Key Geometric Features Research Articles

The Modular Relative Orbit Design Method for Spacecraft Proximity Motion

Abstract This paper presents a modular relative motion trajectory design tool for proximity operation of on-orbit service spacecrafts. First, a series of commonly used relative motion trajectories are defined. The dynamics are solved such that each type of trajectory is encapsulated into different modules. The input parameters of each module include the key geometrical features, such as the initial position, desired final position, and flying time. Different modules are connected via the same position and velocity. The modular orbit design tool is able to cover most of the typical proximity operation scenarios. Following that, two notational on-orbit service missions are given as examples to describe the specific usage process of the tool. It includes three major steps, namely, determining the appropriate module sequence according to the mission requirements, designing the pending input parameters of each module by taking consideration of constraints, and computing the details of module sequence to generate relative trajectories. Compared with the traditional maneuver-optimized orbit design approach, the modular method has fewer parameters to solve. Additionally, the modular application tool is versatile and can be used for a variety of different on-orbit service missions. This tool is of great importance in standardizing the space operations.

Machine-learning-enabled geometric compliance improvement in two-photon lithography without hardware modifications

In recent years, two-photon lithography (TPL) has emerged as a practical and promising micro- and nano-fabrication technique for a wide range of applications. Numerous studies have reported improving the process control and printed feature size of TPL, including by incorporating some degree of hardware improvements, which may be prohibitive for commercial systems. However, the geometric accuracy of TPL-fabricated 3D structures has not been well understood. In this study, a general machine-learning-based framework is presented to quantitatively model and improve the geometric compliance in TPL. The framework quantifies the spatial variation in geometric compliance of fabricated 3D structures, and then designs compensation strategies to improve the geometric compliance. Two experimental case studies, one at the microscale and the other at the nanoscale, are presented to demonstrate the effectiveness of the framework. It is revealed for the first time that systematic geometric errors exist in TPL-fabricated structures and such errors exhibit a strong spatial correlation. The produced compensation strategies reduce the average errors in key geometric features at the microscale and nanoscale by up to 79.7% and 47.4%, respectively. The case studies demonstrate that the proposed framework can effectively improve the geometric compliance without introducing any modifications to the hardware or process parameters, thereby facilitating more widespread adoption.

A novel U‐shaped encoder–decoder network with attention mechanism for detection and evaluation of road cracks at pixel level

AbstractAs the most common road distress, cracks have a substantial influence on the integrity of pavement structures. Accurate identification of crack existence and quantification of crack geometry are thus critical for the decision‐making of maintenance measures. This paper proposes a novel neural network for the detection and evaluation of road cracks at pixel level, which combines the advantage of encoding–decoding network and attention mechanism and can thus extract the crack pixels more accurately and efficiently. The proposed network achieves an excellent detection performance with IOU = 92.85%, precision = 96.90%, recall = 95.36%, F1 = 95.53%. Compared with the other advanced networks, the accuracy of the proposed method is substantially enhanced. The quantitative estimation of key geometrical features of cracks including length, width, and area is successfully realized with the development of a prototype of an intelligent mobile system. Compared with the ground truth, the maximum crack width shows the lowest relative error rate, which ranges −31.75%∼28.57%.

An integrated data mining-based approach to identify key building and urban features of different energy usage levels

Building and urban geometries, prerequisite at the design phase, are the key determinants of building energy consumptions. However, the key building and urban features of different energy consumption levels is rarely studied. This study proposed a data mining-based method to explore the significant building features of different building groups. In this approach, clustering classifies buildings into three clusters according to energy consumption, and the clustering results contribute a base for principal component analysis (PCA) and random forest (RF) to discover key building features affecting different energy consumption levels. To demonstrate the availability of the framework, it is applied to on a city dataset in China. The results indicate that the key geometric features for low and medium residential energy consumption clusters are Orientation, HW-South, HW-West, HW-North and HW-East, while the key determinants for high residential energy consumption cluster are Orientation and HW-South. The key features for public buildings are similar to those for residential buildings with exception of HW-East. The findings provide insights into the key influence geometric features of different building energy usage levels, which can guide the passive design of urban buildings to efficiently reduce energy consumptions at design stage.

Front Cover Picture: Chiral Iron Complexes in Asymmetric Organic Transformations (Adv. Synth. Catal. 4/2022)

The front cover picture illustrates chiral iron catalysts featuring tetrahedral and octahedral stereocenters, which are reviewed in the accompanying article for their use in enantioselective organic transformations. The configurational stability of these complexes can be achieved by multidentate ligands in octahedral complexes and cyclopentadienyl anions in tetrahedral structures. These key geometrical features are recalled here by the graphical representations of a piano stool and of a three-blade propeller, while the Fe symbol at the core of the illustration highlights the fundamental role of the configuration of the metal center. Details can be found in the Review by Piarulli and co-workers (G. M. Fusi, S. Gazzola, U. Piarulli, Adv. Synth. Catal. 2022, 364, 696–714; DOI: 10.1002/adsc.202100995).

