Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “variability”

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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Exact Solution for a Magnetogasdynamical Cylindrical Shock Wave in a Self-Gravitating Rotating Perfect Gas with Radiation Heat Flux and Variable Density

An exact similarity solution for a magnetoradiative cylindrical shock wave in a self-gravitating rotating perfect gas is obtained. The density, azimuthal velocity, and magnetic field strength are assumed to vary in an undisturbed medium. It is shown that the flow variables, namely, the radial velocity, pressure, magnetic field strength, azimuthal velocity, mass, and the radiation flux, decrease from the highest values at the shock front to zero; however, the density tends to infinity as the symmetry axis is approached. The effects of variation in the magnetic field strength, gravitational parameter, rotational parameter, and in the adiabatic exponent on the flow variables and shock strength are discussed. The solutions obtained for self-gravitating and nongravitating media are compared. The total energy of the shock wave is shown to be not constant.

42 ENGINEERING↗
DOE OSTI2019

Mixed structures of optical breather and rogue wave for a variable coefficient inhomogeneous fiber system

Breathers and rogue waves are two special types of soliton and play significant role in optical fibers. In this work, the mixed breather and rogue wave solution is constructed for a variable coefficient inhomogeneous fiber system via the Darboux transformation method. The impacts on the mixed wave of the first- and second-order group velocity dispersions (GVD), are discussed, respectively. The spectral eigenvalue parameter control is also studied. The pulse wave width, amplitude and wave density can be controlled by the variable GVD functions and spectral eigenvalue parameter. Especially, two types of rogue wave solutions can be derived from the mixed solution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Multiaxial creep–fatigue Life Prediction Under Variable Amplitude Loading at High Temperature

Based on the time–temperature parameter method, a modified von Mises stress is proposed to calculate the multiaxial creep damage at high temperature. By employing the strain-based multiaxial fatigue damage models, the modified von Mises stress is used to predict the creep–fatigue life under variable amplitude multiaxial loading at high temperature. The experimental data of GH4169 superalloy and 304 stainless steel conducted under both constant and variable amplitude loadings at high temperature are used to verify the proposed method, and a good agreement is obtained with the experimental data.

42 ENGINEERING↗
DOE OSTI2019

Fatigue of Composite Materials Subjected to Variable Loadings

A model describing the fatigue behavior of composite materials under variable loadings is presented. The approach is based on a two-parameter model fulfilling the principal responses’ features of composite materials subjected to constant amplitude cyclic loadings. It is shown that when coupled with the equivalent residual strength assumption the model can describe the strength degradation of composite materials subjected to variable amplitude loadings without simplifications or substantial reductions of the loading spectrum. Compared to the linear Miner’s rule, the results highlight the importance of the sequence of loads and the approach discriminates the ascending, descending and random patterns. It is shown that for the case under study the ordered descending (high to low) sequence showed the worst scenario in terms of damage accumulation extent, while the random spectrum resulted in an intermediate response between ordered ascending and descending sequences.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Discrete Hahn polynomials for numerical solution of two-dimensional variable-order fractional Rayleigh–Stokes problem

The main aim of this paper is to find the numerical solutions of 2D Rayleigh–Stokes problem with the variable-order fractional derivatives in the Riemann–Liouville sense. The presented method is based on collocation procedure in combination with the new operational matrix of the variable-order fractional derivatives, in the Caputo sense, for the discrete Hahn polynomials. The main advantage of the proposed method is obtaining a global approximation for spatial and temporal discretizations, and it reduced the problem to an algebraic system, which is easier to solve. Also, the profit of approximating a continuous function by Hahn polynomials is that for computing the coefficients of the expansion, we only have to compute a summation and the calculation of coefficients is exact. The error bound for the approximate solution is estimated. Finally, we evaluate results of the presented method with other numerical methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A semi-agnostic ansatz with variable structure for variational quantum algorithms

Quantum machine learning—and specifically Variational Quantum Algorithms (VQAs)—offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Newton trust-region methods with primary variable switching for simulating high temperature multiphase porous media flow

