 0.385 wt.%) were completely reversed due to the formation of {delta} hydride. - Research Highlights: {yields} A novel method for determining {delta} hydride in Ti-6Al-4V alloy is presented. {yields} The critical hydrogen content of {delta} hydride formation is 0.385 wt.%. {yields} The lattice parameter of {beta} phase can be expressed as follows: a=0.323(1+9.9x10{sup -2}C{sub H}) . {yields} Precipitation of {delta} hydride has a significant influence on the microstructure. {yields} The {alpha}/{beta} interfaces of transformed {beta} phase became fuzzy in the hydrogenated alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Variation of carbon concentration in proeutectoid ferrite during austenitization in hypoeutectoid steel

The variation of the C concentration in proeutectoid ferrite (α{sub PF}) during austenitization in hypoeutectoid steels was quantitatively investigated using the massive transformation start temperature (T{sub m}) of α{sub PF} to austenite (γ) measured by high-temperature confocal laser scanning microscopy and hardness of α{sub PF}. The C concentration in α{sub PF} at T{sub m} in hypoeutectoid steels increased with increasing total C concentration up to approximately 0.2 wt.% during heating. The hardness of α{sub PF} with isothermal holding time at 775 °C in S20C steel revealed C enrichment in α{sub PF} at the early stage of isothermal holding and its reduction with further holding. These results explain the redistribution of the C in α{sub PF} during austenitization as follows: free C atoms released from cementite during pearlite decomposition diffuse excessively into neighboring α{sub PF} as well as pearlitic ferrite. The supersaturated C concentration in α{sub PF} is reduced during the long-range diffusive transformation of α{sub PF} to γ. However, some of the excess C atoms still remain in α{sub PF} until α{sub PF} starts to massively transform to γ. - Highlights: • Massive transformation of αPF to γ in hypoeutectoid steels was observed using CLSM. • C content in αPF during austenitization was analyzed by measured Tm and hardness. • Tm decreases and C content in αPF at Tm increases with increasing total C. • C atoms released from θ during formation of P to γ diffuse excessively into αPF. • Supersaturated C content in αPF is reduced during transformation of αPF to γ.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Variations of target volume definition and daily target volume localization in stereotactic body radiotherapy for early-stage non–small cell lung cancer patients under abdominal compression

We aimed to compare gross tumor volumes (GTV) in 3-dimensional computed tomography (3DCT) simulation and daily cone beam CT (CBCT) with the internal target volume (ITV) in 4-dimensional CT (4DCT) simulation in stereotactic body radiotherapy (SBRT) treatment of patients with early-stage non–small cell lung cancer (NSCLC) under abdominal compression. We retrospectively selected 10 patients with NSCLC who received image-guided SBRT treatments under abdominal compression with daily CBCT imaging. GTVs were contoured as visible gross tumor on the planning 3DCT and daily CBCT, and ITVs were contoured using maximum intensity projection (MIP) images of the planning 4DCT. Daily CBCTs were registered with 3DCT and MIP images by matching of bony landmarks in the thoracic region to evaluate interfractional GTV position variations. Relative to MIP-based ITVs, the average 3DCT-based GTV volume was 66.3 ± 17.1% (range: 37.5% to 92.0%) (p < 0.01 in paired t-test), and the average CBCT-based GTV volume was 90.0 ± 6.7% (daily range: 75.7% to 107.1%) (p = 0.02). Based on bony anatomy matching, the center-of-mass coordinates for CBCT-based GTVs had maximum absolute shift of 2.4 mm (left-right), 7.0 mm (anterior-posterior [AP]), and 5.2 mm (superior-inferior [SI]) relative to the MIP-based ITV. CBCT-based GTVs had average overlapping ratio of 81.3 ± 11.2% (range: 45.1% to 98.9%) with the MIP-based ITV, and 57.7 ± 13.7% (range: 35.1% to 83.2%) with the 3DCT-based GTV. Even with abdominal compression, both 3DCT simulations and daily CBCT scans significantly underestimated the full range of tumor motion. In daily image-guided patient setup corrections, automatic bony anatomy-based image registration could lead to target misalignment. Soft tissue-based image registration should be performed for accurate treatment delivery.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Wetting mechanism and alteration of nano-sized shale pores: Insights from contrast variation small angle neutron scattering

Wettability of tight shale is crucial for fluid flow and mass transport process in energy geosciences. However, understanding the interfacial chemistry and wetting mechanisms at sub-nano-pore scales remains a formidable challenge. Here, in this study, the Contrast Variation technique of Small Angle Neutron Scattering (CV-SANS) is employed to investigate shale’s interfacial chemistry using reagents that possess a range of different polarities, including water, n-decane, toluene, and dimethyl methanamide. Through five different experimental strategies, we have demonstrated a successful modification of shale wettability, ranging from enhancement, weakening, to reversal. Delving into the mechanisms, we illustrated the crucial role of pre-existing liquid films in these changes, where the uniquely co-existing polar and non-polar functional groups in dimethyl methanamide acted as a conduit for interfacial chemistry adjustments. Furthermore, a solvent immersion led to matrix dilation as well as liberation of residual oil-occupied pores, resulting in altered pore size distributions, with hydrogen bonding playing a significant role in the polar groups. Interestingly, despite shale exhibiting a stronger affinity for oil over water, hydrophilic solvents induced more substantial dilation than lipophilic ones. Collectively, this work elucidates the dynamic change of interfacial chemistry via the configuration of polarity using chemical reagents, and the CV-SANS technique underscores its invaluable utilities in decoding the interfacial wettability traits in nanopore space of shale.

58 GEOSCIENCES↗
DOE OSTI2018

Learning from crowds with variational Gaussian processes

Solving a supervised learning problem requires to label a training set. This task is traditionally performed by an expert, who provides a label for each sample. The proliferation of social web services (e.g., Amazon Mechanical Turk) has introduced an alternative crowdsourcing approach. Anybody with a computer can register in one of these services and label, either partially or completely, a dataset. The effort of labeling is then shared between a great number of annotators. However, this approach introduces scientifically challenging problems such as combining the unknown expertise of the annotators, handling disagreements on the annotated samples, or detecting the existence of spammer and adversarial annotators. All these problems require probabilistic sound solutions which go beyond the naive use of majority voting plus classical classification methods. In this work we introduce a new crowdsourcing model and inference procedure which trains a Gaussian Process classifier using the noisy labels provided by the annotators. Variational Bayes inference is used to estimate all unknowns. The proposed model can predict the class of new samples and assess the expertise of the involved annotators. Moreover, the Bayesian treatment allows for a solid uncertainty quantification. Since when predicting the class of a new sample we might have access to some annotations for it, we also show how our method can naturally incorporate this additional information. Furthermore, a comprehensive experimental section evaluates the proposed method with synthetic and real experiments, showing that it consistently outperforms other state-of-the-art crowdsourcing approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Combining Poisson singular integral and total variation prior models in image restoration

In this study, a novel Bayesian image restoration method based on a combination of priors is presented. It is well known that the Total Variation(TV) image prior preserves edge structures while imposing smoothness on the solutions.However,it tends to oversmooth textured areas.To alleviate this problem we propose to combine the TV and the Poisson Singular Integral(PSI)models, which,as we will show, preserves the image textures. The PSI prior depends on a parameter that controls the shape of the filter. A study on the behavior of the filter as a function of this parameter is presented. Our restoration model utilizes a bound for the TV image model based on the majorization–minimization principle,and performs maximum a posteriori Bayesian inference. In order to assess the performance of the proposed approach,in the experimental section we compare it with other restoration methods.

97 MATHEMATICS AND COMPUTING↗