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DOE OSTI2020

Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.

DERs↗

DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗

NASA NTRS2014

A Systems Engineering Approach to Architecture Development

Architecture development is conducted prior to system concept design when there is a need to determine the best-value mix of systems that works collectively in specific scenarios and time frames to accomplish a set of mission area objectives. While multiple architecture frameworks exist, they often require use of unique taxonomies and data structures. In contrast, this presentation characterizes architecture development using terminology widely understood within the systems engineering community. Using a notional civil space architecture example, it employs a multi-tier framework to describe the enterprise level architecture and illustrates how results of lower tier, mission area architectures integrate into the enterprise architecture. It also presents practices for conducting effective mission area architecture studies, including establishing the trade space, developing functions and metrics, evaluating the ability of potential design solutions to meet the required functions, and expediting study execution through the use of iterative design cycles.

Architecture Development↗

NASA NTRS1999

NSBRI Radiation Effects: Carcinogenesis in Sprague-Dawley Rats Irradiated with Iron Ions, Protons, or Photons

Our ability to confidently develop appropriate countermeasures for radiations in space in terms of shielding and design of a spacecraft, the mission scenario, or chemoprevention is severely limited by the uncertainties in both the risk itself and the change in that risk with intervention. Despite the fact that the risk of carcinogenesis from exposures of personnel to radiations on long-term missions is considered one of the worst hazards in space, only a limited amount of in-vivo data exist for tumor induction from exposures to protons or energetic heavy ions (HZEs) at lower doses. The most extensive work remains the landmark study. for tumor development in the harderian gland of the mouse. The objective of this study is to characterize the level of risk for tumor induction in another relevant animal model. Subsequent experiments are designed to test the hypothesis that the level of risk can be reduced by pharmaceutical intervention in the promoting and progressing stages of the disease rather than in the initiating stage. The work presented here results from a cooperative effort on the part of investigators from two projects of the Radiation-Effects Team of the National Space Biomedical Research Institute (NSBRI). The collaborating projects are the Core Project which is investigating the risk of carcinogenesis in Sprague-Dawley rats and the Chemoprevention Project which is investigating the ability of Tamoxifen to reduce the number of malignant tumors in the irradiated animals. Research at the cellular and subcellular levels is being conducted in two other projects of the Radiation-Effects Team, Cytogenetics with J. R. Williams as Principal Investigator and Mutations from Repeated DNA Sequences. Results for these other projects also are being presented at this Workshop.

Dicello, J. F.↗

NASA NTRS2016

A First Look at the Upcoming SISO Space Reference FOM

Simulation is increasingly used in the space domain for several purposes. One example is analysis and engineering, from the mission level down to individual systems and subsystems. Another example is training of space crew and flight controllers. Several distributed simulations have been developed for example for docking vehicles with the ISS and for mission training, in many cases with participants from several nations. Space based scenarios are also used in the "Simulation Exploration Experience", SISO's university outreach program. We have thus realized that there is a need for a distributed simulation interoperability standard for data exchange within the space domain. Based on these experiences, SISO is developing a Space Reference FOM. Members of the product development group come from several countries and contribute experiences from projects within NASA, ESA and other organizations. Participants represent government, academia and industry. The first version will focus on handling of time and space. The Space Reference FOM will provide the following: (i) a flexible positioning system using reference frames for arbitrary bodies in space, (ii) a naming conventions for well known reference frames, (iii) definitions of common time scales, (iv) federation agreements for common types of time management with focus on time stepped simulation, and (v) support for physical entities, such as space vehicles and astronauts. The Space Reference FOM is expected to make collaboration politically, contractually and technically easier. It is also expected to make collaboration easier to manage and extend.

Crues, Edwin↗

DOE OSTI2024

A nuclear power plant digital twin for developing robot navigation and interaction

As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention.

