Complex Motion Research Articles

PixelPrint 4D : A 3D printing method of fabricating patient-specific deformable CT phantoms for respiratory motion applications.

All in-vivo medical imaging is impacted by patient motion, especially respiratory motion, which has a significant influence on clinical workflows in diagnostic imaging and radiation therapy. Many technologies such as motion artifact reduction and tumor tracking algorithms have been developed to compensate for respiratory motion during imaging. To assess these technologies, respiratory motion phantoms (RMPs) are required as preclinical testing environments, for instance, in computed tomography (CT). However, current RMPs are highly simplified and do not exhibit realistic tissue structures or deformation patterns. With the rise of more complex motion compensation technologies such as deep learning-based algorithms, there is a need for more realistic RMPs. This work introduces PixelPrint 4D , a 3D printing method designed to fabricate lifelike, patient-specific deformable lung phantoms for CT imaging. The phantom demonstrated accurate replication of patient lung structures, textures, and attenuation profiles. Furthermore, it exhibited accurate nonrigid deformations, volume changes, and attenuation changes under compression. PixelPrint 4D enables the production of highly realistic RMPs, surpassing existing models to offer more robust testing environments for a diverse array of novel CT technologies.

Interference mechanism of trailing edge flap shedding vortices with rotor wake and aerodynamic characteristics

A robust Reynolds-Averaged Navier-Stokes (RANS) based solver is established to predict the complex unsteady aerodynamic characteristics of the Active Flap Control (AFC) rotor. The complex motion with multiple degrees of freedom of the Trailing Edge Flap (TEF) is analyzed by employing an inverse nested overset grid method. Simulation of non-rotational and rotational modes of blade motion are carried out to investigate the formation and development of TEF shedding vortex with high-frequency deflection of TEF. Moreover, the mechanism of TEF deflection interference with blade tip vortex and overall rotor aerodynamics is also explored. In non-rotational mode, two bundles of vortices form at the gap ends of TEF and the main blade and merge into a single TEF vortex. Dynamic deflection of the TEF significantly interferes with the blade tip vortex. The position of the blade tip vortex consistently changes, and its frequency is directly related to the frequency of TEF deflection. In rotational mode, the tip vortex forms a helical structure. The end vortices at the gap sides co-swirl and subsequently merge into the concentrated beam of tip vortices, causing fluctuations in the vorticity and axial position of the tip vortex under the rotor. This research concludes with the investigation on suppression of Blade Vortex Interaction (BVI), showing an increase in miss distance and reduction in the vorticity of tip vortex through TEF phase control at a particular control frequency. Through this mechanism, a designed TEF deflection law increases the miss distance by 34.7% and reduces vorticity by 11.9% at the target position, demonstrating the effectiveness of AFC in mitigating BVI.

AI-ENHANCED TRACKSEGNET AN ADVANCED MACHINE LEARNING TECHNIQUE FOR VIDEO SEGMENTATION AND OBJECT TRACKING

Video segmentation and object tracking are critical tasks in computer vision with applications spanning surveillance, autonomous driving, and interactive media. Traditional methods often struggle with the dynamic nature of video data, where object occlusions, variations in illumination, and complex motion patterns present significant challenges. Existing segmentation and tracking systems frequently suffer from inaccuracies in handling real-time video sequences, particularly in distinguishing and tracking multiple overlapping objects. The limitations of current models in addressing these issues necessitate the development of more advanced techniques that can effectively manage dynamic scenes and improve tracking accuracy. To address these challenges, we propose an advanced machine learning technique, AI-Enhanced TrackSegNet, which integrates deep learning with novel attention mechanisms for improved video segmentation and object tracking. Our method utilizes a combination of Convolutional Neural Networks (CNNs) for feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. We introduce an attention-based mechanism to dynamically focus on relevant features, enhancing the model's ability to handle occlusions and varying object appearances. The model was trained on a diverse dataset of video sequences, incorporating both synthetic and real-world footage. The AI-Enhanced TrackSegNet demonstrated significant improvements in performance compared to existing techniques. Our method achieved an average Intersection over Union (IoU) score of 86.7% for segmentation and a tracking precision rate of 91.3% on the MOT17 benchmark dataset. These results represent a 10.2% improvement in IoU and a 7.5% increase in tracking precision compared to state-of-the-art methods. The model also exhibited enhanced robustness in complex scenes, handling occlusions and motion variations with greater accuracy.

