Search NASA⌕ Search

SEARCH · Search NASA

Results for “quick”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Build plate conduction cooling for thermal management of wire arc additive manufactured components

Wire arc additive manufacturing (WAAM) allows for quick, large component manufacturing with fast deposition rates while leveraging readily available wire feedstock that is significantly cheaper than metal powder. However, the increased deposition rate of this process requires enhanced thermal management as failures can occur due to overheating. A common strategy to mitigate overheating is to dwell, or pause, between individual layers; however, this can significantly increase build times and eliminate the advantage of additive manufacturing being able to manufacture components quickly. To help mitigate this issue, this study explores the use of active cooling to maintain process control and to decrease overall build time. In this work, conductive cooling applied to either the bottom or side of the print substrate was explored. Results from this study showed that bottom build plate active cooling can be used to decrease dwell times by up to 50% and decrease cool-down to room temperature after the building process by up to 75%. Results from this study demonstrate that the use of active cooling strategies for WAAM can be used for better thermal control over the process and should be further investigated.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optical follow-up of gravitational wave triggers with DECam during the first two LIGO/VIRGO observing runs

Gravitational wave (GW) events detectable by LIGO and Virgo have several possible progenitors, including black hole mergers, neutron star mergers, black hole–neutron star mergers, supernovae, and cosmic string cusps. A subset of GW events is expected to produce electromagnetic (EM) emission that, once detected, will provide complementary information about their astrophysical context. To that end, the LIGO–Virgo Collaboration (LVC) sends GW candidate alerts to the astronomical community so that searches for their EM counterparts can be pursued. The DESGW group, consisting of members of the Dark Energy Survey (DES), the LVC, and other members of the astronomical community, uses the Dark Energy Camera (DECam) to perform a search and discovery program for optical signatures of LVC GW events. DESGW aims to use a sample of GW events as standard sirens for cosmology. Due to the short decay timescale of the expected EM counterparts and the need to quickly eliminate survey areas with no counterpart candidates, it is critical to complete the initial analysis of each night’s images as quickly as possible. We discuss our search area determination, imaging pipeline, and candidate selection processes. We review results from the DESGW program during the first two LIGO–Virgo observing campaigns and introduce other science applications that our pipeline enables.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Online evolutionary neural architecture search for multivariate non-stationary time series forecasting

Time series forecasting (TSF) is one of the most important tasks in data science. TSF models are usually pre-trained with historical data and then applied on future unseen datapoints. However, real-world time series data is usually non-stationary and models trained offline usually face problems from data drift. Models trained and designed in an offline fashion can not quickly adapt to changes quickly or be deployed in real-time. To address these issues, this work presents the Online NeuroEvolution-based Neural Architecture Search (ONE-NAS) algorithm, which is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Without any pre-training, ONE-NAS utilizes populations of RNNs that are continuously updated with new network structures and weights in response to new multivariate input data. ONE-NAS is tested on real-world, large-scale multivariate wind turbine data as well as the univariate Dow Jones Industrial Average (DJIA) dataset. These results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods, including online linear regression, fixed long short-term memory (LSTM) and gated recurrent unit (GRU) models trained online, as well as state-of-the-art, online ARIMA strategies. Additionally, results show that utilizing multiple populations of RNNs which are periodically repopulated provide significant performance improvements, allowing this online neural network architecture design and training to be successful.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Influence of polycarboxylate superplasticizer, citric acid and their combination on the hydration and workability of calcium sulfoaluminate cement

Highlights: • The dispersion efficiency of polycarboxylate superplasticizers drops quickly over time. • Citric acid maintains the dispersion efficiency of PCE over time. • Competitive adsorption between citric acid and PCE occurs. • The combination of citric acid with PCE decreases the initial dispersion efficiency of PCE. PCEs are well known to improve the initial fluidity of CSA. However, their dispersion efficiency drops quickly over time. This issue can be solved by incorporating retarders. In this context, this paper deals with the influence of citric acid, used as a retarder, PCE and their combination on the hydration and workability of CSA. Isothermal calorimetry, XRD and TG analysis were used to describe the hydration process, while workability was characterized with the mini-cone test. Adsorption behavior was investigated using total organic carbon analyzer coupled with ion chromatography. Results show that the introduction of citric acid retained the dispersion efficiency of PCE over time. However, the initial dispersion efficiency of PCE was decreased by citric acid as the latter tend to adsorb first on the surface of cement grains, inhibiting the adsorption of PCE. A dispersion model was proposed to describe the acting mechanism of these admixtures on CSA.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A fast computational framework for the design of solvent-based plastic recycling processes

