HVAC Systems Research Articles

Dimensionless correlations of indoor thermal stratification in a non-enclosed large-space building under heating and cooling conditions

Indoor thermal stratification is a prevailing phenomenon in large-space buildings, which poses great pressure on their energy consumption and indoor built environment. This paper revealed the mechanism of indoor thermal stratification in a non-enclosed large-space building under heating and cooling conditions. It can be abstracted as a joint effect of the cold and hot air inflow (i.e., HVAC system and outdoor air infiltration), which are respectively depicted by two newly-defined Archimedes numbers (i.e., Arc and Arh). Then, we developed dimensionless correlations between the indoor thermal stratification (quantified by the dimensionless factor of the vertical temperature profile, CT) and the two streams of air inflow (quantified by Arc and Arh). For application, the dimensionless correlations can be coupled with our previously proposed theoretical model to accurately predict the air infiltration rate in a large-space building. Moreover, the dimensionless analysis reveals a principle of indoor vertical temperature profile control to minimize the buoyancy driving force of air infiltration in large-space buildings: Achieving a vertically uniform indoor thermal environment under heating condition and an effectively stratified indoor thermal environment under cooling condition. These findings help to provide practical guidelines to minimize buoyancy-driven air infiltration in large-space buildings.

An interpretable feature selection method integrating ensemble models for chiller fault diagnosis

Timely diagnosis of chiller faults is crucial for indoor comfort and building energy saving with the number of sensors being minimized. To address this challenge and select the top important features for chiller diagnosis (FD), a novel feature selection method (FSM) with interpretability is proposed for chiller FD. The proposed method integrates four ensemble models, namely, random forest, XGBoost, LightGBM, and CatBoost in the feature selection process. A feature sets fusion strategy is designed based on SHAP analysis and a novel normalized feature weight calculation (NFWC) method. An interpretability analysis is performed using SHAP. The proposed NFWC method integrates the weights of four feature subsets based on SHAP values. Subsequently, the method of joint correlation analysis of feature weights is leveraged to eliminate redundant features. To evaluate the effectiveness of the proposed FSM, ten models, including traditional machine learning and deep learning, are used for FD performance validation. The experiment results show that the top ten features selected by the proposed FSM have obvious advantages compared to existing methods, especially in the early stage of FD. This work effectively eliminates 84% of redundant features for chiller FD, significantly improving the efficiency of HVAC system maintenance.

THE ROLE OF AGILE PROJECT MANAGEMENT IN DRIVING INNOVATION IN ENERGY-EFFICIENT HVAC SOLUTIONS

This review delves into the significance of Agile Project Management (APM) in fostering innovation within the realm of energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) solutions. As the global focus intensifies on sustainability and energy conservation, the HVAC industry stands at the forefront of addressing environmental challenges through innovative technologies. Agile methodologies, originally developed for software development, have transcended their conventional domains to revolutionize project management across various industries. This review explores how the application of Agile principles can catalyze innovation in energy-efficient HVAC solutions. Firstly, it delineates the core tenets of Agile Project Management, emphasizing iterative development, adaptive planning, and customer collaboration. By prioritizing flexibility and responsiveness to change, Agile methodologies empower HVAC project teams to swiftly adapt to evolving customer needs and technological advancements. This agility is particularly crucial in the HVAC sector, where rapid technological innovations necessitate quick iterations and constant feedback loops to refine energy-efficient solutions. Furthermore, the review elucidates how Agile frameworks such as Scrum and Kanban facilitate cross-functional collaboration and enhance communication within HVAC project teams. By fostering a collaborative environment that encourages transparency and knowledge sharing, Agile methodologies cultivate a culture of innovation wherein team members can experiment with novel ideas and swiftly pivot based on market feedback. Moreover, this review underscores the role of Agile Project Management in mitigating risks associated with innovation in energy-efficient HVAC solutions. Through frequent iterations and incremental delivery, Agile methodologies enable HVAC project teams to identify potential bottlenecks and address them proactively, thereby reducing project risks and enhancing overall project success rates. This review highlights the transformative potential of Agile Project Management in driving innovation within the energy-efficient HVAC industry. By embracing Agile methodologies, HVAC project teams can navigate the complexities of innovation more effectively, accelerate time-to-market for energy-efficient solutions, and ultimately contribute to the sustainable transformation of the HVAC sector.
 Keywords: Agile, Project Management, Innovation, Energy-Efficient, HVAC, Review.

