Grey Box Research Articles

Gray Box Time Variant Clogging behaviour and Pressure Drop Prediction of the Air Filter in the HVAC System

Identification and prediction of clogging behavior in heating, ventilation, and air conditioning (HVAC) filters is crucial to avoid issues such as system overheating, energy waste, lower indoor air quality, etc. Researchers are focusing more on the particle loading characteristics of a filter medium in a laboratory environment under steady-state conditions, fixed particle concentrations, area of porosity, dust feed and volumetric flow rate. However, recent research still shows uncertainties in modeling as well as the implementation problems of constructing the HVAC laboratory test bench and equipment. In addition, subjects such as non-uniform particle deposition depreciation of the condition and various type of mechanical filters such as fibrous, fabric, granular, and membrane filter or electrostatic filters which typically used in HVAC systems perform under some assumptions and still need more research. The studies become even more difficult acquiring a large number of time-varying and noisy signals. Another approach among studies is data-driven knowing that Building Automation System (BAS) is not equipped with appropriate sensor measuring the clogging, it is needed to drive the clogging mathematical model from the pressure drop signal. This paper bridges the gap between particle-size study and black box modeling of HVAC filter which has not received much attention from authors. The proposed method assumes that the pressure drop is the result of two time-varying functions; f(t), which represents the dynamics of clogging and, g(t), which refers to dynamics of remained terms. The exponential and polynomial of second order functions are proposed to express the clogging behavior. The software package based on Particle Swarm Optimization Artificial Bee Colony (PSOABC) algorithm, is developed and implemented to estimate the coefficients of the clogging functions based on smallest RMSE, high coefficient of correlation and acceptable tracking. Five Air Handling Unit (AHUs) are selected for practical verification of the model and the results show that the applied method can successfully predict clogging and pressure drop behaviour of HVAC filters.

A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

This paper presents a prediction error-based power forecasting (PEBF) method for a Photovoltaic (PV) system, using Photovoltaics for Utility Scale Applications (PVUSA) model based grey box neural network (GBNN). First, the differential equation based PVUSA model is transformed into a neural network. In the proposed PEBF scheme, the neural network is set to train whenever the difference between predicted and output powers increases from a certain threshold defined based on system dynamics and requirements. The unique design of the PVUSA model based grey box neural network takes far less training time than usual black-box neural network based models. This gives the proposed prediction scheme an advantage of updating the prediction model parameters from frequent training of neural networks with the change in metrological variables. The effectiveness of the proposed prediction scheme is demonstrated by a real case study regarding a 20MW grid-connected PV system located in Dongying city of Shandong province China. To evaluate the efficiency of the developed scheme, different assessment metrics, mean absolute error (MAE), root mean square error (RMSE), weighted mean absolute error (WMAE) and coefficient of determination ( R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) are applied. The average values of MAE, RMSE, and WMAE were 0.12 %, 0.20% and 0.23% respectively for all cases. The results demonstrate that the proposed scheme predicts the PV power efficiently within the defined error tolerance level, which shows the effectiveness and feasibility of the proposed prediction scheme. The prediction accuracy of the proposed scheme has been compared with the conventional black box neural network models and reveals outperformed performance with respect to prediction accuracy improvement. The proposed prediction scheme will help to balance power production and demands across integrated networks through economic dispatch decisions between the power sources.

Framework for Automated Data-Driven Model Adaption for the Application in Industrial Energy Systems

Increasing flexibility and efficiency of energy-intensive industrial processes is generally seen as a big lever towards a decarbonized energy system of the future. However, to leverage these potentials, the accurate prediction of unit behavior is essential to be able to close the gap between supply and demand. Not only pose nonlinear relations a serious challenge in thermal systems engineering and optimization but real-world unit behavior furthermore changes during operation due to wear, fouling and other effects. In the present work, a novel framework for automated data-driven model adaption is presented which is capable of automating fast and accurate predictions of current system behavior. The framework is based on open protocol bidirectional live communication and mechanistic grey box modeling. While especially thermal energy storage is considered a solution to increase flexibility, it is very challenging for operation optimization. A packed bed thermal energy storage operated under severe conditions leading to continuous fouling acts as proof of concept of the proposed framework. The obtained results indicate major improvement for storage output prediction with the novel framework compared to a conventional approach without readjustment. Furthermore, the presented framework is perfectly suitable and an essential foundation for live condition monitoring, fault prediction, predictive maintenance, and operation optimization.

