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This article contains the results of mathematical modeling of the self-ignition delay (τc sum) of a single droplet for various fuels, and the results of measurement verification (τc) of this modeling in diesel engines. The result of modeling the τc sum (as a function of the diameter and ambient temperature of the fuel droplet) revealed two physical and two chemical stages that had different values of the weighting factor (WFi) in relation to the total delay of self-ignition. It was also found that the WFi values of individual phases of the self-ignition delay differed for different fuels (conventional and alternative), and in the total value of τc sum. The measured value of the self-ignition delay (τc) was determined in tests using two diesel engines (older—up to EURO II and newer generation—from EURO IV). The percentage difference in the Δτc sum value obtained from modeling two fuels with different cetane number values was compared with the percentage difference in the Δτc value for the same fuels obtained during the engine measurements. Based on this analysis, it was found that the applied calculation model of the self-ignition delay for a single fuel droplet can be used for a comparative analysis of the suitability of different fuels in the real conditions of the cylinder of a diesel engine. This publication relates to the field of mechanical engineering.

Recent advances in computational petrological modeling provide accurate methods for computing seismic velocities and density within the lithospheric and sub‐lithospheric mantle, given the bulk composition, temperature, and pressure within them. Here, we test an integrated geophysical‐petrological inversion of Rayleigh‐ and Love‐wave phase‐velocity curves for fine‐scale lithospheric structure. The main parameters of the grid‐search inversion are the lithospheric and crustal thicknesses, mantle composition, and bulk density and seismic velocities within the crust. Conductive lithospheric geotherms are computed using P‐T‐dependent thermal conductivity. Radial anisotropy and seismic attenuation have a substantial effect on the results and are modeled explicitly. Surface topography provides information on the integrated density of the crust, poorly constrained by surface waves alone. Investigating parameter inter‐dependencies, we show that accurate surface‐wave data and topography can constrain robust lithospheric models. We apply the inversion to central Mongolia, south of the Baikal Rift Zone, a key area of deformation in Asia with debated lithosphere‐asthenosphere structure and rifting mechanism, and detect an 80–90 km thick lithosphere with a dense, mafic lower crust and a relatively fertile mantle composition (Mg# < 90.2). Published measurements on crustal and mantle Miocene and Pleistocene xenoliths are consistent with both the geotherms and the crustal and lithospheric mantle composition derived from our inversion. Topography can be fully accounted for by local isostasy, with no dynamic support required. The mantle structure constrained by the inversion indicates no major thermal anomalies in the shallow sub‐lithospheric mantle, consistent with passive rifting in the Baikal Rift Zone. Key Points Petro‐physical inversion reduces non‐uniqueness of seismic surface‐wave inversion No evidence for thermal anomaly in the uppermost mantle in central Mongolia Topography is consistent with local isostasy with no dynamic component required

This article aims to advance the understanding of particle interactions in low retention time flocculators and proposes a new flocculation model that appropriately considers the influence of retention time in flocculation processes. This consideration is important for units with flocculation time lower than 1 min, as seen in helically coiled tube flocculators (HCTFs), whose retention time is significantly lower than conventional flocculation units (about 30 min). With this, it was possible to obtain a more adherent model, reducing deviations between results obtained by physical modelling (using HCTFs, 48 tests) and those obtained with the proposed model, when compared with results obtained using the flocculation models commonly used for this purpose. The decreasing-rising behaviour presented by experimental data for process efficiency versus retention time, not verified in the benchmark models, was satisfactorily addressed by the proposed model. Furthermore, maximum and average absolute percentage deviations obtained using the model proposed in this study were less than or equal to the results obtained with the benchmark models and less for experimental uncertainty (10%). The results obtained indicate that this model can be a useful tool to support the rational design of low retention time units, including applications for the water industry and water recycling systems.

This study demonstrates the research and development of a visualization method called thermal performance simulation. The objective of this study is providing the results of thermal performance simulation results into building information modeling (BIM) models, displaying a series of thermal performance results, and enabling stakeholders to use the BIM tool as a common user interface in the early design stage. This method utilizes a combination of object-oriented physical modeling (OOPM) and BIM. To implement the suggested method, a specific BIM authoring tool called the application programming interface (API) was adopted, as well as an external database to maintain the thermal energy performance results from the OOPM tool. Based on this method, this study created a prototype called the thermal energy performance visualization (TEPV). The TEPV translates the information from the external database to the thermal energy performance indicator (TEPI) parameter in the BIM tool. In the TEPI, whenever BIM models are generated for building design, the thermal energy performance results are visualized by color-coding the building components in the BIM models. Visualization of thermal energy performance results enables non-engineers such as architects to explicitly inspect the simulation results. Moreover, the TEPV facilitates architects using BIM as an interface in building design to visualize building thermal energy performance, enhancing their design production at the early design stages.

Research on wave forces attacking a vertical structure has been conducted worldwide. Morison's equation commonly used to describe the phenomenon of the action for offshore structures, while for nearshore structures Goda's equation is more reliable. Wave impact on vertical breakwaters is dangerous for vertical structures, both for walls and columns. Wave pressure distinguished for wave crest and wave trough, assumed to be distributed as a trapezoidal shape like along the vertical wall. The wave force consists of wave pressure on the front of the vertical wall and buoyancy, and uplift pressure in the vertical direction. In this research, a 2-dimensional physical modelling is carried out to observe the response of a vertical structure due to a wave action. Wave forces are measured using a flexi force sensor for both horizontal and vertical forces. Time series of incident wave and waveforces acting on the structure are recorded simultaneously and it clearly depicts the relation between them. The wave forces at the structure are linear to the height of the action waves. Periodical wave action results in the pushing forces at the structure to be higher than the pulling forces, as extra drift forces appear due to the shallow water wave condition.

