Generation Of Engines Research Articles

Hybrid magnetron sputtering of ceramic superlattices for application in a next generation of combustion engines

A hybrid magnetron sputtering process (dcMS/HiPIMS) was developed to manufacture nanostructured CrN/Cr1-xAlxN multilayers, motivated by improving the low-emission efficiency when applied on gas-nitrided diesel piston rings of a next-generation of combustion engines. In order to improve the mechanical, tribological, and corrosion behavior of the multilayers, the hybrid dcMS/HiPIMS process was designed by selecting the optimal sputtering procedure applied to AISI 440 base steel. The effect of substrate bias and carousel rotational speed on the phase composition, crystallographic texture, residual stresses, surface roughness, coating periodicity and densification, instrumented hardness, elastic modulus, as well as wear and corrosion resistance was determined. The results have demonstrated that hybrid magnetron sputtering produces multilayers with a superlattice structure, which outperforms commercial PVD coatings of CrN for diesel piston rings manufactured by cathodic arc evaporation. Also, multilayer periodicities in the range of 5 to 10 nm yield the best tribological performance under bench tests for the piston ring/cylinder liner system.

Biochemical and Molecular Dynamics Study of a Novel GH 43 α-l-Arabinofuranosidase/β-Xylosidase From Caldicellulosiruptor saccharolyticus DSM8903.

The complete hydrolysis of xylan can be facilitated by the coordinated action of xylanase and other de-branching enzymes. Here, a GH43 α-l-arabinofuranosidase/β-xylosidase (CAX43) from Caldicellulosiruptor saccharolyticus was cloned, sequenced, and biochemically investigated. The interaction of the enzyme with various substrates was also studied. With a half-life of 120 h at 70°C, the produced protein performed maximum activity at pH 6.0 and 70°C. The enzyme demonstrated a higher activity (271.062 ± 4.83 U/mg) against para nitrophenol (pNP) α-L-arabinofuranosides. With xylanase (XynA), the enzyme had a higher degree of synergy (2.30) in a molar ratio of 10:10 (nM). The interaction of the enzyme with three substrates, pNP α-L-arabinofuranosides, pNP β-D-xylopyranosides, and sugar beet arabinan, was investigated using protein modeling, molecular docking, and molecular dynamics (MD) simulation. During the simulation time, the root mean square deviation (RMSD) of the enzyme was below 2.5 Å, demonstrating structural stability. Six, five, and seven binding-interacting residues were confirmed against pNP α-L-arabinofuranosides, pNP β-D-xylopyranosides, and arabinan, respectively, in molecular docking experiments. This biochemical and in silico study gives a new window for understanding the GH43 family’s structural stability and substrate recognition, potentially leading to biological insights and rational enzyme engineering for a new generation of enzymes that perform better and have greater biorefinery utilization.

NEW TECHNOLOGIES AND THEIR INTERCONNECTION IN THE CREATION AND PROCESSING OF 3DMODELS AND SCENES

Abstract. This article focuses on the content and processing of the prepared dissertation thesis, which deals with the possibilities of using new technologies and software tools such as procedural generation of 3D models, game engines, rendering programs, virtual and augmented reality, eye tracking and BIM in cartographic processing and 3D landscape visualization. The dissertation focuses on the interconnection of these technologies, on new methods of processing and on the combination of the use of modern software equipment with already existing methods of processing into one complete process. For these purposes, the possibilities of using, among other things, software development kits (SDK), plugins and extensions of existing programs will be explored. Great emphasis will be placed on the Unity game engine and the ability to use its individual parts (rendering engine, physics engine, scripting, simulation, and AI) to create new workflows and new types of processing / outputs. The main goal of this dissertation is to find and document the possibilities of interconnection of individual technologies, especially those that are not commonly used or can be considered new (no scientific articles were found that would use these methods of interconnection). The result of the dissertation will be new working procedures and more realistic visualizations of 3D models and scenes corresponding to the technologies available today. An equally important part of the dissertation will be the creation of sample projects for individual programs, test data, work procedures and general teaching materials to simplify the use of these technologies in teaching and in departmental projects.

