Mass Airflow Research Articles

Prognostic value of the post-exercise heart rate recovery and BHDE-index in chronic obstructive pulmonary disease

BackgroundThe BODE index, consisting of body mass index (B), airflow obstruction (O), dyspnea score (D), and exercise capacity (E), can predict outcomes in COPD. However, when spirometry was restricted to prevent cross-infection such as COVID-19 pandemic, a modified index would be needed. Because cardiovascular dysfunction is associated with poor clinical outcomes in COPD, we conducted a novel BHDE-index by replacing spirometry with post-exercise heart rate recovery (HRR, H) and evaluated its predictive performance in this observational study.MethodsFrom January 2019 to December 2019, enrolled patients were analyzed as a derivation cohort for the setup of the model. This model was verified in another group of patients generated between January 2020 and December 2020, as the validation cohort. The post exercise HRR was defined as the difference of heart rate immediately after and 1 min after test cessation.ResultsA total of 447 patients with COPD were enrolled. Patients with abnormal HRR were older, with more severe airway obstruction, severe airway symptoms, faster resting heart rate, shorter 6-min walk distance and higher frequency of severe acute exacerbation in previous one year. The prediction performance of the BHDE-index for one-year severe COPD exacerbation was similar to that of the BODE-index in both the derivation and validation groups [area under the receiver operating characteristic curve (AUROC) 0.76 vs. 0.75, p = 0.369; AUROC 0.74 vs. 0.79, p = 0.05]. The prediction performance for 1 year mortality was also similar between BHDE-index and BODE-index in both cohorts [AUROC 0.80 vs. 0.77, p = 0.564; 0.76 vs. 0.70, p = 0.234]. Univariate and multivariate analyses also showed that the BHDE-index was an independent and important predictor of annual severe COPD exacerbation in the derivation and validation cohorts.ConclusionsThe BHDE-index is a good and easy-to-perform prediction model for the risk of severe acute exacerbation and 1-year mortality in COPD wherever spirometry results are unavailable.

Spatial and temporal distribution of HCHO and its pollution sources based on satellite remote sensing: a case study of the Yangtze River Economic Belt

Abstract In recent years, with the acceleration of industrialization and the expansion of urban scale, air pollution including formaldehyde (HCHO) becomes more and more serious. In order to study HCHO pollution in the Yangtze River Economic Belt (YEB), the temporal and spatial evolution of atmospheric HCHO and its influencing factors were analyzed by using the Ozone Monitoring Instrument (OMI) during 2012–2021. The results showed that the concentration of YEB HCHO column was unevenly distributed, with high values concentrated in Anhui, Jiangsu, Yunnan and Hubei provinces. During the past 10 years, the concentration of YEB HCHO column varied between 10.28 and 17.19 × 1015 molec cm−2, and the lowest concentration of HCHO column was 13.16 × 1015 molec cm−2 in 2015. However, it reached the peak value in 2018 (14.93 × 1015 molec cm−2). In natural sources, normalized vegetation index (NDVI) and leaf area index (LAI) had greater influence on YEB HCHO, and the correlation was −0.91 ∼ 0.97 and −0.9 ∼ 0.95, respectively. The positive correlation area between HCHO and Mean annual temperature (MAT) reached 93%. The contribution of high-intensity human activity areas to HCHO cannot be underestimated. Industrial and civil sources have great influence on HCHO. In addition, the potential source of HCHO in Shanghai is affected by local emission sources, trans-regional potential sources, northwest air mass and ocean airflow.

The Relationship between Exertional Desaturation and Pulmonary Function, Exercise Capacity, or Medical Costs in Chronic Obstructive Pulmonary Disease Patients.

