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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

An efficient tensor transpose algorithm for multicore CPU, Intel Xeon Phi, and NVidia Tesla GPU

An efficient parallel tensor transpose algorithm is suggested for shared-memory computing units, namely, multicore CPU, Intel Xeon Phi, and NVidia GPU. The algorithm operates on dense tensors (multidimensional arrays) and is based on the optimization of cache utilization on x86 CPU and the use of shared memory on NVidia GPU. From the applied side, the ultimate goal is to minimize the overhead encountered in the transformation of tensor contractions into matrix multiplications in computer implementations of advanced methods of quantum many-body theory (e.g., in electronic structure theory and nuclear physics). A particular accent is made on higher-dimensional tensors that typically appear in the so-called multireference correlated methods of electronic structure theory. Depending on tensor dimensionality, the presented optimized algorithms can achieve an order of magnitude speedup on x86 CPUs and 2-3 times speedup on NVidia Tesla K20X GPU with respect to the na ve scattering algorithm (no memory access optimization). Furthermore, the tensor transpose routines developed in this work have been incorporated into a general-purpose tensor algebra library (TAL-SH).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

VTAnDeM: A python toolkit for simultaneously visualizing phase stability, defect energetics, and carrier concentrations of materials

Phase stability, defect formation energies, and carrier concentrations are closely interrelated features of semiconductors. Due to their joint dependence on the multidimensional chemical potential space, it is challenging to quantitatively establish patterns between these quantities in a given semiconductor, especially when the semiconductor is comprised of multiple elements. To enable synchronous visualization and analysis of these complementary material properties and their interdependence, we developed the Visualization Toolkit for Analyzing Defects in Materials (VTAnDeM). This python-based toolkit allows users to interactively explore how defect formation energies and carrier concentrations vary across the composition and chemical potential spaces of multicomponent semiconductors. Here, we illustrate the computational workflow that employs VTAnDeM as a post-processing tool for first-principles calculations and describe the data organization and theory underlying the visualization scheme. Furthermore, we believe that this software will serve as a useful tool for simultaneously visualizing the often complex and non-intuitive chemical potential – defect – carrier concentration phase space of semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A parallel and performance portable implementation of a full-field crystal plasticity model

We have developed a parallel implementation of an Elasto-Viscoplastic Fast Fourier Transform-based (EVPFFT) micromechanical solver to enable computationally efficient crystal plasticity modeling for polycrystalline materials. Our primary focus lies in achieving performance portability, allowing a single EVPFFT implementation to run optimally on various homogeneous architectures, including multi-core Central Processing Units (CPUs), as well as on heterogeneous computer architectures comprising multi-core CPUs and Graphics Processing Units (GPUs) from different vendors. To accomplish this goal, we have leveraged MATAR, a C++ software library that simplifies the creation and utilization of multidimensional dense or sparse matrix and array data structures. These data structures are designed to be portable across diverse architectures through the use of Kokkos, a performance-portable library. Additionally, we have employed the Message Passing Interface (MPI) to efficiently distribute the computational workload among processors. The heFFTe (Highly Efficient FFT for Exascale) library is used to facilitate the performance portability of the fast Fourier transforms (FFTs) computation. The computational performance of EVPFFT is evaluated and presented in terms of parallel scalability and simulation runtime on different high-performance computing (HPC) architectures. As a result, the utility of the developed framework to efficiently simulate the micro-mechanical fields in polycrystalline microstructures in engineering applications is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2016

The social impacts of dams: A new framework for scholarly analysis

No commonly used framework exists in the scholarly study of the social impacts of dams. This hinders comparisons of analyses and thus the accumulation of knowledge. The aim of this paper is to unify scholarly understanding of dams' social impacts via the analysis and aggregation of the various frameworks currently used in the scholarly literature. For this purpose, we have systematically analyzed and aggregated 27 frameworks employed by academics analyzing dams' social impacts (found in a set of 217 articles). A key finding of the analysis is that currently used frameworks are often not specific to dams and thus omit key impacts associated with them. The result of our analysis and aggregation is a new framework for scholarly analysis (which we call ‘matrix framework’) specifically on dams' social impacts, with space, time and value as its key dimensions as well as infrastructure, community and livelihood as its key components. Building on the scholarly understanding of this topic enables us to conceptualize the inherently complex and multidimensional issues of dams' social impacts in a holistic manner. If commonly employed in academia (and possibly in practice), this framework would enable more transparent assessment and comparison of projects.

