Trend Detection Research Articles

A comprehensive setting method for the optimal setpoint of controlled variables in the zinc smelting solution purification process under multi-variation inlet status

The zinc smelting solution purification process regulates the oxidation-reduction reactions (characterized by the oxidation-reduction potential (ORP)) by controlling the amount of zinc powder added to remove the impurity ions. In order to ensure the optimal operation of the process, the target key technical indicators should be converted into the optimal setpoint of controlled variables (that is, ORP). Based on the mechanism knowledge, fuzzy rules, and preference-guided predictive optimization, a comprehensive setting method for the optimal setpoint of controlled variables under multi-variation inlet status is proposed. It includes a hybrid trend detection method for inlet status, a mechanism model under multiple operating modes, and a fuzzy correction method and a preference-guided predictive optimization method to deal with slight and violent fluctuations in the inlet status, respectively. The experimental results show that the proposed method can obtain the optimal setpoint of controlled variables and reduce zinc powder consumption by approximately 3.6%.

Innovative polygon trend analysis for the monthly rainfall in the Mae Klong River Basin, Thailand

ABSTRACT Historical fluctuations in hydroclimatic variables can reveal important information about water resource changes in any basin. The innovative polygon trend analysis (IPTA) method was used to analyze 45-year rainfall data from eight monitoring stations in the Mae Klong River Basin, Thailand. The IPTA graphics were developed for the mean monthly rainfall and the standard deviation of the monthly rainfall for each station. In addition, the IPTA results were compared with the Mann–Kendall (MK) test and the Sen's slope method. The IPTA graphics showed no regular polygon for any rainfall station, which indicated variability of rainfall over the years. The IPTA method showed increasing trends for the month of April while decreasing trends in the month of October for the majority of the stations (7 of 8). A good agreement for detection of trends was found between the IPTA method and the MK test. Both the methods found trends (increasing/decreasing) for 69 months (72%) and showed no trend for 27 months (28%) out of a total of 96 months. The findings of this study could benefit water supply and management, drought monitoring, and agricultural production activities in the Mae Klong River Basin in the future.

An inversion-based clustering approach for complex clusters

BackgroundThe choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications.MethodsIn this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index.ResultsThe simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains.ConclusionsThe inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.

Benefits of Mann–Kendall trend analysis for vibration-based condition monitoring

Rank-based statistics offer an appealing framework to non-parametric robust trend detection. Especially, the Mann–Kendall test detects the presence of a trend in a series. This paper investigates the benefits of using the Mann–Kendall test, which seems to have remained largely unnoticed in the context of vibration-based condition monitoring. Two contributions structure the present paper. First, theoretical foundations of rank-statistics are reminded and the Z-score is introduced, a statistical metric measuring the presence of a trend in a given series. The performance of the Mann–Kendall test is investigated and confronted to different numerical cases involving series of increasing condition indicators. Second, based on this understanding, the Z-score metric is exploited to design two trend-oriented signal processing tools dedicated to vibration-based condition monitoring. Namely, a new tool named the Mannagram is introduced as a dyadic filterbank representation of the trend of indicators series. It shows effective in the informative band selection problem and unveils more interpretable indicators. Besides, a concise representation of trend by frequency bin is introduced and coined the Kendrum. This representation is shown to help the diagnosis by summarising the trend information of series of spectra. Both methods are demonstrated on run-to-failure series of vibration signals from industrial wind turbines.

Opinion: New directions in atmospheric research offered by research infrastructures combined with open and data-intensive science