StorSeismic: A New Paradigm in Deep Learning for Seismic Processing

Machine learned tasks on seismic data are often trained sequentially and separately, even though they utilize the same features (i.e. geometrical) of the data. We present StorSeismic, as a framework for seismic data processing, which consists of neural network pre-training and fine-tuning procedures. We, specifically, utilize a neural network as a preprocessing model to store seismic data features of a particular dataset for any downstream tasks. After pre-training, the resulting model can be utilized later, through a fine-tuning procedure, to perform tasks using limited additional training. Used often in Natural Language Processing (NLP) and lately in vision tasks, BERT (Bidirectional Encoder Representations from Transformer), a form of a Transformer model, provides an optimal platform for this framework. The attention mechanism of BERT, applied here on a sequence of traces within the shot gather, is able to capture and store key geometrical features of the seismic data. We pre-train StorSeismic on field data, along with synthetically generated ones, in the self-supervised step. Then, we use the labeled synthetic data to fine-tune the pre-trained network in a supervised fashion to perform various seismic processing tasks, like denoising, velocity estimation, first arrival picking, and NMO. Finally, the fine-tuned model is used to obtain satisfactory inference results on the field data.

Cr/PCCP-catalysed selective ethylene oligomerization: analysis of various conformations and the hemilabile methoxy group

This work reports a DFT study on the effects of the hemilabile methoxy group in Cr/PCCP and Cr/PCCPOMe catalyzed ethylene tri- and tetramerization. The ensemble of conformers and the key geometrical features of the lowest conformer were discussed.

Transferable, Deep-Learning-Driven Fast Prediction and Design of Thermal Transport in Mechanically Stretched Graphene Flakes.

Piling graphene sheets into a bulk form is essential for achieving massive applications of graphene in flexible structures and devices, and the arbitrary shape, random distributions, and adjacent overlaps of graphene sheets are yet challenging the prediction of its fundamental properties that are strongly coupled by mechanical strength and thermal or electronic transport. Here, we present a deep neural network (DNN)-based machine learning (ML) approach that enables the prediction of thermal conductivity of piled graphene structures with a broad range of geometric configurations and dimensions in response to external mechanical loading. A physics-informed pixel value matrix is developed to capture the key geometric features of piled graphene structures and is incorporated into the DNN to train the ML model with the only training data ratio of 12.5% but the prediction accuracy of 94%. The ML model is further extended with the transferred knowledge from primitive training data sets to predict the thermal transport of piled graphene in a custom data set. Extensive demonstrations in search of piled graphene structures with desirable thermal conductivity and its response to mechanical loading are presented and illustrate the capability and accuracy of the DNN-ML model for establishing a mechanically adaptive structure: responsive thermal property paradigm in piled graphene. This work lays a foundation for quantitatively evaluating thermal conductivity of piled graphene in response to mechanical loadings through an ML model and also offers a rational route for exploring mechanically tunable thermal properties of nanomaterial-based bulk forms, potentially useful in the design of flexible thermal structures and devices with controllable thermal management performance.

Simple models for simulating shear key arrangement in nonlinear seismic analysis of arch dams

Shear keys are commonly designed on contraction joints to limit joint slipping and improve the overall seismic behavior of arch dams. However, shear keys are usually over-simplified in the current nonlinear seismic analysis. This paper proposes a geometric simplification method for simulating shear key arrangement. Actual shear keys on a contraction joint are simplified into the combination of large oblique-type shear keys along the cantilever direction and horizontal direction. Key geometric features and progressive failure of shear keys can be naturally considered using the contact boundary model and the plastic-damage model. Nonlinear seismic analysis of the Xulong arch dam is implemented with different shear key models for verification and comparison. The proposed method is firstly verified by analyzing seismic responses of the dam with geometrically-simplified shear keys and fine shear keys, respectively. Furthermore, seismic responses of the dam with geometrically-simplified shear keys, free-slipping plane joints (ignoring shear keys but considering surface friction), and non-slipping plane joints (modeling perfect shear keys) are compared to investigate the effects of shear key modeling methods. Ignoring shear keys and modeling perfect shear keys underestimate and overestimate the seismic capacity of the dam, respectively. The nonlinear dynamic behavior of shear keys can be more reasonably represented with the proposed method without much increasing modeling efforts.

