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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Weight-adapted ultra-low-dose pancreatic perfusion CT: radiation dose, image quality, and perfusion parameters

Purpose: We evaluate the reliability and feasibility of weight-adapted ultra-low-dose pancreatic perfusion CT. Methods: A total of 100 (47 men, 53 women) patients were enrolled prospectively and were assigned to five groups (A, B, C, D, and E) with different combination of tube voltage and tube current according to their body weight. Radiation dose parameters including volume CT dose index (CTDI) and dose-length product (DLP) were recorded. Image quality was evaluated both subjectively and objectively (noise, signal-to-noise ratio, contrast-to-noise ratio). Perfusion parameters including blood flow (BF), blood volume (BV), and permeability (PMB) were measured. The dose, image quality measurements, and perfusion parameters were compared between the five groups using one-way analysis of variance (ANOVA). Results: Radiation dose reached 8.7 mSv in patients under 50 kg and was 18.9 mSv in patients above 80 kg. The mean subjective image quality score was above 4.45 on a 5-point scale with good agreement between two radiologists. Groups A–D had equivalent performance on objective image quality (P > 0.05), while Group E performed even better (P < 0.05). No significant differences emerged in comparison with perfusion parameters (BF, BV, PMB) of normal pancreas parenchyma between the five groups. Conclusion: Weight-adapted ultra-low-dose pancreatic perfusion CT can effectively reduce radiation dose without prejudice to image quality, and the perfusion parameters of normal parenchyma are accurate and reliable.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Parallel anisotropic mesh adaptation with boundary layers for automated viscous flow simulations

This paper presents a set of parallel procedures for anisotropic mesh adaptation accounting for mixed element types used in boundary layer meshes, i.e., the current procedures operate in parallel on distributed boundary layer meshes. The procedures accept anisotropic mesh metric field as an input for the desired mesh size field and apply local mesh modifications to adapt the mesh to match/satisfy the specified mesh size field. The procedures fully account for the parametric geometry of curved domains and maintain the semi-structured nature of the boundary layer elements. The effectiveness of the procedures is demonstrated on three viscous flow examples that include the ONERA M6 wing, a heat transfer manifold, and a scramjet engine.

Sahni, Onkar↗
DOE OSTI2018

Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets

New manufacturing technologies such as additive manufacturing require research and development to minimize the uncertainties in the produced parts. The research involves experimental measurements and large simulations, which result in huge quantities of data to store and analyze. We address this challenge by alleviating the data storage requirements using lossy data compression. We select wavelet bases as the mathematical tool for compression. Unlike images, additive manufacturing data is often represented on irregular geometries and unstructured meshes. Thus, we use Alpert tree-wavelets as bases for our data compression method. We first analyze different basis functions for the wavelets and find the one that results in maximal compression and miminal error in the reconstructed data. We then devise a new adaptive thresholding method that is data-agnostic and allows a priori estimation of the reconstruction error. Finally, we propose metrics to quantify the global and local errors in the reconstructed data. One of the error metrics addresses the preservation of physical constraints in reconstructed data fields, such as divergence-free stress field in structural simulations. While our compression and decompression method is general, we apply it to both experimental and computational data obtained from measurements and thermal/structural modeling of the sintering of a hollow cylinder from metal powders using a Laser Engineered Net Shape process. The results show that monomials achieve optimal compression performance when used as wavelet bases. The new thresholding method results in compression ratios that are two to seven times larger than the ones obtained with commonly used thresholds. Overall, adaptive Alpert tree-wavelets can achieve compression ratios between one and three orders of magnitude depending on the features in the data that are required to preserve. Furthermore, these results show that Alpert tree-wavelet compression is a viable and promising technique to reduce the size of large data structures found in both experiments and simulations.

