This systematic review provides a comprehensive analysis of the advancements, challenges, and future directions of smart HVAC systems by examining 125 peer-reviewed articles following the PRISMA methodology. The study focuses on five critical areas: IoT-enabled HVAC systems, machine learning algorithms for predictive maintenance, renewable energy integration, data privacy and security concerns, and the economic feasibility of implementation. The findings highlight that IoT-enabled HVAC systems significantly enhance energy efficiency by leveraging real-time data from interconnected sensors, leading to energy savings between 20% and 40% through adaptive heating, cooling, and ventilation strategies. Additionally, machine learning algorithms in predictive maintenance play a crucial role in reducing system failures, cutting unscheduled maintenance costs by up to 40%, and improving energy efficiency by 15% to 25% through automated fault detection and optimized maintenance scheduling. Furthermore, renewable energy integration in HVAC systems, particularly hybrid solutions combining solar, geothermal, and wind energy, has demonstrated remarkable potential, with some systems achieving energy savings of up to 60%. However, challenges such as high upfront installation costs, the need for advanced energy storage solutions, and infrastructure limitations continue to hinder widespread adoption. The review also identifies data privacy and security vulnerabilities as a major concern in IoT and AI-powered HVAC systems. 20 reviewed articles highlight risks associated with data transmission, unauthorized access, and cyber threats, emphasizing the need for robust encryption techniques, blockchain-based security, and AI-driven authentication mechanisms to enhance protection and user trust. Moreover, the economic feasibility of smart HVAC solutions remains a key factor influencing adoption. While these systems require higher initial investments, findings from 15 reviewed studies indicate that the return on investment (ROI) typically falls within five to seven years, particularly for large-scale commercial applications where energy cost savings and predictive maintenance benefits accumulate more rapidly. Policy incentives, tax rebates, and government subsidies have also been recognized as crucial enablers for accelerating adoption, making smart HVAC technologies more accessible for residential and small business users. This review synthesizes existing knowledge, evaluates emerging trends, and identifies persistent barriers in smart HVAC technologies. The findings provide valuable insights for researchers, industry stakeholders, and policymakers to develop scalable, cost-effective, and energy-efficient HVAC solutions. Future research should focus on enhancing system interoperability, improving data security frameworks, and developing innovative financing models to facilitate the transition toward more sustainable and intelligent HVAC solutions in the built environment.

This study explores the integration of Artificial Intelligence (AI) in aggregate planning across diverse industrial sectors, with a particular focus on identifying cross-sectoral trends, implementation challenges, and performance outcomes. Drawing upon an in-depth comparative analysis of eight real-world case studies, this research investigates how AI-driven tools such as machine learning, deep learning, reinforcement learning, fuzzy logic, and heuristic optimization are transforming demand forecasting, inventory management, production scheduling, and resource allocation in sectors including manufacturing, retail, automotive, pharmaceutical, and food industries. The study reveals that while AI enhances forecast accuracy, operational agility, and strategic decision-making, its effectiveness is often mediated by organizational readiness, regulatory environments, and the maturity of digital infrastructure. Resistance to adoption, lack of interpretability, and fragmented data systems were noted as common barriers. In contrast, firms with integrated data ecosystems, leadership support, and workforce upskilling strategies demonstrated greater success in embedding AI into their planning processes. The findings highlight the sector-specific nuances of AI implementation and underline the urgent need for a standardized cross-industry framework to guide the scalable and ethical adoption of AI in aggregate planning. This research contributes valuable insights to both academia and industry by bridging theoretical models with practical applications and by emphasizing the human, technological, and strategic factors critical to unlocking AI’s full potential in supply chain and operations management.

Combustion involves the study of multiphysics phenomena that includes fluid and chemical kinetics, chemical reactions and complex nonlinear processes across various time and space scales. Accurate simulation of combustion is essential for designing energy conversion systems. Nonetheless, due to its multiscale, multiphysics nature, simulating these systems at full resolution is typically difficult. The massive and complex data generated from experiments and simulations, particularly in turbulent combustion, presents both a challenge and a research opportunity for advancing combustion studies. Machine learning facilitates data-driven techniques to manage the substantial amount of combustion data that is either obtained through experiments or simulations, and thereby can find the hidden patterns underlying these data. Alternatively, machine learning models can be useful to make predictions with comparable accuracy to existing models, while reducing computational costs significantly. In this era of big data, machine learning is rapidly evolving, offering promising opportunities to explore its integration with combustion research. This work provides an in-depth overview of machine learning applications in turbulent combustion modeling and presents the application of machine learning models: Decision Trees (DT) and Random Forests (RF), for the spatio-temporal prediction of plasma-assisted ignition kernels, based on the initial degree of ionization, with model validations against DNS data. The results demonstrate that properly trained machine learning models can accurately predict the spatio-temporal ignition kernel profile based on the initial energy deposition and distribution.