Planning and implementation of a digital shadow for the friction factor quantification of the ECAP process using a grey box modeling approach and finite element analysis

This paper describes the possibility of analytical friction factor generation using a Grey Box modelling approach. A preprocessor is used, which automatically collects measured data via a Smart Production Network and calculates the effective friction coefficient as a function of all significant process parameters of the Equal-Channel-Angular-Pressing (ECAP) process using a data-driven evaluation algorithm. The results of this calculation are then automatically implemented in an efficiently programmed finite element analysis. This Digital Shadow then serves as the basis for the implementation of a Digital Twin, which is described shortly at the end.

Energy, environmental and economic analysis of electric vapour compression and gas driven absorption heat pumps for single-family houses

The aim of this paper is to compare energy consumptions, CO2 emissions, and operative costs of condensing boilers, electric vapour compression heat pumps and gas driven absorption heat pumps to provide space heating and domestic hot water. The analysis is performed for 140 m2 single-family houses in five different Italian cities whose envelope features depend on the location. For each location, two different envelope conditions are considered. The first one is a non-insulated building, while the second one is the same building, but an external thermal insulation is added on vertical walls and roof. To avoid internal renovation, radiators are maintained as emission system. Combined dynamic simulations are performed to appreciate building and system interactions. A 6 second time step is set to evaluate properly interactions and the DHW profile demand. In addition, the GHP dynamic model is a grey box model experimentally validated. The results show that electric vapour compression heat pumps reach the highest non-renewable primary energy savings (&gt;32%) compared to condensing boilers, but their operative costs are higher due to the higher specific cost of electricity in Italy. Gas driven absorption heat pumps achieve a lower consumption reduction than electric heat pumps (&gt;22%), but they have also the minimum operative cost among the three technologies.

Artificial neural network modelling for cream cheese fermentation pH prediction at lab and industrial scales

The mechanism of cream cheese fermentation has a complex, which makes its modelling a challenging task. Researchers have proposed different approaches to modelling the fermentation process. The developed models can be categorized into white, grey, and black box models. In this paper, we studied these models and investigated their application by using lab and industrial scale data which were obtained in the presence of disturbances. The results showed that using the states of the white box model for predicting pH is challenging because of the complexity of the cream cheese compound. Although this problem was solved in the grey box model, there were difficulties in applying both white and grey box models mainly due to the lack of online measurements of states. Unlike white and grey box models, a black box model, an ANN model, was developed based on pH data, which are measured online. Using the experimental pH data, ANN model configurations with optimal feedback and time intervals were used to predict industrial fermentation pH dynamics. The ANN model provided reliable pH predictions at both lab and industrial scales.

Grey box aggregation modeling of wind farm for wideband oscillations analysis

The power electronic converter-based wind farms have imposed great risks on the small-signal stability of the modern power system, mainly characterized by wideband oscillations ranging from one Hertz to several thousand Hertz. To analyze such wideband oscillations problems, a consensus has been reached that the aggregation of the wind farm is indispensable. However, most of the present aggregation studies are focused on the time-domain consistency (e.g., output active/reactive power) before and after aggregation, while the frequency-domain consistency, closely associated with the accuracy of wideband oscillations analysis, has not been paid much attention. To fulfill this gap, in this paper, a wideband grey-box aggregation model of the wind farm with its parameter identification method are presented, which can be acquired without knowing the details of the wind farm. The identified aggregation model shows a good conformity with the large scope frequency characteristics (e.g., 1–2500 Hz) of the detailed wind farm, suitable for the wideband oscillation analysis. Firstly, the trajectory sensitivity and wideband characteristics of the wind farm are studied, based on which a reduced-order grey-box aggregation model is established in low-medium and high frequency band, respectively. Secondly, a vector fitting-based parameter identification method is presented for the established grey-box aggregation model. At last, the influences of the wind speed distribution and transmission line length on the accuracy of the proposed aggregation method are studied, where a comprehensive deviation analysis is carried out statistically.

