Dynamic Engine Research Articles

Transparent Intrinsic Barrier Polymer via Dynamic Pressure Engineering

Polymers with desirable barrier performances are urgently demanded for various applications, especially in dealing with COVID19. Current art mostly focuses on copolymerization, crystallization, melt blending, and surface modification, ultimately leading to compromised transparency, mechanical performances, and potential medical safety. In this work, we report a dynamic pressure driven approach for the first time can translate conventional nonbarrier polyethylene terephthalate (PET) into intrinsic barrier (ib-PET) material while maintaining its outstanding transparence by a single step. The oxygen permeability and water vapor transmission rate are 88% and 43% lower than those for conventional technique-processed nonbarrier PET, respectively, resulting from slower chain dynamics and reduction in the free volume. This work can construct a prospective avenue toward rational design for pristine materials with outstanding barrier properties and provide in-depth insight into the perception of dynamic pressure engineering, as well as provide a novel concept for tailoring and design of advanced polymers from the energy point of view.

Factors Influencing Cost Performance Improvement on the Concept Of Green Retrofitting High-Rise Offices Using Structural Equationg Modelling-Part Least Square (SEM-PLS)

The increase in energy consumption in the operation of the building construction industry globally has increased by a percentage of 35%, so that this increase can lead to an increase in carbon emissions which was originally at a percentage of 28% to 38%. The concepts of Green Retrofitting, Dynamic Systems and Value Engineering can be researched based on the influence on improving the cost performance of Green Retrofitting in high-rise (existing) office buildings and connected with the Structural Equation Modelling-Partial Least Square (SEM-PLS) analysis model. PLS-SEM is very appropriate to be used in research aimed at developing theories. The results of this study show that in the application of the Green Retrofitting concept in high-rise office buildings using Dynamic Systems and Value Engineering has a significant effect on improving the cost performance of Green Retrofitting and get the most influential factors are Top Management Support, Energy Efficiency, Water Efficiency, Indoor Air Quality, Retrofitting Planning, Retrofitting Initial Costs, Bill Of Quantity, Dynamic System Model Generation, Alternative Material Selection, and Material Cost Reduction

RANS-AD based ANN surrogate model for wind turbine wake deficits

Inside wind farms, wake effects are the primary source of turbine interactions, and as such, they constitute one of the most important aspects of wind farm operations. The two most widespread methods for calculating the wind farm wake flow are computational fluid dynamics (CFD) methods and engineering models. Both methods have drawbacks; CFD methods can be very accurate but are computationally expensive. Vice versa, engineering models sacrifice accuracy by simplifying the physics, thereby improving computational efficiency.In cases where many evaluations of the flow are needed, this trade-off is a hindrance. One such case is wind farm layout optimization problems. It has been shown that the estimation of wake flows can be improved by surrogate modeling. Recently, Artificial Neural Networks (ANN) have been demonstrated to predict accurate wakes over a mesh. In this work, a new mesh-free ANN-based wake model is proposed. This new model can predict the flow everywhere in the domain, and as it employs smooth activation functions, it is suitable for gradient-based optimization.Two ANNs were trained with data generated by Reynolds-Averaged Navier-Stokes with Actuator Disc simulations for several yaw angles. The first ANN predicts streamwise wake velocity induction/deficit, the second ANN predicts added wake turbulence intensity. Both ANNs predict with a low error.

Experimental characterization of the turbulent intake jet in an engine flow bench

The turbulent intake flow of an optically accessible internal combustion engine is modeled using an air flow bench to reduce the complexity in the number of variables inherent within engine flows. By removing the piston and introducing a new optically accessible housing and outlet channel, the flow bench design simulates engine flows in the region just downstream of the intake valves and offers the possibility to measure and calculate quantities that would be difficult or impossible to obtain in the unsteady environment of a dynamic engine. Velocity data obtained via high-speed particle image velocimetry of the flow bench in the symmetry and valve planes are compared with data from a base operating condition of the motored engine at an intake pressure of 0.95 bar and a speed of 800 rpm at - 270°CA (270 crank-angle-degrees before compression top dead center), beginning with stationary valves at the corresponding valve lift of - 270°CA, then with moving valves. Analysis of the intake jet turbulence for increasing mass flow rates reveals a coherent flapping of the jet at a frequency of 752.5 Hz for only the 100% mass flow rate case. The vortex shedding frequency of the valve stem is estimated to being in the range of 634–799 Hz, indicating a possible link between the coherent jet flapping and the vortex shedding surviving the acceleration through the valve gap. Through comprehensive analysis, this study provides valuable validation data and insight into the intake flows of internal combustion engines.