Background and Objectives: Exertional desaturation (ED) is common and is associated with poorer clinical outcomes in chronic obstructive pulmonary disease (COPD). The age, dyspnea, airflow obstruction (ADO) and body mass index, airflow obstruction, dyspnea, and exercise (BODE) indexes are used to predict the prognosis of COPD patients. This study aimed to investigate the relationship between these indexes, pulmonary function, medical costs, and ED in COPD patients. Materials and Methods: Data were collected from the electronic database of the Kaohsiung Chang Gung Memorial Hospital. This retrospective study included 396 patients categorized as either ED (n = 231) or non-ED (n = 165). Variables (including age, smoking history, body mass index (BMI), pulmonary function test, maximum inspiratory pressure (MIP) and maximum expiratory pressure (MEP), six minutes walking test distance (6MWD), SpO2, COPD Assessment Test (CAT) score, ADO index, BODE index, Charlson comorbidity index (CCI), and medical costs) were compared between the two groups, and their correlations were assessed. ED was defined as SpO2 less than 90% or SpO2 decrease of more than 4% compared to baseline levels during 6MWT. Results: A significant statistical difference was found regarding a lower score of the ADO index and the BODE index (both p < 0.001), better pulmonary function (forced expiratory volume in the first second (FEV1), p < 0.001; FEV1/ forced vital capacity (FVC), p < 0.001; diffusion capacity of the lung for carbon monoxide (DLCO), p < 0.001), and higher minimal oxygen saturation (p < 0.001) in non-ED COPD patients. No difference was found in the distance of the 6MWT (p = 0.825) and respiratory muscle strength (MIP; MEP, p = 0.86; 0.751). However, the adjusted multivariate logistic regression analysis showed that only SpO2 (minimal) had a significant difference between of the ED and non-ED group (p < 0.001). There was either no difference in the medical expenses between ED and non-ED COPD patients. Conclusions: SpO2 (minimal) during the 6MWT is the independent factor for ED. ED is related to BODE and ADO indices, but is not related to medical expense.

Lung volume reduction surgery versus endobronchial valves: a randomised controlled trial.

Lung volume reduction surgery (LVRS) and bronchoscopic lung volume reduction (BLVR) with endobronchial valves can improve outcomes in appropriately selected patients with emphysema. However, no direct comparison data exist to inform clinical decision making in people who appear suitable for both procedures. Our aim was to investigate whether LVRS produces superior health outcomes when compared with BLVR at 12 months. This multicentre, single-blind, parallel-group trial randomised patients from five UK hospitals, who were suitable for a targeted lung volume reduction procedure, to either LVRS or BLVR and compared outcomes at 1 year using the i-BODE score. This composite disease severity measure includes body mass index, airflow obstruction, dyspnoea and exercise capacity (incremental shuttle walk test). The researchers responsible for collecting outcomes were masked to treatment allocation. All outcomes were assessed in the intention-to-treat population. 88 participants (48% female, mean±sd age 64.6±7.7 years, forced expiratory volume in 1 s percent predicted 31.0±7.9%) were recruited at five specialist centres across the UK and randomised to either LVRS (n=41) or BLVR (n=47). At 12 months follow-up, the complete i-BODE was available in 49 participants (21 LVRS/28 BLVR). Neither improvement in the i-BODE score (LVRS -1.10±1.44 versus BLVR -0.82±1.61; p=0.54) nor in its individual components differed between groups. Both treatments produced similar improvements in gas trapping (residual volume percent predicted: LVRS -36.1% (95% CI -54.6- -10%) versus BLVR -30.1% (95% CI -53.7- -9%); p=0.81). There was one death in each treatment arm. Our findings do not support the hypothesis that LVRS is a substantially superior treatment to BLVR in individuals who are suitable for both treatments.

