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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Human influence on joint changes in temperature, rainfall and continental aridity

Despite the pervasive impact of drought on human and natural systems, the large-scale mechanisms conducive to regional drying remain poorly understood. Here we use a multivariate approach to identify two distinct externally forced fingerprints from multiple ensembles of Earth system model simulations. The leading fingerprint, F(M1)(x), is characterized by global warming, intensified wet–dry patterns and progressive large-scale continental aridification, largely driven by multidecadal increases in greenhouse gas (GHG) emissions. The second fingerprint, F(M2)(x), captures a pronounced interhemispheric temperature contrast, associated meridional shifts in the intertropical convergence zone and correlated anomalies in precipitation and aridity over California, the Sahel and India. F(M2)(x) exhibits nonlinear temporal behaviour: the intertropical convergence zone moves southwards before 1975 in response to increases in hemispherically asymmetric sulfate aerosol emissions, and it shifts northwards after 1975 due to reduced sulfur dioxide emissions and the GHG-induced warming of Northern Hemisphere landmasses. Both fingerprints are statistically identifiable in observations of joint changes in temperature, rainfall and aridity during 1950–2014. We show that the reliable simulation of these changes requires combined forcing by GHGs, direct and indirect effects of aerosols, and large volcanic eruptions. Our results suggest that GHG-induced aridification may be modulated regionally by future reductions in sulfate aerosol emissions.

Human influence↗
NASA NTRS2021

Contribution of the World's Main Dust Source Regions to the Global Cycle of Desert Dust

Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, the relative contributions of the world's major source regions to the global dust cycle remain poorly constrained. This problem hinders accounting for the potentially large impact of regional differences in dust properties on clouds, the Earth's energy balance, and terrestrial and marine biogeochemical cycles. Here, we constrain the contribution of each of the world's main dust source regions to the global dust cycle. We use an analytical framework that integrates an ensemble of global aerosol model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth (DAOD). We obtain a dataset that constrains the relative contribution of nine major source regions to size-resolved dust emission, atmospheric loading, DAOD, concentration, and deposition flux. We find that the 22–29 Tg (1 standard error range) global loading of dust with a geometric diameter up to 20 µm is partitioned as follows: North African source regions contribute ∼ 50 % (11–15 Tg), Asian source regions contribute ∼ 40 % (8–13 Tg), and North American and Southern Hemisphere regions contribute ∼ 10 % (1.8–3.2 Tg). These results suggest that current models on average overestimate the contribution of North African sources to atmospheric dust loading at ∼ 65 %, while underestimating the contribution of Asian dust at ∼ 30 %. Our results further show that each source region's dust loading peaks in local spring and summer, which is partially driven by increased dust lifetime in those seasons. We also quantify the dust deposition flux to the Amazon rainforest to be ∼ 10 Tg yr−1, which is a factor of 2–3 less than inferred from satellite data by previous work that likely overestimated dust deposition by underestimating the dust mass extinction efficiency. The data obtained in this paper can be used to obtain improved constraints on dust impacts on clouds, climate, biogeochemical cycles, and other parts of the Earth system.

Dust↗
NASA NTRSDate not supplied

High Latitude Atmospheric Rivers: Teleconnections and Impacts

Atmospheric rivers (ARs) are long, narrow synoptic scale features that act as moisture transport vehicles by moving water vapor poleward. They are typically found in storm tracks and often have profound implications for regional hydroclimate. For high latitude locales such as Antarctica and the Arctic, ARs are fewer in number but often have an outsized impact by either producing significant snow accumulation events, or, depending on their thermal characteristics, initiating melt events on ice sheets, ice shelves, and sea ice. In this study, we apply both reanalysis and climate model data to evaluate high latitude ARs for both the Arctic and Antarctic in the context of teleconnections and modes of variability. Specifically, we fold in both MERRA-2 and ERA5 reanalysis as well as historical and climate change simulations from the CESM2 (Community Earth System Model, Version 2) and E3SMv2 (Energy Exascale Earth System Model, v2) with multiple ensemble members to better understand natural variability versus climate change signals. An overview of AR climatology and impacts will be presented, including a baseline uncertainty quantification analysis using data from ARTMIP (the Atmospheric River Tracking Method Intercomparison Project). AR impacts during specific modes of variability will be characterized by diagnosing precipitation and boundary layer temperature associated with Ars influencing the cryosphere. Modes of variability investigated include the SAM (Southern Annular Mode), PDO (Pacific Decadal Oscillation), PSA2 (Pacific South American Mode 2), the IOD (Indian Ocean Dipole), and the AO (Arctic Oscillation).