Abstract. The acquisition and dissemination of essential information for understanding global biogeochemical interactions between the atmosphere and ecosystems and how climate–ecosystem feedback loops may change atmospheric composition in the future comprise a fundamental prerequisite for societal resilience in the face of climate change. In particular, the detection of trends and seasonality in the abundance of greenhouse gases and short-lived climate-active atmospheric constituents is an important aspect of climate science. Therefore, easy and fast access to reliable, long-term, and high-quality observational environmental data is recognised as fundamental to research and the development of environmental forecasting and assessment services. In our opinion article, we discuss the potential role that environmental research infrastructures in Europe (ENVRI RIs) can play in the context of an integrated global observation system. In particular, we focus on the role of the atmosphere-centred research infrastructures ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure), IAGOS (In-service Aircraft for a Global Observing System), and ICOS (Integrated Carbon Observation System), also referred to as ATMO-RIs, with their capabilities for standardised collection and provision of long-term and high-quality observational data, complemented by rich metadata. The ATMO-RIs provide data through open access and offer data interoperability across different research fields including all fields of environmental sciences and beyond. As a result of these capabilities in data collection and provision, we elaborate on the novel research opportunities in atmospheric sciences which arise from the combination of open-access and interoperable observational data, tools, and technologies offered by data-intensive science and the emerging collaboration platform ENVRI-Hub, hosted by the European Open Science Cloud (EOSC).

Nonstationarity in Extreme Precipitation Return Values along the U.S. Gulf and Southeastern Coasts

Abstract This study estimates extreme rainfall trends across the Gulf Coast and southeastern coast of the United States while applying methods for extending the temporal record and aggregating across spatial trend variations. Nonstationary generalized extreme value (GEV) models are applied to historical annual daily maximum precipitation data (1890–2019) while using CMIP5 global mean surface temperature (GMST) as the covariate. County composites and multicounty regions are used for local data record extension and pooling. Unlike most previous studies, return periods as long as 100 years are analyzed. The local trend estimates themselves are found to be too noisy to be reliable as estimates of climate-driven trends. However, application of a Gaussian process model to the spatial distribution of observed trends yields overall trend detection at the 95% significance level. The overall historical increase due to nonstationarity across the study region, with associated 95% confidence intervals, is 9% (3%, 15%) for the 2-yr return period and 16% (4%, 26%) for the 100-yr return period. A trend is also detectable in the Gulf Coast subregion, but not in the smaller southeast subregion. Recent weather events and nonstationarity have caused the official return value estimates for parts of North and South Carolina to be much lower than the return values estimated here. Significance Statement Protection of people and infrastructure from flooding relies on accurate estimates of potential extreme rainfall intensity. Some official estimates of extreme rainfall near the Gulf Coast and southeastern coast of the United States are over 20 years old. We show that, across this region, there is a clear trend in daily rainfall so extreme that it only has a 1% chance of happening in any given year (the so-called 100-yr rainfall). This trend means that many existing estimates of extreme rainfall are too low, both now and in the future, so flooding risks based on those estimates would be underestimated as well.

ВІД АНАЛІЗУ ТЕКСТУ ДО МОДЕЛЮВАННЯ ПРИРОДНОЇ МОВИ: КОМПЛЕКСНЕ ДОСЛІДЖЕННЯ

The article presents a comprehensive analysis of modern techniques and technologies used to analyze the tonality of textual data. Through a series of experiments, basic machine and deep learning algorithms such as sentiment analysis were developed to automatically detect and classify emotional tones in texts. Practical applications of these methods are also explored, ranging from social network monitoring to expert analysis and analysis of user reviews. Next, the article delves into the classification of textual data using modern machine and deep learning methods. This section of the paper reviews several classification models, including the Bayesian most-narrow classifier, support vector methods, and neural networks, highlighting their advantages and limitations. In addition, it emphasizes the importance of implementing text classification in various fields, including social network analysis, news article categorization, and automated document processing. In addition, clustering of similar textual data is considered for further analysis. Various clustering algorithms, such as k-means, hierarchical clustering, and spectral clustering, are compared, with special emphasis on their application to large text corpora. A demonstration of the practical application of text clustering, including data organization, topic search, and style identification in paper works, was also demonstrated. Moving on, the article considers the processing of individual thematic structures in textual data and its further analysis. An in-depth analysis of topic modeling techniques, such as the realized Dirichlet distribution (LDA) model, as well as its capabilities and limitations, is explored. Practical applications of topic modeling are demonstrated in various ways, including text collection analysis, news trend detection, and automatic document categorization. Finally, the article discusses some challenges and future prospects for the development of thematic modeling.