A correctly scaling rigorously spin-adapted and spin-complete open-shell CCSD implementation for arbitrary high-spin states.

In this paper, we report on a correctly scaling novel coupled cluster singles and doubles (CCSD) implementation for arbitrary high-spin open-shell states. The chosen cluster operator is completely spin-free, i.e., employs spatial substitutions only. It is composed of our recently developed Löwdin-type operators [N. Herrmann and M. Hanrath, J. Chem. Phys. 153, 164114 (2020)], which ensure (1) spin completeness and (2) spin adaption, i.e., spin purity of the CC wave function. In contrast to the proof-of-concept matrix-representation-based implementation presented there, the present implementation relies on second quantization and factorized tensor contractions. The generated singles and doubles operators are embedded in an equation generation engine. In the latter, Wick's theorem is used to derive prefactors arising from spin integration directly from the spin-free full contraction patterns. The obtained Wick terms composed of products of Kronecker deltas are represented by special non-antisymmetrized Goldstone diagrams. Identical (redundant) diagrams are identified by solving the underlying graph isomorphism problem. All non-redundant graphs are then automatically translated to locally-one term at a time-factorized tensor contractions. Finally, the spin-adapted and spin-complete (SASC) CCS and CCSD variants are applied to a set of small molecular test systems. Both correlation energies and amplitude norms hint toward a reasonable convergence of the SASC-CCSD method for a Baker-Campbell-Hausdorff series truncation of order four. In comparison to spin orbital CCSD, SASC-CCSD leads to slightly improved correlation energies with differences of up to 1.292mEH (1.10% with respect to full configuration identification) for quintet CH2 in the cc-pVDZ basis set.

Design of fuel molecules based on variational autoencoder

A variational autoencoders (VAE) has been developed to reversibly represent hydrocarbon molecules in the mathematic form of continuous multidimensional vector for fuel design. The characteristics of a hydrocarbon molecule can be efficiently captured to establish the latent space of hydrocarbon fuels by the VAE. The physicochemical properties of a hydrocarbon fuel were automatically calculated by a piece of python code based on the group contribution method, and the energy properties were quickly and accurately predicted by a multilayer perceptron jointly trained with the VAE. Handily manipulating of the fuel molecules have been realized in the latent space, such as sampling new structures, decoding random vectors, perturbing a given fuel structure, and interpolating between fuel molecules. A large database (CH-02) containing 11,291,051 hydrocarbon molecules and their fuel properties have been established. Moreover, an approximate-concentric-sphere method was developed to realize multi-objective optimization of a given fuel molecule. This work opens new avenues in the de novo design of new hydrocarbon fuels to meet the requirements of next generation engine.

Design of Hybrid-Electric Small Air Transports

The potential benefits of hybrid-electric or full-electric propulsion have led to the proliferation of many concepts over the past decade. The lack of conceptual design methods capable of grasping the effects of the new propulsive technologies is often due to the absence of industrial data supporting the research. In the present work, two objectives are pursued. On the one hand, the introduction of semi-empirical methods for weight estimation based on the data provided by the industrial partners of the ELICA CS2 project. On the other hand, the establishment of generative engineering as a conceptual design tool for propulsive system architecting and fault tolerance analysis. The applications of the proposed conceptual design methods deal with two different years of entrance in service.