ISO 15016:2015-Based Method for Estimating the Fuel Oil Consumption of a Ship

Recently, interest in the design and construction of smart ships has been widely increasing. Optimal route planning is a widely studied essential aspect of smart ship technology. Planning an optimal route requires an accurate estimation of the fuel oil consumption of a ship. Various studies have suggested methods for theoretically estimating the fuel oil consumption. However, the calculation methodology and accuracy are different for each method. In addition, in commercial software, a statistical model based mainly on operating data has been used. Therefore, in this study, we propose a method based on ISO 15016:2015 for estimating the fuel oil consumption of a ship by improving the ISO 15016:2002 method—which has been predominantly used in existing studies. Moreover, the accuracy of the proposed method is examined by comparing it with a gray box model based on operating data. The results confirm that the proposed method can be used for estimating the fuel oil consumption of a ship.

An ANFIS based inverse modeling for pneumatic artificial muscles

Pneumatic Artificial Muscles (PAM) are soft actuators with advantages of high force to weight ratio, flexible structure and low cost. On the other hand, their inherent nonlinear characteristics yield difficulties in modeling and control actions, which is an important factor restricting use of PAM. In literature, there are various modeling approaches such as virtual work , empirical and phenomenological models. However, they appear as either much complicated or are approximate ones as a variable stiffness spring for model with nonlinear input-output relationship. In this work, the behaviour of PAM is interpreted as an integrated response to pressure input that results in a simultaneous force and muscle length change. The integrated response behaviour of PAM is not combined effectively in terms of simultaneous resultant force and muscle contraction in many existing models. In order to implement that response, standard identification methods , for instance NNARX, are not suitable for modeling this behaviour. Moreover, an inverse modeling with grey box approach is proposed in order to utilize the model in control applications. Since Neuro-Fuzzy inference systems are universal estimators, the modeling is implemented by an ANFIS structure using the experimental data collected from PAM test bed. According to implementation results, the ANFIS based inverse model has yielded satisfactory performance deducing that it could be a simple and effective solution for PAM modeling and control issue.

Захист інформаційно-комунікаційної мережі установи від несанкціонованого доступу

У статті розглянуто питання можливості проникнення до інформаційно-комунікаційної мережі установи, проаналізовано та симульовано дії реального порушника контурів захисту інформації на прикладі реальної інформаційної інфраструктури, наведено загальний порядок оцінювання та забезпечення вдосконалення систем захисту інформації шляхом проведення тестування на проникнення. Запропоновані методи проведення тестування на проникнення можуть використовуватись при розробці та тестуванні нових систем захисту інформації, а також оцінювання ефективності та вдосконалення вже існуючих систем. Результати можуть бути використані в галузі захисту інформації, зокрема в сучасних інформаційно-комунікаційних системах та мережах (ІКСМ) для оцінювання та підвищення ефективності застосування комплексних систем та окремих засобів захисту інформації в сучасних ІКСМ.

Security ambiguity and vulnerability in G2C eGovernance system: Empirical evidences from Indian higher education

