Air Data Research Articles

APLIKASI PENCATATAN PENJUALAN PENGDINGIN RUANGAN BERBASIS JAVA PADA PT. UNIE SARANA MANDIRI


 Unie Sarana Mandiri is a company engaged in the sale, installation and routine maintenance of air conditioners, therefore, sales and data collection units for air conditioners still use the manual method, so there are often difficulties in finding data, collecting data on goods, transactions and making reports, all of which are still recorded. in Ms. Excel and Ms. Offices. The method used in this research is the Waterfall method. For this reason, the researcher designed an application using NeatBeans. The design of this sales application is in the form of a desktop by presenting data collection of goods and sales reports. This application is designed using Neatbeans which is a support program for creating Java-based applications using Black Box in testing. Java-based air conditioner sales applications have had good performance, efficiency, application suitability with testing. This application can help employee performance in sales report item data by utilizing information technology to complement existing deficiencies in air conditioning sales, as media and information at PT. Unie Sarana Mandiri.

GPS-free roll and pitch estimation through pressure altitude aided inertial navigation system for a jet aircraft

This paper presents a novel approach for estimating roll and pitch angles of an aircraft by regulating the dead-reckoning drift using pressure altitude measurements. The proposed algorithm uses measurements from rate gyroscopes, pressure altitude, and an air data system to estimate the roll and pitch angles of an aircraft. This unique combination of sensor data offers a new alternative to traditional navigation approaches relying on accelerometers and satellite systems to estimate the aircraft attitude. Multiple flight data sets from a high-speed crewed US Navy T-38 jet are used to validate the proposed approach. Comparable levels of attitude estimation accuracy are reported thus demonstrating that the proposed algorithm is a viable alternative for attitude estimation purposes. Therefore, this approach can be used to improve the robustness and accuracy of attitude estimation algorithms in scenarios where GPS coverage is denied, unreliable, spoofed and/or jammed.

Multimedia fate simulation of mercury in a coastal urban area based on the fugacity/aquivalence method

Due to intensive industrial production and living activities, urban areas are the main anthropogenic mercury (Hg) emission sources. After entering the environment through exhaust gases, wastewater or waste residues, Hg can migrate and transform among different environmental compartments in various species, such as elemental mercury (Hg0), divalent mercury (Hg2+) and methylmercury (MeHg). Studies have yet to report on the multimedia behaviors of Hg in urban areas due to the complexity of the processes involved. In this study, the atmospheric Hg emission in Dalian, a coastal city in Northeast China, was estimated by an anthropogenic emission inventory, and a Level III multimedia model was constructed based on the fugacity/aquivalence method to simulate the fate of Hg in air, water, soil, sediment, vegetation and film. The total annual atmospheric emission was 9.91 t, of which coal combustion and non-coal sources accounted for 70.1 % and 29.9 %, respectively. Atmospheric emission and advection were dominated by Hg0, and aquatic emission and advection were dominated by Hg2+. The migration of air–vegetation, vegetation–soil and soil–air were three important pathways of Hg in urban areas. The model was validated by collecting local soil and vegetation samples and regional air, seawater and sediment monitoring data. The scenario simulation indicated that the local load would decrease to different extents with a 21.0 % reduction in atmospheric Hg emission by implementing the “coal-to-gas” measures. Our developed model can characterize the fate of Hg in coastal urban areas and provide a reference for control strategies.

HYAQP: A Hybrid Meta-Heuristic Optimization Model for Air Quality Prediction Using Unsupervised Machine Learning Paradigms

In the current decade, presence of air pollution leads towards serious health conditions, including respiratory ailments, cardiovascular disorders, and lung cancer, which impacts both the lifespan and overall well-being of individuals. Moreover, the air pollution has the potential to have detrimental effects on the environment, resulting in destruction to ecosystems, decreased agricultural output and so on. Emission control systems, better fuels, stronger laws, energy efficiency improvements, and renewable energy promotion are helping industries reduce air pollution. To monitor the quality of air methods such as Particulate Matter (PM2.5), PM10, Ozone (O3), and other meteorological indicators exists. These measures pose real-time air quality however it fails to predict air quality. Predicting air quality helps reduce air pollution by enabling timely interventions and preventive measures to mitigate pollution peaks. Thus, in this research a hybrid version of optimization algorithm namely Hybrid Air Quality Prediction system (HYAQP) which is a combination of k-means clustering algorithm and meta-heuristic algorithm Sine Cosine Algorithm (SCA) is proposed. The HYAQP holds SCA integrated with k-means algorithm to find optimal cluster centroid for grouping the air data into three clusters good, poor, and moderate quality. Then the cluster which is nearer to the test instance is found and the instances present in those clusters are passed to K-Nearest Neighbor Regressor (K-NNR). Comparing HYAQP on mean absolute error it outperforms 62.9% than Multiple Linear Regression (MLR), 58.5% than Support Vector Regression (SVR), 45.5% than Vanilla-Long Short-Term Memory (Vanilla-LSTM), 44.4% than Sparrow Search Algorithm based-LSTM (SSA-LSTM) and 53.8% than K-Nearest Neighbor (KNN).