Computer Aided Intelligent Control of Inlet Air Cooling System in Combined Cycle Power Plant

In hot climate, the overall efficiency of combined cycle power plant (CCPP) decreases dramatically owing to the rise of environment temperature, which decreases the mass air flow to the compressor. Therefore, the gross power and thermal efficiency drop. Worldwide practice uncovers that gas turbines generate up to 20% less power in summer than in winter. A brief introduction of the CCPP and analysis of influence of ambient temperature are provided in this work. Furthermore, this essay gives a outline of the combined cycle based on several inlet air cooling technologies and why they are necessary. Since 1987, more and more inlet air cooling systems have been devised, each of which has its own advantages and limitations. They are mainly divided into 2 classes. Low cost and ease of operation are the main advantages of evaporation systems. However, they are strictly restricted by the wet-bulb temperature. In terms of the mechanical systems, which are not limited to it, command high initial and operating cost. The absorption chiller utilizes the waste heat of exhaust gases, which means they have great potential. In the end, economic criteria and problems to be solved are mentioned.

Prevalence of Metabolic Syndrome in Chronic Obstructive Pulmonary Disease and its Correlation with Body Mass Index, Airflow Obstruction, Dyspnea, and Exercise Index and C-Reactive Protein

Context: Chronic obstructive pulmonary disease (COPD) is a widespread reason of disease and death in developing countries like India, although it is preventable, controllable, and treatable. Objectives: We studied the occurrence of metabolic syndrome (MetS) among cases of stable COPD and determined the correlation between stable COPD with MetS and body mass index, airflow obstruction, dyspnea, and exercise (BODE index) and C-reactive protein (CRP) titer. Methods: The study was a case–control analysis conducted on 90 patients from November 2019 to August 2020. Results: Seventy percentage of our case population belonged to Global Initiative for Chronic Obstructive Lung Disease (GOLD) Grade 1 and 2. A significant correlation between forced expiratory volume 1 s (FEV1) with body mass index (BMI), triglycerides (TGs), high-density lipoproteins (HDL), systolic blood pressure (SBP), and fasting blood sugar (FBS) was found. The higher the GOLD grade (lower FEV1), the higher the values of BMI, TG, HDL, SBP, and FBS. A negative association with MetS is related to small study population. Conclusion: Patients with MetS had significantly lower FEV1, higher mean SI, higher mean waist circumference, higher mean BMI, higher mean SBP and diastolic blood pressure, higher FBS, and higher HDL and TG. Patients with MetS showed higher BODE index and CRP titer compared to ones without it. Apart from routine vaccination, assessment of systemic comorbidities for early detection of MetS plays a pivotal role to provide best possible quality of life and utmost care to COPD patients and helps reduce mortality and morbidities of COPD.

Multiple fault detection and isolation using artificial neural networks in sensors of an internal combustion engine

Technology advances rapidly in vehicles with internal combustion engines; therefore, detecting vehicles’ faults becomes more complex, primarily due to the increased usage of sensors and actuators to improve engine performance. Specialized instruments for detecting vehicle faults can sometimes show errors in multiple sensors when there is only one issue in the system, and they are not responsible for isolating faults. Then, it must add superior algorithms for fault detection and isolation (FDI) in the Internal Combustion Engine to these diagnostic instruments. For this reason, this research focuses on designing an individual and multiple fault detection and isolation system based on artificial neural networks for an internal combustion engine. The proposed scheme detects individual and multiple faults in the throttle position sensors (TPS), mass air-flow (MAF), and manifold absolute pressure (MAP). The FDI system uses five artificial neural networks (ANN). Each ANN estimates the value of two sensors using the engine speed and the air–fuel ratio (AFR) signal to generate analytical redundancy. When a sensor fault occurs, the FDI system substitutes the faulty signal with a healthy signal estimate provided by an ANN. Suppose a second fault arises in another sensor. The FDI system can replace the faulty signal with the ANN’s estimated signal, allowing the internal combustion engine to run even with multiple faults.