ФУНКЦІОНАЛЬНІ МОЖЛИВОСТІ СИСТЕМ КЕРУВАННЯ БАЗАМИ ДАНИХ ЧАСОВИХ РЯДІВ

This work provides a comprehensive overview of the capabilities of time series databases, with a special emphasis on InfluxDB –a leading time series database known for its efficiency and support for complex analyses through the Flux query language, including data aggregation, filtering, trend detection, and forecasting. The practical application of InfluxDB is demonstrated through various examples, showcasing its versatility in performing complex data operations, such as data filtering, identifying significant changes in data, and conducting stochastic analysis. These functionalities highlight the potential of InfluxDB across a broad range of applications, from IoT telemetry to financial analysis, underscoring the critical role of time series databases in modern data management and analysis strategies.

Future Slower Reduction of Anthropogenic Aerosols Enhances Extratropical Ocean Surface Warming Trends

AbstractGlobal surface temperature short‐term trends fluctuate between cooling and fast‐warming under the combined action of external forcing and internal variability, significantly influencing the detectability of near‐term climate change. A key driver of these variations is anthropogenic aerosols (AAs), which have undergone a non‐monotonic evolution with rapid reduction in recent decades. However, their reduction is projected to decelerate under a high carbon emission scenario, yet the impact on surface temperature trends remains unknown. Here, using initial‐perturbation large ensembles, we find that future slowdown in AA reduction over Europe and North America expedites the subpolar North Atlantic surface warming by intensifying the Atlantic meridional overturning circulation. Further, it accelerates the South Indian Ocean and Southern Ocean surface warming through positive low‐cloud feedback and oceanic dynamical adjustment, triggered by the poleward migration of westerlies under interhemispheric energy constraint. These AA‐driven warmings exacerbate greenhouse warming, significantly enhancing the detectability of local decadal warming trends.

Trend detection and depletion effects evidence in time series of groundwater levels in the southern sector of the left bank of the Tagus-Sado Basin (Portugal, Iberian Peninsula)

In the last 20 years in Portugal, water resources have been affected to the point that water storage has decreased by 20% since 2000. Creating strategies to manage water resources requires a comprehensive understanding of the factors influencing water storage and their effects over time. This study is focused on the evolution of Groundwater Deep Levels (GDL) by applying a two-phase trend analysis methodology to examine the dynamic changes in GDL within a series of monitoring wells located in the Central and Southern sectors of the Left Bank of the Tagus-Sado Cenozoic age Basin, situated in Portugal In the initial phase of trend analysis, Factorial Analysis of Mixed Data (FAMD) was employed and posteriorly the Hierarchical Classification Analysis (HCA). These techniques enabled us to identify distinct GDL trend profiles and generate interpretative maps illustrating their spatial distribution. In the second phase, the non-parametric Mann–Kendall Analysis (MKA) and Innovative Trend Analysis (ITA) were applied, allowing for a quantified confirmation of the different trend profiles previously detected. These techniques allowed the identification of positive and negative hydrodynamic trends in distinct sections of the Basin. In the SE sector they are characterized by a significative increase of GDL associated with overexploitation and in the Central sector with a decrease of GDL. Nevertheless, significant depletion effects can result from natural factors such as prolonged droughts, and in certain regions, changes in geological and hydrothermal dynamics, such as Alpine-age faults, graben, and horst structures, may account for these alterations.

Scanner: Simultaneously temporal trend and spatial cluster detection for spatial‐temporal data

Scanner: Simultaneously temporal trend and spatial cluster detection for spatial‐temporal data

Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective

Sales prediction plays a paramount role in the decision-making process for organizations across various industries. Nonetheless, accurately predicting sales is challenging because of trends and seasonality in sales data. The prime objective of this research paper was to explore machine learning methodologies and techniques that can efficiently address seasonality and trend detection in predictive sales forecasting. The research focused on pinpointing suitable features based on correlation coefficients, which were then adopted to train the three different models: random forests, linear regression, and gradient boosting. From the performance evaluation, gradient boosting displayed relatively superior performance compared to the other two regarding R2 score and accuracy. These results highlighted the capability of sales prediction through machine learning, offering vital insights for decision-making processes. The findings of this empirical research provide an extensive guideline for executing machine learning techniques in sales forecasting and addressing seasonality and trend detection, especially when working with large datasets. Furthermore, the study shed light on possible challenges and issues encountered in the process. By resolving these issues, retailers can reinforce the reliability and accuracy of their sales predictions, thereby enhancing their decision-making capabilities in the context of sales management.

