Wide bandgap power devices have emerged as an often superior alternative power switch technology for many power electronic applications. These devices theoretically have excellent material properties ...enabling power device operation at higher switching frequencies and higher temperatures compared with conventional silicon devices. However, material defects can dominate device behavior, particularly over time, and this should be strongly considered when trying to model actual characteristics of currently available devices. Compact models of wide bandgap power devices are necessary to analyze and evaluate their impact on circuit and system performance. Available compact models, i.e., models compatible with circuit-level simulators, are reviewed. In particular, this paper presents a review of compact models for silicon carbide power diodes and MOSFETs.
Beaches are thought to be a large reservoir for marine plastics. To protect vulnerable beaches, it is advantageous to have information on the sources of this plastic. Here, we develop a universally ...applicable Bayesian framework to map sources of plastic arriving on a specific beach. In this framework, we combine Lagrangian backtracking simulations of drifting particles with estimates of plastic input from coastlines, rivers and fisheries. The advantage over traditional Lagrangian simulations is that the Bayesian framework can consider information on known sources, and thus facilitates spatiotemporal source attribution for plastic arriving at the specified beach. We show that the main sources for our target beach in southwest Netherlands are the east coast of the UK, the Dutch coast, the English Channel (fisheries) and the Thames, Seine, Rhine and Trieux (rivers). We also show that floating time is a major uncertainty in source attribution using backtracking.
Plain Language Summary
A large part of plastic in the ocean is located at or near beaches. This plastic can break down into micro‐plastics or be ingested by animals. Therefore, it is important to clean up these beaches. The easiest way to do so is to prevent the plastic from entering the oceans initially by interfering at the source. In this study, we develop a framework to find these sources for a given beach. We first simulate the path that plastic has taken to reach this beach. We do this by releasing virtual plastic particles at the beach where they end up. Next, we calculate their paths back in time, computing their trajectories until they reach this beach. We then combine these simulations with data on the sources of plastic: where and when did plastic enter the ocean? We apply this framework to a beach in southwest Netherlands, near the town of Domburg. We quantify seasonal effects, where time‐varying currents cause the plastic to come from different sources. Lastly, we study how plastic sources vary with plastic age (the time between the plastic entering the ocean and beaching at its final location).
Key Points
Combined oceanic backtracking and Bayesian statistics supports source attribution of beached plastic
Strong seasonal variability in likely sources is found, due to variability in plastic input and currents
Floating time remains a major uncertainty in determining the origin of beached plastic via backtracking
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The decline in global emissions of carbon dioxide due to the COVID‐19 pandemic provides a unique opportunity to investigate the sensitivity of the global carbon cycle and climate system to emissions ...reductions. Recent efforts to study the response to these emissions declines has not addressed their impact on the ocean, yet ocean carbon absorption is particularly susceptible to changing atmospheric carbon concentrations. Here, we use ensembles of simulations conducted with an Earth system model to explore the potential detection of COVID‐related emissions reductions in the partial pressure difference in carbon dioxide between the surface ocean and overlying atmosphere (ΔpCO2), a quantity that is regularly measured. We find a unique fingerprint in global‐scale ΔpCO2 that is attributable to COVID, though the fingerprint is difficult to detect in individual model realizations unless we force the model with a scenario that has four times the observed emissions reduction.
Plain Language Summary
The COVID‐19 pandemic is slowing the rate of fossil fuel use, and thus impacting the carbon dioxide concentration in the atmosphere. Here we explore what this change in fossil fuel use does to carbon in the ocean. We use a climate model to estimate the change in ocean‐atmosphere carbon exchange and ocean acidity. Since we don’t yet know how much we will slow our fossil fuel use due to COVID, we make several informed guesses and see how our model ocean responds to each. We use the model to investigate whether the change that we model would be detectable in real world observations. We find that it is nearly impossible to detect a COVID‐related change in ocean acidity with observations. It might be possible to detect a COVID‐related change in ocean‐atmosphere carbon exchange, but only if we use the most extreme guess, and only if we have enough observation stations in place to record it.
Key Points
COVID‐related emissions reductions will be imperceptible in surface ocean pH observations
The CanESM5 COVID ensemble predicts a unique fingerprint of COVID‐related emissions reductions in global mean ΔpCO2 (pCO2oc ‐ pCO2atm)
The fingerprint is potentially detectable in global‐scale observations of ΔpCO2, but only with large emissions reductions
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
•Applications of modeling and simulation in EAOPs for wastewater treatment are few.•Role of modeling and simulation in the design of membrane-EAOPs was outlined.•Models and simulations are powerful ...tools for the optimization of EAOPs.•Models and simulations are faster and greener than running multiple experiments.