Experimental Investigation of Thermal Performance Parameters of Drying Air Unit with a Different Flowrate of Liquid Desiccant Solution

Liquid desiccant air-conditioning systems remove directly the latent load from moist air and remove indirectly sensible load of this air by using an indirect evaporative cooling process. The liquid desiccant must subsequently be regeneration once again with a low-grade heat source like solar energy. In this study, calcium chloride solution was used as a desiccant. For the desiccant solution regeneration process, a flat plate solar collector was employed. Different variables, such as the primary airflow rate and desiccant flow rate, were changed during the experiments. The impact of these variables on the performance parameters of the desiccant systems such as moisture removal rate, moisture efficiency, enthalpy efficiency, sensible heat ratio, and the mass transfer coefficient was studied. The obtained results revealed that as the flow rate of primary air increases, the moisture removal rate, sensible heat ratio, and the mass transfer coefficient increase. For example, from 0.006 m/s to 0.0125m/s when the mass airflow rate increased from 0.1 kg/s to 0.18 kg/s.

A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions.

Vehicles are the major source of air pollution in modern cities, emitting excessive levels of CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles, the continuous emission of such pollutants can be indirectly measured over time, although accuracy has been an important design issue when performing this task due the nature of the retrieved data. In this scenario, soft-sensor approaches can be adopted to process engine combustion data such as fuel injection and mass air flow, processing them to estimate pollution and transmitting the results for further analyses. Therefore, this article proposes a soft-sensor solution based on an embedded system designed to retrieve data from vehicles through their OBD-II interface, processing different inputs to provide estimated values of CO2 emissions over time. According to the type of data provided by the vehicle, two different algorithms are defined, and each follows a comprehensive mathematical formulation. Moreover, an unsupervised TinyML approach is also derived to remove outliers data when processing the computed data stream, improving the accuracy of the soft sensor as a whole while not requiring any interaction with cloud-based servers to operate. Initial results for an embedded implementation on the Freematics ONE+ board have shown the proposal’s feasibility with an acquisition frequency equal to 1Hz and emission granularity measure of gCO2/km.

Deep Learning Model for Prediction of Air Mass Deviation Faults

Major Systems of an internal combustion Engine are Air System, Fuel system, and Exhaust system. Any malfunction in these systems increases emissions. OBD legislation mandates to monitor these systems for any faults and appropriate action should be taken in case of the any faults which increase vehicle emissions. The idea of the paper is to find the Air mass flow deviation faults using datamining and machine learning based approach. Detection of fault is classifying whether system is faulty or not. Objective is to create a deep learning model using the available vehicle data to classify the system for a fault. Three main inputs for the Air Mass flow in an internal combustion Engine are1) Fresh Air which measure using Mass Air Flow sensor2) Low Pressure EGR3) High Pressure EGRDuring vehicle lifetime, due to different real vehicle operating conditions and environmental conditions, deviation in the set point of air mass flow and actual mass flow are possible to an extent, which can affect vehicle emissions. Deviation in the Air Mass flow can be caused by intake Air mass, LP-EGR, HP-EGR. The Aim of the project is to create the deep learning model for Air Mass Flow Hi and Low faults using the available data, and associate the fault to the component in the Intake Air System.

Assessment of Various Trombe Wall Geometries with CFD Study

An investigation of the optimal geometric configuration of a Trombe wall is presented with simulation of the transfer phenomena, that take place during its operation, using computational fluid dynamics. A numerical model is developed for a 2D steady-state simulation of a Trombe wall cross-section operation, and it is validated against an energy balance model’s results. Then the developed model is used for the evaluation of 10 different geometrical configurations examining various air gap widths, storage wall thicknesses, ventilation slots distances, and ventilation slots diameters. The examined geometries were evaluated with respect to the achieved temperature at the air gap exit and at the room facing storage wall surface, the achieved mass air flow in the air gap, and the ability of warm air stream from the gap to enter the test room. The aim was to ventilate the whole space without leaving large areas where the air just recirculates unaffected by the Trombe wall operation. According to the above-described criteria, optimum solution is an air gap width of 5 to 8 cm with increased distance between ventilation slots and a configuration of upper ventilation slot with an inclination of 30 degrees.