Nonlinear trends in signatures characterizing non-perennial US streams

Stream drying patterns – including duration, timing, and dry-down rates – affect aquatic ecosystems and nutrient exports in non-perennial streams. Because hydrologic processes are often nonlinear, changes in drying may also be nonlinear, but analyses of historical changes in stream drying to date have not characterized the frequency or functional forms of nonlinear change. Understanding the extent of nonlinear change in non-perennial streams is essential for advancing our fundamental knowledge of hydrological processes, aquatic ecosystems, and watershed functioning under a warming climate. This paper uses a polynomial-based trend detection technique (PolyTrend) to analyze the linear and nonlinear trend behaviors of three intermittency signatures (annual no-flow days specifying longer or shorter drying duration, day of first no-flow occurrence specifying timing of stream drying, and days from peak to no-flow specifying dry-down rates) at 540 non-perennial gage stations over 38 years (1980–2017) across the continental United States (CONUS). Additionally, we carried out a breakpoint analysis to characterize the discontinuities in the time series of each intermittency signature. Analysis of annual no-flow days shows that about 37 % of the total streamflow stations are drying for longer each year, whereas about 22 % are wetter for longer than in the past. The day of first no-flow occurrence analysis shows that 10 % of the streams are drying earlier, and 19 % are drying later. On the other hand, analysis of days from peak to no-flow shows that 14 % of streams are drying faster, and 17 % are drying more slowly. For all these metrics, among the significant trends, at least half of the relationships were nonlinear. For annual no-flow days, the breakpoint analysis shows more discontinuities in the second half of the analysis period (1999 to 2017) than in the first half, with more discontinuities in the Southern Great Plains than in other regions. The other two signatures demonstrate less frequent discontinuities in the second half of the analysis period, suggesting decreased nonlinear dynamics in recent years. Nonlinear no-flow duration trends are common in Mediterranean California, and the dry-down rate has increased in recent decades. Our findings indicate that nonlinear change in stream drying is widespread and must be accounted for in watershed planning and management.

Analysis of rainfall and temperature variabilities in Sidama regional state, Ethiopia

The objective of the study was to examine local-scale fluctuation in precipitation and temperature in selected districts of Sidama regional state. Specifically, it focuses on three districts—Hawassa Zuriya, Wonsho, and Hula—using precipitation and temperature records obtained from the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) database which covers the period from 1981 to 2022. Various statistical measures such as mean, standard deviation, as well as coefficient of variation was employed to detect fluctuation. For trend detection, the Mann-Kendall (MK) and Sen's slope tests were also employed. Observations revealed that the average yearly precipitation spatially varied from 1331 mm in Hula, followed by 1275 mm in Wonsho, and 1013 mm at Hawassa Zuriya. Rainfall was bimodal which 53% rains in Kiremt and 33% in Belg season respectively. Annual rainfall show relatively low variability (<20%) for Hula and Wonsho districts, and moderate variability (CV˃20%) for Hawassa Zuriya respectively. The findings also revealed noticeable rising tendencies (p < 0.05) for average temperature across all three agroecosystems over the years under consideration with the highest slope at Hawassa Zuriya (0.038 °C/year), followed by Hula (0.031 °C/year), and Wonsho (0.022 °C/year) respectively.Moreover, both temperature and rainfall exhibited spatial and inter-annual variability. The results of this study necessitate farmers for systematic planning and implementing location specific crop calendar in the context of fluctuating climatic settings. Policy-makers as well as development practitioners can also utilize the finding to better devise and execute plans for adapting and minimizing the effects of climate change.