42 ENGINEERING↗
DOE OSTI2018

Convergence and Efficiency of Adaptive Importance Sampling Techniques with Partial Biasing

We propose a new Monte Carlo method to efficiently sample a multimodal distribution (known up to a normalization constant). We consider a generalization of the discrete-time Self Healing Umbrella Sampling method, which can also be seen as a generalization of well-tempered metadynamics. The dynamics is based on an adaptive importance technique. The importance function relies on the weights (namely the relative probabilities) of disjoint sets which form a partition of the space. These weights are unknown but are learnt on the fly yielding an adaptive algorithm. In the context of computational statistical physics, the logarithm of these weights is, up to an additive constant, the free-energy, and the discrete valued function defining the partition is called the collective variable. The algorithm falls into the general class of Wang–Landau type methods, and is a generalization of the original Self Healing Umbrella Sampling method in two ways: (i) the updating strategy leads to a larger penalization strength of already visited sets in order to escape more quickly from metastable states, and (ii) the target distribution is biased using only a fraction of the free-energy, in order to increase the effective sample size and reduce the variance of importance sampling estimators. We prove the convergence of the algorithm and analyze numerically its efficiency on a toy example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Adaptive Wavelet Decomposition of Matrix Flows

Adaptive algorithms for constructing spline-wavelet decompositions of matrix flows from a linear space of matrices over a normed field are presented. The algorithms suggested provides for an a priori prescribed estimate of the deviation of the basic flow from the initial one. Comparative bounds of the volumes of data in the basic flow for various irregularity characteristics of the initial flow are obtained in the cases of pseudo-equidistant and adaptive grids. Limit characteristics of the above-mentioned volumes are given in the cases where the initial flow is generated by differentiable functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Memory-efficient nonsmooth dynamic optimization using adaptive randomized compression

Dynamic optimization problems arise in many applications including flow control, full waveform inversion, and medical imaging. These problems are plagued by significant computational challenges. One such challenge — and the focus of this work — is the memory limitation induced by the size of the underlying dynamical system. In particular, the entire dynamic trajectory is required for derivative computation and therefore must be stored or recomputed using, e.g., checkpointing. Although recent work demonstrated the use of adaptive randomized sketching to overcome the memory challenge, that work only applies to smooth unconstrained problems, prohibiting its use for nonsmooth regularized and constrained problems. The inclusion of nonsmooth regularizers and constraints is critical as they often arise in an attempt to preserve certain physical properties or to promote sparsity. To solve these problems, we introduce a trust-region algorithm for minimizing the sum of a smooth nonconvex function and a nonsmooth convex function that leverages randomized sketching to compress the dynamical system trajectories and adaptively adjust the sketch rank to satisfy a gradient inexactness condition. We prove convergence of this algorithm and demonstrate that it achieves substantial memory reduction on three discretized PDE-constrained optimization applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Reduction of amplified spontaneous emission noise using a statistical signal adaptive filter

This paper proposes a statistical signal adaptive filter (SSAF) based on adaptive total variation to reduce amplified spontaneous emission (ASE) noise in optical amplifiers, e.g. erbium doped fiber amplifiers. Non-return-to-zero on–off keying signal to noise ratio (SNR) is degraded by ASE noise. However, this can be improved by squeezing ASE using the proposed SSAF after photodetection to effectively remove the noise by analyzing the signal movement statistically. We obtained 4-dB SNR gain by reducing ASE beat noise compared with not applying SSAF. Simultaneous optical bandpass filter and SSAF application reduced the 2-dB power penalty for a 20-km optical link more than when only SSAF was used.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Numerical simulation and analysis of femtosecond pulse evolution in liquid-core photonic crystal fiber based on adaptive step-size methods

The adaptive step-size methods including conservation quantity error method (CQEM) and local error method (LEM) are described and used to investigate the femtosecond pulse evolution in liquid-core photonic crystal fibers. The efficiency and accuracy of the numerical results obtained from CQEM and LEM in frequency domain (FD) and time domain (TD) are compared and discussed under the fixed numerical parameters respectively. The numerical results represent that LEM has the higher accuracy due to the integration of nonlinear operator that is more efficient than CQEM. The femtosecond pulse evolution in liquid-core photonic crystal fiber is described and analyzed using LEM. Moreover, the influences of the peak power of pulse and length of liquid-core photonic crystal fibers on the supercontinuum generation are also obtained based on LEM-FD. This study is helpful optimizing the numerical process in ultra-short pulse evolution based on adaptive step-size methods.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Scale-Adaptive Simulation of Transient Two-Phase Flow in Continuous-Casting Mold