Energy & Fuels↗

NASA NTRS2015

The SMART-NAS Testbed

The SMART-NAS Testbed for Safe Trajectory Based Operations Project will deliver an evaluation capability, critical to the ATM community, allowing full NextGen and beyond-NextGen concepts to be assessed and developed. To meet this objective a strong focus will be placed on concept integration and validation to enable a gate-to-gate trajectory-based system capability that satisfies a full vision for NextGen. The SMART-NAS for Safe TBO Project consists of six sub-projects. Three of the sub-projects are focused on exploring and developing technologies, concepts and models for evolving and transforming air traffic management operations in the ATM+2 time horizon, while the remaining three sub-projects are focused on developing the tools and capabilities needed for testing these advanced concepts. Function Allocation, Networked Air Traffic Management and Trajectory Based Operations are developing concepts and models. SMART-NAS Test-bed, System Assurance Technologies and Real-time Safety Modeling are developing the tools and capabilities to test these concepts. Simulation and modeling capabilities will include the ability to assess multiple operational scenarios of the national airspace system, accept data feeds, allowing shadowing of actual operations in either real-time, fast-time and/or hybrid modes of operations in distributed environments, and enable integrated examinations of concepts, algorithms, technologies, and NAS architectures. An important focus within this project is to enable the development of a real-time, system-wide safety assurance system. The basis of such a system is a continuum of information acquisition, analysis, and assessment that enables awareness and corrective action to detect and mitigate potential threats to continuous system-wide safety at all levels. This process, which currently can only be done post operations, will be driven towards "real-time" assessments in the 2035 time frame.

Trajectory Based Operations↗

DOE OSTI2021

A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. Here, the framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Challenges in simulating economic effects of climate change on global agricultural markets

Abstract Many approaches have been used to investigate climate change impacts on agriculture. However, several caveats remain in this field: (i) analyses focus only on a few major crops, (ii) large differences in yield impacts are observed between projections from site-based crops models and Global Gridded Crop Models (GGCMs), (iii) climate change impacts on livestock are rarely quantified, and (iv) several causal relations among biophysical, environmental, and socioeconomic aspects are usually not taken into account. We investigate how assumptions about these four aspects affect agricultural markets, food supply, consumer well-being, and land use at global level by deploying a large-scale socioeconomic model of the global economy with detailed representation of the agricultural sector. We find global welfare impacts several times larger when climate impacts all crops and all livestock compared to a scenario with impacts limited to major crops. At the regional level, food budget can decrease by 10 to 25% in developing countries, challenging food security. The role of land area expansion as a major source of adaptation is highlighted. Climate impacts on crop yields from site-based process crop models generate more challenging socioeconomic outcomes than those from GGCMs. We conclude that the agricultural research community should expand efforts to estimate climate impacts on many more crops and livestock. Also, careful comparison of the GGCMs and traditional site-based process crop models is needed to understand their major implications for agricultural and food markets.

Gurgel, Angelo C. (ORCID:0000000183319508)↗

DOE OSTI2024

Crosstalk-robust quantum control in multimode bosonic systems

High-coherence superconducting cavities offer a hardware-efficient platform for quantum information processing. To achieve universal operations of these bosonic modes, the requisite nonlinearity is realized by coupling them to a transmon ancilla. However, this configuration is susceptible to crosstalk errors in the dispersive regime, where the ancilla frequency is Stark shifted by the state of each coupled bosonic mode. This leads to a frequency mismatch of the ancilla drive, lowering the gate fidelities. To mitigate such coherent errors, we employ quantum optimal control to engineer ancilla pulses that are robust to the frequency shifts. These optimized pulses are subsequently integrated into a recently developed echoed conditional displacement protocol for executing single- and two-mode operations. Through numerical simulations, we examine two representative scenarios: the preparation of single-mode Fock states in the presence of spectator modes and the generation of two-mode entangled Bell-cat states. Our approach markedly suppresses crosstalk errors, outperforming conventional ancilla control methods by orders of magnitude. These results provide guidance for experimentally achieving high-fidelity multimode operations and pave the way for developing high-performance bosonic quantum information processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2017

Commercial Crew Vehicle Ascent Abort Simulation and Analysis

SpaceX and Boeing have been selected to develop and operate crew vehicles to transport astronauts to and from the International Space Station. Their design work is to be analyzed to ensure that they are meeting all of the safety and operational requirements put forth by NASA. Throughout my time here, I worked familiarized myself with the SpaceX Dragon Abort system, as well as the NASA Human-Systems Integration Requirements (HSIR). This included understanding the different abort scenarios, and how each one could potentially impact the astronaut crew. In addition, I familiarized myself with the simulation developed my NASA to test and analyze the Guidance Navigation and Control (GN&C) systems developed by SpaceX and Boeing.