Characterizing and utilizing oxygen-dependent promoters for efficient dynamic metabolic engineering

Promoters adjust cellular gene expression in response to internal or external signals and are key elements for implementing dynamic metabolic engineering concepts in fermentation processes. One useful signal is the dissolved oxygen content of the culture medium, since production phases often proceed in anaerobic conditions. Although several oxygen-dependent promoters have been described, a comprehensive and comparative study is missing. The goal of this work is to systematically test and characterize 15 promoter candidates that have been previously reported to be induced upon oxygen depletion in Escherichia coli. For this purpose, we developed a microtiter plate-level screening using an algal oxygen-independent flavin-based fluorescent protein and additionally employed flow cytometry analysis for verification. Various expression levels and dynamic ranges could be observed, and six promoters (nar-strong, nar-medium, nar-weak, nirB-m, yfiD-m, and fnrF8) appear particularly suited for dynamic metabolic engineering applications. We demonstrate applicability of these candidates for dynamic induction of enforced ATP wasting, a metabolic engineering approach to increase productivity of microbial strains that requires a narrow level of ATPase expression for optimal function. The selected candidates exhibited sufficient tightness under aerobic conditions while, under complete anaerobiosis, driving expression of the cytosolic F1-subunit of the ATPase from E. coli to levels that resulted in unprecedented specific glucose uptake rates. We finally utilized the nirB-m promoter to demonstrate the optimization of a two-stage lactate production process by dynamically enforcing ATP wasting, which is automatically turned on in the anaerobic (growth-arrested) production phase to boost the volumetric productivity. Our results are valuable for implementing metabolic control and bioprocess design concepts that use oxygen as signal for regulation and induction.

Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining

Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular research. For large assemblies, ultra coarse models are often knowledge-based, relying on a priori information to parameterize models thus hindering general predictive capability. Here, we present substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG2. SBCG2 utilizes a revitalized formulation of the topology representing network which makes high-granularity modeling possible, preserving atomistic details that maintain assembly characteristics. Further, we present a method of granularity selection based on charge density Fourier Shell Correlation and have additionally developed a refinement method to optimize, adjust and validate high-granularity models. We demonstrate our approach with the conical HIV-1 capsid and heteromultimeric cofilin-2 bound actin filaments. Our approach is available in the Visual Molecular Dynamics (VMD) software suite, and employs a CHARMM-compatible Hamiltonian that enables high-performance simulation in the GPU-resident NAMD3 molecular dynamics engine.

Intransitivity as a dynamic assembly engine of competitive communities

Historically, those ecological communities thought to be dominated by competitive interactions among their component species have been assumed to exhibit transitive competition, that is, a hierarchy of competitive strength from most dominant to most submissive. A surge of recent literature takes issue with this assumption and notes that some species in some communities are intransitive, where a rock/scissors/paper arrangement characterizes some components of some communities. We here propose a merging of these two ideas, wherein an intransitive subgroup of species connects with a distinct subcomponent that is organized hierarchically, such that the expected eventual takeover by the dominant competitor in the hierarchy is thwarted, and the entire community can be sustained. This means that the combination of transitive and intransitive structures can maintain many species even when competition is strong. Here, we develop this theoretical framework using a simple variant on the Lotka-Volterra competition equations to illustrate the process. We also present data for the ant community in a coffee agroecosystem in Puerto Rico, that appears to be organized in this way. A detailed study on one typical coffee farm illustrates an intransitive loop of three species that seems to maintain a distinct competitive community of at least 13 additional species.