External Synchronization of Distributed Redundant Flight Control Computers

<div>This work introduces a practical approach to external synchronization for flight control computers (FCCs) deployed in a decentralized fashion. The internal synchronization among the FCCs in distributed flight control systems needs to be extended for specific applications, necessitating an urgent need for an external synchronization mechanism. For instance, when the air data and attitude reference system (ADAHRS) and the flight control computer (FCC) are not synchronized, a dead time or time offset occurs between the time the ADAHRS transmits data and the time the FCC begins executing its control functions, which can impair flight control system performance or even cause system instability, particularly for the system with incremental control approaches, such as incremental nonlinear dynamic inversion (INDI). Therefore, an external synchronization technique that does not rely on establishing a global view of time that is accurately synchronized with an external reference device has been proposed and implemented for distributed flight control systems. The findings of the implemented system demonstrate that the suggested approach can synchronize the distributed FCCs to an external reference device with high synchronization precision and deal with varying clock drift rates.</div>

The Development of Drone Techology and its Regulation in the European Union

Abstract Unmanned Aerial Vehicles (UAVs), or drones, have rapidly transformed from niche gadgets to versatile tools with widespread applications across various sectors. This paper investigates the regulatory landscape of drones within the European Union (EU) and its intricate balance between fostering technological innovation and addressing safety, privacy, security, and environmental concerns. Drones’ exponential growth in Europe’s airspace has prompted the EU to establish a legal framework that addresses their multifaceted implications. While drones offer unprecedented opportunities in areas like disaster response and remote sensing, they also pose challenges such as airspace congestion, potential privacy breaches, and ecological disturbances. This paper examines the evolving legal frameworks, policy developments, and technological advancements that characterize drone regulation in the EU. The study delves into key considerations including air traffic management, data protection, privacy preservation, environmental sustainability, and international collaboration. By analysing EU regulations, such as the European Aviation Safety Agency (EASA) standards, the General Data Protection Regulation (GDPR), and European initiatives on unmanned aircraft systems, this paper uncovers the complex interplay of legal, technical, and ethical dimensions.

Theoretical Foundations of Development of the Air Data System with the Fixed Flush-Mounted Receiver

Theoretical Foundations of Development of the Air Data System with the Fixed Flush-Mounted Receiver

Methods for Parametric Identifi cation of Systematic Errors of Onboard Measurements of Aerodynamic Angles in Aircraft Flight Tests

Methods of the actual values of the angles of attack and slip determination in the technology of air data systems estimation using satellite navigation systems during aircraft flight tests of are stated. Also methods of parametric identification of mathematical models of on-board aerodynamic angles measurements errors are presented. The technology has been developed and implemented in the practice of flight tests at Flight Research Institute. The technology shows high efficiency of solving tasks of air data on-board systems, vertical separation systems, flight performance and takeoff and landing characteristics estimation in flight tests. The technology covers a wide range of issues of aerometric measurements, many of which have already been published. Here, attention is paid to measurements of aerodynamic angles. The features of determining the angles of attack and slip using data from air data systems, satellite and inertial navigation systems are shown. Equations for determining the angles of attack and slip during testing, factors of measurement errors, the structure of the mathematical model of the aerodynamic errors of the angle of attack on-board measurements are presented in a stationary approximation. Algorithms for solving problems in steady and unsteady modes of flight, takeoff and landing are considered. The completeness of the solution for the wind speed vector is noted. Estimates of methodological errors in determining aerodynamic angles using the technology are made. Compliance with modern requirements is showing. The effectiveness of the technology is confirmed by the materials of flight tests of a short-range aircraft. Typical examples of test materials presentation, features of measurements depending on the type of test mode, aerodynamic influence factors are shown.

Community Health Impacts From Natural Gas Pipeline Compressor Stations.