Psychometrics of Questionnaires Designed for the Evaluation of Safety Levels and Safety Culture Case Study: Power Plant Generator Manufacturing Company

The psychometrics of instruments in safety performance evaluation is essential for the accreditation of an organization’s safety evaluation and has been emphasized in many studies. Psychometrics pertains to the validity and reliability of an evaluation instrument and describes its precision and consistency. The aim of the present study was to evaluate the psychometrics of safety level and safety culture questionnaires to provide a reliable and valid instrument for safety performance evaluation in industries. This descriptive cross sectional study was conducted with the intention of psychometric instruments used to evaluate the safety level and the safety climate at the Mapna Pars power plant generator manufacturing and engineering company. The face validity of the questionnaires was analyzed quantitatively and qualitatively. In the quantitative method, the importance of the influence score was applied and the content validity was calculated using the Lawshe method. Content Validity Ratio (CVR) and Content Validity Index (CVI) were also used. For this purpose, elicitation was obtained from experts within the professional health and safety community. The reliability of the instruments was determined via the Cronbach's alpha test and the Interclass Correlation Coefficient (ICC) test. Finally, the standardized questionnaires were used to evaluate the safety level and safety climate of the industry as a case study. The obtained data were analyzed using SPSS software solution v20. Based on the quantitative face validity results obtained at the Mapna Pars Company, one question was removed from each of the final instruments. The content reliability analysis revealed that the safety level questionnaire had a CRI of 0.99, CVR of 0.91, and was accepted. The safety climate questionnaire had an acceptable CVI of 0.95 and an acceptable CVR of 0.82. Regarding the reliability analysis, a Cronbach's alpha of 0.72 and 0.89 was obtained for the safety level questionnaire and the safety climate questionnaire, respectively. It can be concluded that both questionnaires had an acceptable level of internal consistency. The re-application of the questionnaires after two weeks revealed a relatively consistent safety level (ICC=0.90) and safety climate (ICC=0.74). Analyzing the data obtained in the present study showed that the safety level questionnaire with 66 questions and the safety climate questionnaire with 93 questions had acceptable validity and reliability. Thus, it may provide a useful approach for safety evaluations in similar industries.

Enhanced photocatalytic reduction of carbon dioxide into carbon monoxide by electric field generation and defect engineering in TiO2

Enhanced photocatalytic reduction of carbon dioxide into carbon monoxide by electric field generation and defect engineering in TiO2

Optimum intensity measures for probabilistic seismic demand model of subway stations with different burial depths

In this paper, optimum intensity measures for probabilistic seismic demand model of subway stations with different burial depths are investigated. A three-story three-span subway station which is commonly seen in the metro system in China, is selected as a typical case. The subway station is embedded in two generic engineering sites corresponding to site classes II and III specified in Chinese code for seismic design of urban rail transit structures. Five different burial depths (4 m, 8 m, 12 m, 16 m, and 20 m) are considered in this study. Nonlinear soil-structure interaction time history analyses are conducted on the two-dimensional numerical model of the subway station embedded in two different engineering sites. Based on the theory of wave propagation in homogeneous and elastic media, seismic excitations at the engineering bedrock for the two generic sites are back calculated from an ensemble of 50 pairs of as-recorded seismic motions at bedrock outcrop. The efficacy of twenty-one commonly-used scalar intensity measures (IMs) for seismic response prediction of underground structures are evaluated by the metrics of efficiency, practicality, proficiency and sufficiency. The numerical results reveal that for subway stations embedded in site class II, velocity spectrum intensity (VSI) is the optimum IM with different burial depths. Furthermore, for subway station embedded in site class III, VSI and peak ground velocity (PGV) are the optimum IMs with a burial depth of 4 m, and VSI and compound velocity-related intensity measure are the optimum IMs with burial depths of 8 m–20 m. Moreover, for the three well-recognized amplitude IMs, peak ground acceleration (PGA), PGV, peak ground displacement, with the increase of the burial depth of underground structures, the proficiency of PGV is gradually enhanced compared with PGA. Finally, based on the theory of one-dimensional wave propagation in homogeneous and elastic half-space, the preliminary mechanism explanation of the optimum amplitude IMs for variation of burial depth is given.