Coupling multiphase flow with energy transport due to high temperature heat sources introduces significant new challenges since boiling and condensation processes can lead to dry-out conditions with subsequent re-wetting. The transition between two-phase and single-phase behavior can require changes to the primary dependent variables adding discontinuities as well as extending constitutive nonlinear relations to extreme physical conditions. Practical simulations of large-scale engineered domains lead to Jacobian systems with a very large number of unknowns that must be solved efficiently using iterative methods in parallel on high-performance computers. Performance assessment of potential nuclear repositories, carbon sequestration sites and geothermal reservoirs can require numerous Monte-Carlo simulations to explore uncertainty in material properties, boundary conditions, and failure scenarios. Due to the numerical challenges, standard NR iteration may not converge over the range of required simulations and require more sophisticated optimization method like trust-region. In this study, we use the open-source simulator PFLOTRAN for the important practical problem of the safety assessment of future nuclear waste repositories in the U.S. DOE geologic disposal safety assessment Framework. The simulator applies the PETSc parallel framework and a backward Euler, finite volume discretization. We demonstrate failure of the conventional NR method and the success of trust-region modifications to Newton’s method for a series of test problems of increasing complexity. Trust-region methods essentially modify the Newton step size and direction under some circumstances where the standard NR iteration can cause the solution to diverge or oscillate. Furthermore, we show how the Newton Trust-Region method can be adapted for Primary Variable Switching (PVS) when the multiphase state changes due to boiling or condensation. The simulations with high-temperature heat sources which led to extreme nonlinear processes with many state changes in the domain did not converge with NR, but they do complete successfully with the trust-region methods modified for PVS. This implementation effectively decreased weeks of simulation time needing manual adjustments to complete a simulation down to a day. Finally, we show the strong scalability of the methods on a single node and multiple nodes in an HPC cluster.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Variable Polytropic gas cosmology

We mainly study a cosmological scenario represented by the variable Polytropic gas (VPG) unified energy density proposal. To reach this aim, we start with reconstructing a variable form of the original Polytropic gas (OPG) definition. We show that this model is a generalization of the OPG, cosmological constant plus cold dark matter (ΛCDM) and two different Chaplygin gas models. Later, we fit the auxiliary parameters given in the model and discuss essential cosmological features of the VPG proposal. Besides, we compare the VPG with the OPG by focusing on recent observational dataset given in literature including Planck 2018 results. We see that the VPG model yields better results than the OPG description and it fits very well with the recent experimental data. Moreover, we discuss some thermodynamical features of the VPG and conclude that the model describes a thermodynamically stable system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO 2 sequestration accelerated with machine learning

Successful geologic CO 2 storage projects depend on numerical simulations to predict reservoir performance during site selection, injection verification, and post-injection monitoring phases of the project. These numerical simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence, individual simulations often require tens to hundreds of hours to complete on high-performance computing clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative permeability exhibit substantial variability, even within the same rock type. This combination of computational expense and wide-ranging parametric variability means that there remains substantial uncertainty in the behavior of multi-phase CO 2 -water systems, particularly in the context of feedbacks between relative permeability and capillary pressure. To bridge this knowledge gap, here we develop a novel workflow that utilizes physics-based numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate parameter space that governs both capillary pressure and relative permeability. With this approach, the ANN is trained to emulate both fluid pressure distribution and CO 2 saturation, which are then interrogated quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this study initially show that capillary entry pressure is the dominant control on both CO 2 plume geometry and fluid pressure propagation when considering the combined effects of capillary pressure and relative permeability, particularly when phase interference is low and residual CO 2 saturation is high. Moreover, the ANN emulator provides tremendous computational speed-up by computing 2691 individual simulations in several minutes; whereas, the same simulation ensemble would have required ~3 years of simulation time using only physics-based simulation methods (25,000 times speed up).