Atmospheric Rivers↗
NASA NTRS2023

Characterization of Slat Noise Radiation from a High-Lift Common Research Model

A test campaign was conducted in the open-jet test section of the NASA Langley 14- by 22-Foot Subsonic Tunnel on a 10%-scale, semispan version of a High-Lift Common Research Model (CRM-HL) incorporating a leading-edge slat, trailing-edge flap and removable high-fidelity main landing gear. Aerodynamic and far field acoustic measurements were obtained on baseline and acoustically treated model configurations using an ensemble of time-averaged and unsteady surface pressure sensors paired with a traversing 97-microphone phased array viewing the pressure side of the airframe. A modified version of the DAMAS deconvolution method incorporating corrections for shear layer acoustic wave decorrelation was employed to determine the locations and strengths of relevant noise sources along the span of the slat and in the vicinity of the flap edges and landing gear. The main goal of the test campaign was to use the surface pressure and array data to evaluate the effectiveness of various slat noise reduction concepts. It was found that slat-gap fillers produced significant broadband noise reduction with minimal aerodynamic performance penalties for all Mach numbers and airfoil angles of attack that were investigated. The test campaign clearly demonstrated the noise reduction benefits that can be obtained by applying appropriate treatments to leading-edge slats on commercial transport-class aircraft.

Aeronautics Research Mission Directorate↗
NASA NTRS2023

Impacts of Stratospheric Ozone Recovery on Southern Ocean Temperature and Heat Budget

The impacts of stratospheric ozone recovery on Southern Ocean surface and interior temperature, heat content, heat uptake, and heat transport are investigated by contrasting two ensemble chemistry-climate model simulations in 2005–2099: one with fixed ozone depleting substances (ODSs) and another with decreasing ODSs. In our simulations ozone recovery significantly affects Southern Ocean temperature, with large latitudinal and vertical variations. Ozone recovery causes a dipole change of the full-depth ocean heat content (OHC) with an increase south of 60°S and a decrease between 45°S and 60°S. Integrated over latitudes south of 40°S, OHC decreases in response to ozone recovery. This ocean heat loss is shown to be driven by weakened poleward ocean heat transport (OHT) across 40°S, which is partly canceled by enhanced heat uptake. The weakening of poleward OHT into the Southern Ocean is caused by the ozone-induced equatorward shift of the meridional overturning circulation.

stratospheric ozone↗
NASA NTRS2024

Global Nitrogen Needs to Improve Wheat Yield Under Climate Change

Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with improved yield traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The improved yield traits increased yield by 16% with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century high warming climate scenario (RCP8.5), fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.

Nitrogen↗
DOE OSTI2020

Efficient Distance-based Global Sensitivity Analysis for Terrestrial Ecosystem Modeling

Sensitivity analysis in terrestrial ecosystem modeling is important for understanding controlling processes, guiding model development, and targeting new observations to reduce parameter and prediction uncertainty. Complex and computationally expensive terrestrial ecosystem models (TEM) limit the number of ensemble simulations, requiring sophisticated and efficient methods to analyze sensitivities of multiple model responses to different types of parameter uncertainties. In this study, we propose a distance-based global sensitivity analysis (DGSA) method. DGSA first classifies model response samples into a small set of discrete classes and then calculates the distance between parameter frequency distributions in different classes to measure the parameter sensitivity. The principle is that, if the parameter distribution is the same in each class, then the model response is insensitive to the parameter, while a large difference in the distributions indicates the parameter is influential to the response. Built on this idea, DGSA can be applied to analyze sensitivity of a single and a group of responses to different kinds of parameter uncertainties including continuous, discrete and even stochastic. Besides the main-effect sensitivity from a single parameter, DGSA can also quantify the sensitivity from parameter interactions. Additionally, DGSA is computationally efficient which can use a small number of model evaluations to obtain an accurate and statistically significant result. We applied DGSA to two TEMs, one having eight parameters and three kinds of model responses, and the other having 47 parameters and a long-period response. We demonstrated that DGSA can be used for sensitivity problems with multiple responses and high-dimensional parameters efficiently.