Modeling of wound ballistics in biological tissues simulators

Modern weapons cause severe damage, accompanied by high rates of complications and mortality. The investigation of such kinds of weapons is in high demand considering the ongoing active phase of russia's war against Ukraine since February 2022. In order to understand the pathological processes that occur in and outside the gunshot wound, we conducted an experimental study using mathematical simulation. The results presented in the article will help to choose the appropriate surgical management and improve the results of treatment. The aim of this study was to investigate and evaluate the damaging effect of a 5.45 mm 7N6M bullet and a 5.45 mm V-max expansive bullet using numerical modeling of wound canals in ballistic plasticine. The Ansys Explicit Dynamics engineering complex was used to simulate the dynamics of the bullet’s motion. The basic equations, solved by the explicit dynamic analysis, express the conservation of mass, momentum, and energy in Lagrange coordinates. Together with the material model and the set of initial and boundary conditions, they determine the complete solution to the problem. Taking into account that the initial velocity of the bullet is 1185 m/s at a mass of 3.9 g, we obtain energy 2740 J. All this energy acts at the area of the wound canal with a depth of 150 mm. Injury with a conventional 7N6M bullet is characterized by the fact that it passes through the block and loses only part of the kinetic energy. The simulation results showed that the velocity of the bullet at the outlet is 220 m/s. Taking into account the initial velocity of the bullet 918 m/s with a mass of 3.4 g, we obtain the kinetic energy acting on the walls of the wound canal with a depth of 200 mm of about 830 J. Mathematic analyses showed that the expansive bullet has a soft core that deforms and transfers all the kinetic energy to the tissues immediately after penetration into the tissues. The loss of kinetic energy of the bullet (ΔE, J) is defined as the difference between the kinetic energy at the time of injury (Ec, J) and the residual energy of the bullet when leaving the material (Er, J). Numerical modeling of wound ballistics in biological tissue simulators allows us to determine with high accuracy the features of wound canal formation and tissue response to damage of bullets having different kinetic energy, which contributes to the choice of adequate surgical management during surgery for gunshot wounds.

New Method of Transformer Differential Protection Based on Graph Fourier Transform

In order to enhance the anti-saturation capability of transformer differential protection, a new method based on graph Fourier transform is proposed. First, by analyzing the characteristics of the similarity of the differential protection of the side current and the eigenvector. Secondly, the graph Fourier transform calculates the spectral coefficient for the differential current diagram signal, and constructs the action component and braking component of the differential protection, which can more accurately and effectively identify the faults in the differential protection area, and significantly improves the anti-saturation ability. The dynamic imitation and engineering cases have proved that the new criterion has high sensitivity and maximum TA saturation ability compared to the current mode value and maximum braking methods widely used in the current engineering.

Lithiophilic interface dynamic engineering to inhibit Li dendrite growth for intrinsically safe Li-metal batteries

Lithium (Li) metal is deemed as attractive anode due to its low electrochemical potential and high theoretical capacity. Nevertheless, the uncontrollable Li dendrite growth causes safety concern and battery failure, which severely impedes the commercialization of Li metal batteries (LMBs). Herein, 3D functional matrix comprising NiFe-layered-double-hydroxide nanosheet arrays (NiFe-LDH) evenly dispersed on acidified carbon cloth (NiFe-LDH@ACC) via hydrothermal method is designed to manipulate uniform Li electrodeposition morphology. Vertical NiFe-LDH nanosheets possess high accessible surface area to supply vast Li nucleation sites, thus effectively reducing the local current density and decreasing the nucleation energy barrier to suppress Li dendrites growth, which has been validated by simulation. Moreover, 3D structure of carbon cloth provides adequate space for storing Li metal, substantially abating volume fluctuation during repeated Li plating/stripping process. Consequently, the Li@NiFe-LDH@ACC electrode manifests ultra-long lifespan over 3500 h and low-voltage hysteresis in symmetric cells. Additionally, the full cell with limited Li@NiFe-LDH@ACC anode and commercial LiFePO4 cathode (mass loading: 8.35 mg cm−2) displays good rate capacity from 0.2 C to 5 C, and can steadily operate for 75 cycles at 2 C with a capacity of 131 mAh g−1, revealing the practical feasibility of the surface modification strategy for constructing advanced Li metal anode with high safety and appealing durability.