Development of a test-rig for advanced rotor–stator seals used in jet engines: Part II

Advanced rotor–stator seals are becoming more and more common in the latest generation of jet engines. In order to develop new radial seals and fully understand existing ones, testing needs to be done under full-scale conditions. It is necessary to be able to evaluate a new seal concept at “scale one”, under representative operating temperatures, pressure and rotational speed. This article – published in two instalments – presents details of an advanced new test-rig that is able to achieve this goal. It describes many facets of its design, construction, commissioning and use, and the challenges associated with its development.

Linear kitchen layout design via machine learning

AbstractThe main objective of this paper is to develop a novel approach for linear kitchen layout design which utilizes information from existing layouts via machine learning algorithms. With the growing popularity of large-scale virtual 3D environments for architectural visualization and the game industry, the manual interior design of virtual scenes becomes prohibitively expensive in terms of time and resources. In our approach, the machine learning model automatically generates layout suggestions. The proposed procedural kitchen generation (PKG) model is a pipeline of six Machine Learning (ML) classifiers that are trained and tested on a kitchen layout dataset created by interior designers. The performances of the model are evaluated for the following classifiers: Random forest, Decision tree, AdaBoost, Naive Bayes, MLP, SVM, and L2 Logistic regression. Random forest, as the best performing classifier is used in the final PKG model, and integrated into Unity Engine for automatic 3D kitchen generation and presentation. The PKG model is evaluated in the quantitative and perceptual study, showing better performance than the prior rule-based method. The perceptual study results demonstrate that our tool can be used to speed up designer's work, improve communication with clients, and educate interior design students.

Production of Producer Gas from Densified Agricultural Biomass in Downdraft Gasifier and Its Application to Small Diesel Engines

Biomass is becoming one of the most popular renewable energy sources, especially from agricultural wastes. These wastes can be gasified and utilized in various industries. This experimental study investigated producer gas generation from densified agricultural fuels such as corncobs, rice husks, wood chips, and oil palm fronds in a 50 kWth throatless downdraft gasifier. This system produced combustible gases such as H2, CO, and CH4, which were utilized as a substitute for diesel fuel in a small diesel engine for power generation. The results showed that the gasifier performs successfully and seems to prefer pellets to briquettes. Producer gas contains 18%–20% carbon monoxide, 1%–6% hydrogen, and 0.9%–1.9% methane. Maximum gasification efficiencies of 53%–71% were achieved with biomass pellets from wood chips, corncobs, oil palm fronds, and rice husks, with producer gas calorific value of 2.94–3.85 MJ/Nm3. The average fuel consumption rate was between 6.72–14.43 kg/h, while the producer gas yield was between 2–3 Nm3/kg. The average gravimetric concentration of biomass tar in the raw product gas was found to be in the range of 23–50 g/Nm3, in which pelletized fuel appeared to show slightly lower tar than briquette fuel. The tar was primarily composed of five compounds: Benzo (a) pyrene, chrysene, pyrene, phenanthrene, 1-methylnaphthalene, and several other polycyclic aromatic compounds. The producer gas from oil palm frond briquettes and biodiesel were tested in a gas engine system in a dual fuel mode. A thermal efficiency of 22.21% was achieved with 2500 W electric load and a 72% biodiesel displacement rate, respectively.

An Energy-Efficient Reconfigurable AI-Based Object Detection and Tracking Processor Supporting Online Object Learning

This letter presents an energy-efficient reconfigurable AI-based object detection and tracking processor for smart drone/robot applications. Several techniques have been proposed to achieve high energy efficiency while supporting flexible object detection and tracking tasks with online object learning, including a reconfigurable object detection and tracking architecture with reconfigurable neural network (NN) engine, an online object learning architecture with shared NN inference and learning engine and automatic label generation engine, and a layer- and stride-aware NN computing technique. Compared with several state-of-the-art designs, the proposed design achieves better energy efficiency (2.13 mJ/frame), while supporting flexible object detection and tracking tasks with online object learning.

