bluuebunny/crossref_metadata_2025 huggingface dataset:
Review of Transfer Learning in Additive Manufacturing Modeling
Authors: Tang Yifan , Dehaghani M. Rahmani , Wang G. Gary
Published: November 2022

The process-structure-property modeling of additive manufacturing (AM) products plays an important role in process and quality control. In practice however, only limited data are available for each product due to its expensive material and time-consuming fabricating process, which becomes an obstacle to achieve high quality models. Transfer learning (TL) is a new and promising approach that the model of one product (source) may be reused for another product (target) with limited new data on the target. This paper focuses on reviewing applications of TL in AM modeling in order to help further research in this area. To clarify the specific topic, the problem definition is presented, as well as the differences between TL, multi-fidelity modeling, and multi-task learning. Then current applications of TL in AM modeling are summarized according to different TL approaches. To better understand the performances of different TL approaches, several representative TL-assisted AM modeling methods are reproduced and tested on an open-source dataset. Based on the test results, their effectiveness and limitations are discussed in detail. Finally, future research directions about TL in AM modeling are discussed in hope to explore more potential of TL in boosting the AM model performance.
Optimization of Self-Heated Vacuum Membrane Distillation Using Response Surface Methodology
Authors: Rahmadhanty Shaneza Fatma , T. M. Subrahmanya , Hung Wei-Song , Lin Po Ting
Published: November 2022

Currently, technology is increasingly advanced and has penetrated into several fields such as Membrane Distillation (MD) in water treatment, especially to purify saline water, in the face of the increasing scarcity of clean water needed for consumption, both in the domestic, agriculture, and industrial sectors. One of the configurations in MD systems, self-heated Vacuum Membrane Distillation (VMD) system, which employs a Graphene-PVDF membrane heated by power supply, in this case using DC power with low voltage, to eliminate feed pre-feed heating and temperature polarization, is interesting to learn. In addition, to enhance the performances, such as optimizing permeate flux (JW), Temperature Polarization Factor (TPF), Specific Heating Energy (QSH), and Gain Output Ratio (GOR), several different designs are made and compared. In this study, four parameters are selected: DC power supply’s voltage, feed flow rate, the length, and the width of the cell-body’s slot. Ansys FLUENT software is utilized to simulate the system, followed by Minitab software to analyze the results using Response Surface Method (RSM) which aims to achieve the optimal design parameters. The simulation data was validated by experimental data and determination of the optimum conditions of the self-heated VMD system led to the best performances such as maximizing JW, TPF, and GOR, as well as minimizing QSH.
Decision Fusion Method for the Failure Form of Retired Parts Based on Cloud Model Optimization D-S Evidence Theory
Authors: Wang Lei , Xu Chengying , Zhang Zelin , Xia Xuhui
Published: November 2022

The decision-making fusion of multi-source failure information for retired parts is vital to classify the failure forms of retired parts. There are various failure information detection methods for retired parts, however the results obtained by different detection methods are biased, one-sided, and uncertain, resulting in difficulty in decision making. Aiming at improving the classification accuracy and reliability of the failure forms for retired parts, we propose a fusion method for retired parts failure form decision-making based on the cloud-model optimization D-S evidence theory (CM-D-S). The cloud model solves the problem of strong subjectivity in the traditional D-S evidence theory and overcomes the negative effects caused by random factors. Through image processing, magnetic memory, and ultrasonic testing, the multi-source failure information of retired parts can be obtained, and the failure forms of retired parts are divided into fracture, corrosion, wear and deformation. The cloud model parameters are used to characterize different failure feature quantities sequentially, obtain the basic probability assignment matrix in the D-S evidence theory, and substituted into the D-S evidence theory model to combine and optimize multi-source failure information, and output the result of decision fusion, which is the multi-source failure form of the retired parts. The case shows that the CM-D-S decision-making method has high accuracy for failure form recognition, which proves the applicability and feasibility of the method.
Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
Authors: Chen Wei (Wayne) , Lee Doksoo , Balogun Oluwaseyi , Chen Wei
Published: November 2022

Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. Past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the “real-world”, “free-form” uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of 1) building a universal uncertainty quantification model compatible with both shape and topological designs, 2) modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and 3) allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performances after fabrication.
The Need for Desalination in Humanitarian Crises
Authors: Bessette Jonathan T. , Winter Amos G.
Published: November 2022