13 HYDRO ENERGY↗
DOE OSTI2018

Ranking the importance of Wildfires' human drivers through a multi-model regression approach

Highlights: • Spatial variability of wildfire regimes is closely related to human activities. • Fire variables have different correlation structures with socioeconomic variables. • Rural poverty, economic disparities and social conflicts are associated to high fire risk. • Landscape structure, land-use and cropping systems influence local-scale fire regimes. • Local unemployment rates discriminate among different wildfire regimes. - Abstract: Wildfires are a major ecological disturbance in Mediterranean environments, and affect together natural resources, ecosystem services and human activities. The impact of socioeconomic forces on wildfire regimes is generally less investigated than the effects of biophysical drivers. Being grounded on a multi-model regression analysis of socioeconomic and territorial indicators, the present study identifies relevant factors influencing local-scale wildfires' regimes in Italy. An economically-disadvantaged context with persistent unemployment, rural poverty, social inequalities and population aging proved to be associated to more frequent fire events. Additionally, our analysis points out that occurrence, average size and density of wildfires reflect different correlation profiles with the local-scale socioeconomic context. Along with the socioeconomic profile of local communities, the empirical outcomes of our study show the importance of landscape structure, land-use and cropping systems in local-scale fire regimes. The empirical results of this study justify a multidimensional analysis of relevant socioeconomic dimensions in fire risk assessment and contribute to an informed approach to wildfire management. Moreover, our study provides basic knowledge advancing research on fire prevention, and informing spatial planning and developmental policies aimed at increasing preparedness to large fires.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Environmental impact assessment of online advertising

Highlights: • A practical framework for assessing the environmental impact of any Internet service • Online advertising consumed between 20.38 to 282.75 TWh of energy and 11.53 – 159.93 million tons of CO{sub 2}e was emitted to produce the electricity consumed. • Invalid traffic associated with online advertising resulted in 2.65 – 36.78 million tons of CO{sub 2}e emissions. • The energy burden of online advertising is considerable and urgently requires attention from researchers and industries. - Abstract: There are no commonly agreed ways to assess the total energy consumption of the Internet. Estimating the Internet's energy footprint is challenging because of the interconnectedness associated with even seemingly simple aspects of energy consumption. The first contribution of this paper is a common modular and layered framework, which allows researchers to assess both energy consumption and CO{sub 2}e emissions of any Internet service. The framework allows assessing the energy consumption depending on the research scope and specific system boundaries. Further, the proposed framework allows researchers without domain expertise to make such an assessment by using intermediate results as data sources, while analyzing the related uncertainties. The second contribution is an estimate of the energy consumption and CO{sub 2}e emissions of online advertising by utilizing our proposed framework. The third contribution is an assessment of the energy consumption of invalid traffic associated with online advertising. The second and third contributions are used to validate the first. The online advertising ecosystem resides in the core of the Internet, and it is the sole source of funding for many online services. Therefore, it is an essential factor in the analysis of the Internet's energy footprint. As a result, in 2016, online advertising consumed 20–282 TWh of energy. In the same year, the total infrastructure consumption ranged from 791 to 1334 TWh. With extrapolated 2016 input factor values without uncertainties, online advertising consumed 106 TWh of energy and the infrastructure 1059 TWh. With the emission factor of 0.5656 kg CO{sub 2}e/kWh, we calculated the carbon emissions of online advertising, and found it produces 60 Mt CO{sub 2}e (between 12 and 159 Mt of CO{sub 2}e when considering uncertainty). The share of fraudulent online advertising traffic was 13.87 Mt of CO{sub 2}e emissions (between 2.65 and 36.78 Mt of CO{sub 2}e when considering uncertainty). The global impact of online advertising is multidimensional. Online advertising affects the environment by consuming significant amounts of energy, leading to the production CO{sub 2}e emissions. Hundreds of billions of ad dollars are exchanged yearly, placing online advertising in a significant role economically. It has become an important and acknowledged component of the online-bound society, largely due to its integration with the Internet and the amount of revenue generated through it.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Deconvolving distribution of relaxation times, resistances and inductance from electrochemical impedance spectroscopy via statistical model selection: Exploiting structural-sparsity regularization and data-driven parameter tuning