Hydroclimatic Trends and Streamflow Response to Recent Climate Change: An Application of Discrete Wavelet Transform and Hydrological Modeling in the Passaic River Basin, New Jersey, USA

The exigency of the current climate crisis demands a more comprehensive approach to addressing location-specific climate impacts. In the Passaic River Basin (PRB), two bodies of research—hydroclimatic trend detection and hydrological modeling—have been conducted with the aim of revealing the basin’s hydroclimate patterns as well as the hydrologic response to recent climate change. In a rather novel application of the wavelet transform tool, we sidelined the frequently used Mann–Kendal (MK) trend test, to identify the hidden monotonic trends in the inherently noisy hydroclimatic data. By this approach, the use of MK trend test directly on the raw data, whose results are almost always ambiguous and statistically insignificant in respect of precipitation data, for instance, no longer poses a challenge to the reliability of trend results. Our results showed that, whereas trends in temperature and precipitation are increasing in the PRB, streamflow trends are decreasing. Based on results from the hydrological modeling, streamflow is more sensitive to actual evapotranspiration (ET) than it is to precipitation. In periods spanning decades with sufficient water availability, energy governs actual evapotranspiration rates, rendering streamflow more sensitive to increases in precipitation. Conversely, during meteorologically stressed decades, water availability dictates actual evapotranspiration, consequently amplifying streamflow sensitivity to fluctuations in actual evapotranspiration. We found that the choice of baseline condition constitutes an important source of uncertainty in the sensitivities of streamflow to precipitation and evapotranspiration changes and should routinely be considered in any climate impact assessment.

Damage Detection in Glass Fibre Composites Using Cointegrated Hyperspectral Images.

Hyperspectral imaging (HSI) is a remote sensing technique that has been successfully applied for the task of damage detection in glass fibre-reinforced plastic (GFRP) materials. Similarly to other vision-based detection methods, one of the drawbacks of HSI is its susceptibility to the lighting conditions during the imaging, which is a serious issue for gathering hyperspectral data in real-life scenarios. In this study, a data conditioning procedure is proposed for improving the results of damage detection with various classifiers. The developed procedure is based on the concept of signal stationarity and cointegration analysis, and achieves its goal by performing the detection and removal of the non-stationary trends in hyperspectral images caused by imperfect lighting. To evaluate the effectiveness of the proposed method, two damage detection tests have been performed on a damaged GFRP specimen: one using the proposed method, and one using an established damage detection workflow, based on the works of other authors. Application of the proposed procedure in the processing of a hyperspectral image of a damaged GFRP specimen resulted in significantly improved accuracy, sensitivity, and F-score, independently of the type of classifier used.

UbiHeart: A novel approach for non-invasive blood pressure monitoring through real-time facial video

Monitoring blood pressure (BP) is an essential component of evaluating cardiovascular health, aiding in the early detection and management of hypertension-related complications. Traditional methods of BP measurement often involve invasive or cumbersome devices, leading to discomfort and reduced compliance. We propose a framework to monitor BP non-invasively, analyzing the face video captured by a webcam or smartphone camera leveraging the relationship of image-based Pulse Transit Time (iPTT) and Heart Rate Variability (HRV) with BP. We have built a dataset of 90 sets of collected videos using a mobile phone front camera and BP data from a standard digital BP monitor from 12 individuals from an approved Institutional Review Board (IRB) to evaluate our system. We have got a Mean Absolute Error (MAE) of 10.35+/−2.5 mmHg for systolic BP (SBP) and 7.8+/−1.5 mmHg for diastolic BP (DBP) while using the HRV representation RMSSD. On the other hand, an MAE of 8.25+/−3.5 mmHg for SBP and 7.7+/−2.5 mmHg for DBP while using the HRV representation SDRR. Finally, we have developed a framework and built a real-time system to monitor BP as a mobile and web-based application that can facilitate early detection of trends and anomalies, allowing healthcare providers to intervene promptly and personalize treatment plans.