Humanitarian crises ranging from political unrest to natural disasters are becoming increasingly prevalent with global climate change. Correspondingly, there are an increasing number of regions that consist both of high crises risk and saline water contamination. Such regions include the Middle East, Subsaharan Africa (particularly along the Great Rift Valley), Southeast Asia (including the Mekong Delta and Pacific Islands), and coastal regions. However, there is a lack of robust, deployable desalination technologies for humanitarian crises. This is mainly attributed to the highly-constrained environment which necessitate: minimization of consumables, rapid speed of deployment and simplification of operation and maintenance. Such constraints are often secondary thoughts, are difficult to traditionally quantify, and differ from stable commercial situations where operations are supported by an accessible supply chain and network of technicians. These barriers have particularly hindered the adoption of membrane technology and thus, high volume desalination and chemical contaminant removal. This work justifies the need for desalination technology in humanitarian crises via geospatial analysis of saline water databases and exploration of regional case studies, formulates design requirements for an emergency-use desalination system based on needs extracted from open-interviews of stakeholders and literature review, evaluates some of the gaps within currently employed deployable desalination systems and explores the potential opportunities of other desalination technology.
Human Grasping Force Prediction, Measurement, and Validation for Human-Robot Lifting
Authors: Arefeen Asif , Quarnstrom Joel , Syed Shahbaz P Qadri , Bai He , Xiang Yujiang
Published: November 2022

In this study, a 13 degrees of freedom (DOFs) three-dimensional (3D) human arm model and a 10 DOFs 3D robotic arm model are used to validate the grasping force for human-robot lifting motion prediction. The human arm and robotic arm are modeled in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The human-box and robot-box interactions are characterized as a collection of grasping forces. The joint torque squares of human arm and robot arm are minimized subjected to physics and task constraints. The design variables include (1) control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box; and (2) the discretized grasping forces during lifting. Both numerical and experimental human-robot liftings were performed with a 2 kg box. The simulation reports the human arm’s joint angle profiles, joint torque profiles, and grasping force profiles. The comparisons of the joint angle profiles and grasping force profiles between experiment and simulation are presented. The simulated joint angle profiles have similar trends to the experimental data. It is concluded that human and robot share the load during lifting process, and the predicted human grasping force matches the measured experimental grasping force reasonably well.
Mobility Prediction of Off-Road Ground Vehicles Using a Dynamic Ensemble of NARX Models
Authors: Liu Yixuan , Barthlow Dakota , Mourelatos Zissimos P. , Gorsich David , Singh Amandeep , Zeng Jice , Hu Zhen
Published: November 2022

Mobility prediction of off-road Autonomous Ground Vehicles (AGV) in uncertain environments is essential for their model-based mission planning especially in the early design stage. While surrogate-modeling methods have been developed to overcome the computational challenge in simulation-based mobility prediction, it is very challenging for a single surrogate model to accurately capture the complicated vehicle dynamics. With a focus on vertical acceleration of an AGV under off-road conditions, this paper proposes a surrogate modeling approach for AGV mobility prediction using a dynamic ensemble of Nonlinear Autoregressive Network with Exogenous inputs (NARX) models over time. Synthetic vehicle mobility data of an AGV are first collected using a limited number of high-fidelity simulations. The data are then partitioned into different segments using a variational Gaussian mixture model in order to represent different vehicle dynamic behaviors. Based on the partitioned data, multiple surrogate models are constructed under the NARX framework with different numbers of lags. The NARX models are then assembled together dynamically over time to predict the mobility of the AGV under new conditions. A case study demonstrates the advantages of the proposed method over the classical NARX models for AGV mobility prediction.
Using Coaxial Melt Pool Monitoring Images to Estimate Cooling Rate for Powder Bed Fusion Additive Manufacturing
Authors: Yang Zhuo , Lane Brandon , Lu Yan , Yeung Ho , Kim Jaehyuk , Ndiaye Yande , Krishnamurty Sundar
Published: November 2022

Cooling rate is a decisive index to characterize melt pool solidification and determine local microstructure formation in metal powder bed fusion processes. Traditional methods to estimate the cooling rate include in-situ temperature measurement and thermal simulation. However, these methods may not be accurate or efficient enough under complex conditions in real-time. This paper proposes a method to approximate the melt pool cooling rate using temperature profile acquired via thermally-calibrated melt pool camera, and based on continuous pixel tracking result. The proposed method can estimate the temperature and associated cooling rate for every pixel immediately, which is potentially applicable for real-time process monitoring. This paper focuses on investigating image data processing, method development, and cooling condition analysis. This work presents the preliminary result of the cooling rate estimation under different conditions such as position, layer number, and overhanging.
Toward Formal Qualitative Reasoning to Support Functional Decomposition
Authors: Mao Xiaoyang , Sen Chiradeep
Published: November 2022