A study of signal noise reduction of the mass air flow sensor using the flow conditioner on the air induction system of heavy-duty truck

The mass air flow meter is a critical sensor that works based on thermal hot wire technology, used to determine the fuel to be injected into the cylinder and calculate the fuel-air ratio. In order to measure the airflow rate accurately, the flow should be uniform and smooth upstream of the sensor. The flow disturbance with a short straight length upstream of the flow meter results in the noise of the sensor signal. This noise causes unstable mass flow measurement on the system. Flow conditioners can be used to smooth the velocity profile of the flow. In this study, experimental and numerical methods were used to characterize the performance and operating accuracy of the mass flow meter used in heavy-duty truck applications. The flow conditioners were implemented to smooth the velocity profile around the mass flow meter that was disrupted by bends. The flow structures with and without flow conditioner were examined using Particle Image Velocimetry (PIV) to measure the time-averaged velocity. As well as the validated computational fluid dynamics (CFD) model provides data to understand the flow uniformity effect of the conditioner on the mass airflow (MAF) sensor. The optimization study was performed using a full factorial design of experiment (DOE) for flow conditioner design. A robust methodology was developed for the flow conditioner characteristics and mass airflow sensor implementation on the air induction system.

COPD and comorbidities in the Republic of Moldova

BACKGROUND AND AIM: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide, and the majority of patients have at least one comorbid condition of clinical significance. Therefore, we studied its prevalence and implication based on experience from the Republic of Moldova. METHODS: The study was a prospective cohort study that included 435 patients with COPD from 2015 to 2017. RESULTS: We found heart failure in 38.62% of the patients, hypertension in 50.11%, coronary artery disease in 23.45%, diabetes mellitus in 10.11%, renal failure in 1.15%, rheumatoid arthritis in 3.22%, depression in 4.83%, cognitive impairment in 4.37%, obesity in 29.89%, and cachexia in 3.22%. Only 24.65% of patients did not have comorbidities. One comorbidity was found in 23.73%, two in 24.19%, three or more in 27.42%. The Charlson comorbidity index (CCI) had a medium negative correlation with the 6-minute walking test (r=–0.37, p<0.001) and a weak correlation with the rate of exacerbations (r=0.17, p=0.016). CCI had a strong correlation with ADO (age, dyspnea and airflow obstruction) (r=0.75, p<0.001); moderate with BODE (body mass index, airflow obstruction, dyspnea, and exercise) (r=0.3, p<0.001); and weak with BODEx (body mass index, airflow obstruction, dyspnea, and exacerbations), CODEX (comorbidity, obstruction, dyspnea, and previous severe exacerbations), and DOSE (dyspnea, obstruction, smoking, and exacerbation). CCI had a medium correlation with St. George’s Respiratory Questionnaire (SGRQ) activity (r=0.36, p<0.001), impact (r=0.34, p<0.001), and total (r=0.37, p<0.001) scores, and the overall quality of life assessed by SGRQ and Clinical COPD Questionnaire. CONCLUSIONS: Patients with COPD require a multidisciplinary approach to assess and manage a variety of conditions, which influence the evolution and prognosis of COPD. Patients often have one or two comorbidities of clinical significance, and they are predominantly cardiovascular and metabolic. Patients with comorbidities tend to have a poorer health-related quality of life. Comorbidities can be assessed by multidimensional indexes such as ADO and BODE.

Risk factors as outcome predictors of pulmonary rehabilitation in patients with chronic obstructive pulmonary disease