Membrane technology coupled with electrocatalytic oxidation processes also known as membrane-EAOPs became a trendy alternative employed in the treatment of organic contaminants in aqueous liquids. In juxtaposition, modeling and simulations have been widely used for system improvements and design of electrochemical systems such as batteries and capacitors, proton exchange membrane fuel cells (PEMFC), etc. Howbeit, the utilization of modeling and simulations in EAOPs is still very minor when knowing the extent of their potential. In this review, the role of modeling and simulations in the design of membrane-electrodes combined with electrocatalytic oxidation processes for the treatment of organic polluted water is outlined. Modeling and simulation techniques for membrane-EAOPs parameters such as the electrode materials, the geometrical construction of the porous layer, the electrode–electrolyte interactions, the voltammogram, the electrochemical impedance spectroscopy, the catalysts coating, and the electrocatalytic degradation of pollutants are briefly discussed. Study cases of some implementations of modeling and simulation in real EAOP applications are also discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Global monitoring of seasonal snow water equivalent (SWE) has advanced significantly over the past decades. However, challenges remain when estimating SWE from passive and active microwave ...signatures, because a priori characterization of snow properties is required for SWE retrievals. Numerical experiments have shown that utilizing physical snow models to acquire snowpack characterization can potentially improve microwave‐based SWE retrievals. This study aims to identify the challenges of assimilating active and passive microwave signatures with physical snow models, and to examine solutions to those challenges. Guided by observations from a point‐based study, we designed a sensitivity experiment to quantify the effects of changes in the physically modeled SWE—and of corresponding changes to other snowpack properties—to the microwave‐based SWE retrievals. The results indicate that assimilating microwave signatures with physical snow models face some critical challenges associated with the physical relationship between SWE and snow microstructure. We demonstrate these challenges can be overcome if the microwave algorithms account for these relationships.
Key Points
Simulated snow properties from SnowModel are used in microwave‐based snow water equivalent (SWE) retrievals by MEMLS3&a
Biases in physically modeled SWE can induce larger biases in microwave‐based SWE retrievals
The challenges can be mitigated when microwave algorithms account for the physical relationship of snow properties
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
One of the biggest challenges in cyber-physical system (CPS) design is their intrinsic complexity, heterogeneity, and multidisciplinary nature. Emerging distributed CPSs integrate a wide range of ...heterogeneous aspects such as physical dynamics, control, machine learning, and error handling. Furthermore, system components are often distributed over multiple physical locations, hardware platforms, and communication networks. While model-based design (MBD) has tremendously improved the design process, CPS design remains a difficult task. Models are meant to improve understanding of a system, yet this quality is often lost when models become too complicated. In this paper, we show how to use aspect-oriented (AO) modeling techniques in MBD as a systematic way to segregate domains of expertise and cross-cutting concerns within the model. We demonstrate these concepts on actor-oriented models of an industrial robotic swarm application and illustrate the use of AO modeling techniques to manage the complexity. We also show how to use AO modeling for design-space exploration.
Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and ...resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models 'decouple' individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.
Many nations responded to the COVID-19 pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol ...precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. Twelve models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID- 19-related emission reductions on near-term climate.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Measurement-based black box behavioral models are widely used nowadays. To handle nonlinear effects efficiently, such models often rely on approximation techniques. Polyharmonic distortion (PHD) ...modeling emerged as a viable approach for describing a nonlinear mapping. Typical PHD-based models, such as the well-known <inline-formula> <tex-math notation="LaTeX">X </tex-math></inline-formula>-parameter model, are gained from linearization while operating on a certain large signal (LS) operational point. This limits the accuracy, especially for hard nonlinearities. However, quadratic terms can be added, which result in the quadratic PHD (QPHD) model. This enables highly accurate models for devices in strongly nonlinear operation, even in highly mismatched environments. In this paper, the accuracy of such models is investigated by predicting typical nonlinear measures, such as load-pull contours and intermodulation distortion, to assess the model accuracy for both static and dynamic stimulus. Furthermore, the LS matching problem is solved for both the <inline-formula> <tex-math notation="LaTeX">X </tex-math></inline-formula>-parameter and the QPHD model. This allows to predict the optimum matching analytically, without performing load-pull analysis. To verify the accuracy of the model, the results are presented by comparing the model prediction with verification measurements for a commercially available GaN HEMT.
Annual forest area burned (AFAB) in the western United States (US) has increased as a positive exponential function of rising aridity in recent decades. This non‐linear response has important ...implications for AFAB in a changing climate, yet the cause of the exponential AFAB‐aridity relationship has not been given rigorous attention. We investigated the exponential AFAB‐aridity relationship in western US forests using a new 1984–2019 database of fire events and 2001–2020 satellite‐based records of daily fire growth. While forest‐fire frequency and duration grow linearly with aridity, the exponential AFAB‐aridity relationship results from the exponential growth rates of individual fires. Larger fires generally have more potential for growth due to more extensive firelines. Thus, forces that promote fire growth, such as aridification, have more potent effects on larger fires. As aridity increases linearly, the potential for growth of large fires accelerates, leading to exponential increases in AFAB.
Plain Language Summary
Though a natural phenomenon in the western United States (US), wildfires have burned over increasingly large forested areas as the climate has warmed and dried in recent decades, straining fire management and putting humans at risk. An important characteristic of the wildfire response to climate is that as fuels dry–mainly from low precipitation and heat–the amount of annual forest area burned increases exponentially. Although scientists frequently use this relationship to project wildfire responses to climate change, the cause of the exponential relationship has not been robustly investigated. We show here that the exponential response of annual burned area to fuel dryness is related to how individual wildfires spread. Fire growth is a dispersion phenomenon–similar to how the area of a circle increases exponentially as the radius grows incrementally, wildfires tend to grow at compounding rates; the larger a fire, the more potential it has for rapid growth. As western US forest fires have grown under climate change, larger fires have grown more rapidly than smaller fires and increases in annual forest‐fire area have therefore accelerated. Annual western US forest area burned will likely continue to increase due to warming and drying until fuel availability becomes a limiting factor.
Key Points
We developed a database of western United States wildfires and examined why annual forest area burned grows exponentially with aridity
Individual fires grow at compounding rates, so fire enlargement driven by aridification is most rapid among large fires
Approximately two‐thirds of the increase in 1984–2019 forest burned area is shaped by each year's largest 10% of forest fires
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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