Compressor stations maintain pressure along natural gas pipelines to sustain gas flow. Unfortunately, they present human health concerns as they release chemical pollutants into the air, sometimes at levels higher than national air quality standards. Further, compressor stations are often placed in rural areas with higher levels of poverty and/or minority populations, contributing to environmental justice concerns. In this paper we investigate what chemical pollutants are emitted by compressor stations, the impacts of emitted pollutants on human health, and local community impacts. Based on the information gained from these examinations, we provide the following policy recommendations with the goal of minimizing harm to those affected by natural gas compressor stations: the Environmental Protection Agency (EPA) and relevant state agencies must increase air quality monitoring and data transparency; the EPA should direct more resources to monitoring programs specifically at compressor stations; the EPA should provide free indoor air quality monitoring to homes near compressor stations; the EPA needs to adjust its National Ambient Air Quality Standards to better protect communities and assess cumulative impacts; and decision-makers at all levels must pursue meaningful involvement from potentially affected communities. We find there is substantial evidence of negative impacts to strongly support these recommendations.

Evaluation of meteorological characteristics and the influence of the Southern Annular Mode at newly established Bharati Station, East Antarctica

The Indian Antarctic program, a multi-disciplinary and multi-institutional program initiated in 1981 with the first expedition to Antarctica, has been instrumental in advancing our understanding of Antarctic meteorology and its global climate implications. This study focuses on the latest meteorological characteristics of newly established research base Bharati aiming to unravel the climate dynamics of Bharati station in East Antarctica. The meteorological data for the period December 2014 to December 2022 have been analysed in present study. We observed the highest maximum temperature of 9.9 °C on January 5, 2018, and the lowest minimum temperature of −40.3 °C on September 6, 2016. The monthly mean temperature exhibits pronounced seasonal variation, with a summer maximum of approximately 0 °C in December and January, and a winter minimum of about −19 °C from May to September. While the eight-year observations are not sufficient for studying long-term trends, we identify a slight increasing tendency in temperature at Bharati. In addition to temperature, we examined the characteristics of pressure, relative humidity, precipitation, wind, and blizzards. The analysis reveals a negative correlation between the Southern Annular Mode (SAM) and temperature and pressure at Bharati. Station experiences predominantly easterly and northeasterly winds. The upper air data were obtained using radiosonde and ozonesonde ascents, enhancing our understanding of the atmospheric conditions in the region. These results provide important knowledge about the weather in Bharati and how it relates to the Southern Annular Mode. Understanding this helps us comprehend how East Antarctica weather influences global climate patterns.

Extreme weather—Wildfires & pregnancy

As described in the previous chapter, Chapter 4: Air pollution and pregnancy, there is robust literature on the adverse health impacts of ambient air pollution on perinatal outcomes. With climate change contributing to more extreme weather patterns, wildfire events are becoming more intense and frequent. Wildfire smoke is a major contributor to poor air quality and data are beginning to emerge with respect to the negative impact on perinatal outcomes. The aim of this chapter is to provide an overview of the current literature on wildfire smoke exposure in pregnancy and associated adverse outcomes.

Practical examples for the flight data compatibility check

Accurate information about aircraft speed, altitude, and aerodynamic flow angles is essential for evaluating aircraft performance and handling qualities. These quantities are determined from air data measurements taken by sensors normally located near the aircraft cockpit. Since these sensors are affected by the distorted flow field around the fuselage, a correction must be applied. Before the first flight, a set of calibration parameters is usually determined from wind tunnel experiments or CFD calculations. However, the Data Compatibility Check (DCC) method allows a more accurate air data sensor calibration during the certification flight test. This method reconstructs air data quantities from inertial acceleration, angular rate measurements and the flight path. By comparing the reconstructed quantities with the measured ones, the structure and parameters of air data sensor models can be identified. In this paper, an introduction to the data compatibility check method and the setup used in a flight test for system identification is given. The DCC is applied on data gathered from a test campaign with the new DLR research aircraft Dassault Falcon 2000LX ISTAR. Use cases for the calibration of the nose boom airflow vanes and the correction of sensors during large sideslip maneuvers will be presented in this paper.