Multicomponent plastics cannot be processed using mechanical recycling technologies, hindering efforts to deal with plastic waste. Multicomponent plastics include multilayer plastic films, which are widely used for food and healthcare packaging. Multilayer films combine several layers (potentially dozens) of different polymers to protect products from external factors (e.g., oxygen, water, temperature, shock, and light). Solvent-based separation processes have emerged as a promising alternative to recycle these complex materials. For instance, the Solvent-Targeted Recovery and Precipitation (STRAP TM ) process uses sequential solvent washes to selectively dissolve and separate constituent polymers from multicomponent plastic waste, including films. STRAP TM process design (separation sequence, type of solvents, and operating conditions) changes significantly depending on the design of the multilayer plastic film (e.g., number, types, and proportions of polymers). The ability to quickly quantify the economic and environmental benefits of diverse STRAP TM process designs is essential to accelerate the development of sustainable recycling processes and more recyclable multilayer film products. In this work, we present a fast computational framework that integrates molecular-scale models, process modeling, and techno-economic and life cycle analysis to quickly evaluate STRAP TM designs. The computational framework is general and can be used to study the processing of complex multilayer plastic waste streams that contain many layers. Furthermore, we highlight the different uses of the framework via targeted case studies.

Computational framework↗
DOE OSTI2022

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Spatiotemporal features of traffic help reduce automatic accident detection time

Quick and reliable automatic detection of traffic accidents is of paramount importance to save human lives in transportation systems. However, automatically detecting when accidents occur has proven challenging, and minimizing the time to detect accidents (TTDA) by using traditional features in machine learning (ML) classifiers has plateaued. We hypothesize that accidents affect traffic farther from the accident location than previously reported. Therefore, leveraging traffic signatures from neighboring sensors that are adjacent to accidents should help improve their detection. We confirm this hypothesis by using verified ground-truth accident data, traffic data from radar detection system sensors, and light and weather conditions and show that we can minimize the TTDA while maximizing classification performance by considering spatiotemporal features of traffic. Specifically, we compare the performance of different ML classifiers (i.e, logistic regression, random forest, and XGBoost) when controlling for different numbers of neighboring sensors and TTDA horizons. We use data from interstates 75 and 24 in the metropolitan area that surrounds Chattanooga, TN. Our results show that the XGBoost classifier produces the best results by detecting accidents as quickly as 1.0 min after their occurrence with an area under the receiver operating characteristic curve of up to 83% and an average precision of up to 49%. We describe limitations, open challenges, and how the proposed framework can be used for quicker operational accident detection.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Independent temperature and humidity control of a precooled desiccant air cooling system with proportional and fuzzy logic + proportional based controllers

Providing room comfort quickly by using less energy in cooling systems is important both in terms of efficiency and environment. In this study, independent temperature/humidity control strategies are designed for precooled desiccant based evaporative cooling system to provide room comfort quickly with less energy. Therefore, the speed controls of the system's actuators become important depending on the room, cooler and atmosphere conditions. The mathematical models are obtained to determine the relation between voltages of actuators and the variations in the room's temperature and humidity. After that, new control strategies are developed based on Proportional and Fuzzy+Proportional techniques and the control performance is increased by adding extra modes to the operation of the actuators. The effects of the control techniques on the cooling performance and energy consumption were investigated for different cases. It has been observed that the room temperature and relative humidity reach the set values in a short time for both techniques and are successfully kept at these values under the disturbances. By using Fuzzy+Proportional controller, the robustness of the cooler increases due to more precisely adjustment of controller's parameter depending the system conditions. Finally, both control strategies provide more than 20% energy savings compared to the traditional ON/OFF control.