Introduction/Objective. Chronic obstructive pulmonary disease (COPD) is a primary lung disease. Today, pulmonary rehabilitation (PR) is the basis for non-pharmacological treatment of these patients, with numerous confirmed effects on the most significant symptoms of the disease and the quality of life (QoL). The aim of this study was to determine the relationship between certain risk factors and the outcome of PR, as well as to determine the percentage of respondents who had a positive outcome of PR. Methods. The study included 500 patients with COPD, determined according to the Global Initiative for Chronic Obstructive Lung Disease guidelines, all stages (I?IV), in the stable phase of the disease, who completed the outpatient PR program. Disease stage, comorbidities, forced expiratory volume in the first second, six-minute walk test (6MWT), COPD Assessment Test (CAT), and Medical Research Council dyspnea scale, body mass index, airflow obstruction, dyspnea and exercise capacity (BODE) index, were measured before and after the program. The last four parameters have been observed as risk factors that affect the outcome of PR, but also as parameters by which we monitor the outcome of PR. Results. A successful outcome of PR was achieved by as many as 452 (90.4%) patients. The following were determined as independent predictors of a positive outcome of PR: lower number of comorbidities, absence of heart failure, higher BMI, and CAT ? 10. Conclusions. PR in our group of patients leads to statistically significant improvements in most of the examined subjective and objective parameters, in patients in all stages of the disease.

ТЕСТОВЫЙ КОНТРОЛЬ ДАТЧИКОВ МАССОВОГО РАСХОДА ВОЗДУХА СОВРЕМЕННЫХ ДВИГАТЕЛЕЙ ВНУТРЕННЕГО СГОРАНИЯ

The authors have considered typical designs of sensors of mass air flow rate used in motor vehicles and established that the most perfect are film-type sensors. Analysis of modern means and methods for diagnostics of mass air flow rate sensors showed that there are no methods for their nondestructive inspection. The study presented in the paper is based on the use of a developed measuring device for creation of test modes. As controlled parameters the authors have selected a mass air flow rate, voltage from sensor test terminal, a position of the butterfly throttle, hourly consumption of fuel at variation of the ICE crankshaft rotation speed at the position of the butterfly throttle from 0 to 100 %. It is established that the most sensitive parameters are the mass air flow rate and the voltage of the sensor control contact.

Applied Mathematical Model for Solar Energy Collection and Thermal Storage

Solar thermal storage systems (STSS) are renewable energy systems that provide a continuous, controllable source of heat for many applications. The inherent variability of solar insolation poses a challenge to the use of direct solar power for continuous operations. A dynamic non-linear model of the STSS was initialised via a steady state analysis and verified by drying 1000 kg of cocoa beans per batch from 60% to 8% moisture content. Ambient temperature and insolation data for Trinidad in 2017 were used as inputs. This was achieved by modifying flow rates, varying aspect ratio of the storage tank, and observing their effect on storage tank temperature profiles and drying air temperatures. The steady state analysis determined the tank volume as 12.57 m3 and a required mass airflow rate of 1020 kg/h. A dynamic model of the STSS revealed an optimal tank aspect ratio of 2:4 m (D: H) and that three AE-40 solar collectors were sufficient. The effect of the circulation rate between the storage tank and solar collectors on energy storage was found to be negligible. The need for temperature control was demonstrated and a control strategy developed. A pilot plant was built using recommended specifications, albeit without temperature control. As predicted, drying was more than sufficient, but poor control led to burnt cocoa beans. The application of this work extends well beyond the cocoa bean test case, and the open-source models built can be applied to optimise the design of any solar heating application.

The course of birch pollen seasons in Sosnowiec (Southern Poland) in 1997\u20132020 in relation to meteorological conditions

The study's main objective was to specify the extent to which weather conditions were related to the course of birch pollen seasons in the years 1997–2020. The impact of atmospheric conditions on the daily concentrations of birch pollen grains, the Annual pollen integral (APIn), and the length of pollen seasons were studied. The dependency between each meteorological condition and various features of the birch pollen season was determined using Spearman’s rho correlation, the Kruskal–Wallis test, and cluster analysis with the k-means method. It has been shown that the duration of sunshine and average air temperature occurring within 14 days preceding the season has the most significant influence on the beginning of a birch pollen season. The value of daily birch pollen concentrations in Sosnowiec showed a statistically significant positive correlation with the duration of sunlight and the average and maximum wind speed. The daily concentration also depended on the synoptic situation: the mass airflow direction, the type of air mass inflow, and the type of weather front. The near-ground temperature influenced the APIn of birch pollen grains during the period of 14 days before the beginning of the season and the meteorological conditions occurring in the summer of the preceding year such as the maximum temperature, duration of sunlight, the maximum and average wind speed, and the relative air humidity. It was concluded that the length of birch pollen seasons decreased year by year.