LEVERAGING PROJECT MANAGEMENT TOOLS FOR ENERGY EFFICIENCY IN HVAC OPERATIONS: A PATH TO CLIMATE RESILIENCE

Efficient management of heating, ventilation, and air conditioning (HVAC) systems is paramount for mitigating energy consumption and enhancing climate resilience in modern infrastructure. This review explores the significance of leveraging project management tools to optimize energy efficiency within HVAC operations, thus fostering a pathway towards climate resilience. The integration of project management methodologies offers a systematic approach to address challenges associated with energy consumption in HVAC systems, thereby facilitating informed decision-making processes. Firstly, the review delves into the pressing need for energy conservation in HVAC operations amidst escalating concerns over climate change and its adverse impacts. The imperative to minimize energy consumption in buildings, particularly through HVAC systems, is highlighted as a pivotal step towards achieving climate resilience and sustainability goals. Subsequently, the review outlines the role of project management tools in orchestrating effective strategies to enhance energy efficiency within HVAC operations. By employing techniques such as project scheduling, risk management, and resource allocation, project managers can streamline the implementation of energy-efficient measures, thereby optimizing HVAC system performance and reducing carbon emissions. Moreover, the review underscores the importance of data analytics and technological advancements in augmenting the efficacy of project management tools for energy efficiency in HVAC operations. Leveraging real-time data monitoring, predictive analytics, and IoT-enabled devices enables proactive maintenance and continuous optimization of HVAC systems, thereby maximizing energy savings and bolstering climate resilience. Furthermore, the review elucidates the potential benefits and challenges associated with the adoption of project management tools for energy efficiency in HVAC operations. While improved cost-effectiveness, environmental sustainability, and operational performance emerge as primary benefits, challenges such as initial investment costs, technological complexities, and organizational inertia necessitate careful consideration. This review advocates for the integration of project management tools as a viable approach to foster energy efficiency in HVAC operations, thereby paving the way towards enhanced climate resilience and sustainability in built environments. By embracing innovative methodologies and technological solutions, stakeholders can mitigate energy consumption, reduce greenhouse gas emissions, and fortify infrastructure against the impacts of climate change.
 Keywords: HVAC, Climate Resilience, Project Management, Energy, Review.

Microclimate-Monitoring: Examining the Indoor Environment of Greek Museums and Historical Buildings in the Face of Climate Change

The preservation of cultural artifacts within museums and historical buildings requires control of microclimatic conditions, and the constantly evolving climate certainly poses a challenge to maintaining recommended conditions. Focused on the Archaeological Museum of Delphi and the Church of Acheiropoietos in Greece, our study evaluates the hygrothermal behavior of these buildings with a specific emphasis on the preservation of cultural heritage objects hosted there. An innovative approach to the real-time analysis of data is utilized, aiming to achieve a timely detection of extreme temperature and humidity levels. A one-year monitoring campaign was carried out to achieve a detailed assessment of the indoor climate in selected museums and historical buildings in Greece. The monitoring campaign was performed using dataloggers that were set to measure and record temperature (T) and relative humidity (RH) values hourly. The results allowed for the detection of extreme temperature and relative humidity values, pinpointing the time period that requires more attention. The museum’s heating, ventilation, and air conditioning (HVAC) systems provide temperature control for visitor comfort, but the temperature still rises in summer, highlighting the impact of external climate factors. The church’s lack of HVAC systems widens the temperature range compared to the museum, but significant hourly fluctuations are not observed, underlining the building’s high thermal mass and inertia. Both buildings demonstrate a significant response to changes in outdoor temperature, emphasizing the need for future adaptation to climate change. The HMRhs and PRD indices indicate minimal microclimate risk in both buildings for temperature and RH, reducing the probability of material damage. The church’s slightly higher HMRhs index values, attributed to relative humidity, increases susceptibility due to sensitive materials. Overall, the study highlights the importance of managing microclimatic conditions in historical buildings and proposes careful adaptations for the protection of cultural heritage.

Modelling building HVAC control strategies using a deep reinforcement learning approach

Heating, ventilation, and air-conditioning (HVAC) systems are responsible for a considerable proportion of total building energy consumption but are also vital for improved indoor temperature comfort, indoor air quality and well-being of building occupants. Thus, developing control strategies for HVAC systems is critical for the total life cycle of any building projects. Particularly, HVAC and building operations are not stationary but are filled with fuelled by environmental dynamisms and unexpected disruptions such as users' activities, weather conditions, occupancy rate, and operation of machinery and systems. This research aims to develop and propose a strategic control learning framework for HVAC systems using the deep reinforcement learning (DRL) approach. The results show that the proposed Phasic Policy Gradient (PPG) based method is more adaptive to changes in real building's environments. Notably, PPG performs better and more reliable than the conventional method for HVAC control optimization with about 2-14% in energy consumption reduction and indoor temperature comfort enhancement, along with a 66% faster convergence rate. Overall, our findings demonstrate that our proposed DRL approach is less resource intensive and much easier than the conventional approach in deriving solutions for HVAC control optimization driven by energy efficiency and indoor temperature comfort.