The distribution of relaxation times (DRT) has drawn increasing attention for interpreting electrochemical impedance spectroscopy (EIS). Deconvolution of DRT from EIS is a challenging ill-posed problem that requires regularization methods. In this work, we formulate DRT reconstruction task as a statistical model selection problem with structural-sparsity penalties. We utilize the Elastic net regularization that simultaneously benefits from Ridge and Lasso regularizations with optimal tuning parameter automatically determined by the information criteria. We benchmark our approach on four synthetic experiments (a ZARC element, ZARC mixtures, a RC circuit and a Fractal element) and two real EIS datasets of a Lithium ion battery and an organic-inorganic halide class of perovskites in oxygen environment at different gas pressures. We demonstrate the superiority of proposed model selection procedure, that is capable of eliminating pseudo peaks and representing asymmetries in DRT as well as precisely estimating resistances. Here, we highlight our approach is robust to reducing and subsampling EIS frequency range, making it a promising tool for timing-resolved, localized and large scale EIS data analysis. For the Lithium ion battery data analysis, we extend the classical DRT model to incorporate the inductive effect and illustrate DRT as a guidance for equivalent circuit modeling to refine impedance reconstruction at low risks of overfitting. Furthermore, the structural-sparsity regularization could be extended for multidimensional and Bayesian EIS data analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Keras2c: A library for converting Keras neural networks to real-time compatible C

With the growth of machine learning models and neural networks in measurement and control systems comes the need to deploy these models in a way that is compatible with existing systems. Existing options for deploying neural networks either introduce very high latency, require expensive and time consuming work to integrate into existing code bases, or only support a very limited subset of model types. We have therefore developed a new method called Keras2c, which is a simple library for converting Keras/TensorFlow neural network models into real-time compatible C code. It supports a wide range of Keras layers and model types including multidimensional convolutions, recurrent layers, multi-input/output models, and shared layers. Keras2c re-implements the core components of Keras/TensorFlow required for predictive forward passes through neural networks in pure C, relying only on standard library functions considered safe for real-time use. The core functionality consists of ~ 1500 lines of code, making it lightweight and easy to integrate into existing codebases. Keras2c has been successfully tested in experiments and is currently in use on the plasma control system at the DIII-D National Fusion Facility at General Atomics in San Diego.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Associations between bacterial communities of house dust and infant gut

The human gut is host to a diverse and abundant community of bacteria that influence health and disease susceptibility. This community develops in infancy, and its composition is strongly influenced by environmental factors, notably perinatal anthropogenic exposures such as delivery mode (Cesarean vs. vaginal) and feeding method (breast vs. formula); however, the built environment as a possible source of exposure has not been considered. Here we report on a preliminary investigation of the associations between bacteria in house dust and the nascent fecal microbiota from 20 subjects from the Canadian Healthy Infant Longitudinal Development (CHILD) Study using high-throughput sequence analysis of portions of the 16S rRNA gene. Despite significant differences between the dust and fecal microbiota revealed by Nonmetric Multidimensional Scaling (NMDS) analysis, permutation analysis confirmed that 14 bacterial OTUs representing the classes Actinobacteria (3), Bacilli (3), Clostridia (6) and Gammaproteobacteria (2) co-occurred at a significantly higher frequency in matched dust–stool pairs than in randomly permuted pairs, indicating an association between these dust and stool communities. These associations could indicate a role for the indoor environment in shaping the nascent gut microbiota, but future studies will be needed to confirm that our findings do not solely reflect a reverse pathway. Although pet ownership was strongly associated with the presence of certain genera in the dust for dogs (Agrococcus, Carnobacterium, Exiguobacterium, Herbaspirillum, Leifsonia and Neisseria) and cats (Escherichia), no clear patterns were observed in the NMDS-resolved stool community profiles as a function of pet ownership.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Effect of fuel composition and EGR on spark-ignited engine combustion with LPG fueling: Experimental and numerical investigation