Effect of forceful suction and air disinfection machines on aerosol removal

BackgroundsDental procedures involving drilling and grinding can produce a significant amount of suspended aerosol particles (PM) and bioaerosols. This study aims to analyze the size and concentration of aerosol particles generated during drilling and to investigate the effectiveness of two air exchange systems, namely forceful suction (FS) and air disinfection machines (DM), in removing PM.MethodsFor this study, 100 extracted permanent teeth were collected and divided into three groups: without suction (n = 50), suction with forceful suction (n = 25), and suction with air disinfection machines (n = 25). The removal rate of suspended aerosol particles was analyzed using particle counters and air data multimeter.ResultsWhen drilling and grinding were performed without vacuum, 0.75% of the aerosol particles generated were PM2.5-10, 78.25% of total suspended aerosol particles (TSP) were PM2.5, and 98.68% of TSP were PM1. The nanoanalyzer measurements revealed that the aerodynamic diameter of most aerosol particles was below 60 nm, with an average particle diameter of 52.61 nm and an average concentration of 2.6*1011 ultrafine aerosol particles. The air change per hour (ACH) was significantly lower in the air disinfection machines group compared to the forceful suction group. Additionally, the number of aerosol particles and mass concentration was significantly lower in the air disinfection machines group compared to the forceful suction group in terms of PM2.5 levels. However, the forceful suction group also reduced the mass concentration in PM10 level than the air disinfection machines group.ConclusionIn conclusion, the air exchange system can reduce the aerosol particles generated during drilling and grinding. Comparing the two air exchange systems, it was found that the air disinfection machines group reduces the number of aerosol particles and mass concentration in PM2.5 levels, while the forceful suction group reduces the mass concentration in PM10 level.

ATMOSPHERE HEMISPHERIC ANALYSIS OF PARTICLES POLLUTION OVER AFRICA

Particulate Matters (PM) or particle pollution are mixture of microscopic solid and liquid particles present in air. The presence of PM in air can be monitored through modern devices known as Air Quality Sensors. In this study, we seek to investigate the concentration of PM at different latitude in some regions in Africa using purple air data available at map.purpleair.com downloaded for a period of 365 days in some African countries namely; Nigeria, Ghana, Ethiopia, Tanzania, Morocco and Zambia. Using Pearson correlational analysis, findings have revealed that the correlation coefficient r between the average PM concentration in some countries within the Northern hemisphere shows -0.91 and 1 for countries lying in the Southern hemisphere. Taken together, these results suggest that there is an association between the PM and latitude. It can therefore be inferred that the higher the country from the northern hemisphere, the lower the concentration of PM and vice versa.

An adaptive serial cascaded autoencoder and LSTM with multivariate regression for ambient air quality prediction with improved flow direction algorithm

ABSTRACT Forecasting of air quality is an emerging process to evade the entailment of several defects for human as well as environmental resources. Because of the poor quality of air substances, air pollution occurs. Living beings and other economic resources get affected by this polluted air very frequently. Therefore, air quality prediction is a trendsetting method to maintain a healthy life and infrastructure. Though multiple existing models are implemented, managing high-level data and deploying such standard models become cumbersome. Rather than indoor air quality, the ambient (outdoor) air quality should require the prediction process as it exists in the open environment. Thus, an intelligent ambient air quality prediction model is needed, which is designed in this paper by adopting a heuristic-aided deep learning model. The original air data is initially fetched from the three diverse data sources. It is followed by the data pre-processing stage with standard techniques. Subsequently, the resultant data is given to Adaptive Serial Cascaded Autoencoder and Long Short-Term Memory (LSTM) with Multivariate Regression (ASCA-LSMR), in which some of the hyper-parameters are tuned by proposing the novel algorithm as Fitness-based Improved Flow Direction Algorithm (FIFDA) to produce the better prediction results. Finally, experimental results indicate that our method enables more accurate predictions than all the listed traditional models and performs better in predictive performance. The RMSE of the designed FIFDA-ASCA-LSMR method attains 31.9%, 33.5%, 6.66%, and 23.7% elevated than SSA-ASCA-LSMR, DHOA-ASCA-LSMR, EHO-ASCA-LSMR, and FDA-ASCA-LSMR, for dataset 2. Thus, the designed ambient air quality prediction model reveals better performance than the other baseline approaches.

Cfd Tools as Applied to Indian Combat Aircraft Configuration-an Experience

Computational Fluid Dynamics (CFD) tools are extensively used by aircraft industries all over the world in design and development of airplanes, with emphasis on reducing both the cost and design cycle time. This paper deals with the use of CFD tools at Aeronautical Development Agency (ADA) in design and development of Indian combat aircraft. The applications include: (1) evaluation of incremental changes in aerodynamic configuration, (2) wave drag evaluation, (3) aerodynamic loads, (4) flow field around air data sensor locations, (5) Euler calculation for coupled internal and external flow field, and (6) viscous computation of high angles of attack flows. In the initial design phase, codes based on the potential theory were used for wing design and fuselage shape optimization. Later indigenously developed Euler and Navier-Stokes codes were employed for the various applications. This paper brings out the strength and weakness of CFD as experienced at ADA and its comparison with the Wind Tunnel and Flight Data.