Lu, Dan↗
DOE OSTI2023

Robust design of semi-automated clustering models for 4D-STEM datasets

Materials discovery and design require characterizing material structures at the nanometer and sub-nanometer scale. Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM) resolves the crystal structure of materials, but many 4D-STEM data analysis pipelines are not suited for the identification of anomalous and unexpected structures. This work introduces improvements to the iterative Non-Negative Matrix Factorization (NMF) method by implementing consensus clustering for ensemble learning. We evaluate the performance of models during parameter tuning and find that consensus clustering improves performance in all cases and is able to recover specific grains missed by the best performing model in the ensemble. The methods introduced in this work can be applied broadly to materials characterization datasets to aid in the design of new materials.

Bruefach, Alexandra (ORCID:0000000209323477)↗
DOE OSTI2024

Characterizing the multisectoral impacts of future global hydrologic variability

There is significant uncertainty in how global water supply will evolve in the future, due to uncertain climate, socioeconomic, and land use change drivers and variability of hydrologic processes. It is critical to characterize the potential impacts of uncertainty in future water supply given its importance for food and energy production. In this work, we introduce a framework that integrates stochastic hydrology and human-environmental systems to characterize uncertainty in future water supply and its multisector impacts. We develop a global stochastic watershed model and demonstrate that this model can generate a large ensemble of realizations of basin-scale runoff with global coverage that preserves the mean, variance, and spatial correlation of a historical benchmark. We couple this model with a well-known human-environmental systems model to explore the impacts of runoff variability on the water and agricultural sectors across spatial scales. We find that the impacts of future hydrologic variability vary across sectors and regions. Impacts are felt most strongly in the water and agricultural sectors for basins that are expected to have unsustainable water use in the future, such as the Indus River basin. For this basin, we find that the variability in future irrigation water withdrawals and irrigated cropland increase over time due to uncertainty in renewable water supply. We also use the Indus basin to show how our stochastic ensemble can be leveraged to explore the global multisector consequences of local extreme runoff conditions. This work introduces a novel technique to explore the propagation of future hydrologic variability across human and natural systems and spatial scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast

Uncertainty quantification of long-term modeled wind speed is essential to ensure stakeholders can best leverage wind resource numerical data sets. Offshore, this need is even stronger given the limited availability of observations of wind speed at heights relevant for wind energy purposes and the resulting heavier relative weight of numerical data sets for wind energy planning and operational projects. In this analysis, we consider the National Renewable Energy Laboratory's 21-year updated numerical offshore data set for the US East Coast and provide a methodological framework to leverage both floating lidar and near-surface buoy observations in the region to quantify uncertainty in the modeled hub-height wind resource. We first show how using a numerical ensemble to quantify the uncertainty in modeled wind speed is insufficient to fully capture the model deviation from real-world observations. Next, we train and validate a random forest to vertically extrapolate near-surface wind speed to hub height using the available short-term lidar data sets in the region. We then apply this model to vertically extrapolate the long-term near-surface buoy wind speed observations to hub height so that they can be directly compared to the long-term numerical data set. We find that the mean 21-year uncertainty in 140 m hourly average wind speed is slightly lower than 3 m s -1 (roughly 30m% of the mean observed wind speed) across the considered region. Atmospheric stability is strictly connected to the modeled wind speed uncertainty, with stable conditions associated with an uncertainty which is, on average, about 20 % larger than the overall mean uncertainty.

17 WIND ENERGY↗
DOE OSTI2021

Soil Water Potentials (1990-2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama

This dataset has ELM-FATES simulation outputs and scripts that accompany and reproduce the manuscript, Chitra-Tarak et al. (2021) Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytologist. https: //doi.org/10.1111/nph.17464”. This dataset is organized into four parts: (1) source code for the model, Energy Exascale Earth System Land Model-Functionally Assembled Advanced Terrestrial simulator (ELM-FATES) (2) R scripts associated with ELM-FATES simulations: to prepare files for parameter ensemble simulation runs, calibrate the model with observed hydrological fluxes observed at BCI, re-run the model with tuned parameters and obtain long-term soil matric potentials by depth (3) ELM-FATES hydrological simulation outputs at PA-BCI, Panama (1990-2018, daily), and (4) R scripts for inverse rooting depth modeling and post-processing: prepare datasets and inversely model tree rooting depths at BCI, test hypotheses about the relationship of rooting depths with plant hydraulic traits and drought-induced mortality, and reproduce the manuscript itself. n.b. 1. Data needed to run R scripts in (2) and (4) and thus to reproduce the manuscript are sourced from various public datasets. Refer to the data availability section of the accompanying manuscript. 2. This dataset version (prepared on May 12, 2021) matches the revised and published version of the accompanying manuscript (published May 16, 2021) replacing a previous version. In the publication, this dataset is cited as follows: Chitra-Tarak R, Xu C, Aguilar S, Anderson-Teixeira K, Chambers J, Detto M, Faybishenko B, Fisher R, Knox R, Koven C et al. 2020. Soil water potentials (1990–2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama. 2.0. NGEE Tropics Data Collection. doi: 10.15486/ngt/1696806

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Persistent ocean anomalies as a response to Northern Hemisphere heating induced by biomass burning variability

Biomass burning aerosol (BBA) emissions in the Coupled Model Intercomparison Project Phase 6 (CMIP6) historical forcing fields have enhanced temporal variability during the years 1997–2014 compared to earlier periods. Recent studies document that the corresponding inhomogeneous shortwave forcing over this period can cause changes in clouds, permafrost, and soil moisture, which contribute to a net terrestrial Northern Hemisphere warming relative to earlier periods. Here, we investigate the ocean response to the hemispherically asymmetric warming, using a 100-member ensemble of the Community Earth System Model version 2 Large Ensemble forced by two different BBA emissions (CMIP6 default and temporally smoothed over 1990–2020). Differences between the two subensemble means show that ocean temperature anomalies occur during periods of high BBA variability and subsequently persist over multiple decades. In the North Atlantic, surface warming is efficiently compensated for by decreased northward oceanic heat transport due to a slowdown of the Atlantic Meridional Overturning Circulation. In the North Pacific, surface warming is compensated for by an anomalous cross-equatorial cell (CEC) that reduces northward oceanic heat transport. The heat that converges in the South Pacific through the anomalous CEC is shunted into the subsurface and contributes to formation of long-lasting ocean temperature anomalies. The anomalous CEC is maintained through latitude-dependent contributions from narrow western boundary currents and basin-wide near-surface Ekman transport. Furthermore, these results indicate that interannual variability in forcing fields may significantly change the background climate state over long timescales, presenting a potential uncertainty in CMIP6-class climate projections forced without interannual variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies

The current pandemic demands a search for therapeutic agents against the novel coronavirus SARS-CoV-2. In this work, we present an efficient computational strategy that combines machine learning (ML)-based models and high-fidelity ensemble docking studies to enable rapid screening of possible therapeutic ligands. Targeting the binding affinity of molecules for either the isolated SARS-CoV-2 S-protein at its host receptor region or the S-protein:human ACE2 interface complex, we screen ligands from drug and biomolecule data sets that can potentially limit and/or disrupt the host-virus interactions. Top scoring one hundred eighty-seven ligands (with 75 approved by the Food and Drug Administration) are further validated by all atom docking studies. Important molecular descriptors ( 2 χ n , topological surface area, and ring count) and promising chemical fragments (oxolane, hydroxy, and imidazole) are identified to guide future experiments. Overall, this work expands our knowledge of small-molecule treatment against COVID-19 and provides a general screening pathway (combining quick ML models with expensive high-fidelity simulations) for targeting several chemical/biochemical problems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Protecting Spin Coherence in a Tunable Heisenberg Model

In this work, using an ensemble of atoms in an optical cavity, we engineer a family of nonlocal Heisenberg Hamiltonians with continuously tunable anisotropy of the spin-spin couplings. Therefore, we thus gain access to a rich phase diagram, including a paramagnetic-to-ferromagnetic Ising phase transition that manifests as a diverging magnetic susceptibility at the critical point. The susceptibility displays a symmetry between Ising interactions and XY (spin-exchange) interactions of the opposite sign, which is indicative of the spatially extended atomic system behaving as a single collective spin. Images of the magnetization dynamics show that spin-exchange interactions protect the coherence of the collective spin, even against inhomogeneous fields that completely dephase the noninteracting and Ising systems. Our results underscore prospects for harnessing spin-exchange interactions to enhance the robustness of spin squeezing protocols.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom

Energy-efficient ventilation control plays an important role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representations of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO 2 prediction and deep learning–based reduced-order models, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2018

Recent Updates in the SMAP Level-4 Soil Moisture Algorithm

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level-4 Soil Moisture (L4_SM) product. The L4_SM data are published with a mean latency of ~2.5 days from the time of observation and provide global, three-hourly, 9-km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. The L4_SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF). In 2018, the L4_SM algorithm was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high-quality, space-borne remote sensing observations. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of surface soil moisture from below under non-equilibrium conditions, which brings the model's surface soil moisture more in line with the SMAP Level-2 and in situ soil moisture. Moreover, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4, and the "catchment deficit" model variable was removed from the EnKF state vector to avoid degrading the model's groundwater estimates.Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation-minus-forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4_SM product and the lessons learned from the effort.

Reichle, Rolf H.↗
NASA NTRS2023

Overview and Statistical Analysis of Boundary Layer Clouds and Precipitation Over the Western North Atlantic Ocean

Due to their fast evolution and large natural variability in macro- and microphysical properties, the accurate representation of boundary layer clouds in current climate models remains a challenge. One of the regions with large intermodel spread in the Coupled Model Intercomparison Project Phase 6 ensemble is the western North Atlantic Ocean. Here, statistically representative in situ measurements can help to develop and constrain the parameterization of clouds in global models. To this end, we performed comprehensive measurements of boundary layer clouds, aerosol, trace gases, and radiation in the western North Atlantic Ocean during the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. In total, 174 research flights with 574 flight hours for cloud and precipitation measurements were performed with the HU-25 Falcon during three winter (February–March 2020, January–April 2021, and November 2021–March 2022) and three summer seasons (August–September 2020, May–June 2021, and May–June 2022). Here we present a statistical evaluation of 16 140 individual cloud events probed by the fast cloud droplet probe and the two-dimensional stereo cloud probe during 155 research flights in a representative and repetitive flight strategy allowing for robust statistical data analyses. We show that the vertical profiles of distributions of the liquid water content and the cloud droplet effective diameter (ED) increase with altitude in the marine boundary layer. Due to higher updraft speeds, higher cloud droplet number concentrations (N liquid ) were measured in winter compared to summer despite lower cloud condensation nucleus abundance. Flight cloud cover derived from statistical analysis of in situ data is reduced in summer and shows large variability. This seasonal contrast in cloud coverage is consistent with a dominance of a synoptic pattern in winter that favors conditions for the formation of stratiform clouds at the western edge of cyclones (post-cyclonic). In contrast, a dominant summer anticyclone is concomitant with the occurrence of shallow cumulus clouds and lower cloud coverage. The evaluation of boundary layer clouds and precipitation in the N liquid ED phase space sheds light on liquid, mixed-phase, and ice cloud properties and helps to categorize the cloud data. Ice and liquid precipitation, often masked in cloud statistics by a high abundance of liquid clouds, is often observed throughout the cloud. The ACTIVATE in situ cloud measurements provide a wealth of cloud information useful for assessing airborne and satellite remote-sensing products, for global climate and weather model evaluations, and for dedicated process studies that address precipitation and aerosol–cloud interactions.

Aerosol↗
DOE OSTI2021

Modeling laser-driven ion acceleration with deep learning

Developments in machine learning promise to ameliorate some of the challenges of modeling complex physical systems through neural-network-based surrogate models. High-intensity, short-pulse lasers can be used to accelerate ions to mega-electronvolt energies, but to model such interactions requires computationally expensive techniques such as particle-in-cell simulations. Multilayer neural networks allow one to take a relatively sparse ensemble of simulations and generate a surrogate model that can be used to rapidly search the parameter space of interest. In this study, we created an ensemble of over 1,000 simulations modeling laser-driven ion acceleration and developed a surrogate to study the resulting parameter space. A neural-network-based approach allows for rapid feature discovery not possible for traditional parameter scans given the computational cost. A notable observation made during this study was the dependence of ion energy on the pre-plasma gradient length scale. While this methodology harbors great promise for ion acceleration, it has ready application to all topics in which large-scale parameter scans are restricted by significant computational cost or relatively large, but sparse, domains.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