This paper presents an experimental and numerical investigation of a spark-ignited (SI) cooperative fuel research (CFR) engine fueled with different liquefied petroleum gas (LPG) fuels and exhaust gas recirculation (EGR). Here, the effects of LPG fuel composition on engine combustion characteristics are initially evaluated at two different compression ratios (CR). Results show normal combustion at CR 7 and heavy knocking combustion at CR 10 for all the tested fuels, with a more substantial impact for the LPG fuel with high proportions of n-butane species. The Livengood-Wu (LW) integral method is then used to analyze the knock occurrence risk of individual fuel based on the reactivity of the tested fuels. The introduction of EGR then demonstrates the potential of knock intensity reduction below the borderline knock limit. A zonal-based kinetic interactions study is also performed to understand the knock mitigation effectiveness of EGR over the pressure–temperature domain relevant to SI engine operation. Finally, a multidimensional, computational fluid dynamics (CFD) simulation model is shown to predict the LPG combustion characteristics and presents the evolution of in-cylinder temperature and chemical species to demonstrate the development of end-gas autoignition events without and with EGR.

02 PETROLEUM↗
DOE OSTI2026

Real-time plasma monitoring framework for advanced plasma control and ML-research in DIII-D

Real-time and adaptive plasma control is crucial for robust tokamak operation, requiring sensitivity and tolerance measurements of the plasma state. This paper presents the implementation of an integrated real-time plasma monitoring framework on the DIII-D tokamak to support advanced control approaches, including machine-learning (ML) methods. The system is built on the SHIELD framework, a high-performance modular architecture that provides a unified pipeline for integrating diverse diagnostics. The framework leverages high-bandwidth digitizers, fast numerical processing, and deterministic, low-latency interconnects to stream high-fidelity data from diagnostics such as electron cyclotron emission (ECE), beam emission spectroscopy (BES), CO interferometers, and a visible tangential divertor camera (TangTV). The system’s validity is demonstrated through direct comparisons of real-time and offline data. Furthermore, we present two key applications of the developed plasma monitoring system with ML-based plasma control strategies, including real-time divertor detachment and active Alfvén Eigenmode control. As a result, this work presents a robust and scalable approach for integrating high-frequency, multidimensional diagnostics into advanced control algorithms for future fusion devices.

AI/ML↗
DOE OSTI2024

Three-dimensional reconstruction of implosion stagnation in laser direct drive on OMEGA

Multidimensional effects on hot-spot formation must be considered to better understand the current limits on the performance of direct-drive inertial confinement fusion experiments on OMEGA with cryogenically layered solid deuterium–tritium targets. A comprehensive reconstruction effort has been established at the Laboratory for Laser Energetics to infer hot-spot and shell conditions at stagnation from a large collection of x-ray, neutron, and particle detectors along multiple lines of sight. Several time-gated and time-integrated x-ray imagers are being used to record the shape of the hot-spot plasma. A 3D hot-spot x-ray emission tomography technique has been developed to infer low-mode drive asymmetries from the hot-spot shape. A suite of neutron diagnostics is used to provide measurements of hot-spot flow velocity, ion temperature, and areal density. Here, the information obtained from the x-ray and neutron detectors will be combined into a coherent model of the shape of the hot spot and shell assembly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Common risk segment mapping: Streamlining exploration for carbon storage sites, with application to coastal Texas and Louisiana