A Variable Gain Complementary Filtering Fusion Algorithm Based on Distributed Inertial Network and Flush Air Data Sensing

Aiming at the problem that the accuracy of flight parameters calculated by the traditional flush air data sensing (FADS) and inertial navigation system (INS) complementary filter cannot meet the fine real-time control of the aircraft, a data fusion algorithm based on distributed inertial network and FADS is proposed. This method converts the inertial navigation parameters calculated by the distributed inertial network into flight parameters, and using the least squares fitting theory, the flight parameters are obtained from the pressure data measured by the FADS pressure hole. Then, by adjusting the filter constant of the complementary filtering algorithm, the flight parameters calculated by the inertial network are fused with the flight parameters calculated by the air data sensing to obtain high-precision flight parameters. Finally, the simulation results show that the proposed filtering algorithm can keep the flight parameter estimation error less than 0.01 degrees throughout the flight phase. Compared with the traditional complementary filter, the estimation error of the proposed algorithm is smaller.

Fault-tolerant FADS system development for a hypersonic vehicle via neural network algorithms

Hypersonic vehicles suffer from extreme aerodynamic heating during flights, especially around the area of leading edge due to its small curvature. Therefore, flush air data sensing (FADS) system has been developed to perform accurate measurement of the air data parameters. In the present study, the method to develop the FADS algorithms with fail-operational capability for a sharp-nosed hypersonic vehicle is provided. To be specific, the FADS system implemented with 16 airframe-integrated pressure ports is used as a case study. Numerical simulations of different freestream conditions have been conducted to generate the database for the FADS targeting in 2≤Ma≤5 and 0km≤H≤30km. Four groups of neural network algorithms have been developed based on four different pressure port configurations, and the accuracy has been validated by 280 groups of simulations. Particularly, the algorithms based on the 16-port configuration show an excellent ability to serve as the main solver of the FADS, where 99.5% of the angle-of-attack estimations are within the error band ±0.2∘. The accuracy of the algorithms is discussed in terms of port configuration. Furthermore, diagnosis of the system health is present in the paper. A fault-tolerant FADS system architecture has been designed, which is capable of continuously sensing the air data in the case that multi-port failure occurs, with a reduction in the system accuracy.

Improving Indoor Air Quality through Standardization

Human beings experience a large fraction of their exposure to air pollutants in indoor environments. Air pollution is a large environmental health risk, and exposure to ambient air pollution and indoor air pollution contribute equally to the total number of fatalities worldwide. Although legislative authorities have established limit values for ambient outdoor air and stack emissions, there are inconsistent and variable national and regional limit values for gaseous substances and airborne particulate matter in the built environment (schools, homes, healthcare facilities, offices, and other public spaces). This lack of regulation is unsurprising, because indoor spaces are characterized by complex air chemistry, and their construction materials and types of activities vary significantly. The current understanding of indoor pollutants, including short-lived oxidants, degradation of VOCs, particle formation, and particle composition, is incomplete. It is necessary to identify and assess emerging pollutants and their toxicity, and to consider new consumer products and green construction materials and their impact on indoor air quality (IAQ). Learning from IAQ surveys and audit protocols, research methodologies should be regularized for cross-research comparisons. Some indoor air quality guidance and standards have been written, and several more are in development, with the international ISO 16000 series of indoor standards leading the way for improving indoor air data quality. The WHO has established some ambient air limit values which can mostly be translated into indoor limit values. The built environment needs to harmonize energy efficiency, thermal comfort and air quality standards and guidance. In this review, we discuss the next steps for improving international, regional and national standards and guidance, leading to better and more complete indoor air quality regulations.

Real-time estimation of altitude and airspeed under large airflow angles in flight test

In the flight test, many test subjects need to enter the large airflow angle. Due to the large airflow angle, the position error of air data sensor is big, and the altitude and airspeed of the aircraft are not accurate, which not only affects the effectiveness of the test results, but also affects the safety of the test flight. This paper proposes two real-time altitude and airspeed estimation methods, including the GPS method and constant wind field method. The GPS method is commonly used in the airspeed calibration, which mainly uses the GPS height change to correct the static pressure, but it depends on the aircraft total pressure sensor to get the airspeed. The constant wind field method directly calculates the true airspeed not depending on the pressure measurement, but it is greatly affected by the wind stability. During the flight test, using this method, the real-time monitoring can provide pilots with accurate altitude and airspeed reference.