DycSe: A Low-Power, Dynamic Reconfiguration Column Streaming-Based Convolution Engine for Resource-Aware Edge AI Accelerators

Edge AI accelerators are utilized to accelerate the computation in edge AI devices such as image recognition sensors on robotics, door lockers, drones, and remote sensing satellites. Instead of using a general-purpose processor (GPP) or graphic processing unit (GPU), an edge AI accelerator brings a customized design to meet the requirements of the edge environment. The requirements include real-time processing, low-power consumption, and resource-awareness, including resources on field programmable gate array (FPGA) or limited application-specific integrated circuit (ASIC) area. The system’s reliability (e.g., permanent fault tolerance) is essential if the devices target radiation fields such as space and nuclear power stations. This paper proposes a dynamic reconfigurable column streaming-based convolution engine (DycSe) with programmable adder modules for low-power and resource-aware edge AI accelerators to meet the requirements. The proposed DycSe design does not target the FPGA platform only. Instead, it is an intellectual property (IP) core design. The FPGA platform used in this paper is for prototyping the design evaluation. This paper uses the Vivado synthesis tool to evaluate the power consumption and resource usage of DycSe. Since the synthesis tool is limited to giving the final complete system result in the designing stage, we compare DycSe to a commercial edge AI accelerator for cross-reference with other state-of-the-art works. The commercial architecture shares the competitive performance within the low-power ultra-small (LPUS) edge AI scopes. The result shows that DycSe contains 3.56% less power consumption and slight resources (1%) overhead with reconfigurable flexibility.

Spatio-temporal variations of a meandering channel morphodynamics: A study along the Kaljani River in Bhuchungmari-Moamari Section of North Bengal Dooars, West Bengal (India)

Rivers being the most dynamic and natural engines receive the maximum outcome of process-form activities over space and time. Displacement and shifting of course of the alluvial channels over the lower gradients result into severe bed-bank scouring activities temporally. River Kaljani within the neo tectonically active zone of North Bengal plains is an example of temporal variations of high sinuosity and braiding over the softer lithology. The present work is focusing on a temporal study from 1970 to 2019. The SOI Topographical map of 1:50,000 scales has been the base layer along with temporal satellite imageries (1990, 2000, 2010 and 2019). The schematization has followed digitization, rectification and overlaying of conformal layers extracted from the USGS website under UTM Projection system and WGS of 1984 datum for north 45 zones under GIS platform. Multi-temporal satellite data identified serious course changes of the selected reach of lower segment of Kaljani River. The rectified multi-spectral bands are used to make the NDWI for identification of the water content on earth surface followed by selective morphometric analysis e.g. sinuosity index, braided index and roundness of curvature to access the spatio-temporal changes accomplishing some derivative mathematical equations. Descriptive statistics viz. mean, standard deviation (SD), coefficient of variation (CV), skewness and kurtosis were used and explained maximum course oscillations. The authors tried to examine the fact for six temporal frames i.e. 1970, 1990, 2000, 2006, 2010 and 2019 and received high sinuosity of all categories. Conclusively the work claims to set a documentary approach to examine the morphodynamics of an alluvial channel of Sub-Himalayan foothills.

DynQ: a dynamic query engine with query-reuse capabilities embedded in a polyglot runtime

Language-integrated query (LINQ) frameworks offer a convenient programming abstraction for processing in-memory collections of data, allowing developers to concisely express declarative queries using popular programming languages. Existing LINQ frameworks rely on the type system of statically typed languages such as C^sharp or Java to perform query compilation and execution. As a consequence of this design, they do not support dynamic languages such as Python, R, or JavaScript. Such languages are however very popular among data scientists, who would certainly benefit from LINQ frameworks in data-analytics applications. The gap between dynamic languages and LINQ frameworks has been partially bridged by the recent work DynQ, a novel query engine designed for dynamic languages. DynQ is language-agnostic, since it is able to execute SQL queries on all languages supported by the GraalVM platform. Moreover, DynQ can execute queries combining data from multiple sources, namely in-memory object collections as well as on-file data and external database systems. The evaluation of DynQ shows performance comparable with equivalent hand-optimized code, and in line with common data-processing libraries and embedded databases, making DynQ an appealing query engine for standalone analytics applications and for data-intensive server-side workloads. In this work, we extend DynQ addressing the problem of optimizing high-throughput workloads in the context of fluent APIs. In particular, we focus on applications which make use of data-processing libraries mostly for executing many queries on small batches of datasets, e.g., in micro-services, as well as applications which make use of data-processing libraries within recursive functions. For this purpose, we present reusable compiled queries, a novel approach to query execution which allows reusing the same dynamically compiled code for different queries. As we show in our evaluation, thanks to reusable compiled queries, DynQ can also speed up applications that heavily use data-processing libraries on small datasets using a typical fluent API.