Gnam, Christopher↗

DOE OSTI2024

SMART Deliverable 6.1.2a: Application of the ORION tool to the IBDP Carbon Storage Site

Forecasting and managing potential induced seismic activity is one of the challenges facing commercialscale geologic carbon sequestration (GCS), as well as other geologic energy extraction and byproduct disposal technologies. Historically, the process to develop robust, science-based forecasts of induced seismicity has required an integrated effort from experts in seismology, geomechanics, and reservoir engineering to manage data, develop and evaluate models of subsurface processes, and to calibrate and interpret the results from a range of models to understand site behavior relative to prescribed standards and in the context of uncertainty in geologic characterization data, forecasting models, and operational scenario uncertainty. The Operational Forecasting of Induced Seismicity (ORION) toolkit is an open-source, observation-based forecasting toolkit that is being co-developed by two U.S. DOE-funded initiatives: the National Risk Assessment Partnership (NRAP) and the Scienceinformed Machine Learning for Accelerating Real Time Decisions in Subsurface Applications (SMART) Initiative. ORION is designed to provide functionality to support decision making about seismic hazard analysis and risk management for GCS stakeholders ranging from the public to site operators to expert seismologists. The tool, which is written as open-source code in the Python programming language, is composed of a desktop graphical user interface (GUI) and an underlying forecasting engine. The forecasting engine uses available reservoir properties, well and fluid injection scenario details, and observed seismic catalog data as inputs to produce a set of temporal and spatio-temporal seismic forecasts.

58 GEOSCIENCES↗

NASA NTRS2012

Using Remotely Sensed Data and Watershed and Hydrodynamic Models to Evaluate the Effects of Land Cover Land Use Change on Aquatic Ecosystems in Mobile Bay, AL

Alabama coastal systems have been subjected to increasing pressure from a variety of activities including urban and rural development, shoreline modifications, industrial activities, and dredging of shipping and navigation channels. The impacts on coastal ecosystems are often observed through the use of indicator species. One such indicator species for aquatic ecosystem health is submerged aquatic vegetation (SAV). Watershed and hydrodynamic modeling has been performed to evaluate the impact of land cover land use (LCLU) change in the two counties surrounding Mobile Bay (Mobile and Baldwin) on SAV stressors and controlling factors (temperature, salinity, and sediment) in the Mobile Bay estuary. Watershed modeling using the Loading Simulation Package in C++ (LSPC) was performed for all watersheds contiguous to Mobile Bay for LCLU scenarios in 1948, 1992, 2001, and 2030. Remotely sensed Landsat-derived National Land Cover Data (NLCD) were used in the 1992 and 2001 simulations after having been reclassified to a common classification scheme. The Prescott Spatial Growth Model was used to project the 2030 LCLU scenario based on current trends. The LSPC model simulations provided output on changes in flow, temperature, and sediment for 22 discharge points into the estuary. These results were inputted in the Environmental Fluid Dynamics Computer Code (EFDC) hydrodynamic model to generate data on changes in temperature, salinity, and sediment on a grid throughout Mobile Bay and adjacent estuaries. The changes in the aquatic ecosystem were used to perform an ecological analysis to evaluate the impact on SAV habitat suitability. This is the key product benefiting the Mobile Bay coastal environmental managers that integrates the influences of temperature, salinity, and sediment due to LCLU driven flow changes with the restoration potential of SAVs. Data products and results are being integrated into NOAA s EcoWatch and Gulf of Mexico Data Atlas online systems for dissemination to coastal resource managers and stakeholders. Objective 1: Develop and utilize Land Use scenarios for Mobile and Baldwin Counties, AL as input to models to predict the affects on water properties (temperature,salinity,)for Mobile Bay through 2030. Objective 2: Evaluate the impact of land use change on seagrasses and SAV in Mobile Bay. Hypothesis: Urbanization will significantly increase surface flows and impact salinity and temperature variables that effect seagrasses and SAVs.