ASSESSING THE IMPACT OF CLIMATE CHANGE ON HVAC SYSTEM DESIGN AND PROJECT MANAGEMENT

In the face of rapidly changing climate conditions, the field of HVAC (Heating, Ventilation, and Air Conditioning) system design and project management is confronted with a myriad of challenges. This review delves into the critical importance of assessing the impact of climate change on these aspects and highlights key considerations for industry professionals. Climate change poses multifaceted challenges to HVAC system design and project management, primarily due to its influence on temperature patterns, extreme weather events, and evolving energy demands. Rising global temperatures necessitate innovative approaches to cooling systems, with a focus on energy efficiency and sustainable practices. Furthermore, the increasing frequency and intensity of heatwaves and extreme cold spells demand HVAC systems that can adapt dynamically to fluctuating environmental conditions. The review underscores the significance of incorporating climate projections and future-proofing strategies into HVAC system design. By leveraging predictive modeling techniques and climate data, engineers can optimize system performance, minimize energy consumption, and enhance indoor air quality amidst changing climatic conditions. Additionally, the integration of renewable energy sources such as solar and geothermal technologies is explored as a means to mitigate the environmental impact of HVAC systems and reduce reliance on fossil fuels. Effective project management in the context of climate change entails proactive risk assessment and mitigation strategies. Project managers must anticipate potential disruptions arising from extreme weather events, supply chain disruptions, and regulatory changes influenced by climate policies. Adopting adaptive project management methodologies enables stakeholders to respond swiftly to emerging challenges and ensure project resilience in the face of climatic uncertainties. This review underscores the imperative for industry stakeholders to embrace a proactive approach towards assessing the impact of climate change on HVAC system design and project management. By prioritizing resilience, sustainability, and innovation, professionals can navigate the complexities posed by climate change and contribute towards a more resilient built environment.
 Keywords: Climate Change, HVAC, Project Management, System Design, Review.

A novel probabilistic q-rung orthopair linguistic neutrosophic information-based method for rating nanoparticles in various sectors

The idea of probabilistic q-rung orthopair linguistic neutrosophic (P-QROLN) is one of the very few reliable tools in computational intelligence. This paper explores a significant breakthrough in nanotechnology, highlighting the introduction of nanoparticles with unique properties and applications that have transformed various industries. However, the complex nature of nanomaterials makes it challenging to select the most suitable nanoparticles for specific industrial needs. In this context, this research facilitate the evaluation of different nanoparticles in industrial applications. The proposed framework harnesses the power of neutrosophic logic to handle uncertainties and imprecise information inherent in nanoparticle selection. By integrating P-QROLN with AO, a comprehensive and flexible methodology is developed for assessing and ranking nanoparticles according to their suitability for specific industrial purposes. This research contributes to the advancement of nanoparticle selection techniques, offering industries a valuable tool for enhancing their product development processes and optimizing performance while minimizing risks. The effectiveness of the proposed framework are demonstrated through a real-world case study, highlighting its potential to revolutionize nanoparticle selection in HVAC (Heating, Ventilation, and Air Conditioning) industry. Finally, this study is crucial to enhance nanoparticle selection in industries, offering a sophisticated framework probabilistic q-rung orthopair linguistic neutrosophic quantification with an aggregation operator to meet the increasing demand for precise and informed decision-making.

Aerosol collection efficiency enhancement in HVAC systems through electrical field-Induced filter media polarization

Electrically enhanced media filters improve filtration efficiency in heating, ventilation, and air conditioning (HVAC) systems. Enhancement is accomplished by placing a uniquely formulated media material which can be polarized between arrays of wire electrodes. We explore the implementation of an electrically enhanced filtration system to both quantify the level of enhancement in filtration efficiency achievable in conditions representative of HVAC systems, and to determine the mechanism of collection enhancement. Measurements were carried out on a system comprised of a MERV 11 base filter, two arrays of upstream electrodes, and one array of downstream electrodes. Collection efficiency measurements reveal a strong dependency on the applied voltage across all examined face velocities. At a particle diameter of 0.3 μm, the collection efficiencies showed marked increases of 51.6%, 47.2%, and 30.7% from the zero-field level to a voltage of 10 kV at flow rates of 850, 1700, and 2550 m3 hr−1, respectively. Notably, this enhancement in collection efficiency is maintained even after particle loading on the filter. Charge state examinations revealed minimal changes in particle charge levels within the filter unit, demonstrating that the enhancement of filter performance is driven by dielectrophoretic motion and to a lesser extent electrophoretic motion.

