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Recent Submissions
A Computational Fluid Dynamics Model to Simulate Wood Combustion
Banagiri, Shrikar (Virginia Tech, 2025-04-28)
Residential wood stoves and wood heaters are being used by nearly 3 billion people worldwide. In the United States, residential wood heaters are used by only 9 % of the households. However, according to the US EPA, they contribute to around 7 % of the cumulative PM2.5 emissions. Furthermore, firebrand generation from wood combustion is the primary mechanism for wild land fire spread. In order to predict various performance parameters of residential heaters and predict wood degradation behavior for fire safety, a comprehensive understanding of wood combustion is necessary. Wood combustion involves various coupled phases, namely, dehydration, pyrolysis, char oxidation, and gas phase combustion. These phases involve several heat transfer processes including radiation from the surrounding flames and surfaces onto the wood surface, convection with the bulk gas flow, radiation losses from the wood surface, and energy generation due to the exothermic char oxidation reactions. Along with these heat transfer processes, several mass transfer processes including production of water vapor (from dehydration), production of volatile combustion gases (from pyrolysis), consumption of surface oxygen (from char oxidation), and production of CO, CO2 (from char oxidation) are involved. These processes are interdependent and may occur simultaneously, thus making them strongly coupled.
The aim of this dissertation is to formulate a comprehensive kinetics based numerical model to simulate wood combustion. To this end, a reduced three-step wood pyrolysis mechanism was developed using several microscale experiments and model-fitting algorithms. The reduced mechanism accounts for both the solid phase heat flow and the gas phase heat release. For char oxidation, a conjugate heat transfer driven UDF model was developed in ANSYS Fluent. This char oxidation model was validated against wind tunnel experiments of smoldering firebrands at various wind speeds. The pyrolysis and char oxidation models were coupled together with a gas phase methane combustion mechanism and implemented into the UDF framework. This coupled wood combustion model was validated against mesoscale cone calorimeter experiments with different sample sizes. The validated wood combustion model was implemented in a top-lit updraft (TLUD) wood stove being designed by the University of Alabama (UA). The TLUD wood stove simulations were validated against the steady-state gasification rate and thermocouple temperature data collected for different operating conditions. Using the TLUD wood stove simulations, soot and CO emissions at various locations in the combustor were also quantified. The wood combustion model formulated in this dissertation can be readily applicable to various fire safety and clean energy applications.
Performance Evaluation of Numerical Weather Prediction Models in Forecasting Rainfall Events in Kerala, India
Nitha, V.; Pramada, S. K.; Praseed, N. S.; Sridhar, Venkataramana (MDPI, 2025-03-25)
Heavy rainfall events are the main cause of flooding, especially in regions like Kerala, India. Kerala is vulnerable to extreme weather due to its geographical location in the Western Ghats. Accurate forecasting of rainfall events is essential for minimizing the impact of floods on life, infrastructure, and agriculture. For accurate forecasting of heavy rainfall events in this region, region-specific evaluations of NWP model performance are very important. This study evaluated the performance of six Numerical Weather Prediction (NWP) models—NCEP, NCMRWF, ECMWF, CMA, UKMO, and JMA—in forecasting heavy rainfall events in Kerala. A comprehensive assessment of these models was performed using traditional performance metrics, categorical precipitation metrics, and Fractional Skill Scores (FSSs) across different forecast lead times. FSSs were calculated for different rainfall thresholds (100 mm, 50 mm, 5 mm). The results reveal that all models captured rainfall patterns well for the lower threshold of 5 mm, but most of the models struggled to accurately forecast heavy rainfall, especially for longer lead times. JMA performed well overall in most of the metrics except False Alarm Ratio (FAR). It showed high FAR, which revealed that it may predict false rainfall events. ECMWF demonstrated consistent performance. NCEP and UKMO performed moderately well. CMA, and NCMRWF had the lowest accuracy either due to more errors or biases. The findings underscore the trade-offs in model performance, suggesting that model selection should depend on the accuracy required or rainfall event prediction capability. This study recommends the use of Multi-Model Ensembles (MME) to improve forecasting accuracy, integrate the strengths of the best-performing models, and reduce biases. Future research can also focus on expanding observational networks and employing advanced data assimilation techniques for more reliable predictions, particularly in regions with complex terrain such as Kerala.