Scale-adaptive simulation (SAS) of the transient gas–liquid two-phase flow in a laboratory-scale continuous-casting mold is presented. The main objective is to investigate the applicability of the scale-adaptive unsteady Reynolds-averaged Navier–Stokes turbulent model (URANS SAS) for predicting the transient multiscale turbulent structures in a two-phase flow. Good quantitative agreements with the experimental data and the large eddy simulation (LES) results are obtained both for the time-averaged velocity field and for the transient turbulent characteristics. The introduction of the von Karman length-scale into the turbulence-scale equation allows the SAS model to dynamically adjust to the resolved turbulent structures. The LES-like pulsating behavior of the air gas and the large-scale liquid eddy magnitudes in the unsteady regions of flow field are captured by the SAS model. The classical − 5/3 law of power spectrum density (PSD) of the axial velocity is kept properly for the single-phase turbulent flow. For two-phase flow, the decay of PSD is too steep at the high-frequency region; the predicted PSD obtained with SAS is damped stronger than that estimated by LES. The SAS model offers an attractive alternative to the existing LES approach or to the other hybrid RANS/LES models for strongly unsteady flows.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization

Here, we consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients using finite differences of stochastic function evaluations within a common random number framework. We develop modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations and provide global convergence results to the neighborhood of a locally optimal solution. We present numerical experiments on simulation optimization problems to illustrate the performance of the proposed algorithm. When compared with classical zeroth-order stochastic gradient methods, we observe that our strategies of adapting the sample sizes significantly improve performance in terms of the number of stochastic function evaluations required.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Adaptive primal–dual control for distributed energy resource management

With the increased adoption of distributed energy resources (DERs) in distribution networks, their coordinated control with a DER management system (DERMS) that provides grid services (e.g., voltage regulation, virtual power plant) is becoming more necessary. One particular type of DERMS using primal–dual control has recently been found to be very effective at providing multiple grid services among an aggregation of DERs; however, the main parameter, the primal–dual step size, must be manually tuned for the DERMS to be effective, which can take a considerable amount of engineering time and labor. To this end, we design a simple method that self-tunes the step size(s) and adapts it to changing system conditions. Additionally, it gives the DER management operator the ability to prioritize among possibly competing grid services. Here we evaluate the automatic tuning method on a simulation model of a real-world feeder in Colorado with data obtained from an electric utility. Through a variety of scenarios, we demonstrate that the DERMS with automatically and adaptively tuned step sizes provides higher-quality grid services than a DERMS with a manually tuned step size.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience

In an extended main grid outage spanning multiple days, load shedding serves as a critical mechanism for islanded microgrids to maintain essential power and energy reserves that are indispensable for fulfilling reliability and resiliency mandates. However, using load shedding for such purposes leads to increasing occurrence of cold load pickup (CLPU) events. Here, this study presents an innovative adaptive CLPU model that introduces a method for determining and incorporating parameters related to CLPU power and energy requirements into a two-stage microgrid unit commitment (MGUC) algorithm. In contrast to the traditional fixed-CLPU-curve approach, this model calculates CLPU duration, power, and energy demands by considering outage durations and ambient temperature variations within the MGUC process. By integrating the adaptive CLPU model into the MGUC problem formulation, it allows for the optimal allocation of energy resources throughout the entire scheduling horizon to fulfill the CLPU requirements when scheduling multiple CLPU events. The performance of the enhanced MGUC algorithm considering CLPU needs is assessed using actual load and photovoltaic (PV) data. Simulation results demonstrate significant improvements in dispatch optimality evaluated by the amount of load served, customer comfort, energy storage operation, and adherence to energy schedules. These enhancements collectively contribute to reliable and resilient microgrid operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Increase in proteins involved in mitochondrial fission, mitophagy, proteolysis and antioxidant response in type I endometrial cancer as an adaptive response to respiratory complex I deficiency