BIM-based automated fault detection and diagnostics of HVAC systems in commercial buildings

In order to meet the growing demand for effective Automated Fault Detection and Diagnostics (AFDD) for HVAC systems, innovative approaches are needed to address limitations in data diversity and access to contextual information. This study introduces a methodology that leverages Building Information Modeling (BIM) to enhance the development of the AFDD model. Feature engineering techniques are utilized to generate dynamic BIM features, compensating for the lack of sensory and contextual data in Building Management Systems (BMS). By integrating AFDD analytics with BIM, a comprehensive digital twin of the facility is created, which enables facility managers to compare, reuse, and develop AFDD models for HVAC systems. The proposed methodology demonstrates the potential of leveraging BIM-based knowledge models to overcome the challenges associated with the limited sensor and contextual information availability by utilizing BIM for feature generation and, conversely, updating the BIM model with AFDD analytics.

PROJECT MANAGEMENT STRATEGIES FOR ACCELERATING ENERGY EFFICIENCY IN HVAC SYSTEMS AMIDST CLIMATE CHANGE

Amidst the escalating impacts of climate change, optimizing energy efficiency in Heating, Ventilation, and Air Conditioning (HVAC) systems emerges as a critical imperative. This review explores project management strategies aimed at accelerating energy efficiency in HVAC systems amidst climate change challenges. In response to climate change, the urgency to mitigate energy consumption in HVAC systems has intensified. Project management plays a pivotal role in orchestrating systematic approaches to address this challenge. Firstly, effective project management entails comprehensive planning. This involves conducting thorough energy audits and assessments to identify inefficiencies and prioritize improvement opportunities. Utilizing advanced technologies such as Building Energy Management Systems (BEMS) can enhance data collection and analysis for informed decision-making. Secondly, stakeholder engagement is essential for successful project implementation. Collaboration between building owners, facility managers, engineers, and contractors facilitates alignment of goals and resources. Clear communication channels and regular progress updates foster accountability and momentum throughout the project lifecycle. Thirdly, project management methodologies like Agile or Lean can streamline HVAC efficiency initiatives. Iterative development allows for continuous improvement, enabling teams to adapt to evolving requirements and incorporate feedback effectively. Moreover, risk management strategies are vital to anticipate and mitigate potential obstacles. Contingency plans for supply chain disruptions, regulatory changes, and unforeseen technical challenges ensure project resilience and minimize disruptions. Lastly, monitoring and evaluation mechanisms are indispensable for tracking project performance and verifying energy savings. Utilizing Key Performance Indicators (KPIs) and performance benchmarks enables stakeholders to measure success and adjust strategies as needed. Effective project management strategies are indispensable for accelerating energy efficiency in HVAC systems amidst climate change. By emphasizing comprehensive planning, stakeholder engagement, agile methodologies, risk management, and continuous evaluation, organizations can navigate challenges and realize substantial energy savings in HVAC operations.
 Keywords: Project Management, HVAC, Climate Change, Energy, Efficiency, Review.

Data-driven approach enabling post-operation evaluation of air conditioning performance regarding thermal conditions attained indoors

Data-driven approaches become more and more attractive in building performance analysis including performance assessment of heat, ventilation, and air conditioning (HVAC) systems. Their popularity is associated with the increasing availability of large amounts of high-quality building-related measurement data. The study is focused on a data-driven approach that enables post-operation evaluation of AC performance based on indoor air monitoring. It allows for the identification of the classifying solution which recognizes the monitoring data that is representative of thermal conditions during AC operation. The major novelty consists in proposing the classification performance criterion, which is F1w-Score (the weighted F1-Score), and the procedure of weights selection. The procedure utilizes Hellinger distance (H). It is applied to measure the similarity between the binary empirical probability distribution of T and RH (EPDtr), associated with AC operation, and the prediction of this distribution (EPDes), made by the classifier. The approach was tested on three years of data from continuous indoor air monitoring in the IT engineers' room equipped with an AC unit. The best weights of F1w-Score were wPPV = 0.85 and wTPR = 0.15. The best classifying solution was identified using F1w-Score. It involved: Logistic Regression as the classifier and feature vector being Fourier spectrum of 16 min long fragments of RH and T time series. The performance of the best classifying solution was high as indicated by F1w-Score = 0.89 ± 0.05. The corresponding H(EPDtr, EPDes) = 0.17 ± 0.04 pointed at the capability to identify the measurement data that characterizes indoor conditions during AC operation well. This work contributes to the development of data-driven approaches serving diagnostics of HVAC systems. It draws attention to the problem of defining the classification performance criterion which takes into account the ultimate, domain-specific use of the classification results.