Analysis of Factors Influencing Knowledge Transfer between the Product and Production System Development as well as Production

The product development process is characterized by accelerating development cycles and increasing customer demands for a wide range of product variants. In addition, it is very knowledge-intensive and characterized by the reuse of knowledge in product generation engineering. Developing new products based on existing references, e.g. knowledge about design parameters or manufacturing technologies, requires effective and efficient transfer of knowledge. In a knowledge transfer, people of different domains, here product and production system development as well as production, make parts of their mental model tangible for others. When doing so, problems can occur that can cause information loss. Knowledge transfer has been reviewed in literature by multiple disciplines and defined differently amongst various understandings of its design. In this work, knowledge transfer includes the identification, transmission, and application of knowledge and thus addresses the problem of distributing knowledge within a company. To optimize knowledge transfer within the product engineering process to reduce information loss and knowledge deficits, factors that impact knowledge transfer must be considered. Therefore, this contribution examines factors that either influence the knowledge transfer positively or negatively, especially between product and production system development as well as production. In addition to a literature-based identification of influencing factors, a qualitative study interviewing experts in those fields enhances the findings. Furthermore, the collection of factors was assigned to four clusters: people, organization, technology, and knowledge and transfer. By linking the factors of each cluster, a model was created to be able to investigate the impact of changing factors within and between clusters providing a basis for closing knowledge deficits to enable effective and efficient knowledge transfer.

Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries

The purpose of this study is to investigate the effects of contextual features on automatic detection accuracy of online recruitment frauds in Australian job market. In addition, the study aims to unearth the significance of localisation of such approaches. The study first generates a dataset based on a local and semi-structured advertising platform in Australia. The labelled dataset is then used to train a learning model on several content-based and contextual features. The existence of advertising body in relevant government and non-government registries in Australia, along with the internet presence of the advertiser, were considered as contextual features. The extraction process of such contextual features was automated as well. The study concludes that the inclusion of contextual features improves the performance measures of the automated online recruitment fraud detection model. The practical implication of the study is two-folds. Firstly, the contextual feature space generation engine can be used with any dataset, with minimal localisation efforts. Secondly, such learning models can be used in the back end of online job recruitment portals to detect and prevent online recruitment frauds. The study not only demonstrates the positive impact of using contextual features in fraud detection using a real-life dataset, but it also demonstrates how these contextual features can be extracted automatically from the web, based on localised company registries. The generated dataset has been made available for further research in the domain.

Allometric Vegetation Modeling and SAR Image Simulation for Polarimetry and Interferometry

This paper establishes a low degrees-of-freedom allometric vegetation model based on the Biomass and allometry database (BAAD). It consists of a 5-bit species encoding scheme and a 5-parameter relationship derived from the BAAD data records. Combined with opensource fractal tree generation engine, it can produce realistic tree samples of a large variety of species. A coherent electromagnetic scattering calculation method is developed for the vegetation model where the generalized Rayleigh-Gans (GRG) approximation and the infinite cylinder approximation are used to calculate the scattering matrix of leaves and branches/trunk. Four-path multiple scattering mechanisms between vegetation and the ground are considered and attenuations through vegetation canopy are also considered. The scattering model is validated against numerical methods. In addition, an end-to-end simulation tool is developed. Optical image is used to extract individual trees with center positions and crown diameters. The rest parameters are generated using the derived allometry model. Virtual 3D scene with vegetation on digital elevation map (DEM) can be generated and SAR images can be simulated. Several case studies are carried out for both polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) and multi-temporal interferometric SAR (InSAR). One case of the Mount Fuji area is simulated and validated against ALOS-2 data and demonstrates an average scattering coefficient error of less than 3dB. Additional cases of Southwest China, Hainan Island and the Great Khingan Mountain demonstrate the feasibility of the proposed simulation scheme for various application scenarios.

Genetic Engineering of Streptomyces ghanaensis ATCC14672 for Improved Production of Moenomycins.