The Higher Education Institutions (HEIs) in India enhanced dependency on Information Communication Technology (ICT) based on G2C eGovernance applications persistently raised apprehension regarding cyber-attacks and breach of security. Objectives:The present study assesses the status of ambiguity and vulnerability pertaining to security aspects of HEIs G2C eGovernance web portals. Methods:Five prominent central and state HEIs i.e. Malaviya National Institute of Technology - Jaipur (MNIT), National Institute of Technology - Kurukshetra (NITK), Guru Nanak Dev University -Amritsar (GNDU), Maharshi Dayanand University – Rohtak (MDU), and Bhagat Phool Singh Mahila Vishwavidyalaya – Sonipat (BPSMV) were included in the study and Grey Box penetration testing method through open-source software’s Whois.sc, Yougetsignal.com, Kali Linux, Builtwith.com, NMAP, and Google Hacking Database (GHDB), etc. along with social engineering testing through external penetration strategy was applied to assess the ambiguity and vulnerability of HEIs. Findings:The analysis revealed that login IDs and passwords related to web portals, eResources, networks, etc. are freely available and shared without any authorization which is a major cause of security breach. The vulnerability test depicted unencrypted communication between the HEIs portals and servers and the absence of well-articulated security and privacy. Novelty: The study exhibits the lenient view of HEIs administration towards security aspects of G2C eGovernance projects and the outcome would enable the HEIs of India to develop a comprehensive security policy for enriching and securing the G2C eGovernance System. Keywords: eGovernance; security; ambiguity; vulnerability; privacy policy; social engineering testing

Meningkatkan Keamanan Sistem Informasi Puskesmas Terpadu dengan Metode Grey-Box Penetration Test Menggunakan Computer Assisted Audit Techniques

Keamanan sistem informasi merupakan hal yang wajib diperhatikan oleh pemiliknya agar terhindar dari kejahatan siber. Sistem informasi yang memiliki kerentanan keamanan dapat mengancam infrastruktur penting suatu organisasi. Kerentanan keamanan adalah segala jenis celah yang memungkinkan penyerang untuk dapat masuk kedalam sistem secara illegal dan melakukan tidakan yang tidak diinginkan. Penetration test pada sistem informasi perlu dilakukan untuk memastikan keamanannya. Penelitian ini bertujuan untuk meningkatkan keamanan sistem informasi puskesmas terpadu Kota Payakumbuh sehingga data dan informasi yang ada terjamin keamanannya. Metode yang digunakan adalah grey box penetration test menggunakan computer assisted audit techniques. Hasil penelitian ini ditemukan 97 kerentanan kategori tinggi, 1 kerentanan kategori sedang dan 26 kerentanan kategori rendah. Dinas Komunikasi dan Informatika Kota Payakumbuh dapat memanfaatkan hasil penetration test sebagai referensi untuk meningkatkan keamanan sistem informasi puskesmas terpadu Kota Payakumbuh.

Nonlinear and linearized gray box models of direct-write printing dynamics

PurposeThe purpose of this study is to develop nonlinear and linearized models of DW printing dynamics that capture the complexity of DW while remaining integrable into control schemes. Control of material metering in extrusion-based additive manufacturing modalities, such as positive displacement direct-write (DW), is critical for manufacturing accuracy. However, in DW, transient flows are poorly controlled due to capacitive pressure dynamics – pressure is stored and slowly released over time from the build material and other compliant system elements, adversely impacting flow rate start-ups and stops. Thus far, modeling of these dynamics has ranged from simplistic, potentially omitting key contributors to the observed phenomena, to highly complex, making usage in control schemes difficult.Design/methodology/approachThe authors present nonlinear and linearized models that seek to both capture the capacitive and nonlinear resistive fluid elements of DW systems and to pose them as ordinary differential equations for integration into control schemes. The authors validate the theoretical study with experimental flow rate and material measurements across a range of extrusion nozzle sizes and materials. The authors explore the contribution of the system and build material bulk modulus to these dynamics.FindingsThe authors show that all tested models accurately describe the measured dynamics, facilitating ease of integration into future control systems. Additionally, the authors show that system bulk modulus may be substantially reduced through appropriate system design. However, the remaining build material bulk modulus is sufficient to require feedback control for accurate material delivery.Originality/valueThis study presents new nonlinear and linear models for DW printing dynamics. The authors show that linear models are sufficient to describe the dynamics, with small errors between nonlinear and linear models. The authors demonstrate control is necessary for accurate material delivery in DW.