Optimization of liquid jet ejector geometry and its impact on flow fields

Liquid jet liquid (LJL) ejectors have wide industrial application and the improvement in their efficiency is sought to increase the viability of their use. This study performs the optimization of the entire geometry of an LJL ejector with multiple parameters to maximize energy efficiency. The approach with multiple parameters allows to identify key geometrical features and interdependent parameters, like nozzle position and mixing chamber length, with respect to performance. Computational fluid dynamics (CFD) and optimization simulations were performed to optimize the parameters of Bézier curves that characterize the device's geometry. The optimization results showed that the ejector efficiency curve is sensitive to the nozzle and the suction chamber geometries. Simulations that consider the nozzle diameter and the nozzle position along ejector axis (NXP) as parameters of the optimization process resulted in higher efficiency values than those that kept these parameters fixed. The optimization of the diffuser curve, including the diameter of the diffuser and the length of the mixing chamber, also contributed to increase the efficiency of the device. It was observed that the increase in the length of the mixing chamber and the spacing of the nozzle imply similar effects in the efficiency and in the pressure, velocity and energy dissipation rate profiles confirming some correlation of these parameters on the performance. One may observe the effect of geometry modification through optimization on the flow profiles in key sections of the ejector: the flow profiles in the optimized geometry tend to be more homogenous, hence less dissipative, and it is also confirmed by local energy dissipation rate.

Laser wobble welding of fluid-based cooling channel joining for battery thermal management

Aluminium alloys are increasingly used to fabricate cooling channels for the thermal management of Li-ion batteries. Cooling channel fabrication involves a number of manufacturing operations including material extrusion, forming and joining/welding. In general, welding of aluminium alloys is challenging as they are both highly reflective and thermally conductive. To address the joining challenges, this paper is focused on developing an optimised joining process to connect a thin, flanged cooling channel to the thick module manifold of the battery thermal management system to create a watertight joint with high mechanical strength. As continuous seam welding was required, laser welding was the preferred as it is a non-contact process combining high speed and precision. For this application, 0.4 mm Al cooling channel was welded with 1.5 mm Al endplate/module manifold using a wobble head integrated with 1 kW CW fibre laser system. The effect of process parameters including line energy, incident angle, laser power, welding speed and beam offset were investigated to optimise both the weld geometry and strength. Microstructure, micro-hardness and grain formation analyses were carried out to understand the metallurgical behaviour of the weld. Beam offset had the most significant effect on the responses such as weld strength, throat thickness and modified throat thickness, and laser power had a significant influence on two key geometric features of the fusion zone, i.e. penetration depth and weld width. Weld strength was optimised using a developed surrogate model and a maximum load of 646.89 N was achieved using 0.2 mm beam offset, 331.82 W laser power and 659.10 mm/min welding speed. Using this optimum combination, a leak-proof cooling channel and module manifold joint were produced for battery thermal management.

Mathematical Analysis of Process Parameters in Drilling of Various Aluminium Matrix Composites Using TOPSIS

Drilling is one of the major material removal processes to produce holes and the key parameters involved in the process are speed of the drilling tool and feed rate of the tool into the work piece to be machined. A conventional study would include the diameter of the drill bit as a key geometric feature that affects the output parameters: thrust, torque, roundness error and the surface roughness. Drilling experiments are performed on three different hybrid composite specimen of Al6061 alloy. Keeping the diameter constant and by varying the point angle of the tool, drilling is performed and the responses of the output parameters are recorded and tabulated. Using the data from the experiment, analysis of the data is done by one of the multi-criteria decision analysis method, TOPSIS. This method is used to determine the optimal condition to drill a particular specimen of the composite. In TOPSIS method, the data is ranked based on performance of the drill, to find the parameters which are ideal to obtain a hole with minimum roughness, circularity error and thrust force. But it is enough to keep the goal as minimum roughness because the other parameters will invariably get minimized as they are dependent

Multi-curvature micropatterns unveil distinct calcium and mitochondrial dynamics in neuronal networks.