Features Importance Analysis of Diesel Vehicles' NOx and CO2 Emission Predictions in Real Road Driving Based on Gradient Boosting Regression Model.

In order to have an accurate and fast prediction of the artificial intelligence (AI) model, the choice of input features is at least as important as the choice of model. The effect of input features selection on the emission models of light diesel vehicles driven on real roads was investigated in this paper. The gradient boosting regression (GBR) model was used to train and to predict the emissions of nitrogen oxide (NOx), carbon dioxide (CO2), and the fuel consumption of real driving diesel vehicles in urban scenarios, the suburbs, and on highways. A portable emissions measurement system (PEMS) system was used to collect data of vehicles as well as environmental conditions. The vehicle was run on two routes. The model was trained with the first route data and was used to predict the emissions of the second route. There were ten features related to the NOx model and nine features associated with the CO2 model. The importance of each feature was sorted, and a different number of features were used as input to train the models. The best NOx model had the coefficient of determination (R2) values of 0.99, 0.99, and 0.99 in each driving pattern (urban, suburbs, and highways). Predictions of the second route had the R2 values of 0.88, 0.89, and 0.96 respectively. The best CO2 model had the R2 values of 0.98, 0.99, and 0.99 in each driving pattern, respectively. Predictions of the second route had the R2 values are 0.79, 0.82, and 0.83, respectively. The most important features for the NOx model are mass air flow rate (g/s), exhaust flow rate (m3/min), and CO2 (ppm), while the important features for the CO2 model are exhaust flow rate (m3/min) and mass air flow rate (g/s). It is noted that the regression models based on the top three features may give predictions very close to the measured data.

Design of a Mass Air Flow Sensor Employing Additive Manufacturing and Standard Airfoil Geometry

This work concerns the design, fabrication, and preliminary characterization of a novel sensor for determining the air intake of low and medium power internal combustion engines employed at various applications in the marine industry. The novelty of the presented sensor focuses on the fabrication process, which is based on additive manufacturing combined with PCB technology, and the design of the sensing elements housing geometry, which is derived through suitable CFD simulations and is based on standard airfoil geometry. The proposed process enables low-cost, fast fabrication, effective thermal isolation, and facile electrical interconnection to the corresponding circuitry of the sensor. For initial characterization purposes, the prototype device was integrated into a DIESEL engine testbed while a commercially available mass air flow sensor was employed as a reference; the proper functionality of the developed prototype has been validated. Key features of the proposed device are low-cost, fast on-site manufacturing of the device, robustness, and simplicity, suggesting numerous potential applications in marine engineering.

The effect of the initial swirl of the supersonic flow of humid air on the adiabatic wall temperature

The paper presents the results of measuring the adiabatic wall temperature of an axisymmetric channel during acceleration of the moist air flow in it to supersonic speed. The initial swirl was imparted to the flow (swirling parameter S = 0.5, 1.0, 2.5). The relative initial humidity (RH) of the flow varied in the range of 10 ÷ 90%. When the flow was accelerated to supersonic speeds, part of the moisture condensed, which influenced the wall temperature. It is shown experimentally that with an increase in the initial moisture content of the flow to certain values, the distribution of the wall temperature for a flow without initial swirl (S = 0) and with swirl with S = 0.5, 1.0 practically coincide. However, from a certain value of RH, the wall temperature in the case of a swirling flow decreases in comparison with a flow without swirling. The maximum decrease in the wall temperature was achieved at RH = 90%. An increase in the initial swirl to S = 2.5 led to a greater decrease in the wall temperature, while the mass air flow through the channel decreased by 26% at an identical pressure drop.