Pathogenic mtDNA mutations associated with alterations of respiratory complex I, mitochondrial proliferation (oncocytic-like phenotype) and increase in antioxidant response were previously reported in type I endometrial carcinoma (EC). To evaluate whether in the presence of pathogenic mtDNA mutations other mitochondrial adaptive processes are triggered by cancer cells, the expression level of proteins involved in mitochondrial dynamics, mitophagy, proteolysis and apoptosis were evaluated in type I ECs harboring pathogenic mtDNA mutations and complex I deficiency. An increase in the fission protein Drp1, in the mitophagy protein BNIP3, in the mitochondrial protease CLPP, in the antioxidant and anti-apoptotic protein ALR and in Bcl-2 as well as a decrease in the fusion protein Mfn2 were found in cancer compared to matched non malignant tissue. Moreover, the level of these proteins was measured in type I EC, in hyperplastic (the premalignant form) and in non malignant tissues to verify whether the altered expression of these proteins is a common feature of endometrial cancer and of hyperplastic tissue. This analysis confirmed in type I EC samples, but not in hyperplasia, an alteration of the expression level of these proteins. These results suggest that in this cancer mitochondrial fission, antioxidant and anti-apoptotic response may be activated, as well as the discharge of damaged mitochondrial proteins as adaptation processes to mitochondrial dysfunction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Adaptive behavior and different thermal experiences of real people: A Bayesian neural network approach to thermal preference prediction and classification

Various observed and unquantifiable factors affect the thermal comfort of occupants in indoor environments and can lead to high uncertainty in the prediction and classification of their thermal preferences. The behavioral adaptation of occupants, by operating window systems for example, changes their thermal experience and expectations and therefore contributes to even higher prediction uncertainty. In this study, we applied a Bayesian neural network (BNN) algorithm to build a predictive model for occupant thermal preference using the ASHRAE Global Thermal Comfort Database II. The Bayesian method allows us to synthesize prior knowledge and available measurements into a unified modeling framework. It also offers a way to express and quantify uncertainty. Here we have performed a systematic study to test the efficiency and robustness of different BNN model configurations. In this study, the results show that the BNN model outperforms conventional thermal comfort models such as Predicted Mean Vote (PMV) and adaptive comfort model. The BNN model tends to produce more confident “prefer cooler” predictions with high possibility and low uncertainty. In contrast, the BNN model produces less certain predictions for “prefer no change” and “prefer warmer” across all occupants. Our findings suggest that linking occupants’ subjective evaluation measures and window opening/closing behavior to thermal comfort modeling effectively improves predictive performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Boosting efficiency and reducing graph reliance: Basis adaptation integration in Bayesian multi-fidelity networks

The computational cost of high-fidelity numerical models makes outer-loop analysis, which requires repeated interrogation of the model such as uncertainty quantification, computationally demanding. Multi-fidelity methods, which construct a surrogate model using data from an ensemble of models of varying cost and accuracy, can substantially reduce the cost of outer-loop analysis. However, these methods can be difficult to apply when the model ensemble does not admit a clear hierarchy a priori and the correlations between models are low. Consequently, in this paper, we present a multi-fidelity method that leverages dimension reduction to enhance the correlation between models, thereby reducing the amount of data needed to train a surrogate from an unordered ensemble of models. Our method utilizes basis adaptation to build low-dimensional polynomial chaos expansions of each model and employs Multi-fidelity Networks to encode the relationships among models. We show that the resulting method exhibit two notable advantages over its counterpart: (1) enhanced accuracy (both reduced bias and variance); and (2) reduced dependency on the graph structure encoding relationships among models. We demonstrate the approach on an analytical test problem and a challenging finite element model for a spent nuclear fuel. Our method produces a surrogate model that is significantly more accurate than either a single-fidelity surrogate or a multi-fidelity surrogate constructed without basis adaptation.