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods. Plain Language Summary In the atmosphere, microphysics—the small‐scale processes affecting cloud and precipitation particles such as their growth by condensation, evaporation, and melting—is a critical part of Earth's weather and climate. Because it is impossible to simulate every cloud particle individually owing to their sheer number within even a small cloud, atmospheric models have to represent the evolution of particle populations statistically. There are critical gaps in knowledge of the microphysical processes that act on particles, especially for atmospheric ice particles because of their wide variety and intricacy of their shapes. The difficulty of representing cloud and precipitation particle populations and knowledge gaps in cloud processes both introduce important uncertainties into models that translate into uncertainty in weather forecasts and climate simulations, including climate change assessments. We discuss several specific challenges related to these problems. To improve how cloud and precipitation particle populations are represented, we advocate a “particle‐based” approach that addresses several limitations of traditional approaches and has recently gained traction as a tool for cloud modeling. Advances in observations, including laboratory studies, are argued to be essential for addressing gaps in knowledge of microphysical processes. We also advocate using statistical modeling tools to improve how these observations are used to constrain model microphysics. Finally, we discuss a hierarchical approach that combines the various pieces discussed in this article, providing a possible blueprint for accelerating progress in how microphysics is represented in cloud, weather, and climate models. Key Points Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertain Two critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physics A possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes

A laboratory‐based physical modeling environment has great potential to reproduce the complex physical hydrologic phenomena and understand the interactions of rainfall‐runoff processes in a visual and informative manner. In this study, a three‐layer physical modeling environment was developed to represent the dynamics of runoff production from the urban drainage system. The three‐layer physical modeling environment consists of a rainfall simulator (the 1st layer), a surface drainage network (the 2nd layer) and a subsurface rainwater pipe network (the 3rd layer). The degree of homogeneity of the spatial rainfall distribution produced by the rainfall simulator ranged from 78.6% to 84.0%, which lies within an acceptable range in the rainfall uniformity. The physical catchment model accurately represented the dynamic characteristics of the catchment response in a natural system associated with differing rainfall intensities within a controlled laboratory modeling environment, particularly the magnitude, volume, and shape of the discharge hydrographs. The three‐layer physical modeling setup was implemented to identify the effects of stormwater management facilities such as the rooftop detention storage and the permeable road pavement on the urban rainfall‐runoff responses. The runoff reduction rates for the peak discharge and the total discharge volume showed a strong linearity with the percentage coverages of the stormwater management facilities. Functional relationships between the variables were established to provide intuitive criteria for the runoff reduction rates for a specific coverage percentage of the rooftop detention storage and the permeable road pavement. These results demonstrate the effectiveness of the three‐layer physical setup for modeling rainfall‐runoff processes within the urban drainage network.

The contribution of sea‐ice biology and impact of Arctic warming on overall primary production in a Pan‐Arctic ocean model are investigated in a 57 year (1950–2006) simulation at coarse resolution using a simple ecosystem model. The ice biology model formally represents the growth and aggregation of micro algae into an ice‐water interface, nearly undisturbed by surface mixed layer dynamics. The importance of this so‐called ‘ice‐algae’ stems from their significant contribution to the total primary production (up to 50% depending on the locations, according to observations described in Gosselin et al. (1997). Simple 1D tests reveal that, depending on their initial biomass and light availability, ice algae can affect the temporal variation of surface nutrients, while they marginally impact the total primary production, or the long term position of the nutricline. The sea‐ice primary production is found in the model to be as high as 40% of the total primary production depending on the location and 7.5% for the whole Arctic. The modeled primary production of the ocean is negatively correlated to the September ice cover whereas the production in the ice is more weakly positively correlated. Because of the negative correlation between sea ice cover and pelagic primary production, the short term response to the continuing ice decline will be an increased total production as seen in the model, while the ice algae production would decline. Key Points Sea‐ice biology contributes significantly to the Arctic primary production Marginal impact of ice algae on total primary production or nutricline Overall production will increase with declining ice but longer term is uncertain

A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI‐ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low‐level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO2 over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO2 forcing, which nonetheless can be represented by a simple two‐layer model. Key Points An updated version of the Max Planck Institute for Meteorology Earth System Model (MPI‐ESM1.2) is presented The model includes both code corrections and parameterization improvements Despite this, the model maintains an equilibrium climate sensitivity, which rises with warming

Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives. Brain-inspired computing today encompasses a class of approaches ranging from using novel nano-devices for computation to research into large-scale neuromorphic architectures, such as TrueNorth, SpiNNaker, BrainScaleS, Tianjic, and Loihi. While implementation details differ, spiking neural networks -- sometimes referred to as the third generation of neural networks -- are the common abstraction used to model computation with such systems. Here we describe the second generation of the BrainScaleS neuromorphic architecture, emphasizing applications enabled by this architecture. It combines a custom analog accelerator core supporting the accelerated physical emulation of bio-inspired spiking neural network primitives with a tightly coupled digital processor and a digital event-routing network.