Large-scale deployment of Carbon Capture and Storage (CCS) will require a commensurately large number of sites. Efficient screening methods are needed to create investment assurance and focus efforts on the most promising sites. The problem is similar to petroleum exploration, for which there are well-developed (though seldom published) workflows, including Common Risk Segment (CRS) mapping. In brief, the process requires 1) defining the key play elements; 2) identifying candidate geologic intervals for each; 3) creating fact-based maps for those intervals; 4) determining minimum criteria for the success of each element; 5) reinterpreting the fact-based maps in terms of chance of success; and 6) combining the individual maps to form a composite, basin-scale view of prospectivity. We adapt the CRS process to screening for CO 2 storage sites. Critically, we redefine the process in terms of cost of characterization and development, rather than chance of success. For illustration, we apply the process to the example of the Lower Miocene on the Texas and Louisiana Gulf Coast. We show that the predictions are consistent with historic hydrocarbon production volumes and rates. The power of the CRS method is that it creates a systematic approach to geologic evaluation and translates complex, multidimensional analysis into clear, graphical and easily comprehended business inputs. The results highlight sweet spots and identifies critical risks, suggesting a focus for further data collection and analysis. Furthermore, the method developed here can be applied to both surface and subsurface factors anywhere that there is interest in geologic storage of CO 2 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Modelling and analysis of salt-convection effect on oxide reduction process for uranium oxides using smoothed particle hydrodynamics

Oxygen ion transport via molten salt continuum is a key kinetic feature of electrochemical reduction process for uranium oxides in molten salt. During the electrolysis, the evolution of oxygen ion concentration field in molten salt continuum appears from porous metallic layer to open bulk salt. Despite the important role affecting process throughput, its effect coupled with controlled salt movement was not adequately investigated. To capture the reactive oxygen ion transport phenomena, a multidimensional reduction model based on smoothed particle hydrodynamics (SPH) was combined with the advection-diffusion model. A pelletized oxide fuel bed capturing interconnected salt continuum is considered as a reactant material geometry, which allows forced convective delivery of molten salt through the reactant materials with a relatively low pressure head. Here, in this study, reduction time of a pelletized-fuel bed was quantified with respect to key design parameters of the reactant material configuration. The simulation results demonstrated the advantage of salt convection which effectively removes oxygen ion products from the reactant materials and consequently accelerates the reduction process. Moreover, the reduction-time correlation of a single pelletized fuel was developed as a function of relevant dimensionless parameters and was extended to the application for a pelletized-fuel bed. Finally, several insights and implications in the use of a pelletized oxide fuel were presented. The developed SPH-based framework enabled multi-physics analysis of governing dynamics in appreciating and screening candidate reactant material configurations in a cost-effective manner.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Development of a new ion mobility time-of-flight mass spectrometer

Complex samples require multidimensional measurements with high resolution for full characterization of biological and environmental systems. To address this challenge, we developed a drift tube-based ion mobility spectrometry-Orbitrap mass spectrometry (IMS-Orbitrap MS) platform. To circumvent the timing difference between the fast IMS separation and the slow Orbitrap MS acquisition, we utilized a dual gate and pseudorandom sequence to multiplex ions into the drift tube and Orbitrap. The instrument was designed to operate in signal averaging (SA), single multiplexing (SM) and double multiplexing (DM) IMS modes to fully optimize the signal-to-ratio of the measurements. For the SM measurements, a previously developed algorithm was used to reconstruct the IMS data, while a new algorithm was developed for the DM analyses. The new algorithm is a two-step process that first recovers the SM data from the encoded DM data and then decoded the SM data. The algorithm also performs multiple refining procedures in order to minimize the demultiplexing artifacts traditionally observed in such scheme. The new IMS-Orbitrap MS platform was demonstrated for the analysis of proteomic and petroleum samples, where the integration of IMS and high mass resolution proved essential for accurate assignment of molecular formulae.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Resistance Exercise and Inflammation in Breast Cancer Patients Undergoing Adjuvant Radiation Therapy: Mediation Analysis From a Randomized, Controlled Intervention Trial