Tangential curvatures are a key geometric feature of tissue folds in the human cerebral cortex. In the brain, these smoother and firmer bends are called gyri and sulci and form distinctive curved tissue patterns imposing a mechanical stimulus on neuronal networks. This stimulus is hypothesized to be essential for proper brain cell function but lacks in most standard neuronal cell assays. A variety of soft lithographic micropatterning techniques can be used to integrate round geometries in cell assays. Most microfabricated patterns, however, focus only on a small set of defined curvatures. In contrast, curvatures in the brain span a wide physical range, leaving it unknown which precise role distinct curvatures may play on neuronal cell signaling. Here we report a hydrogel-based multi-curvature design consisting of over twenty bands of distinct parallel curvature ranges to precisely engineer neuronal networks' growth and signaling under patterns of arcs. Monitoring calcium and mitochondrial dynamics in primary rodent neurons grown over two weeks in the multi-curvature patterns, we found that static calcium signaling was locally attenuated under higher curvatures (k > 0.01 μm-1). In contrast, to randomize growth, transient calcium signaling showed higher synchronicity when neurons formed networks in confined multi-curvature patterns. Additionally, we found that mitochondria showed lower motility under high curvatures (k > 0.01 μm-1) than under lower curvatures (k < 0.01 μm-1). Our results demonstrate how sensitive neuronal cell function may be linked and controlled through specific curved geometric features. Furthermore, the hydrogel-based multi-curvature design possesses high compatibility with various surfaces, allowing a flexible integration of geometric features into next-generation neuro devices, cell assays, tissue engineering, and implants.

Vehicle Position and Context Detection Using V2V Communication

A pre-crash detection and warning system in a host vehicle needs to accurately determine the position of each remote vehicle in its vicinity and the context of the driving environment. ADAS (Advanced driver-assistance systems) have extensively used camera radar and LIDAR for automatic detection of vehicles, pedestrians and other road users and their behaviors. However, these vehicle-resident sensors have short operation ranges and require objects to be within the line-of-sight. V2V communication has emerged to be a promising technology to augment vehicle-resident sensors with extended ability of an overall vehicle safety system by addressing a broader range of crash scenarios with improved warning timing. In this paper we present an intelligent system, Geo+NN, developed using the synergy of neural network and geometric modeling. We extract the key geometric features using an analytic geometric model and use them as input to a neural network that is trained on real-world V2V signals to detect and predict remote vehicles’ positions. Geo+NN system is evaluated on V2V communication data recorded during real-world driving trips by vehicles installed with DSRC devices. Experimental results show that Geo+NN has the capabilities of effectively detecting and predicting remote vehicles within the context of 8 different positions.

Truck and Trailer Classification With Deep Learning Based Geometric Features

In this paper, we present a novel and effective approach to truck and trailer classification, which integrates deep learning models and conventional image processing and computer vision techniques. The developed method groups trucks into subcategories by carefully examining the truck classes and identifying key geometric features for discriminating truck and trailer types. We also present three discriminating features that involve shape, texture, and semantic information to identify trailer types. Experimental results demonstrate that the developed hybrid approach can achieve high accuracy with limited training data, where the vanilla deep learning approaches show moderate performance due to over-fitting and poor generalization. Additionally, the models generated are human-understandable.

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing.

Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.

Syntheses and crystal structures of 2-methyl-1,1,2,3,3-penta-phenyl-2-sila-propane and 2-methyl-1,1,3,3-tetra-phenyl-2-silapropan-2-ol.

The sterically hindered silicon compound 2-methyl-1,1,2,3,3-penta-phenyl-2-sila-propane, C33H30Si (I), was prepared via the reaction of two equivalents of di-phenyl-methyl-lithium (benzhydryllithium) and di-chloro-methyl-phenyl-silane. This bis-benzhydryl-substituted silicon compound was then reacted with tri-fluoro-methane-sulfonic acid, followed by hydrolysis with water to give the silanol 2-methyl-1,1,3,3-tetra-phenyl-2-silapropan-2-ol, C27H26OSi (II). Key geometric features for I are the Si-C bond lengths that range from 1.867 (2) to 1.914 (2) Å and a τ4 descriptor for fourfold coordination around the Si atom of 0.97 (indicating a nearly perfect tetra-hedron). Key geometric features for compound II include Si-C bond lengths that range from 1.835 (4) to 1.905 (3) Å, a Si-O bond length of 1.665 (3) Å, and a τ4 descriptor for fourfold coordination around the Si atom of 0.96. In compound II, there is an intra-molecular C-H⋯O hydrogen bond present. In the crystal of I, mol-ecules are linked by two pairs of C-H⋯π inter-actions, forming dimers that are linked into ribbons propagating along the b-axis direction. In the crystal of II, mol-ecules are linked by C-H⋯π and O-H⋯π inter-actions that result in the formation of ribbons that run along the a-axis direction.

DFT and MD Studies of Formaldehyde-Derived DNA Adducts: Molecular-Level Insights into the Differential Mispairing Potentials of the Adenine, Cytosine, and Guanine Lesions.