Functional decomposition is an important task in early systems engineering and design, where the overall function of the system is resolved into the functions of its components or subassemblies. Conventionally, this task is performed manually, because of multiple possible solution paths and the need for understanding the physics phenomena that could realize the desired effects. This paper presents an approach of developing a formal method for functional decomposition using physics-based qualitative reasoning. The representation includes three parts: (1) a natural language reasoner that detects the changes of physical states of material and energy flows, (2) a set of causation tables that abstract the knowledge of qualitative physics by capturing the causal relations between the various quantities involved in a physical phenomenon or process, and (3) a process-to-subgraph mapping that translate the physical processes into function structure constructs. The algorithm uses the above three representations and some topological reasoning to assemble function models that represent the decomposition of a given black box model. The paper illustrates the potential of this method for functional decomposition using an example of an air-heating device. The paper also discusses the limitations and challenges in maturing this approach into an end-usable design tool.
Concept Maps in Augmented Reality to Improve the Learning Process and the Retrieval of Information
Authors: Carulli Marina , Bordegoni Monica , Spadoni Elena , Rossoni Marco
Published: November 2022

Memorization techniques are of primary importance in education. Two relevant and extensively used techniques are the Concept maps and the method of loci. Both methods are based on the visualization of information, which helps memorization and retrieval. For these reasons, they are also considered inclusive learning tools for people with Specific Learning Disability. Augmented Reality is a technology that has gained popularity in many sectors, from industry to the medical one, for its effectiveness in visualizing graphical items on top of real contexts. The paper demonstrates that Augmented Reality can also be beneficial for representing and interacting with Concept maps in a 3D virtual space that is linked to the real world. Specifically, the authors developed an Augmented Reality application in which the key features of both the Concept maps (such as visual-spatial logic and concepts organization) and of the method of loci are integrated with those of Augmented Reality technologies to improve the learning process and the retrieval of information.
An Innovative Method for Lightweight Design Based on FBS and Conflict Resolution
Authors: Liu Shuang , Guo Xin , Yang Xiwen , Hu Huicong
Published: November 2022

With the rapid development of today’s economy, people’s requirements for products are more diverse, and lightweight requirements are typical of these. Lightweight products play an important role in today’s engineering practice, and many researchers are participating in lightweight research. Schemes generated by conceptual design are used to realize the function, but it is difficult to consider simultaneously the lightweight and the functions’ realization. Especially the structures of the products may conflict with the way of working when the lightweight is considered. Sometimes, choosing which structure to start lightweight design is an important issue for a product; so an innovative design model for lightweight products based on FBS and conflict resolution is offered by the authors which can also take into account the product function while reducing the weight from the structure. The model includes three parts: Firstly, multi requirement analysis and FBS is constructed for the initial scheme. Secondly, if the requirements are not met, the structure in the initial scheme is scored by AHP, and the appropriate structure is selected for preliminary lightweight. Thirdly, the lightweight structure is obtained through conflict resolution, and then the conflict between it and other structures in the initial scheme is solved, to generate a new general structure. This paper takes the vertical frame and brace of a gantry crane as an example to verify the effectiveness of the model.
Generative Adversarial Design Analysis of Non-Convexity in Topology Optimization
Authors: Hertlein Nathan , Gillman Andrew , Buskohl Philip R.
Published: November 2022

Material penalization and filtering schemes are key strategies applied to topology optimization (TO) to promote more discrete and manufacturable designs. However, these modifications introduce fluctuations in the design landscape that amplify non-convexity and influence the local minima identified by TO. Harnessing the machine learning approach of generative adversarial networks (GAN), we investigate the role of penalization and filtering by comparing the designs between TO and GAN-based TO surrogates. A total of 17 GANs were constructed to predict 2D minimum compliance topologies across a set of penalization factors and filters, each interpolating a design space of 270,000 boundary condition and loading scenarios. The prevalence of GAN-predicted topologies with better compliance than TO-calculated topologies was estimated via a random sampling of the design space. GAN ‘over-performance’ occurs across material penalization and filtering conditions, where the frequency tends to increase as penalization increases. Analysis of this test set is leveraged to highlight trends regarding the conditions under which this ‘over-performance’ occurs, and the geometric characteristics these designs exhibit. Collectively, this study presents an alternative method to characterize the effects of penalization and filtering on design outcomes and motivates the use of data-driven surrogates to augment traditional approaches.
Virtual Reality for Delivering Swimming Practice Through Water-Free Immersive Training System
Authors: Li Shuo , Zheng Hongtao , Yuan Wenyu , Han Ting
Published: November 2022

Learning to swim is essential at any age as a great way to exercise and part of safety measures. Not everyone feels comfortable to start practicing from being in the water due to specific psychological or physical difficulties. Similarly, the in-water practice lacks real-time feedback for postures or stroke corrections. However, Virtual Reality (VR) technology presents a great potential to enable a water-free approach as preparation for, or supplementary to, in-water practice. Such technology is still under-explored. This paper proposes a Water-Free Immersive Training System (WITS) which verifies the feasibility of a Water-free Immersive Training System using system construction and experience tests. WITS features whole-body physical feedback (e.g.through distributed resistance materials or controllable air cushions) and body movement tracking to provide visual and embodied immersion and real-time training feedback. A Presence Questionnaire (PQ) is adopted to analyze users immersive experience. This paper discusses the user experience of WITS and the broader directions and implications for future development. This paper is intended to propose a novel design application and expand the possibility of other innovative forms of immersive sports. At the same time, it provides a set of design-oriented insights for creating tangible immersive experiences in VR systems.
Topology Optimization of Permanent Magnets for Generators Using Level Set Methods
Authors: Tian Jiawei , Zhuang Ran , Cilia Juan , Rangarajan Arvind , Luo Fang , Longtin Jon , Chen Shikui
Published: November 2022