Streptomycetes are soil-dwelling multicellular microorganisms famous for their unprecedented ability to synthesize numerous bioactive natural products (NPs). In addition to their rich arsenal of secondary metabolites, Streptomyces are characterized by complex morphological differentiation. Mostly, industrial production of NPs is done by submerged fermentation, where streptomycetes grow as a vegetative mycelium forming pellets. Often, suboptimal growth peculiarities are the major bottleneck for industrial exploitation. In this work, we employed genetic engineering approaches to improve the production of moenomycins (Mm) in Streptomyces ghanaensis, the only known natural direct inhibitors of bacterial peptidoglycan glycosyltransferses. We showed that in vivo elimination of binding sites for the pleiotropic regulator AdpA in the oriC region strongly influences growth and positively correlates with Mm accumulation. Additionally, a marker- and “scar”-less deletion of moeH5, encoding an amidotransferase from the Mm gene cluster, significantly narrows down the Mm production spectrum. Strikingly, antibiotic titers were strongly enhanced by the elimination of the pleiotropic regulatory gene wblA, involved in the late steps of morphogenesis. Altogether, we generated Mm overproducers with optimized growth parameters, which are useful for further genome engineering and chemoenzymatic generation of novel Mm derivatives. Analogously, such a scheme can be applied to other Streptomyces spp.

A Model-Based Design Method for the Correlation between Customer Feedback and Technical Design Parameters in the Context of Systems Engineering

Nowadays considering trends such as digitalization, automated driving as well as electric mobility in products in automotive development processes is a major challenge, which has led to an enormous increase in the number of product functions of technical systems. However, the recognized processes in automotive development are strongly component-oriented and such processes partially support the development of product functions. In order to meet future trends and ensure long term customer satisfaction, a transfer from component-oriented to function-oriented development is necessary. Accordingly, a holistic concept can be useful that enables the integration of customer feedback into the early phase of product development in the context of function-orientation. However, the customer feedback evaluation and their mapping with technical subsystems have been considered mainly in the context of component-oriented development. In this contribution, a method is proposed, which is generated in the context of a product model of product generation engineering. Product Generation Engineering enables the structuring of the development process of a product generation and supports function-oriented development. The Product Model provides customer- oriented development of mechatronic products. The proposed method is achieved in the sense of model-based systems engineering and validated by the exemplarily application of a case study of a specific vehicle. Both the past and current product generations of the specific vehicle are taken into account in the development of the subsequent product generation.

Data Analysis for the Aero Derivative Engines Bleed System Failure Identification and Prediction

Middle size gas/diesel aero-derivative power generation engines are widely used on various industrial plants in the oil and gas industry. Bleed of Valve (BOV) system failure is one of the failure mechanisms of these engines. The BOV is part of the critical anti-surge system and this kind of failure is almost impossible to identify while the engine is in operation. If the engine operates with BOV system impaired, this leads to the high maintenance cost during overhaul, increased emission rate, fuel consumption and loss in the efficiency. This paper proposes the use of readily available sensor data in a Supervisory Control and Data Acquisition (SCADA) system in combination with a machine learning algorithm for early identification of BOV system failure. Different machine learning algorithms and dimensionality reduction techniques are evaluated on real world engine data. The experimental results show that Bleed of Valve systems failures could be effectively predicted from readily available sensor data.

Development of a test-rig for advanced rotor–stator seals used in jet engines: Part I

Advanced rotor–stator seals are becoming more and more common in the latest generation of jet engines. In order to develop new radial seals and fully understand existing ones, testing needs to be done under full-scale conditions. It is necessary to be able to evaluate a new seal concept at “scale one”, under representative operating temperatures, pressure and rotational speed. This article – published in two instalments – presents details of an advanced new test-rig that is able to achieve this goal. It describes many facets of its design, construction, commissioning and use, and the challenges associated with its development. The first instalment, which appears here, looks at key characteristics of the test-rig, provides an overview of its mechanical design and airflow loop, and lists associated references. The second instalment will be published in the January 2022 issue of Sealing Technology.