Intelligent Dynamic Test System for Vehicle Engine Based on 5G Internet of Things

With the development of the automobile industry, the improvement of the reliability test level and the intelligent fault diagnosis level of the engine, as the heart component of the automobile and the assembly with the highest failure rate, becomes more and more important. In a context where technology is being upgraded, the development of intelligent automotive engine inspection system based on IoT thinking is set to become the major trend in the future. The objective of this article would be to have better knowledge about smart car engine system in the 5G IoT era. It is also through the study, a dynamic testing and characterization of the engine. The performance of the automobile engine, especially the dynamic performance, is further optimized. In this article, we focus on the PUMAOPEN test system of AVL and the system under test, the state management of the test process, and the intelligent transformation of key components. The results show that: studies and analyses the dynamic working conditions of intelligent vehicles on the Internet of Things, and compares the dynamic working conditions with the steady working conditions from qualitative to quantitative. By analyzing the influence of throttle opening change rate and speed change rate on dynamic performance and engine dynamic torque estimation, the fast sensing and response-ability in the engine testing process is improved. When the engine is accelerated by the experimental method presented in this article, and when the throttle opening is more than 30% and the engine speed varies at 100 and 200 rpm/s, the output value of the torque is higher than the output value of the steady-state torque or has little difference with the output value of the steady-state torque, and tends to be stable. In deceleration performance, the dynamic output torque of the engine is asymmetric. And it will deviate from the steady-state output torque, showing different dynamic response characteristics in different working sections. Throttle delay, overshoot characteristics, mechanical inertia, inflation coefficient, air–fuel ratio, and ignition advance angle when engine speed changes sharply will make engine dynamic characteristics deviate from steady-state characteristics.

New genetic algorithm-based workflow for multi-objective optimization of Net Zero Energy Buildings integrating robustness assessment

Net Zero Energy Buildings (NZEBs) play a key role to save energy. However, unpredictable scenarios and uncertainties about stability of energy production from renewables frequently cause measured performance of the building-plants system that differs from predictions. So, it is crucial to individuate the best alternatives in the early-stage design of the building and related energy systems, also considering uncertain circumstances. Hence, the aim of this paper is to enhance reliability of the building design process by providing an innovative workflow based on multi-objective optimization that encompasses robustness assessment of different design alternatives against uncertain scenarios. The standard genetic algorithm optimization routine has been deeply improved to set as a key performance indicator the robustness of energy performance of several building-HVAC system configurations, also considering the financial aspects optimization. Compliance with NZEB target of the obtained optimal solutions is then automatically verified by the algorithm. The new methodology combines open-source coding language Python and the dynamic energy simulation engine EnergyPlus. The robustness of each solution is evaluated through Taguchi method. First obtained results are related to energy-efficient solutions for the building envelope. Regarding the HVAC system, ground source (instead of air source) heat pumps and variable refrigerating flow systems are preferable. Finally, medium–high size of photovoltaic panels is preferred, although with high investment costs.

Dynamic adaptive engineering pathways for mitigating flood risks in Shanghai with regret theory

Uncertainty in sea level rise and future extreme climate events presents a great planning challenge for flood defence in coastal mega cities like Shanghai. While academic literature has largely focused on uncertainty analysis, engineering solution design requires effective uncertainty management. Here we incorporate the regret theory of economics and decision science into the dynamic-adaptation-pathways framework and assess the impacts of high rates of changes on the flood defence systems in Shanghai. Specific options are developed to manage flooding on the Huangpu River from tidal water levels, river flows, rainfall, drainage inflows and combinations of these flood sources including sea level rises of up to 3 m. Dynamic adaptation pathways are developed where the timing of tipping points from one intervention to the next depends on the actual changes in sea level, rainfall and other variables that affect the future design. This framework is potentially applicable for planning ‘no regrets’ flood-defence systems in other low-lying coastal cities.