Real-world implementation of a cloud-based MPC for HVAC control in educational buildings

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building’s heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively.

Hybrid Transformer for Anomaly Detection on Railway HVAC Systems Through Feature Ensemble of Spatial–Temporal with Multi-channel GADF Images

Hybrid Transformer for Anomaly Detection on Railway HVAC Systems Through Feature Ensemble of Spatial–Temporal with Multi-channel GADF Images

Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry

Large language models (LLMs) have shown human-level capabilities in solving various complex tasks. However, it is still unknown whether state-of-the-art LLMs master sufficient knowledge related to heating, ventilation and air conditioning (HVAC) systems. It will be inspiring if LLMs can think and learn like professionals in the HVAC industry. Hence, this study investigates the performance of LLMs on mastering the knowledge and skills related to the HVAC industry by letting them take the ASHRAE Certified HVAC Designer examination, an authoritative examination in the HVAC industry. Three key knowledge capabilities are explored: recall, analysis and application. Twelve representative LLMs are tested such as GPT-3.5, GPT-4 and LLaMA. According to the results, GPT-4 passes the ASHRAE Certified HVAC Designer examination with scores from 74 to 78, which is higher than about half of human examinees. Besides, GPT-3.5 passes the examination twice out of five times. It demonstrates that some LLMs such as GPT-4 and GPT-3.5 have great potential to assist or replace humans in designing and operating HVAC systems. However, they still make some mistakes sometimes due to the lack of knowledge, poor reasoning capabilities and unsatisfactory equation calculation abilities. Accordingly, four future research directions are proposed to reveal how to utilize and improve LLMs in the HVAC industry: teaching LLMs to use design tools or software in the HVAC industry, enabling LLMs to read and analyze the operational data from HVAC systems, developing tailored corpuses for the HVAC industry, and assessing the performance of LLMs in real-world HVAC design and operation scenarios.

Resilient Distributed Control Against False Data Injection Attacks for Demand Response

To maintain the power system balance, flexible load resources have been widely employed to provide operating reserves, which is named demand response (DR). The heating, ventilation, and air conditioning (HVAC) loads cover a high percentage of the total power consumption and have a significant regulation potential that deserves further investigation. To dispatch such dispersed HVACs, distributed control techniques are developed in the DR field due to their flexibility and scalability. However, the distributed DR system is vulnerable to potential cyber-attacks, i.e., false data injection (FDI) attacks, which may lead to DR failure. This paper proposes a resilient distributed controller to protect HVACs against FDI attacks. Firstly, an HVAC-based DR system is built by using distributed control to provide operating reserves for the power grid. Then, the adverse effect of FDI attacks is quantified with mathematical derivation. It is found that a small FDI attack can lead to severe power output deviations, which is a lethal problem for DR. On this basis, an FDI attack-resilient distributed controller is designed for HVACs so as to meet the grid's operating reserve requirements even under attacks. Moreover, the convergence of the proposed controller is proved based on the Laplace transform and final value theorem. Finally, case studies validate the effectiveness of the proposed resilient controller.

Stacking Revenues From Flexible DERs in Multi-Scale Markets Using Tri-Level Optimization

Rapid proliferation of flexible Distributed Energy Resources (DERs) as a result of Net Zero Emissions objectives entails a profound shift in the paradigm of local and national energy systems. Currently, DERs' simultaneous participation in multiple markets is generally restricted, which undermines their profitability. With the aim of increasing the number of business cases for them, a tri-level optimization problem that seeks the maximisation of revenues from DERs is proposed. The optimization problem considers simultaneous participation of different flexible DERs, such as, Electric Vehicles (EVs), Battery Energy Storage Systems (BESSs) and Heating, Ventilation and Air Conditioning (HVACs), in national and local markets. Markets are cleared sequentially, and the model is recast into a tractable single-level problem using its dual formulation and strong duality condition. Results from a case study based on the IEEE 14 bus transmission network, a realistic distribution network and SimBench dataset demonstrate the effectiveness of the proposed approach in increasing profits compared with a baseline scenario.