42 ENGINEERING↗
DOE OSTI2021

A refined design concept for sulfur-tolerant Pd catalyst supported on zeolite by shape-selective exclusion and hydrogen spillover for hydrogenation of aromatics

Sulfur poisoning of noble metal catalysts has been a major challenge for decades. This work demonstrates the superior sulfur tolerance of a hybrid zeolite-supported Pd catalyst for hydrogenation of tetralin containing benzothiophene. The hybrid catalyst consists of Pd supported on a large-pore acidic zeolite Y (Pd/HY) and a small-pore zeolite A without (Pd/HA) or with surface metal passivation by chemical vapor deposition of SiO 2 (SiO 2 -Pd/HA) and with potassium ion exchange (SiO 2 -Pd/KA). Pd/HY is very active for tetralin hydrogenation but quickly deactivates after exposure to benzothiophene at high concentration of 100 ppm sulfur. The SiO 2 -coated Pd/HA shows no activity for tetralin hydrogenation, but continues to activate H 2 and serve as a source of hydrogen spillover from Pd/HA since these metal sites are protected from thiophenic sulfur due to size-selective exclusion. Adding K ion-exchanged and SiO 2 -coated catalyst SiO 2 -Pd/KA to Pd/HY is even more effective for enhancing sulfur tolerance, both for tetralin hydrogenation and for isomerization of cis-decalin to trans-decalin in the presence of benzothiophene. The turnover frequencies (TOF) of all the catalysts are similar at the exposed sulfur/Pd atomic ratio of around 0.5. With further increase in sulfur/Pd ratio, rapid decline in TOF was observed on Pd/HY, but SiO 2 -Pd/KA + Pd/HY hybrid catalyst shows a significantly higher TOF than that over Pd/HY, even though SiO 2 -Pd/KA or SiO 2 -Pd/HA alone shows no TOF for tetralin hydrogenation. Even after high-dose poisoning at 400 ppm sulfur, the SiO 2 -Pd/KA + Pd/HY hybrid recovered activity more quickly than SiO 2 -Pd/HA + Pd/HY, while the latter in turn is much better than Pd/HY alone. These results point to the higher activity for hydrogen spillover from SiO 2 -Pd/KA where Pd metal sites are protected from not only thiophenic sulfur but also inorganic sulfur H 2 S by shape-selective exclusion. The present work further establishes the validity of shape-selective exclusion and hydrogen spillover in the design concept proposed for a sulfur-tolerant bimodal acidic zeolite-supported metal catalyst for hydrogenation of naphthalene [C.S. Song, Chemtech, 29 (1999) 26–30], and refines the design concept further for limiting the size of small pore in hybrid catalysts to~3 Å for restricting access of H 2 S but allowing entrance of H 2 . Further, this refined design concept is applicable to other supported monometallic and bimetallic catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Reactive flash sintering of TiZrN and TiAlN ternary metal nitrides

Here, this study demonstrated the reactive flash sintering (RFS) for two powder mixtures: TiN-ZrN (both conducting) and AlN-TiN (TiN conducting but AlN insulating), targeting ternary metal nitrides (TMN) of Ti 0.5 Zr 0.5 N and Ti 0.5 Al 0.5 N, respectively. A constant volage and pressure (e.g., 8 V DC, ∼15 MPa) at room temperature triggered the flash (current density up to 27 A/mm 2 ) without pre-heating, and the entire RFS process finished in a few minutes. For TiZrN, the flash was instantaneous whereas for TiAlN, there was a long incubation before the flash followed by a quick and dramatic flash. Both conventional ex situ XRD and in situ synchrotron study had been carried out. They showed a uniform Ti 0.57 Zr 0.43 N solution formed in RFS and persisted upon cooling, while (Ti, Al) N solid solution formed at high temperature was not stable and likely went through a very quick phase separation in the cooling process. The final products from RFS had been characterized using SEM/EDS for microstructure. Both TiZrN and TiAlN were dense. Distribution of Ti, Zr, and N was uniform for TiZrN; for TiAlN, Ti and N distribution was uniform, while association of Al with oxygen was observed. TGA-DSC revealed the onset oxidation temperature for TiZrN was comparable to TiN and ZrN, while it was higher by ∼200 °C for TiAlN, likely due to the formation of Al 2 O 3 . In terms of mechanical properties such as hardness or fracture toughness, forming a single-phase solid solution (like TiZrN) does not offer obvious benefits. while large grain size from RFS seemed to be unfavorable. Future optimization of RFS condition and in-depth study by both experiments and simulation are needed to fully understand the composition-processing-structure-property relationships for such TMN from the reactive flash sintering process.