Interleaving physics- and data-driven models for power system transient dynamics

The paper explores interleaved and coordinated refinement of physics- and data-driven models in describing transient phenomena in large-scale power systems. We develop and study an integrated analytical and computational data-driven gray box environment needed to achieve this aim. Main ingredients include computational differential geometry-based model reduction, optimization-based compressed sensing, and a finite approximation of the Koopman operator. The proposed two-step procedure (the model reduction by differential geometric (information geometry) tools, and data refinement by the compressed sensing and Koopman theory based dynamics prediction) is illustrated on the multi-machine benchmark example of IEEE 14-bus system with renewable sources, where the results are shown for doubly-fed induction generator (DFIG) with local measurements in the connection point. The algorithm is directly applicable to identification of other dynamic components (for example, dynamic loads).

Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity

An explicit model management framework is introduced for predictive Groundwater Levels (GWL), particularly suitable to Observation Wells (OWs) with sparse and possibly heterogeneous data. The framework implements Multiple Models (MM) under the architecture of organising them at levels, as follows: (i) Level 0: treat heterogeneity in the data, e.g. Self-Organised Mapping (SOM) to classify the OWs; and decide on model structure, e.g. formulate a grey box model to predict GWLs. (ii) Level 1: construct MMs, e.g. two Fuzzy Logic (FL) and one Neurofuzzy (NF) models. (iii) Level 2: formulate strategies to combine the MM at Level 1, for which the paper uses Artificial Neural Networks (Strategy 1) and simple averaging (Strategy 2). Whilst the above model management strategy is novel, a critical view is presented, according to which modelling practices are: Inclusive Multiple Modelling (IMM) practices contrasted with existing practices, branded by the paper as Exclusionary Multiple Modelling (EMM). Scientific thinking over IMMs is captured as a framework with four dimensions: Model Reuse (MR), Hierarchical Recursion (HR), Elastic Learning Environment (ELE) and Goal Orientation (GO) and these together make the acronym of RHEO. Therefore, IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data. The results provide some evidence that (i) IMM at two levels improves on the accuracy of individual models; and (ii) model combinations in IMM practices bring ‘model-learning’ into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.

Beyond Visual Acuity: Development of a Simple Test of the Slow-To-See Phenomenon in Children with Infantile Nystagmus Syndrome

ABSTRACT Purpose Conventional static visual acuity testing profoundly underestimates the impact of infantile nystagmus on functional vision. The slow-to-see phenomenon explains why many patients with nystagmus perform well in non-time restricted acuity tests but experience difficulty in certain situations. This is often observed by parents when their child struggles to recognise familiar faces in crowded scenes. A test measuring more than visual acuity could permit a more real-world assessment of visual impact and provide a robust outcome measure for clinical trials. Methods Children with nystagmus and, age and acuity matched controls attending Southampton General Hospital were recruited for two tasks. In the first, eye-tracking measured the time participants spent looking at an image of their mother when alongside a stranger, this was then repeated with a sine grating and a homogenous grey box. Next, a tablet-based app was developed where participants had to find and press either their mother or a target face from up to 16 faces. Here, the response time was measured. The tablet task was refined over multiple iterations. Results In the eye-tracking task, controls spent significantly longer looking at their mother and the grating (P < .05). Interestingly, children with nystagmus looked significantly longer at the grating (P < .05) but not their mother (P > .05). This confirmed a facial target was key to further development. The tablet-based task demonstrated that children with nystagmus take significantly longer to identify the target; this was most pronounced using a 3-min test with 12-face displays. Conclusion This study has shown a facial target is key to identifying the time-to-see deficit in infantile nystagmus and provides the basis for an outcome measure for use in clinical treatment trials.

