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Computational fluid dynamics (CFD) enables modeling, analysis, and visualization of phenomena that would usually be impossible or extremely expensive to measure experimentally.
Utilities aiming for higher fuel enrichment for power uprates or extended operation times before refueling must conduct a new dose analysis. Current conservative dose estimation standards may cause utilities to exceed regulatory limits for proposed increased fuel enrichment. A more accurate modeling of doses from reactor accidents can lower these conservative assumptions. Prescott et al. (2022) demonstrated that the Event Modeling Risk Assessment using Linked Diagrams (EMRALD) software tool, developed at Idaho National Laboratory (INL), can be coupled with the Modular Accident Analysis Program (MAAP5) for dynamic accident analysis in reactor plants. EMRALD forms models of potential accident scenarios, while MAAP5 simulates the accident progression and dose consequences. By integrating these software tools with utility-specific data, a more precise estimation of dose consequences from plant accidents can be achieved. Preliminary findings indicate that EMRALD provides accurate mean core damage frequencies for generalized accident scenarios. Future work includes expanding the model to account for plant-specific data and mitigation factors.
The ongoing changes in the generation resource mix, driven by the rapid adoption of inverter-based resources (IBR) as well as the early retirement of synchronous generators, are bringing new challenges to the planning and operation of bulk electric power systems. Consequently, there is an increasing need to understand, design, and quantify the reliability service provision from IBRs by performing integrated scheduling and dynamic simulations. However, test systems that have consistent scheduling and dynamic models rarely exist largely because of the decoupled nature of the two simulations on a synchronous generator-dominated system. This paper develops the dynamic model of a reduced Western Electricity Coordinating Council (WECC) system. In conjunction with the existing scheduling model, it is suitable for integrating scheduling and dynamic simulations. The reduced 240-bus WECC model reflects the generation resource mix of the Western Interconnection as of 2018. Moreover, the developed dynamic model is validated against field frequency events measured by FNET/GridEye and preserves the dominant inter-area oscillation mode in WECC.
Understanding the dynamics of acute HIV infection can offer valuable insights into the early stages of viral behavior, potentially helping uncover various aspects of HIV pathogenesis. The standard viral dynamics model explains HIV viral dynamics during acute infection reasonably well. However, the model makes simplifying assumptions, neglecting some aspects of HIV infection. For instance, in the standard model, target cells are infected by a single HIV virion. Yet, cellular multiplicity of infection (MOI) may have considerable effects in pathogenesis and viral evolution. Further, when using the standard model, we take constant infected cell death rates, simplifying the dynamic immune responses. Here, we use four models—1) the standard viral dynamics model, 2) an alternate model incorporating cellular MOI, 3) a model assuming density-dependent death rate of infected cells and 4) a model combining (2) and (3)—to investigate acute infection dynamics in 43 people living with HIV very early after HIV exposure. We find that all models qualitatively describe the data, but none of the tested models is by itself the best to capture different kinds of heterogeneity. Instead, different models describe differing features of the dynamics more accurately. For example, while the standard viral dynamics model may be the most parsimonious across study participants by the corrected Akaike Information Criterion (AICc), we find that viral peaks are better explained by a model allowing for cellular MOI, using a linear regression analysis as analyzed by R 2 . These results suggest that heterogeneity in within-host viral dynamics cannot be captured by a single model. Depending on the specific aspect of interest, a corresponding model should be employed.
Dynamic model reduction in power systems is necessary for improving computational efficiency. Traditional model reduction using linearized models or offline analysis would not be adequate to capture power system dynamic behaviors, especially the new mix of intermittent generation and intelligent consumption makes the power system more dynamic and non-linear. Real time dynamic model reduction emerges as an important need. This paper explores the use of clustering techniques to analyze real-time phasor measurements to determine generator groups and representative generators for dynamic model reduction. Two clustering techniques – graph clustering and evolutionary clustering – are studied in this paper. Various implementations of these techniques are compared and also compared with a previously developed Singular Value Decomposition (SVD)-based dynamic model reduction approach. Various methods exhibit different levels of accuracy when comparing the reduced model simulation against the original model. But some of them are consistently accurate. From this comparative perspective, this paper provides a good reference point for practical implementations.