Flash Sintering↗
DOE OSTI2019

Measurement of PuO 2 film thickness by electron probe microanalysis (EPMA) calibration curve method

Plutonium (Pu) surfaces are highly reactive toward oxygen containing species and, therefore, invariably covered with oxides (e.g. PuO 2 ) during transport and handling. The actual thickness of the surface oxide may dictate if a plutonium part is suitable for a certain application. As a result, a cost-effective, quick, non-destructive, yet reliable means to measure the oxide layer thickness formed on Pu samples is desirable. In this study, the cross-sections of a series of room temperature grown oxides on Pu samples were trenched by focus ion beam (FIB) then observed by scanning electron microscopy (SEM) to measure the surface oxide thicknesses, which were then combined with the corresponding oxygen k-ratios provided by electron probe microanalysis (EPMA) to form calibration curves. Oxide thickness measurements for the calibration curves were made on samples within the typical SEM observable range for PuO 2 (35–400 nm). The portion of the calibration curve in the thinner oxide region (<35 nm) were approximated via Pouchou and Pichoir’s Φ(ρz) theory. Two specimens with micrometer-thick PuO 2 standards (one formed at room temperature and the other at higher temperature with a higher level of crystallinity) were made for the k-ratios in this study, allowing EPMA users to choose the standard that best suits their needs. If the surface corrosion is known to be PuO 2 (from the environment in which the Pu sample is stored) or if the stoichiometry of the surface oxide is confirmed by a preliminary/compliment technique, these calibration curves allow EPMA users to quickly and efficiently determine PuO 2 thicknesses from the measured oxygen k-ratios of their samples. The methodology presented in this study can also be used as a template for creating calibration curves for oxides grown on other actinides.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Epsilon Metal: A Waste Form for Noble Metals from Used Nuclear Fuel

Epsilon metal (e-metal) is the metallic phase that forms as inclusions at the grain boundaries in the UO2 fuel during reactor operation. This metal is composed of Pd, Mo, Rh, Ru, and Tc. These metallic inclusions are insoluble in strong acid and remnants of these metallic inclusions have been found in the UO2 matrix that remains from the natural reactors in Gabon that were active 1.8 billion years ago, therefore e-metal should be an excellent waste form for the immobilization of the long-lived isotopes 107Pd (6.5 ? 106 a) and 99Tc (2.13 ? 105 a), with 99Tc being the isotope of interest for repository performance. Therefore, the chemical durability of this potential waste form is assessed in this study. Typically, corrosion rates for metallic materials are measured electrochemically because they are quick, inexpensive, and can reveal the mechanism by which a metal corrodes, at least initially. However, in a repository the waste form would be subjected to slowly flowing water without an applied electrical potential over long time periods. Therefore, the corrosion rates of e-metal specimens were measured with both electrochemical tests and the single-pass flow-through test (SPFT). Potentiodynamic and potentiostatic polarization results suggest that a thin passive film exists on the alloy surface, which seems to be responsible for its high corrosion resistance. Additionally, X-Ray photoelectron spectroscopic results suggest that Pd oxides are significantly enriched in the passive film Results from the SPFT show that the dissolution rates were weakly dependent on pH. Only Mo and Re were found in solution and were used for the calculation of the dissolution rates. In general, the electrochemically determined corrosion rates agree reasonably well with the initial dissolution rate measured with the SPFT test, but they are about one or two orders of magnitude higher than the steady state rates. The causes for this discrepancy are discussed.

epsilon-metal, nuclear waste form, corrosion, sing↗
DOE OSTI2020

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

In this work, we present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Machine learning model to project the impact of COVID-19 on U.S. motor gasoline demand

Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people's resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020. The COVID 19 pandemic and consequent lockdown has had a substantial impact on mobility and therefore fuel demand and it is not clear when demand will recover. Ou et al. use a machine learning model that integrates health recovery scenarios to project the near-term future of gasoline demand.

COVID-19 pandemic↗
DOE OSTI2023

Combined, time-resolved, in situ neutron reflectometry and X-ray diffraction analysis of dynamic SEI formation during electrochemical N 2 reduction