A Quantum Key Distribution Routing Scheme for a Zero-Trust QKD Network System: A Moving Target Defense Approach
Ghourab, Esraa M.; Azab, Mohamed; Gračanin, Denis (MDPI, 2025-03-26)
Quantum key distribution (QKD), a key application of quantum information technology and “one-time pad” (OTP) encryption, enables secure key exchange with information-theoretic security, meaning its security is grounded in the laws of physics rather than computational assumptions. However, in QKD networks, achieving long-distance communication often requires trusted relays to mitigate channel losses. This reliance introduces significant challenges, including vulnerabilities to compromised relays and the high costs of infrastructure, which hinder widespread deployment. To address these limitations, we propose a zero-trust spatiotemporal diversification framework for multipath–multi-key distribution. The proposed approach enhances the security of end-to-end key distribution by dynamically shuffling key exchange routes, enabling secure multipath key distribution. Furthermore, it incorporates a dynamic adaptive path recovery mechanism that leverages a recursive penalty model to identify and exclude suspicious or compromised relay nodes. To validate this framework, we conducted extensive simulations and compared its performance against established multipath QKD methods. The results demonstrate that the proposed approach achieves a 97.22% lower attack success rate with 20% attacker pervasiveness and a 91.42% reduction in the attack success rate for single key transmission. The total security percentage improves by 35% under 20% attacker pervasiveness, and security enhancement reaches 79.6% when increasing QKD pairs. Additionally, the proposed scheme exhibits an 86.04% improvement in defense against interception and nearly doubles the key distribution success rate compared to traditional methods. The results demonstrate that the proposed approach significantly improves both security robustness and efficiency, underscoring its potential to advance the practical deployment of QKD networks.
Reconsidering the Social in Language Learning: A State of the Science and an Agenda for Future Research in Variationist SLA
Gudmestad, Aarnes; Kanwit, Matthew (MDPI, 2025-03-28)
The current paper offers a critical reflection on the role of the social dimension of the second language (L2) development of sociolinguistic competence. We center our discussion of L2 sociolinguistic competence on variationist approaches to second language acquisition (SLA) and the study of variable structures. We first introduce the framework of variationist SLA and offer a brief overview of some of the social, and more broadly extralinguistic, factors that have been investigated in this line of inquiry. We then discuss the three waves of variationist sociolinguistics and various social factors that have been examined in other socially oriented approaches to SLA. By reflecting on these bodies of research, our goal is to identify how the insights from this work (i.e., research couched in the second and third waves of variationist sociolinguistics and in other socially oriented approaches to SLA) could be extended to the study of L2 sociolinguistic competence. We argue that greater attention to the social nature of language in variationist SLA is needed in order to more fully understand the L2 development of variable structures.
SYNGAP1 Syndrome and the Brain Gene Registry
Greco, Melissa R.; Chatterjee, Maya; Taylor, Alexa M.; Gropman, Andrea L. (MDPI, 2025-03-30)
Background: The human brain relies on complex synaptic communication regulated by key genes such as SYNGAP1. SYNGAP1 encodes the GTPase-Activating Protein (SYNGAP), a critical synaptic plasticity and neuronal excitability regulator. Impaired SYNGAP1 function leads to neurodevelopmental disorders (NDDs) characterized by intellectual disability (ID), epilepsy, and behavioral abnormalities. These variants disrupt Ras signaling, altering AMPA receptor transport and synaptic plasticity and contributing to cognitive and motor difficulties. Despite advancements, challenges remain in defining genotype–phenotype correlations and distinguishing SYNGAP1-related disorders from other NDDs, which could improve underdiagnosis and misdiagnosis. Brain Gene Registry: The Brain Gene Registry (BGR) was established as a collaborative initiative, consolidating genomic and phenotypic data across multiple research centers. This database allows for extensive analyses, facilitating improved diagnostic accuracy, earlier interventions, and targeted therapeutic strategies. The BGR enhances our understanding of rare genetic conditions and is critical for advancing research on SYNGAP1-related disorders. Conclusions: While no FDA-approved treatments exist for SYNGAP1-related disorders, several therapeutic approaches are being investigated. These include taurine supplementation, ketogenic diets, and molecular strategies such as antisense oligonucleotide therapy to restore SYNGAP1 expression. Behavioral and rehabilitative interventions remain key for managing developmental and cognitive symptoms. Advancing research through initiatives like the BGR is crucial for refining genotype–phenotype associations and developing precision medicine approaches. A comprehensive understanding of SYNGAP1-related disorders will improve clinical outcomes and patient care, underscoring the need for continued interdisciplinary collaboration in neurodevelopmental genetics.