Generators are considered as the core application of electromagnetic machines, which require high-cost rare-earth-based permanent magnets. The development of generators is moving toward high efficiency and increased environmental friendliness. Minimizing the use of rare earth materials such as magnetic materials under the premise of machine performance emerges as a challenging task. Topology optimization has been promisingly applied to many application areas as a powerful generative design tool. It can identify the optimal distribution of magnetic material in the defined design space. This paper employs the level-set-based topology optimization method to design the permanent magnet for generators. The machine under study is a simplified 2D outer rotor direct-drive wind power generator. The dynamic and static models of this generator are studied, and the magnetostatic system is adopted to conduct the topology optimization. The optimization goals in this study mainly focused on two aspects, namely the maximization of the system magnetic energy and the generation of a target magnetic field in the region of the air gap. The continuum shape sensitivity analysis is derived by using the material time derivative, the Lagrange multiplier method, and the adjoint variable method. Two numerical examples are investigated, and the effectiveness of the proposed design framework is validated by comparing the performance of the original design against the optimized design.
Design and Research of Intelligent QA System for Flight Crew Operating Manual
Authors: Tan Xin , Zhong Jingshu , Jin Yu , Liang Yan , Zheng Yu , Liu Ying
Published: November 2022

Aviation flight crews rely on a large number of complex standard documents and operation manuals when performing flight tasks. In order to relieve the pressure of manual retrieval of documents, intelligent question-answering technology based on reading comprehension is gradually applied. In this paper, the flight crew operation manual SQuAD dataset is studied and built, based on which the reader-retriever framework of text content-based reading question answering system (TCQA) is analyzed and established. Experiments are conducted to compare the relevant indexes of the QA system with different combinations of reader and retriever models under the open-source tool haystack. Based on the comparison of response speed and retrieval capability, the best model combination is obtained for the flight crew operation manual dataset, and suggestions are made for the model-related performance improvement.
Natural Language Processing for Content Analysis of Communication in Collaborative Design
Authors: Lokesh Sachin H. , Chaudhari Ashish M. , Thekinen Joseph , Panchal Jitesh H.
Published: November 2022

We address the problem of content analysis in text-based engineering design communication. Existing methods to characterize communication content in engineering design are manual or qualitative, which is tedious for large datasets. We formulate the characterization of communication messages as an intent classification task. We identify two intents — Intent 1 captures the presence and flow of information, Intent 2 captures specific topics about design parameters and objectives. We compare the predictive accuracy of convolutional LSTM, character-based convolutional LSTM, XLNet, and BERT models for the intent classification task. The results of our comparison show that the XL-Net model predicts Intents 1 and 2 with 88% and 81% accuracy, respectively, on text data collected from 40 teams in a design experiment with university students. We analyze the differences in communication patterns between high- and low-performing teams. Time-series studies show that high-performing teams have more responsive communication and a higher consistency of information exchange.
QuickProbe: Quick Physical Prototyping-in-Context Using Physical Scaffolds in Digital Environments
Authors: Raina Abhijeet Singh , Vyas Shantanu , Ebert Matthew , Krishnamurthy Vinayak R.
Published: November 2022

In this paper, we introduce a novel prototyping workflow, QuickProbe, that enables a user to create quick-and-dirty prototypes taking direct inspiration from existing physical objects. Our workflow is inspired by the notion of prototyping-in-context using physical scaffolds in digital environments. To achieve this we introduce a simple kinesthetic-geometric curve representation wherein we integrated the geometric representation of the curve with the virtual kinesthetic feedback. We test the efficacy of this kinesthetic-geometric curve representation through a qualitative user study conducted with ten participants. In this study, users were asked to generate wire-frame curve networks on top of the physical shapes by sampling multiple control points along the surface. We conducted two different sets of experiments in this work. In the first set of experiments, users were tasked with tracing the physical shape of the object. In the second set of experiments, the goal was to explore different artistic designs that the user could draw using the physical scaffolding of the shapes. Through our user studies, we showed the variety of designs that the users were able to create. We also evaluated the similarities and differences we observed between the two different sets of experiments. We further discuss the user feedback and the possible design scenarios where our QuickProbe workflow can be used.
In-Process Data Integration for Laser Powder Bed Fusion Additive Manufacturing
Authors: Perisic Milica , Lu Yan , Jones Albert
Published: November 2022