4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone is a potent nicotine-based carcinogen that generates many DNA lesions, including the HOCH2-C, HOCH2-G, and HOCH2-A hydroxymethyl adducts. Despite all lesions containing an altered exocyclic amino group, which allows the hydroxymethyl group to be directed away from the Watson-Crick binding face, only the most persistent adenine adduct is mutagenic. As a first step toward understanding this differential mutagenicity, density functional theory (DFT) and molecular dynamics (MD) simulations were used to gain atomic-level structural details of these DNA damage products. DFT calculations reveal that all three lesions exhibit conformational diversity. However, regardless of the hydroxymethyl-nucleobase orientation, both DFT and MD simulations highlight that HOCH2-C and HOCH2-G form pairs with the canonical complementary base (G and C, respectively) that are structural and energetically preferred over mispairs. In contrast, depending on the hydroxymethyl-nucleobase orientation, the Watson-Crick HOCH2-A:T pair can become significantly destabilized relative to undamaged A:T. As a result, HOCH2-A mispairs with G, C, and A are energetically accessible and maintain key geometrical features of canonical DNA. Overall, our data directly correlate with the reported differential mutagenicity of the hydroxylmethyl lesions and will encourage future studies to further uncover the cellular impact of the most persistent adenine lesion.

Crystal structures of two bis-carbamoyl-methyl-phosphine oxide (CMPO) compounds.

Two bis-carbamoyl-methyl-phosphine oxide compounds, namely {[(3-{[2-(di-phen-yl-phosphino-yl)ethanamido]-meth-yl}benz-yl)carbamo-yl]meth-yl}di-phenyl-phos-phine oxide, C36H34N2O4P2, (I), and diethyl [({2-[2-(di-eth-oxy-phosphino-yl)ethanamido]-eth-yl}carbamo-yl)meth-yl]phospho-nate, C14H30N2O8P2, (II), were synthesized via nucleophilic acyl substitution reactions between an ester and a primary amine. Hydrogen-bonding inter-actions are present in both crystals, but these inter-actions are intra-molecular in the case of compound (I) and inter-molecular in compound (II). Intra-molecular π-π stacking inter-actions are also present in the crystal of compound (I) with a centroid-centroid distance of 3.9479 (12) Å and a dihedral angle of 9.56 (12)°. Inter-molecular C-H⋯π inter-actions [C⋯centroid distance of 3.622 (2) Å, C-H⋯centroid angle of 146°] give rise to supra-molecular sheets that lie in the ab plane. Key geometric features for compound (I) involve a nearly planar, trans-amide group with a C-N-C-C torsion angle of 169.12 (17)°, and a torsion angle of -108.39 (15)° between the phosphine oxide phospho-rus atom and the amide nitro-gen atom. For compound (II), the electron density corresponding to the phosphoryl group was disordered, and was modeled as two parts with a 0.7387 (19):0.2613 (19) occupancy ratio. Compound (II) also boasts a trans-amide group that approaches planarity with a C-N-C-C torsion angle of -176.50 (16)°. The hydrogen bonds in this structure are inter-molecular, with a D⋯A distance of 2.883 (2) Å and a D-H⋯A angle of 175.0 (18)° between the amide hydrogen atom and the P=O oxygen atom. These non-covalent inter-actions create ribbons that run along the b-axis direction.

Design of an Annular-Radial Diffuser for Operation With a Supercritical CO2 Radial Inflow Turbine

Radial inflow turbines are a relevant architecture for energy extraction from supercritical CO2 power cycles for scales less than 10 MW. To ensure stage and overall cycle efficiency, it is desirable to recover exhaust energy from the turbine stage through the inclusion of a suitable diffuser in the turbine exhaust stream. In supercritical CO2 Brayton cycles, the high turbine inlet pressure can lead to sealing challenges at small scale if the rotor is supported from the rotor rear side in the conventional manner. An alternative is a layout where the rotor exit faces the bearing system. While such a layout is attractive for the sealing system, it limits the axial space claim of the diffuser. Designs of a combined annular-radial diffuser are considered as a means to meet the aforementioned packaging challenges of this rotor layout. Diffuser performance is assessed numerically with the use of Reynolds-averaged Navier--Stokes (RANS) and unsteady Reynolds-averaged Navier--Stokes (URANS) calculations. To appropriately account for cross coupling with the stage, a single blade passage of the entire stage is modeled. Assessment of diffuser inlet conditions, and off-design performance analysis, reveals that the investigated diffuser designs are performance robust to high swirl, high inlet blockage, and highly nonuniform mass flux distribution. Diffuser component performance is dominated by the annular-radial bend. The incorporation of a constant sectional area bend is the key geometric feature in rendering the highly nonuniform turbine exit flow (dominated by tip clearance flows at the shroud) more uniform.