The United States stands at a pivotal juncture in its energy transition, with green hydrogen emerging as a critical enabler for decarbonizing hard-to-abate sectors such as heavy industry, long-haul transportation, and long-duration energy storage. This journal review paper provides a comprehensive analysis of the U.S. perspective on the burgeoning green hydrogen economy, synthesizing current advancements, policy landscapes, and socio-economic implications. The review first examines the rapid technological progress underpinning the hydrogen value chain. It explores innovations in electrolysis, including declining costs for Proton Exchange Membrane (PEM) and Alkaline technologies, and the potential of emerging methods like solar-driven water splitting. Advancements in storage, from high-pressure tanks to geological solutions, and transport, via repurposed pipelines and liquid organic hydrogen carriers (LOHCs), are assessed. The paper also details the expanding applications of green hydrogen, from fertilizer production and refining to fuel cell electric vehicles and power generation. While prior studies have examined hydrogen technology or policy in isolation, this review uniquely integrates technological, regulatory, and socio-economic perspectives to outline a cohesive national roadmap for the U.S. hydrogen economy. It conducts a comparative review of national strategies, highlighting the transformative impact of the U.S. Inflation Reduction Act (IRA), particularly its hydrogen production tax credit (45V), which has positioned the nation as a highly competitive market. This is contrasted with the European Union’s targeted demand-side policies and the ambitious export-oriented strategies of nations in the Middle East and North Africa (MENA) region and the Global South, analyzing the implications for global trade and U.S. leadership. The paper concludes by identifying significant knowledge gaps and future research directions. These include the need for robust methodologies to accurately account for emissions across the hydrogen lifecycle (ensuring additionality, temporal, and geographic matching for grid-powered electrolysis), optimizing integrated regional infrastructure, developing standardized safety protocols, and formulating best practices for community engagement and equitable benefit-sharing.

Electrohydrodynamic effects can significantly alter transport processes in reacting flows, even when the plasma is weakly ionized. However, predictive modeling of such plasma–flame interactions remains challenging due to the multiscale coupling among charge transport, fluid motion, and chemical kinetics. This study presents a charge-transport closure model to investigate electrohydrodynamic influences on laminar non-premixed flames. A two-dimensional computational framework in cylindrical coordinates is used to simulate plasma-assisted methane–air diffusion flames under weak electric-field conditions representative of practical combustion environments. To represent plasma–flow coupling in a computationally feasible yet physically consistent manner, a charge-transport formulation based on the drift–diffusion approximation is employed. The model solves transport equations for representative positive and negative charge carriers coupled with Poisson’s equation for the electric potential to obtain a self-consistent electric field. This formulation assumes a weakly ionized regime for low-temperature plasma-assisted combustion, in which neutral species dominate the mass and momentum transport, while ionization chemistry is simplified and charge transport primarily influences the flow through electrohydrodynamic body forces and Joule heating. Assuming a weak electric field, the steady flamelet model is applied, in which plasma effects primarily influence scalar transport and local thermal balance rather than inducing significant bulk ionization dynamics. The governing equations are discretized using a high-order compact finite-difference scheme that provides improved resolution of steep gradients in temperature, species concentration, and space-charge density near thin reaction zones. The canonical laminar flame model configuration was validated using the established laminar methane–air diffusion flame benchmark, and steady-state spatial profiles of key transport properties were evaluated. Two-dimensional analysis identified the discharge coupling location as an important factor. The application of discharge in the fuel-air mixing region leads to a clear restructuring of the flame. When the discharge is activated, electrohydrodynamic forcing and ion-driven momentum transfer produce a highly localized, columnar flame with sharp gradients and a confined reaction zone. Compared with the baseline case, the plasma-assisted flame localizes the OH-rich reaction zone, confines the high-temperature region into a narrow column, and enhances downstream H₂O formation.

Accurately capturing the interaction between fluid motion, chemical reactions, and electric fields is essential for understanding flame behavior in advanced combustion systems relevant to propulsion, energy conversion, and emissions control. In this work, we present a two-dimensional computational framework in cylindrical coordinates for simulating laminar non-premixed flames with electrostatic coupling representative of weakly ionized plasma-assisted combustion environments. The solver employs a high-order compact finite difference scheme for spatial discretization, enabling improved resolution of steep gradients commonly observed in reacting flows, together with a fourth-order temporal integration method to ensure numerical stability and accuracy. Detailed combustion chemistry is incorporated through a steady flamelet formulation, providing an efficient yet robust description of chemical kinetics within the CFD framework. To account for electric field-assisted effects in a simplified and physically consistent manner, the governing equations are extended to include Poisson’s equation for the electric potential and transport equations for one positively and one negatively charged species, assuming a weakly ionized regime in which neutral species dominate the flow dynamics. Electric field effects are modeled by solving species continuity equations for one positively charged and one negatively charged species, coupled with Poisson’s equation for the electric potential to obtain a self-consistent electric field. The resulting electrostatic and plasma-induced effects enter the governing equations through explicit source terms in the momentum and energy equations, accounting for electric body forces and Joule heating for weakly ionized plasma. A systematic parametric study is conducted using a canonical co-flow methane–air diffusion flame to examine the influence of flow conditions and combustor geometry. The results show that plasma forcing leads to a noticeable increase in flame length, as identified by extended OH and CH radical distributions. This behavior is attributed to a combination of electric-field-driven charged species drift, enhanced convective transport, and localized Joule heating, which collectively modify scalar transport and delay radical recombination along the axial direction. Overall, the proposed high-order framework provides a validated and computationally efficient tool for high-resolution simulation of chemically reacting flows with weak plasma coupling, offering new insight into the role of electric fields in laminar non-premixed flame dynamics.