One means of improving performance for electrochemical ammonia production through the Li-mediated N 2 reduction reaction (Li-NRR) is by cycling the current driving the reaction between open-circuit conditions and periods of applied current density. Herein, we have investigated the dynamics of the electrode–electrolyte interface under Li-NRR conditions during current cycling using in situ time-resolved neutron reflectometry and grazing-incidence synchrotron X-ray diffraction. During cycling, measured neutron reflectivity curves indicated bilayer formation in which Li-containing species such as LiOH, Li 2 O, and small quantities of Li 3 N and metallic Li primarily appeared in a thin layer at the cathode surface, above which formed a much larger, porous, ‘solid–electrolyte interface’ (SEI) layer. Upon return to open-circuit conditions, Li-containing species quickly moved out of the thin layer, leaving a compact, stable layer of decomposition products underneath the SEI layer. This SEI layer concomitantly filled with electrolyte or dissolved, becoming indistinguishable from the electrolyte via contrast in scattering-length density (SLD). During the second current cycle, Li-containing species again preferentially deposited directly atop the cathode, with the thick SEI-like layer again appearing within a minute. This SEI layer exhibited a lower SLD more quickly than in the first cycle, which might suggest that Li-containing species become distributed within the porous SEI layer. Furthermore, these time-resolved observations of SEI and plated layers during current cycling suggest that benefits associated with return to open-circuit conditions between periods of applied current density may be related to the concomitant loss of Li-containing species from a thin layer at the cathode surface into a porous SEI layer that becomes filled with electrolyte or dissolves.

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

A two-stage service restoration method for electric power distribution systems

Improving the reliability of power distribution systems is critically important for both utilities and customers. This calls for an efficient service restoration module within a distribution management system to support the implementation of self-healing smart grid networks. Although the emerging smart grid technologies, including distributed generators (DGs) and remote-controlled switches, enhance the self-healing capability and allow faster recovery, they still pose additional complexity to the service restoration problem, especially under cold load pickup (CLPU) conditions. Herein, a novel two-stage restoration framework is proposed to generate a restoration solutions with a sequence of control actions. The first stage generates a restoration plan that supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islanding methods. The second stage generates an optimal sequence of switching operations to bring the outaged system quickly to the final restored configuration. The problem is formulated as a mixed-integer linear program that incorporates system connectivity, operating constraints, and the CLPU models. It is demonstrated that on using a multi-feeder test case, the proposed framework is effective in utilizing all available resources to quickly restore the service and generate an optimal sequence of switching actions to be used by the operator to reach the desired optimal configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Oscillatory Spreading and Inertia in Power Grids

The increase in variable renewable generators (VRGs) in power systems has altered the dynamics from a historical experience. VRGs introduce new sources of power oscillations, and the stabilizing response provided by synchronous generators (SGs, e.g., natural gas, coal, etc.), which help avoid some power fluctuations, will lessen as VRGs replace SGs. These changes have led to the need for new methods and metrics to quickly assess the likely oscillatory behavior for a particular network without performing computationally expensive simulations. This work studies the impact of a critical dynamical parameter - the inertia value - on the rest of a power system's oscillatory response to representative VRG perturbations. We use a known localization metric in a novel way to quantify the number of nodes responding to a perturbation and the magnitude of those responses. This metric allows us to relate the spread and severity of a system's power oscillations with inertia. We find that as inertia increases, the system response to node perturbations transitions from localized (only a few close nodes respond) to delocalized (many nodes across the network respond). We introduce a heuristic computed from the network Laplacian to relate this oscillatory transition to the network structure. We show that our heuristic accurately describes the spread of oscillations for a realistic power-system test case. Using a heuristic to determine the likely oscillatory behavior of a system given a set of parameters has wide applicability in power systems, and it could decrease the computational workload of planning and operation.

dynamical systems↗
DOE OSTI2022

The rapid destruction of toroidal magnetic surfaces

The operation of ITER will require reliable simulations in order to avoid major damage to the device from disruptions. Disruptions are the sudden breakup of magnetic surfaces across the plasma volume—a fast magnetic reconnection. This reconnection can be caused by the growth of perturbations outside of the plasma core causing an ideal perturbation to the core. This causes an increasing ratio of the maximum to the minimum separation, Δmax/Δmin, between neighboring magnetic surfaces. Magnetic reconnection becomes a dominant process when magnetic field lines can quickly interchange connections over a spatial scale ar. This occurs when Δmax/Δmin≳ar/Δd, where Δd is the scale over which non-ideal effects make magnetic field lines indistinguishable. Traditional reconnection theory is fundamentally different. It is a study of the steady-state cancelation of oppositely directed magnetic field components across a thin layer. During more than sixty years, mathematical implications of Faraday's law have been derived that clarify and constrain the physics of fast magnetic reconnection. These are reviewed because they are not commonly known but are needed to understand and to place in context how an ideal magnetic evolution can cause reconnection to quickly become a dominant process no matter how small Δd/ar may be.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