58 GEOSCIENCES↗
DOE OSTI2026

Virtual refrigerant charge sensor for variable-speed heat pumps based on feature selection

The refrigerant charge level in heat pump systems significantly impacts their energy efficiency. Virtual refrigerant charge (VRC) sensing technology has been comprehensively investigated and well-established due to its lower cost compared to physical sensors. However, the previous VRC research often relied on expert judgment and physical reasoning for their variable selection, which can potentially select redundant (or highly correlated) or insignificant features, and it is also primarily focused on single-speed systems. To address these challenges, this study proposes a VRC algorithm for variable-speed heat pumps that selects features through a rigorous feature selection method in combination with physical insights. We also propose a piecewise linear model structure segmented by subcooling temperature to accurately predict charge levels, particularly when subcooling temperatures are substantially low. The proposed algorithm was evaluated using experimental data of a residential R410A heat pump, and the performance was compared with two baseline VRC algorithms. The results are: (1) The proposed algorithm outperforms for the case with subcooling temperature less than 1 °C. (2) The proposed algorithm achieves a tested mean absolute percentage error (MAPE) of 4.23%, and improves the overall accuracy for cooling conditions by approximately 60%, compared with the two baseline algorithms. (3) The proposed algorithm uses two fewer features and improves the accuracy for undercharge cooling conditions by 68.0%, compared with baseline algorithm 2. These improvements enhance prediction accuracy and prevent overfitting, providing a more reliable refrigerant charge level prediction and helping improve the heat pump energy efficiency.

Liang, Chenjiyu↗
DOE OSTI2024

Recent trends and variability of temperature and atmospheric water vapor over South Asia

This study examines the recent trends and variability of temperature and atmospheric water vapor over South Asia during the summer monsoon season from 1970 to 2022. We employ gridded reanalysis and observational datasets to assess the trends and variability in surface temperature and further investigate the associated physical processes. Our results show that the surface temperature over South Asia has increased by 0.5 °C per decade. Notably, we find a statistically significant major transition in the surface temperature series in 1995, such that 1970–1994 (1995–2022) is associated with anomalous cooling (anomalous warming). Since 1995, not only have the temporal variations in regional surface temperature, atmospheric water vapor, and clear-sky downward longwave radiation been synchronous at interannual scales, but their largest spatial variations have been located mainly over the same region. A diagnosis of the atmospheric moisture budget reveals that the observed changes in water vapor convergence over South Asia are dominated by changes in mean circulation dynamics, while the change in thermodynamics is relatively small. The enhancement of the Somali low-level jet may contribute to increased water vapor in South Asia by modulating low-level circulations.

Atmospheric circulations↗
DOE OSTI2018

Novel brain-expressed noncoding RNA, HSTR1, identified at a human-specific variable number tandem repeat locus with a human accelerated region

Highlights: • A novel noncoding RNA gene HSTR1 is expressed in human brain and testis. • The evolutionary accelerated region showed enhanced promoter activity in human. • The variable number tandem repeat in HSTR1 is associated with its RNA stability. The evolutionary conserved genomic sequences that have acquired significantly increased number of nucleotide substitutions specifically in the human lineage, called human accelerated regions (HARs), have been identified as candidate genomic regions that have contributed to the evolution of human-specific traits. A number of HARs were indeed shown to have novel enhancer activity and be associated with human-specific brain development and with cognition and social behavior. It is therefore of great importance to investigate the details of genomic function of each HAR to understand the roles of HARs as critical contributors to the genetic basis of human evolution. In this study, we identified a previously unannotated brain-expressed noncoding RNA gene, HSTR1, at a human-specific tandem repeat locus. Notably, the 5′ flanking sequence of HSTR1 showed the signature of HARs and the dramatic human-specific enhancement of promoter activity, providing the evidence of positive selection to increase the expression level of HSTR1 during human evolution. We also revealed that the tandem repeat number in HSTR1 was highly variable among individual alleles and affected the stability of HSTR1 RNA, suggesting variation in the activity of HSTR1 between human individuals. Our work thus provides a novel candidate gene that potentially contributed to the evolution of the human brain. It may also underpin some of the variation between human brains.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Variable resolution Poisson-disk sampling for meshing discrete fracture networks