Additive manufacturing (AM) is a powerful technology that can create complex metallic parts and has the potential to improve the economic bottom line for various industries. However, due to process instabilities, and the resulting material defects that impact the part quality, AM still isn’t as widely used as it could be. To overcome this situation, it is crucial to develop an environment for easy, in-process monitoring and real-time control to detect process anomalies and predict part defects as quickly as possible. AM in-process monitoring measures various process variables and the sensors generate large volumes of structured or unstructured, 1D, 2D, and 3D data, some of which are acquired at very high frequencies. Integration of such data and their analysis are necessary for effective in-process monitoring and real-time control, but they are facing many challenges due to the characteristics of AM in-process data. This paper provides an overview of different in-process monitoring data sources and their connection methods and addresses the integration issues associated with acquiring and fusing the data for both on-fly control and offline analysis. The paper also presents a guideline to help high-speed data integration. This guideline can help users to decide the best data-integration configuration for a specific use case.
Controlling Microwave Energy Deposition Using Active Plasma Elements for Material Processing Applications
Authors: Graber Benjamin D. , Iliopoulos Athanasios P. , Michopoulos John G. , Steuben John C. , Apetre Nicole A. , Petrov George M. , Johnson Luke A. , Fischer Richard P. , Gorzkowski Edward P. , Patterson Eric A.
Published: November 2022

This work is motivated by the need to modulate microwave beam propagation, phase, shape, and direction using an array of plasma elements. Tailoring microwave beams in this fashion will enable new material processing capabilities such as induced localized heating. An initial process example consists of a cylindrical plasma element inserted into a waveguide between a microwave radiation source and a material of interest. In order to establish the feasibility of the proposed process, an accurate model of an argon mercury plasma including plasma-microwave coupling was developed and described herein. Both microwave plasma heating and magnetic plasma couplings are considered. The required computational framework was implemented within the COMSOL Multiphysics finite element solver. The model is first used to investigate a 2D geometry, before being extended to a 3D geometry. The obtained solutions of the relevant partial differential equations and the associated predictions increased the understanding of the interplay between plasma and electromagnetic properties under consideration in the model. Model implementation confirms that a plasma element can be used to modulate incident microwave radiation, thereby shaping the transmitted beam. The framework also enables analysis of beam shaping techniques under consideration for material processing of ceramics. For ceramic processing, beam shaping techniques are being used to direct microwave radiation to predictable, localized areas in order to sinter the dielectric powders under consideration.
Active Pressure Pulsation Suppression Method by Parallel-Series Structure in DFCU Based on Variable Step Size FXLMS Algorithm
Authors: Yao Jing , Cheng Yuwang , Wang Pei , Zhao Juntao , Wang Yupeng
Published: November 2022

Pressure pulsation is the challenge in digital hydraulics owing to its switching characteristics, which makes control performance worse. This study proposed the parallel-series structure for active pressure pulsation suppression, based on variable step size FXLMS algorithm. The pressure pulsation performance in DFCU is analyzed under PNM and PWM control for better suppression effect. On this basis, the active pressure pulsation suppression control algorithm is designed utilizing the principle of pressure wave destructive interference, so that the reverse pressure pulsation generated by the parallel-series structure, including an in-series valve arranged in line between the DFCU and the load and a by-pass valve arranged in parallel with DFCU, offsets with the pressure pulsation at the load end. The adaptive FXLMS algorithm with variable step size is used to identify the secondary path online for better effect of pressure pulsation suppression and achieve the “alignment” between the reverse pressure signal and the initial pressure signal. The experimental results show it can suppress pressure pulsation by 79%, 54%, 37% and 29% at 10 Hz, 30 Hz, 50 Hz, 70 Hz for single valve and 77% for DFCU. It has been demonstrated that the proposed structure has significant effect and guiding relevance for pressure pulsation suppression of DFCU.
Optimizing Viscosity for Maximum Power in a Hydrostatic Transmission Wind Turbine
Authors: Chen Justin , Mohanty Biswaranjan , Escobar-Naranjo Daniel , Stelson Kim A.
Published: November 2022

In basic hydraulic theory, efficiency is dependent on volumetric and mechanical losses. Components dominated by mechanical losses favor low viscosity fluids, while those dominated by volumetric losses favor high viscosity fluids. For a hydrostatic transmission (HST), efficiency of both the pump and motor must be considered. The goal of this work is to model the HST viscosity-efficiency curve for finding the optimal viscosity and to validate the results with experiments. This paper reports on preliminary results with only partial success.

This study uses a mathematical efficiency model that is dependent on non-dimensional viscosity, two non-dimensional length ratios, and fractional swash plate displacement. Rather than using physical dimensions for the non-dimensional length ratios, these parameters were determined for the pump and motor through constant viscosity experiments. The predicted results of the model were compared to validation experiments where viscosity was varied by changing temperature.