Purpose: To explore the mediating role of inflammatory parameters in the development of fatigue, pain, and potentially related depressive symptoms during radiation therapy for breast cancer and its mitigation by resistance exercise. Methods and Materials: Breast cancer patients scheduled for adjuvant radiation therapy were randomized to 12-week progressive resistance exercise training (EX) or a relaxation control group. Interleukin-6 (IL-6) and interleukin-1 receptor antagonist (IL-1ra) were measured in serum samples collected before, at the end, and 6 weeks after radiation therapy from 103 chemotherapy-naïve participants. Fatigue was assessed with the multidimensional Fatigue Assessment Questionnaire, pain with the European Organization for Research and Treatment of Cancer QLQ-C30, and depressive symptoms with the Center for Epidemiologic Studies Depression Scale. Analysis of covariance models, partial correlations, Freedman-Schatzkin tests, and R{sup 2} effect-size measures for mediation were calculated. Results: The analysis of covariance models revealed a significant intervention effect on IL-6 (P=.010) and the IL-6/IL-1ra ratio (P=.018), characterized by a marked increase during radiation therapy among controls, but no significant change in EX. Interleukin-1 receptor antagonist did not change significantly in either group (P=.88). Increased IL-6 and IL-6/IL-1ra levels at the end of radiation therapy were significantly associated with increased physical fatigue and pain 6 weeks after radiation. We observed significant partial mediation by IL-6 and IL-6/IL-1ra of the effect of resistance exercise on physical fatigue (Freedman-Schatzkin P=.023 and P<.001) and pain (both P<.001). Hereby IL-6 and IL-6/IL-1ra mediated between 15% and 24% of the variance of physical fatigue and pain explained by the intervention. Conclusions: This randomized, controlled trial showed a significantly increased proinflammatory cytokine level after adjuvant radiation therapy in breast cancer patients. This effect was counteracted by progressive resistance exercise training. Interleukin-6 and the IL-6/IL-1ra ratio seemed to mediate the beneficial effect of exercise on physical fatigue and pain but only to a small extent.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Numerical approximation of the Schrödinger equation with the electromagnetic field by the Hagedorn wave packets

In this paper, we approximate the semi-classical Schrödinger equation in the presence of electromagnetic field by the Hagedorn wave packets approach. By operator splitting, the Hamiltonian is divided into the modified part and the residual part. The modified Hamiltonian, which is the main new idea of this paper, is chosen by the fact that Hagedorn wave packets are localized both in space and momentum so that a crucial correction term is added to the truncated Hamiltonian, and is treated by evolving the parameters associated with the Hagedorn wave packets. The residual part is treated by a Galerkin approximation. We prove that, with the modified Hamiltonian only, the Hagedorn wave packets dynamics give the asymptotic solution with error O(ε{sup 1/2}), where ε is the scaled Planck constant. We also prove that, the Galerkin approximation for the residual Hamiltonian can reduce the approximation error to O(ε{sup k/2}), where k depends on the number of Hagedorn wave packets added to the dynamics. This approach is easy to implement, and can be naturally extended to the multidimensional cases. Unlike the high order Gaussian beam method, in which the non-constant cut-off function is necessary and some extra error is introduced, the Hagedorn wave packets approach gives a practical way to improve accuracy even when ε is not very small.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Modeling weakly-ionized plasmas in magnetic field: A new computationally-efficient approach

Despite its success at simulating accurately both non-neutral and quasi-neutral weakly-ionized plasmas, the drift-diffusion model has been observed to be a particularly stiff set of equations. Recently, it was demonstrated that the stiffness of the system could be relieved by rewriting the equations such that the potential is obtained from Ohm's law rather than Gauss's law while adding some source terms to the ion transport equation to ensure that Gauss's law is satisfied in non-neutral regions. Although the latter was applicable to multicomponent and multidimensional plasmas, it could not be used for plasmas in which the magnetic field was significant. This paper hence proposes a new computationally-efficient set of electron and ion transport equations that can be used not only for a plasma with multiple types of positive and negative ions, but also for a plasma in magnetic field. Because the proposed set of equations is obtained from the same physical model as the conventional drift-diffusion equations without introducing new assumptions or simplifications, it results in the same exact solution when the grid is refined sufficiently while being more computationally efficient: not only is the proposed approach considerably less stiff and hence requires fewer iterations to reach convergence but it yields a converged solution that exhibits a significantly higher resolution. The combined faster convergence and higher resolution is shown to result in a hundredfold increase in computational efficiency for some typical steady and unsteady plasma problems including non-neutral cathode and anode sheaths as well as quasi-neutral regions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