Here, we present the near-Maximal Algorithm for Poisson-disk Sampling (nMAPS) to generate point distributions for variable resolution Delaunay triangular and tetrahedral meshes in two and three-dimensions, respectively. nMAPS consists of two principal stages. In the first stage, an initial point distribution is produced using a cell-based rejection algorithm. In the second stage, holes in the sample are detected using an efficient background grid and filled in to obtain a near-maximal covering. Extensive testing shows that nMAPS generates a variable resolution mesh in linear run time with the number of accepted points. We demonstrate nMAPS capabilities by meshing three-dimensional discrete fracture networks (DFN) and the surrounding volume. The discretized boundaries of the fractures, which are represented as planar polygons, are used as the seed of 2D-nMAPS to produce a conforming Delaunay triangulation. The combined mesh of the DFN is used as the seed for 3D-nMAPS, which produces conforming Delaunay tetrahedra surrounding the network. Under a set of conditions that naturally arise in maximal Poisson-disk samples and are satisfied by nMAPS, the two-dimensional Delaunay triangulations are guaranteed to only have well-behaved triangular faces. While nMAPS does not provide triangulation quality bounds in more than two dimensions, we found that low-quality tetrahedra in 3D are infrequent, can be readily detected and removed, and a high-quality balanced mesh is produced.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Analysys of the performance a PEM-type electrolyzer in variable energy supply conditions

Here in this article, the main variables related to the performance of a proton exchange membrane-type electrolyzer are analyzed along with the risk of explosion for different operating conditions, notably when the electrolyzer works with a variable solar energy power supply and at high pressure. A novel mathematical model was developed that considers all mass transport phenomena through the membrane electrode assembly (MEA) for water, hydrogen, and oxygen, as well as the water evaporation in the anode and cathode channels. The main findings of this work are that for a solar energy supply operating at high pressure, the electrolyzer cannot function safely throughout the day, since in the hours when solar irradiation is lower, low current densities favor high hydrogen concentrations at the anode. Finally, it was found that greater thicknesses of the membrane increased the explosion risk due to the increase in the MEA temperature and therefore the hydrogen solubility.

08 HYDROGEN↗
DOE OSTI2020

A numerical study of the meso-structure variability in the compaction process of prepreg platelet molded composites

Compaction deformations in a thermoplastic composite system molded from chopped prepreg platelets were analyzed to obtain statistical distributions of local distortions that define the final consolidated meso-structure. This study provides an insight into and allows quantification of the intrinsic geometric variability of the composite meso-structure that controls the variability of the effective composite mechanical properties. A two-phase viscoelastic finite element model was utilized to treat the behavior of molten prepreg platelets, which were assumed to be incompressible and inextensible in the fiber direction during processing. A two-step simulation procedure with discretely modeled platelets and platelet-to-platelet contacts is proposed. The analysis begins with a drop simulation of the uncontrolled material deposition followed by a 3D finite element analysis of the preform compaction and bulk material flow. The proposed model provides a method for generating a composite meso-structure ab initio starting from a statistical sampling of the initial conditions for platelet deposition. The predicted platelet thickness distribution, intra-platelet fiber waviness and out-of-plane platelet undulations were found to be in good agreement with experimental measurements. An estimate of the volume of resin pockets was inferred from the compaction simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Capturing the spatial variability of noise levels based on a short-term monitoring campaign and comparing noise surfaces against personal exposures collected through a panel study