Characteristics of Dry and Wet Changes and Future Trends in the Tarim River Basin Based on the Standardized Precipitation Evapotranspiration Index

Global changes in drought and wetness and their future trends in arid regions have recently become a major focus of research attention. The Tarim River Basin (TRB) in Xinjiang, China, is among the most climate-sensitive regions in the world. This study uses data from the past 60 years (1962–2021) to analyze the spatial and temporal features of drought and wetness conditions in the TRB, calculating the Standardized Precipitation Evapotranspiration Index (SPEI). Trend detection for SPEI is performed using the BEAST mutation test, identification of drought events using the theory of operations, and spatial and temporal analyses of dry and wet changes using Empirical Orthogonal Function (EOF) decomposition. Additionally, the CMIP6 dataset is used to estimate future changes. The study results indicate the following: (1) From 1962 to 1998, the TRB exhibited a “warm and wet” trend that suddenly shifted from “wet-to-dry” in 1998 and subsequently transitioned to a pronounced “warm and dry” trend. (2) After the “wet-to-dry” shift, the frequency of drought events noticeably increased. The northern section of the basin witnessed more frequent drought events, albeit with lower severity, while the southern part had fewer occurrences but with higher severity. The spatial distribution of drought event frequency and severity is inconsistent. (3) The EOF decomposition results for SPEI-variable fields at 1-, 3-, and 6-month time scales show that the cumulative variance contribution rate of the first three principal spatial modal feature vectors exceeds 70%. The spatial distribution of the modes includes a consistent pattern across the entire basin, a north–south opposite pattern, and an east–west opposite pattern. (4) The future trend of drought in the TRB is expected to intensify, manifesting a spatial pattern characterized by dryness in the middle of the basin and wetness around the periphery. These research findings can provide support for decisions addressing regional drought risks.

Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater

Wastewater surveillance has proven a cost-effective key public health tool to understand a wide range of community health diseases and has been a strong source of information on community levels and spread for health departments throughout the SARS- CoV-2 pandemic. Studies spanning the globe demonstrate the strong association between virus levels observed in wastewater and quality clinical case information of the population served by the sewershed. Few of these studies incorporate the temporal dependence present in sampling over time, which can lead to estimation issues which in turn impact conclusions. We contribute to the literature for this important public health science by putting forward time series methods coupled with statistical process control that (1) capture the evolving trend of a disease in the population; (2) separate the uncertainty in the population disease trend from the uncertainty due to sampling and measurement; and (3) support comparison of sub-sewershed population disease dynamics with those of the population represented by the larger downstream treatment plant. Our statistical methods incorporate the fact that measurements are over time, ensuring correct statistical conclusions. We provide a retrospective example of how sub-sewersheds virus levels compare to the upstream wastewater treatment plant virus levels. An on-line algorithm supports real-time statistical assessment of deviations of virus level in a population represented by a sub-sewershed to the virus level in the corresponding larger downstream wastewater treatment plant. This information supports public health decisions by spotlighting segments of the population where outbreaks may be occurring.

The engine mission simulation system: coupled simulation of aircraft and engine in the mission

AbstractWhen designing future aircraft, the entire system including airframe, propulsion system, energy management and mission control must be considered in a coupled approach at an early design stage. To model and study this complex interaction, the Engine Mission Simulation System (EMSS) environment was created in cooperation with the Bundeswehr Technical Center for Aircraft and Aircraft Equipment. EMSS is intended to serve as a digital, flying test bed at the Institute of Jet Propulsion for the rapid and low-cost evaluation of new concepts for propulsion and power supply systems for new and legacy aircraft designs. This tool chain allows a detailed engine simulation under consideration of its flight condition while using an external propulsion and power control system. First, the software architecture of EMSS is discussed, which involves a propulsion simulation in NPSS® coupled with the X-Plane 11 flight simulator via MATLAB®/Simulink®. The aircraft itself is modeled as a six degrees of freedom simulation using the blade element theory, allowing both complex flight maneuvers and unusual aircraft to be easily simulated. The EMSS achieves real-time capability and enables user-friendly pre-design as well as quick trend detection through the ability to perform focused parametric studies. To evaluate the accuracy of the system’s results, the engine model used is validated against experimental data. Subsequently, the aerodynamic characteristics of the aircraft model are verified with a CFD simulation. Finally, a parametric study is used to demonstrate the mission as well as propulsion evaluation capabilities of the Engine Mission Simulation System.