Impact of Hurricanes, Tropical Storms, and Coastal Extratropical Storms on Indoor Air VOC Concentrations

AbstractUnderstanding vapor intrusion (VI) temporal variability is key for the design of sampling strategies intended to assess reasonable maximum exposure of indoor air concentrations of volatile organic compounds (VOCs) as well as risk evaluation and mitigation planning. VI temporal variability has previously been shown to be dependent on the complex interactions of multiple independent variables—meteorological, hydrogeological, and human behavioral. Several meteorological variables, including barometric pressure, wind speed, and rainfall, are linked during tropical and extratropical storm events. High‐frequency meteorological and indoor VOC data from a series of seven tropical storms and four extratropical storms were collected at a single industrial building with multiple heating, ventilation, and air conditioning (HVAC) zones. The storms and sampling zones showed a variety of effects on trichloroethylene (TCE) concentrations in indoor air. In one zone (supply room), increases in TCE concentrations often, but not always, closely coincided with decreasing barometric pressure, sustained wind speeds over 32 km/h (20 mph), and differential pressures indicating subslab to indoor flow. A second zone, in a restroom, did not show a consistent pattern of temporal correlation between meteorological factors and indoor air concentrations. While peak indoor air concentrations may be associated with the passage of cyclonic storms at some sampling locations, this does not appear to be generalizable to all sampling locations. The observed increase in indoor air concentration potentially attributable to these storms is typically less than an order of magnitude and the duration ranges from a day to a week.

Understanding speech in everyday conditions—Effects of room acoustics and noise

Understanding speech in noisy, indoor settings is a considerable challenge for listeners. Room acoustics, such as reverberation and heating, ventilation, and air conditioning (HVAC) noise, all contribute to such challenges. In the current study, the effects of different backgrounds were examined in anechoic versus reverberant conditions at two different signal-to-noise ratios (SNRs) were examined. 60 monolingual American English listeners transcribed spoken English sentences under different noisy conditions. Overall, we tested two acoustic conditions: anechoic and reverberant, reverberation time = 1.5 s, with four speech conditions (target speech only, or combined with HVAC noise, two-talker babble, or both) and two different SNRs (0 dB and -3 dB). Both the target and babble speech were female voices speaking in English. Complex backgrounds resulted in decreased performance, and the detrimental effects of such masking were accentuated by reverberation and lower SNR. This study serves as a basis for future research exploring the effects of room acoustics on speech intelligibility to gain a more wholistic understanding of speech perception under realistic conditions.

Is line-source modeling suitable for ultraviolet light application in an air cleaner duct?

Ultraviolet-C (UV-C) germicidal light can effectively inactivate airborne pathogens and mitigate the transmission of infectious diseases. As the application of UV-C for disinfection gains popularity, practical estimation of UV irradiance is essential in determining the UV fluence (dose) and designing tubular UV lamp configurations for indoor air treatment. It is generally understood that the inverse square (∼1/d2) law (i.e., irradiance is proportional to the inverse square of the distance) applies well to point light sources. However, there has been a recognition that the ∼1/d2 law does not work well for tubular light sources in the commonly defined near-field applications where the UV source is relatively close to the treated air. Therefore, practical near-field irradiation estimation is needed for designing portable air cleaners and heating, ventilation, and air conditioning (HVAC) ducts with built-in UV light bulbs. This research investigated UV-C light irradiance from tubular (L = 0.9 m) light bulbs at near distances inside an air cleaner prototype duct under three power output (1-, 4-, and 8-bulb) scenarios and conducted theoretical estimation based on a line-source irradiation model. Similarly sized visible fluorescent bulbs were used as a reference. The data were fitted on both ∼1/d2 and ∼1/d correlation of irradiance with distance. Both measured and line source estimated data fit better (i.e., evaluated by R-square, standard errors, root mean squared errors) with the ∼1/d than the ∼1/d2 relationship in the near distance. Although the differences between the measured and the modeled were observed, the pattern of light distribution generally follows an inverse relationship (∼1/d) with distances (d) shorter than two tubular bulb lengths (d < 2L). The pattern applies to both UV and visible light tested in this study. It is recommended that the inverse (∼1/d) correlation be used for near-distance estimation of light distribution, especially for disinfection purposes in air ducting for indoor air quality improvement and airborne disease mitigation.