Highlights: • A Generalized Additive Model (GAM) was developed to generate noise exposure surfaces. • Noise data were collected based on short-term measurements in Toronto, Canada. • Various model specifications were tested in terms of the resulting predictions. • Predictions were validated against data from a panel and from a long-term campaign. • Models that involved manual adjustments resulted in more realistic surfaces. Environmental noise can cause important cardiovascular effects, stress and sleep disturbance. The development of appropriate methods to estimate noise exposure within a single urban area remains a challenging task, due to the presence of various transportation noise sources (road, rail, and aircraft). In this study, we developed a land-use regression (LUR) approach using a Generalized Additive Model (GAM) for LA{sub eq} (equivalent noise level) to capture the spatial variability of noise levels in Toronto, Canada. Four different model formulations were proposed based on continuous 20-min noise measurements at 92 sites and a leave one out cross-validation (LOOCV). Models where coefficients for variables considered as noise sources were forced to be positive, led to the development of more realistic exposure surfaces. Three different measures were used to assess the models; adjusted R{sup 2} (0.44–0.64), deviance (51−72%) and Akaike information criterion (AIC) (469.2–434.6). When comparing exposures derived from the four approaches to personal exposures from a panel study, we observed that all approaches performed very similarly, with values for the Fractional mean bias (FB), normalized mean square error (NMSE), and normalized absolute difference (NAD) very close to 0. Finally, we compared the noise surfaces with data collected from a previous campaign consisting of 1-week measurements at 200 fixed sites in Toronto and observed that the strongest correlations occurred between our predictions and measured noise levels along major roads and highway collectors. Our validation against long-term measurements and panel data demonstrates that manual modifications brought to the models were able to reduce bias in model predictions and achieve a wider range of exposures, comparable with measurement data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A reforecasting-based dynamic reserve estimation for variable renewable generation and demand uncertainty

The installed capacity of renewables-based energy sources has been increasing in traditional power systems. In order to accommodate the increased variability and uncertainty associated with the deeper penetration of renewable sources like solar and wind, adjusted amounts of dynamic reserve are needed. Although probabilistic dynamic reserve estimation methods have been previously developed, most of them consider the uncertainty to be represented by parametric density functions that tend to perform poorly under extreme events and, moreover, neglect uncertainty introduced by the forecasting model itself. Toward addressing these limitations, this work presents, for the first time, a dynamic reserve estimation method for flexibility that incorporates nonparametric density estimation and a machine learning based reforecasting to provide a day-ahead prediction of the mean and spread of uncertainty around the base forecast. The prediction is, in turn, used to estimate the up and down reserve relative to the base forecast. Here, the present method takes various endogenous and exogenous features, including the calendar variables, as input to estimate the day-ahead reserve. Using a combination of reforecasting and dynamic reserve estimation techniques, the method is shown to adjust better to the dynamic nature of reserve requirements providing only what is needed to accommodate the expected deviations. Considering California Independent System Operator (CAISO) solar, wind and load data over an 18 month period, up to 67% reduction in the amount of reserve capacity needed for a one day reserve and reserve penalty for solar uncertainty is demonstrated. Additionally, the risk of reserve insufficiency in meeting the net demand is reduced by 20% with the proposed method.

14 SOLAR ENERGY↗
DOE OSTI2019

Enhanced hydrogen storage by a variable temperature process

We present a simple method of variable temperature process that can potentially enhance the hydrogen storage properties of a large variety of solid state materials. In this approach, hydrogen gas is first introduced at about room temperature, which is followed by a gradual increase to a preset maximum temperature value, T max . Using this approach, we investigated hydrogen absorption properties of vertically aligned arrays of magnesium nanotrees and nanoblades fabricated by glancing angle deposition (GLAD) technique, and conventional Mg thin film. Weight percentage (wt%) storage values were measured by quartz crystal microbalance (QCM). After exposing Mg samples to H 2 at 30 bar and 30 °C, dynamic absorption measurements were conducted as the temperature was increased from 30 °C to maximum values of T max = 100, 200, and 300 °C all within 150 min. QCM measurements revealed that variable temperature method results in significant improvements in hydrogen storage values over the ones obtained by conventional constant temperature process. At a low effective temperature T eff = 165 °C (T max = 300 °C), we achieved storage values of 6.19, 4.76, and 2.79 wt% for Mg nanotrees, nanoblades, and thin film, respectively.

08 HYDROGEN↗