Results showed high accuracy for the pump model at all tested conditions, but the accuracy of the motor model was lacking, deviating significantly from experiments at low viscosities. For future studies, it is recommended to increase the number of data points at low dimensionless viscosity to create a more accurate model fit. Alternative models that provide more realistic results will be considered.
Design and Simulation Analysis of a Pump-Controlled Actuation System for Heavy-Duty Booms
Authors: Han Xu , Leinonen Manu , Minav Tatiana
Published: November 2022

The electrified pump-controlled hydraulics receives increasing attention from both industry and academia. Its combined advantages of hydraulic and electric actuators result in an expanding application in heavy-duty actuation. One example of such actuation application is the boom. Despite the diverse achievements from academia, the commercial application has not started yet. This is partially caused by the absence of the auxiliary functions of the conventional hydraulic actuation, e.g. thermal management, filtration, and maintenance convenience, in proposed academic solutions. This paper proposes a new design of the pump-controlled actuation solution for heavy-duty booms, which takes both the primary and auxiliary function requirements into consideration. Then, the proposed system is modeled and simulated using a crane study case, which requires 50 kW power for moving each axis. The primary functions, e.g. the control performance, the energy efficiency, and the auxiliary functions are both analyzed. The control methods are proposed for supporting the upper-level control modes. A conventional load sensing actuation system is also simulated as a comparison. The simulation results demonstrated around 40–70 % energy-saving advantages and comparable auxiliary functions of the proposed system over the conventional system. The achieved results prove the applicability of the proposed system and promote the application of the pump-controlled actuation in heavy-duty booms.
Accumulator Sizing for the Hybrid Hydraulic Electric Architecture (HHEA) Using Dynamic Programming
Authors: Wills Jackson , Li Perry Y.
Published: November 2022

The Hybrid Hydraulic Electric Architecture (HHEA) is an approach to electrifying and improving that does not require very large electric machines. The majority power is provided hydraulically via a set of common pressure rails while small electric machines are used to modulate that power to meet exact demand. Each common pressure rail has an accumulator, which keeps the pressure of each rail near constant as long as the volume in the accumulator does not change much. A main pump adds or removes fluid from each accumulator as needed to reduce volume changes in the accumulators. It is the operation of this main pump which is investigated here.

In previous analysis it was assumed that the size of the accumulators was large enough to account for any difference in fluid volume without significantly changing the pressure. In this paper, the required accumulator sizes are studied using a dynamic programming approach. At each time step the inlet and outlet of the main pump could be connected to any of the common pressure rails. These decisions for the main pump were optimized to minimize the required accumulator size.

Case studies are conducted using drive cycles from a 22-ton excavator, a 20-ton wheel loader and a 5-ton excavator. The machines could be operated with total accumulator sizes of less than 20, 10, and 5 liters respectively. Required accumulator size was found to vary with pump flow rate, the frequency at which the pump/motor was able to change operating conditions, the tolerance level of pressure deviation and the accumulator’s polytropic index.
A Set of Benchmark Problems for Fluid Power System Simulation
Authors: Manhartsgruber Bernhard
Published: November 2022

Mathematical modelling and simulation have become widespread tools in the fluid power industry. The sheer number of available software packages of commercial and academic nature as well as the abundance of published modeling strategies for certain components gives multiple possibilities for modeling one and the same fluid power system. The mathematical model is required to capture the behavior of the real system with a certain degree of accuracy while using a minimum of computational resources for reaching this goal. This trade-off cannot be assessed by looking on model complexity on the level of differential equations only. The computational efficiency is defined by the combination of mathematical modelling on the high level of differential equations, the choice of a solver, i.e. the time discretization, and the details of software implementation like the optimized compilation of right hand sides for the solvers. In order to compare different modeling strategies on various software platforms, well defined benchmark problems are needed. This paper presents a first attempt towards such a set of benchmark problems targeted at the specific problems of fluid power simulation. In a first benchmark case, a system with reversing flow at an orifice under the additional difficulty of a stiff differential equation system is presented. A second benchmark case uses the coupling between dry friction in a hydraulic cylinder and wave propagation in a transmission line which has been shown to give notorious modelling problems in the literature. The goal of the present publication is to present a proposal for such benchmark systems in order to make the accuracy and computational performance of simulation approaches more comparable.
An Electric-Hydraulic Hybrid Wheel Loader With Mode-Driven Control Strategy
Authors: Wu Zihan , Wang Feng , Xu Bing , Fiebig Wieslaw
Published: November 2022