Al-Hamdan, Mohammad↗

DOE OSTI2022

A Multistage Stochastic Transmission Expansion Algorithm for Wide-Area Planning under Uncertainty

The overall objective for this project was to develop and demonstrate a set of methods for solving the transmission investment problem for a large network considering many possible scenarios of future conditions and multiple decision points when investments can be made. Project sub-objectives achieved this goal through a succession of extending the methods to apply to problems with increasing complexity or additional features, including the number of decision points, whether generation and transmission are co-optimized, and whether AC or DC power flow is used. A transmission model was developed for the Western Electric Coordinating Council (WECC) region, the high-voltage transmission system that serves the western third of the continental U.S. Using a dataset provided by WECC and by researchers from John Hopkins University, we have validated and demonstrated the model and used it to compare the new method for solving multi-stage stochastic transmission planning to several state-of-the-art techniques. The project has resulted in several key outcomes and achievements: The covariance-based method for choosing a small set of hours to represent short-term variability has superior performance in terms of accuracy to existing methods, including K-means clustering and Importance Sampling; The combined partitioning method for long-term uncertainty with the nested clustering approach for choosing representative hours for each long-term group has superior accuracy for equivalent computational effort compared with existing methods; Using the partitioning/clustering method combined with Sample Average Approximation provides both statistical bounds on the quality of the solution and at the same time, a complete investment plan for all contingencies in the full uncertainty set; no existing methods can provide both at the same time; The method is demonstrated to work well for choosing both transmission and generation investments; A variant on the method allows for both scenario selection and simultaneous correction for the error from the DC power flow approximation to provide a tractable method for AC power flow-based transmission planning under uncertainty; The method applied to the WECC case study demonstrates the additional value to the system operator and the consumer of identifying flexible investment options in the near-term decisions. In particular, the case study exhibits significant option value in postponing some transmission additions that appear useful but in some long-term system states create new congestion problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2005

Remote Sensing Characterization of the Urban Landscape for Improvement of Air Quality Modeling

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, in moderating ground-level ozone and air temperature, compared to "business as usual" simulations in which heat island mitigation strategies are not applied. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data has been found to better characterize low densityhburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for fiture scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the state Environmental Protection agency to evaluate how these transportation plans will affect fbture air quality.

Quattrochi, Dale A.↗

DOE OSTI2021

Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency

Recent advances in machine learning and computer vision brought to light technologies and algorithms that serve as new opportunities for creating intelligent and efficient manufacturing systems. In this study, the real-time monitoring system of manufacturing workflow for the Smart Connected Worker (SCW) is developed for the small and medium-sized manufacturers (SMMs), which integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems. Specifically, object detection and text recognition models are investigated and adopted to ameliorate the labor-intensive machine state monitoring process, while artificial neural networks are introduced to enable real-time energy disaggregation for further optimization. The developed system achieved efficient supervision and accurate information analysis in real-time for prolonged working conditions, which could effectively reduce the cost related to human labor, as well as provide an affordable solution for SMMs. The competent experiment results also demonstrated the feasibility and effectiveness of integrating machine learning technologies into the realm of advanced manufacturing systems.

42 ENGINEERING↗

DOE OSTI2023

A review of artificial intelligence applications in manufacturing operations

Abstract Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.

Plathottam, Siby Jose↗

DOE OSTI2022

A Phase-Field Study on Internal to External Oxidation Transition in High-Temperature Structural Alloys

Structural alloys applied at high temperatures rely on an external dense layer of oxide scale for protection. As some reactive alloy components are selectively oxidized internally, understanding how these dispersed metal oxide particles reach the surface to form a protective scale—the so-called internal to external oxidation transition—is crucial for designing these alloys. While the literature is replete with experimental studies on oxidation of alloys, there is a lack of computational studies in this realm due to the complex nature of coupled reaction and diffusion processes in multicomponent multi-phase alloy systems. In this work, we apply a recently developed phase-field model to simulate the oxidation processes under different compositions and nucleation scenarios to gain insights into how a continuous oxide scale can be established. The results show that while alloy composition is critical for internal to external oxidation transition, the oxide nuclei size, shape and distribution also have significant impact on the transition kinetics.

36 MATERIALS SCIENCE↗