Firebrand burning is a complex phenomenon that is influenced by several parameters which are difficult to fully explore experimentally. Computational fluid dynamics models capable of predicting local quantities are essential for accurate prediction of char oxidation in firebrands. This article presents a computational fluid dynamics model to estimate firebrand mass loss, diameter change, and surface temperature during char oxidation. The model was validated using previously conducted wind tunnel experiments. These experiments were conducted for firebrands of two different aspect ratios, which were arranged in three different configurations (single, horizontal array, and vertical array), and for four different wind speeds (0.5, 1, 1.5, and 2 m/s). The computational fluid dynamics results were compared with a previous 1 D model. In all the test cases, the computational fluid dynamics model predicted the physical phenomena with significantly improved accuracy compared to a 1 D model. The char oxidation model presented in this article can be coupled with other models to study firebrand generation and trajectory, biomass pyrolysis, fluidized bed reactors, and coal combustion.
Two computational approaches are proposed in the paper to model dynamic fracture opening by explosive products. The first method assumes that the fractures may be modeled using flow elements embedded along the mesh lines. This method models crack opening in a straightforward way by splitting the nodes of the computational grid. It can account for crack branching; however, the crack directions are constrained by existing mesh faces, which may lead to mesh dependence. Also, the stress in flow elements is calculated explicitly separate from the surrounding solid elements that can impose additional limits on the time step stability condition for explicit integration. The second approach uses embedded flow elements to model the cracks. Typical thickness of the cracks is much smaller than the element size. Therefore, gas pressure in the cracks is assumed to be in stress equilibrium with the element stress. To achieve this, the crack thickness and the state of the gas is updated simultaneously with the state of the solid element which contains the crack. Therefore, the time step is controlled by the explicit solver applied for the solid and does not depend on the thickness of the crack. In conclusion, the main disadvantage of the second approach is due to the complexity of modeling multiple intersecting cracks, which go through the same element. We discuss the areas of possible applications of these 2 methods and the ways to improve and enhance them for future practical applications.
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Fusion dynamics and the onset of quasi-fission in reactions, leading to production of superheavy nuclei are investigated using the constrained molecular dynamics model. Constraints on the parameters of the nuclear equation of state are derived from experimental fusion probabilities. Here, the obtained constraint on the modulus of incompressibility of nuclear matter K 0 = 240 –260 MeV is consistent with the results of previous study using the Boltzmann-Uehling-Uhlenbeck equation and also with constraints derived using the recent neutron star binary collision event GW170817. Unlike the modulus of incompressibility of symmetric nuclear matter, the stiffness of the density-dependence of symmetry energy influences the fusion probability only weakly.
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This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defned based on the characteristic spatial variations of a meteorological feld or on the remote circulation response to a known forcing, LSMPs are defned relative to the occurrence of a specifc phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary infuence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to defnitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many diferent defnitions of “extreme” are in use, making it difcult to directly compare diferent studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identifed and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.
Inverter-based resource (IBR) models are necessary to analyze modern power system stability and create effective control strategies. Modeling IBRs in converter-rich power systems is crucial, yet challenging due to the lack of commercial information on converter topologies and control parameters. This paper proposes novel convolutional neural network (CNN)–based data-driven techniques for modeling IBRs, addressing adaptability and proprietary concerns without requiring internal system physics knowledge. The proposed method is tested using real grid-tied commercial IBR transient data and demonstrates effectiveness and accuracy. Furthermore, the developed modeling approach is integrated and implemented in the open-source power distribution simulation and analysis tool, GridLAB-D, to illustrate the potentiality of dynamic analysis of large-scale power systems with high IBRs.
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