Black Box Modelling and Simulating the Dynamic Indoor Relative Humidity of a Laboratory Using Autoregressive–moving-average (ARMA) Model

Modelling and simulation of the dynamic indoor relative humidity behaviour of a building is essential to test any proposed thermal comfort controller and strategy in the building. Like other plants, the dynamic indoor relative humidity behaviour of a building can be developed based on the white box model, black box model, and grey box model. This research focuses on the usage of autoregressive–moving-average (ARMA) model, a type of black box model to represent the dynamic indoor relative humidity behaviour of Industrial Instrumentation Laboratory at Malaysia-Japan International Institute of Technology (MJIIT), UniversitiTeknologi Malaysia (UTM) Kuala Lumpur and uses the real recorded data from the laboratory and minimal knowledge regarding the physical characteristics of the humidity behaviour in the laboratory. The performance of the ARMA model developed in this research is compared with the real recorded data from the laboratory. Result obtained shows that the ARMA model is enough for modelling and simulating the dynamic indoor relative humidity behaviour of the laboratory

Graph-based modelling and simulation of liquid immersion cooling systems

Currently, most of the existing data centers use chilled air to remove the heat produced by the servers. However, liquids have generally better heat dissipation capabilities than air, thus liquid cooling systems are expected to become a standard choice in future data centers. Designing and managing these cooling units benefit from having control-oriented models that can accurately describe the thermal status of both the coolant and the heat sources. This manuscript derives a control-oriented model of liquid immersion cooling systems, i.e., systems where servers are immersed in a dielectric fluid having good heat transfer properties. More specifically, we derive a general lumped-parameters gray box dynamical model that mimics energy and mass transfer phenomena that occur between the main components of the system. The proposed model is validated against experimental data gathered during the operation of a proof-of-concept immersion cooling unit, showing good approximation capabilities.

A framework for uncertainty quantification in building heat demand simulations using reduced-order grey-box energy models

The sophistication of building energy performance tools has significantly increased the number of user inputs and parameters used to define energy models. There are numerous sources of uncertainty in model parameters which exhibit varied characteristics. Therefore, uncertainty analysis is crucial to ensure the validity of simulation results when assessing and predicting the performance of complex energy systems, especially in the absence of adequate experimental or real-world data. Furthermore, different kinds of uncertainties are often propagated using similar methods, which leads to a false sense of validity. A comprehensive framework to systematically identify, quantify and propagate these uncertainties is missing. The main aim of this research is to formulate an uncertainty framework to identify and quantify different types of uncertainties associated with reduced-order grey box energy models used in heat demand predictions of the building stock. The study introduces an integrated uncertainty approach based on a copula-based theory and nested Fuzzy Monte Carlo approach to address the correlations and separate the different kinds of uncertainties. Nested Fuzzy Monte-Carlo approach coupled with Latin Hypercube Sampling is used to propagate these uncertainties. Results signify the importance of uncertainty identification and propagation within an energy system and thus, an integrated approach to uncertainty quantification is necessary to maintain the relevance of developed building simulation models. Moreover, segregation of relevant uncertainties aids the stakeholders in supporting risk-related design decisions for improved data collection or model improvement.

Combined Analyses of Phenotype, Genotype and Climate Implicate Local Adaptation as a Driver of Diversity in Eucalyptus microcarpa (Grey Box)

Trees are a keystone species in many ecosystems and a critical component of ecological restoration. Understanding their capacity to respond to climate change is essential for conserving biodiversity and determining appropriate restoration seed sources. Patterns of local adaptation to climate between populations within a species can inform such conservation decisions and are often investigated from either a quantitative trait or molecular genetic basis. Here, we present findings from a combined analysis of phenotype (quantitative genetic analysis), genotype (single nucleotide polymorphism (SNP) trait associations), and climate associations. We draw on the strength of this combined approach to investigate pre-existing climate adaptation and its genetic basis in Eucalyptus microcarpa (Grey box), an important tree for ecological restoration in south-eastern Australia. Phenotypic data from a 26-year-old provenance trial demonstrated significant genetic variation in growth and leaf traits at both the family and provenance levels. Growth traits were only associated with temperature, whilst leaf traits were associated with temperature, precipitation and aridity. Genotyping of 40 putatively adaptive SNPs from previous genome-wide analyses identified 9 SNPs associated with these traits. Drawing on previous SNP–climate association results, several associations were identified between all three comparisons of phenotype, genotype and climate. By combining phenotypic with genomic analyses, these results corroborate genomic findings and enhance understanding of climate adaptation in E. microcarpa. We discuss the implication of these results for conservation management and restoration under climate change.