Ultrafast generation of atomistic biomolecular trajectories for methods development.

Validation of new methods requires testing data of high (protein-like) complexity, that also can be exhaustively sampled to obtain a precise reference. In practice, however, these criteria are often mutually exclusive. The expense of integrating the equations of motion for molecular dynamics (MD) simulations, for example, typically makes high-quality sampling of complex systems infeasible. Simpler and faster models such as low dimensional potentials can be sampled extensively, but likely do not capture sufficient complexity to challenge cutting-edge new methods. Building on precedents in the literature, we demonstrate a general synthetic dynamics framework for efficiently propagating dynamics in a low dimensional latent space and projecting back to the full-dimensional coordinate space. This framework can efficiently produce extremely long trajectories, and enables rapid testing of enhanced sampling and analysis algorithms on highly non-trivial models. We demonstrate synthetic MD (synMD) trajectory generation based on a fine-grained Markov state model, stratified along slow coordinates to enforce realistic long-time dynamics. Each discrete state is mapped to an atomistic configuration, leading to reasonably realistic MD-mimicking trajectories. We show generation of atomistic synMD trajectories for the fast-folding miniprotein Trp-cage at a rate of over 8 seconds/day on a single core of a standard workstation CPU. We also demonstrate the use of synMD as a dynamics engine for the popular WESTPA implementation of the weighted ensemble enhanced sampling strategy. Although we currently use MSM generators, our implementation provides a generic interface for implementing different generators, such as have been proposed based on machine-learning approaches.

Manipulating Explosion Dynamics of Dissipative Soliton in an Ultrafast Fiber Laser with Dynamic Dispersion Engineering

AbstractAs one of the most fascinating phenomena in dissipative systems, soliton explosions have been intensively investigated in the field of nonlinear optics owing to their intriguing dynamics. However, few efforts have been dedicated to precisely manipulating the dynamics of soliton explosion in fiber lasers. Herein, the exploding dynamics of dissipative soliton in a passively mode‐locked fiber laser by engineering the intracavity dispersion with a spectral pulse shaper is investigated. It is found that the exploding interval of the dissipative soliton can be well manipulated by dynamically engineering the cavity dispersion. The real‐time spectral dynamics of the soliton explosions are investigated by virtue of the dispersion Fourier transform (DFT) technique. The experimental results are further reproduced by the numerical simulations. These findings demonstrated that the dispersion engineering approach is a promising way to manipulate the soliton exploding behavior, and will also shed new light into the dynamics of soliton explosions in dissipative optical systems.

TIES 2.0: A Dual-Topology Open Source Relative Binding Free Energy Builder with Web Portal

Relative binding free energy (RBFE) calculations are widely used to aid the process of drug discovery. TIES, Thermodynamic Integration with Enhanced Sampling, is a dual-topology approach to RBFE calculations with support for NAMD and OpenMM molecular dynamics engines. The software has been thoroughly validated on publicly available datasets. Here we describe the open source software along with a web portal (https://ccs-ties.org) that enables users to perform such calculations correctly and rapidly.

A Minimum-Control-Trajectory-Deviation Time Grid Reconstruction Strategy for Co-Design Approach

Abstract Optimizing dynamic engineering systems (DESs) is quite challenging due to the increasing pursuit of automation and intelligence in modern industry. However, most of the existing studies generally only focus on plant variables or control variables of DESs, which may fail to explore optimal solutions. In this paper, a novel minimum-control-trajectory-deviation (MCTD) time grid reconstruction strategy is presented for the co-design approach. Three co-design approaches, namely simultaneous, nested, and direct transcription quadratic programming (DTQP) are compared using the MCTD time grid reconstruction strategy. Considering a number of design variables are time-varying in practical dynamic systems, three co-design methods use a special class of numerical analysis methods known as direct transcription (DT) that implies a “discretize-then-optimize” process. Motivated by the inefficiency of the traditional uniform discrete strategy, an MCTD time grid reconstruction strategy is proposed. Combining the presented time grid reconstruction strategy, simultaneous, nested, and DTQP methods are implemented for three test problems. The MCTD time grid reconstruction strategy is verified through a mathematical example, the Van der Pol oscillator, and a machine tool case. All cases have proved the superiority of presented strategy in running cost and solution accuracy.