The powertrain electrification is an effective solution to emission reduction of mobile machines. To overcome the low power density of electric drive, a hydraulic accumulator is introduced as a supplementary power source. In this paper an electric-hydraulic hybrid powertrain solution is studied for wheel loader propulsion system, where it combines the energy density of electric drive and high power density of hydraulic drive. The energy management strategy is essential for hybrid powertrain since it determines how efficiently the power is transferred between different energy sources. In this paper a mode-driven control strategy for electric-hydraulic hybrid wheel loader is proposed to achieve electric power reduction over power follower strategy (baseline strategy), without sacrificing electric energy use and vehicle operation hours. In the strategy four modes are defined and the expected hydraulic SOC (system pressure) profile in each mode is scheduled to provide supplementary power assist and capture regenerative braking energy. By setting the pressure profile, the hydraulic charge sustaining is guaranteed. The system operation with mode-driven strategy is compared with power follower strategy through simulation studies. Results show that peak powers of battery and electric motor with mode-driven strategy are reduced by nearly 30% compared to power follower strategy. Results also show that the vehicle operation hour has been slightly increased by 3% by using mode-driven strategy. These results verify the effectiveness of proposed strategy for electric-hydraulic hybrid wheel loader.
Promoting antibody-dependent cellular phagocytosis for effective macrophage-based cancer immunotherapy
Authors: Cao Xu , Chen Jing , Li Bolei , Dang Jessica , Zhang Wencan , Zhong Xiancai , Wang Chongkai , Raoof Mustafa , Sun Zuoming , Yu Jianhua , Fakih Marwan G. , Feng Mingye
Published: November 2022

Macrophages are essential in eliciting antibody-dependent cellular phagocytosis (ADCP) of cancer cells. However, a satisfactory anticancer efficacy of ADCP is contingent on early antibody administration, and resistance develops along with cancer progression. Here, we investigate the mechanisms underlying ADCP and demonstrate an effective combinatorial strategy to potentiate its efficacy. We identified paclitaxel as a universal adjuvant that efficiently potentiated ADCP by a variety of anticancer antibodies in multiple cancers. Rather than eliciting cytotoxicity on cancer cells, paclitaxel polarized macrophages toward a state with enhanced phagocytic ability. Paclitaxel-treated macrophages down-regulated cell surface CSF1R whose expression was negatively correlated with patient survival in multiple malignancies. The suppression of CSF1R in macrophages enhanced ADCP of cancer cells, suggesting a role of CSF1R in regulating macrophage phagocytic ability. Together, these findings define a potent strategy for using conventional anticancer drugs to stimulate macrophage phagocytosis and promote the therapeutic efficacy of clinical anticancer antibodies.
Blind identification of the spinal cord output in humans with high-density electrode arrays implanted in muscles
Authors: Muceli Silvia , Poppendieck Wigand , Holobar Aleš , Gandevia Simon , Liebetanz David , Farina Dario
Published: November 2022

Invasive electromyography opened a new window to explore motoneuron behavior in vivo. However, the technique is limited by the small fraction of active motoneurons that can be concurrently detected, precluding a population analysis in natural tasks. Here, we developed a high-density intramuscular electrode for in vivo human recordings along with a fully automatic methodology that could detect the discharges of action potentials of up to 67 concurrently active motoneurons with 99% accuracy. These data revealed that motoneurons of the same pool receive common synaptic input at frequencies up to 75 Hz and that late-recruited motoneurons inhibit the discharges of those recruited earlier. These results constitute an important step in the population coding analysis of the human motor system in vivo.
Three-dimensional imaging and analysis of pathological tissue samples with de novo generation of citrate-based fluorophores
Authors: Pac Jinyoung , Koo Dong-Jun , Cho Hyeongjun , Jung Dongwook , Choi Min-ha , Choi Yunjung , Kim Bohyun , Park Ji-Ung , Kim Sung-Yon , Lee Yan
Published: November 2022

Two-dimensional (2D) histopathology based on the observation of thin tissue slides is the current paradigm in diagnosis and prognosis. However, labeling strategies in conventional histopathology are limited in compatibility with 3D imaging combined with tissue clearing techniques. Here, we present a rapid and efficient volumetric imaging technique of pathological tissues called 3D tissue imaging through de novo formation of fluorophores, or 3DNFC, which is the integration of citrate-based fluorogenic reaction DNFC and tissue clearing techniques. 3DNFC markedly increases the fluorescence intensity of tissues by generating fluorophores on nonfluorescent amino-terminal cysteine and visualizes the 3D structure of the tissues to provide their anatomical morphology and volumetric information. Furthermore, the application of 3DNFC to pathological tissue achieves the 3D reconstruction for the unbiased analysis of diverse features of the disorders in their natural context. We suggest that 3DNFC is a promising volumetric imaging method for the prognosis and diagnosis of pathological tissues.
TENT5 cytoplasmic noncanonical poly(A) polymerases regulate the innate immune response in animals
Authors: Liudkovska Vladyslava , Krawczyk Paweł S. , Brouze Aleksandra , Gumińska Natalia , Wegierski Tomasz , Cysewski Dominik , Mackiewicz Zuzanna , Ewbank Jonathan J. , Drabikowski Krzysztof , Mroczek Seweryn , Dziembowski Andrzej
Published: November 2022