42 ENGINEERING↗
DOE OSTI2019

Adaptive mesh refinement for steady flows in Nek5000

Adaptive mesh refinement is performed in the framework of the spectral element method augmented by approaches to error estimation and control. The h-refinement technique is used for adapting the mesh, where selected grid elements are split by a quadtree (2D) or octree (3D) structure. Continuity between parent-child elements is enforced by high-order interpolation of the solution across the common faces. Parallel mesh partitioning and grid management respectively, are taken care of by the external libraries ParMETIS and p4est. Two methods are considered for estimating and controlling the error of the solution. The first error estimate is local and based on the spectral properties of the solution on each element. This method gives a local measure of the L-2-norm of the solution over the entire computational domain. The second error estimate uses the dual-weighted residuals method - it is based on and takes into account both the local properties of the solution and the global dependence of the error in the solution via an adjoint problem. The objective of this second approach is to optimize the computation of a given functional of physical interest. The simulations are performed by using the code Nek5000 and three steady-state test cases are studied: a two-dimensional lid-driven cavity at Re = 7, 500, a two-dimensional flow past a cylinder at Re = 40, and a three-dimensional lid-driven cavity at Re = 2,000 with a moving lid tilted by an angle of 30 degrees. Finally, the efficiency of both error estimators is compared in terms of refinement patterns and accuracy on the functional of interest. In the case of the adjoint error estimators, the trend on the error of the functional is shown to be correctly represented up to a multiplicative constant.

42 ENGINEERING↗
DOE OSTI2019

DFT-FE – A massively parallel adaptive finite-element code for large-scale density functional theory calculations

We present an accurate, efficient and massively parallel finite-element code, DFT-FE, for large-scale ab-initio calculations (reaching ~100,000 electrons) using Kohn-Sham density functional theory (DFT). DFT-FE is based on a local real-space variational formulation of the Kohn-Sham DFT energy functional that is discretized using a higher-order adaptive spectral finite-element (FE) basis, and treats pseudopotential and all-electron calculations in the same framework, while accommodating non-periodic, semi-periodic and periodic boundary conditions. We discuss the main aspects of the code, which include, the various strategies of adaptive FE basis generation, and the different approaches employed in the numerical implementation of the solution of the discrete Kohn-Sham problem that are focused on significantly reducing the floating point operations, communication costs and latency. We demonstrate the accuracy of DFT-FE by comparing the energies, ionic forces and periodic cell stresses on a wide range of problems with popularly used DFT codes. Further, we demonstrate that DFT-FE significantly outperforms widely used plane-wave codes---both in CPU-times and wall-times, and on both non-periodic and periodic systems---at systems sizes beyond a few thousand electrons, with over 5-10 fold speedups in systems with more than 10,000 electrons. In conclusion, the benchmark studies also highlight the excellent parallel scalability of DFT-FE, with strong scaling demonstrated on up to 192,000 MPI tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Adaptation of EVIAVE methodology for monitoring and follow-up when evaluating the environmental impact of landfills

Treatment and final disposal of Municipal Solid Waste can have a significant role in the generation of negative environmental impacts. As a prevention strategy, such activities are subjected to the process of Environmental Impact Assessment (EIA). Still, the follow-up of Environmental Management Plans or mitigation measures is limited, for one due to a lack of methodological approaches. In searching for possibilities, the University of Granada (Spain) developed a diagnostic methodology named EVIAVE, which allows one to quantify, by means of indexes, the environmental impact of landfills in view of their location and the conditions of exploitation. EVIAVE is applicable within the legal framework of the European Union and can be adapted to the environmental and legal conditions of other countries. This study entails its adaptation in Colombia, for the follow-up and control of the EIA process for landfills. Modifications involved inclusion of the environmental elements flora and fauna, and the evaluation of the environmental descriptors in agreement with the concept of vulnerability. The application of the modified EVIAVE in Colombian landfills allowed us to identify the elements affected by the operating conditions and maintenance. It may be concluded that this methodology is viable and effective for the follow-up and environmental control of EIA processes for landfills, and to analyze the associated risks, as it takes into account related environmental threats and vulnerabilities. - Highlights: • A modified methodology is used to monitor and follow-up environmental impacts in landfills. • The improved methodology includes the Vulnerability of Flora and Fauna to evaluate environmental impact of landfills. • The methodology serves to identify and evaluate the sources of risk generated in the construction and siting of landfills. • Environmental vulnerability indicators improve effectiveness of the control and follow-up phases of landfill management. • The follow-up of environmental management plans may help diminish the implementation gap in Environmental Impact Assessment.

54 ENVIRONMENTAL SCIENCES↗