Innate immunity is the first line of host defense against pathogens. Here, through global transcriptome and proteome analyses, we uncover that newly described cytoplasmic poly(A) polymerase TENT-5 (terminal nucleotidyltransferase 5) enhances the expression of secreted innate immunity effector proteins in
Caenorhabditis elegans
. Direct RNA sequencing revealed that multiple mRNAs with signal peptide–encoding sequences have shorter poly(A) tails in
tent-5
–deficient worms. Those mRNAs are translated at the endoplasmic reticulum where a fraction of TENT-5 is present, implying that they represent its direct substrates. Loss of
tent-5
makes worms more susceptible to bacterial infection. Notably, the role of TENT-5 in innate immunity is evolutionarily conserved. Its orthologs, TENT5A and TENT5C, are expressed in macrophages and induced during their activation. Analysis of macrophages devoid of TENT5A/C revealed their role in the regulation of secreted proteins involved in defense response. In summary, our study reveals cytoplasmic polyadenylation to be a previously unknown component of the posttranscriptional regulation of innate immunity in animals.
Spectral imaging at high definition and high speed in the mid-infrared
Authors: Knez David , Toulson Benjamin W. , Chen Anabel , Ettenberg Martin H. , Nguyen Hai , Potma Eric O. , Fishman Dmitry A.
Published: November 2022

Spectral imaging in the mid-infrared (MIR) range provides simultaneous morphological and chemical information of a wide variety of samples. However, current MIR technologies struggle to produce high-definition images over a broad spectral range at acquisition rates that are compatible with real-time processes. We present a novel spectral imaging technique based on nondegenerate two-photon absorption of temporally chirped optical MIR pulses. This approach avoids complex image processing or reconstruction and enables high-speed acquisition of spectral data cubes (xyω) at high-pixel density in under a second.
A persistent neutrophil-associated immune signature characterizes post–COVID-19 pulmonary sequelae
Authors: George Peter M. , Reed Anna , Desai Sujal R. , Devaraj Anand , Faiez Tasnim Shahridan , Laverty Sarah , Kanwal Amama , Esneau Camille , Liu Michael K.C. , Kamal Faisal , Man William D.-C. , Kaul Sundeep , Singh Suveer , Lamb Georgia , Faizi Fatima K. , Schuliga Michael , Read Jane , Burgoyne Thomas , Pinto Andreia L. , Micallef Jake , Bauwens Emilie , Candiracci Julie , Bougoussa Mhammed , Herzog Marielle , Raman Lavanya , Ahmetaj-Shala Blerina , Turville Stuart , Aggarwal Anupriya , Farne Hugo A. , Dalla Pria Alessia , Aswani Andrew D. , Patella Francesca , Borek Weronika E. , Mitchell Jane A. , Bartlett Nathan W. , Dokal Arran , Xu Xiao-Ning , Kelleher Peter , Shah Anand , Singanayagam Aran
Published: November 2022

Interstitial lung disease and associated fibrosis occur in a proportion of individuals who have recovered from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through unknown mechanisms. We studied individuals with severe coronavirus disease 2019 (COVID-19) after recovery from acute illness. Individuals with evidence of interstitial lung changes at 3 to 6 months after recovery had an up-regulated neutrophil-associated immune signature including increased chemokines, proteases, and markers of neutrophil extracellular traps that were detectable in the blood. Similar pathways were enriched in the upper airway with a concomitant increase in antiviral type I interferon signaling. Interaction analysis of the peripheral phosphoproteome identified enriched kinases critical for neutrophil inflammatory pathways. Evaluation of these individuals at 12 months after recovery indicated that a subset of the individuals had not yet achieved full normalization of radiological and functional changes. These data provide insight into mechanisms driving development of pulmonary sequelae during and after COVID-19 and provide a rational basis for development of targeted approaches to prevent long-term complications.
Inducing Self-Poisoning: a Feasible Antibacterial Strategy?
Authors: Caspeta Luis , Bustamante Víctor H.
Published: November 2022

Development of novel antibacterial strategies is required to tackle the alarming threat for global health due to antimicrobial resistance. In this issue of the
Journal of Bacteriology
, Boulanger et al. provide evidence supporting that the blocking of metabolic pathways to induce accumulation of toxic intermediates can be a possible approach to combat bacterial infections (E.
A
Mollicutes
Metagenome-Assembled Genome from the Gut of the Pteropod
Limacina rangii
Authors: Pimentel Zachary T. , Thibodeau Patricia S. , Song Bongkeun , Zhang Ying
Published: November 2022

A nearly complete genome of an uncultured
Mollicutes
sp. was obtained from the metagenome of the gut of
Limacina rangii
(open-ocean snail), an important grazer and prey for higher trophic animals along the rapidly warming region of the western Antarctic Peninsula.