Prediction model of grinding roughness for worm with complicated space tooth surface

Optimum grinding parameters are crucial for achieving high-quality grinding of helical tooth surfaces for worm drives. The complex coupling motion relationship and formation mechanism between the worm blank and the grinding head make it difficult to predict the effect of grinding parameters on tooth surface roughness. Therefore, we developed a prediction model for the worm tooth surface roughness under conjugate grinding and applied it to the Roller Enveloping Worm Reducer (REWR). The forming process of the theoretical worm tooth surface during the grinding process was modeled by using the conjugate meshing principle. Considering the plowing and cutting effects of grinding particles on the tooth surface at different design parameters, feed speeds, and rotational speeds of the grinding head, the roughness prediction equation was established based on the theory of the maximum depth of valley bottom. Through the coupling analysis of the theoretical tooth surface model and the roughness prediction model, the prediction of the worm tooth surface roughness is realized. Based on the validated model, the influences of grinding parameters on the worm tooth surface roughness of REWR were quantitatively discussed. This study is of great significance for optimizing process parameters in the grinding of complex spatial surface transmissions.

Representation Learning and Reinforcement Learning for Dynamic Complex Motion Planning System.

Indoor motion planning challenges researchers because of the high density and unpredictability of moving obstacles. Classical algorithms work well in the case of static obstacles but suffer from collisions in the case of dense and dynamic obstacles. Recent reinforcement learning (RL) algorithms provide safe solutions for multiagent robotic motion planning systems. However, these algorithms face challenges in convergence: slow convergence speed and suboptimal converged result. Inspired by RL and representation learning, we introduced the ALN-DSAC: a hybrid motion planning algorithm where attention-based long short-term memory (LSTM) and novel data replay combine with discrete soft actor-critic (SAC). First, we implemented a discrete SAC algorithm, which is the SAC in the setting of discrete action space. Second, we optimized existing distance-based LSTM encoding by attention-based encoding to improve the data quality. Third, we introduced a novel data replay method by combining the online learning and offline learning to improve the efficacy of data replay. The convergence of our ALN-DSAC outperforms that of the trainable state of the arts. Evaluations demonstrate that our algorithm achieves nearly 100% success with less time to reach the goal in motion planning tasks when compared to the state of the arts. The test code is available at https://github.com/CHUENGMINCHOU/ALN-DSAC.

Nontoxic flexible TENG with robust piezoelectric enhancement through graphitic carbon nitride-incorporated PVDF for wearable sensors and power supplies

Flexible triboelectric nanogenerator (TENG) with superior performance and nontoxicity is desirable for sustainable and wearable electronic applications. Herein, a two-dimensional graphitic-carbon-nitride incorporated poly (vinylidene fluoride) (g-C3N4/PVDF) electronegative layer is synthesized and then assembled with a nylon-11 nanofibers electropositive layer to fabricate a nontoxic flexible TENG. Owing to the synergy of excellent electronegativity of PVDF and robust piezoelectricity enhanced by angle-and-thickness-independent piezoelectric effect of g-C3N4, the optimized TENG exhibits stable and superior output performance including open-circuit voltage (VOC) of 339 V, short-circuit current (ISC) of 14.5 μA, and power density of 0.94 W/m2, better than previously-reported values of PVDF-based and g-C3N4 TENGs. Furthermore, it is demonstrated that the optimized TENG can be used not only as a power supply to drive electronics but also as flexible wearable sensors to detect complex motions in real time. Our results provide a convenient way to fabricate a high-performance nontoxic flexible TENG for wearable sensors and power supplies.

Learning to Walk with Adaptive Feet

In recent years, tasks regarding autonomous mobility favoredthe use of legged robots rather than wheeled ones thanks to their higher mobility on rough and uneven terrains. This comes at the cost of more complex motion planners and controllers to ensure robot stability and balance. However, in the case of quadrupedal robots, balancing is simpler than it is for bipeds thanks to their larger support polygons. Until a few years ago, most scientists and engineers addressed the quadrupedal locomotion problem with model-based approaches, which require a great deal of modeling expertise. A new trend is the use of data-driven methods, which seem to be quite promising and have shown great results. These methods do not require any modeling effort, but they suffer from computational limitations dictated by the hardware resources used. However, only the design phase of these algorithms requires large computing resources (controller training); their execution in the operational phase (deployment), takes place in real time on common processors. Moreover, adaptive feet capable of sensing terrain profile information have been designed and have shown great performance. Still, no dynamic locomotion control method has been specifically designed to leverage the advantages and supplementary information provided by this type of adaptive feet. In this work, we investigate the use and evaluate the performance of different end-to-end control policies trained via reinforcement learning algorithms specifically designed and trained to work on quadrupedal robots equipped with passive adaptive feet for their dynamic locomotion control over a diverse set of terrains. We examine how the addition of the haptic perception of the terrain affects the locomotion performance.

Light-Driven Multidirectional Bending in Artificial Muscles.

Using light to drive polymer actuators can enable spatially selective complex motions, offering a wealth of opportunities for wireless control of soft robotics and active textiles. Here, the integration of photothermal components is reported into shape memory polymer actuators. The fabricated twist-coiled artificial muscles show on-command multidirectional bending, which can be controlled by both the illumination intensity, as well as the chirality, of the prepared artificial muscles. Importantly, the direction in which these artificial muscles bend does not depend on intrinsic material characteristics. Instead, this directionality is achieved by localized untwisting of the actuator, driven by selective irradiation. The reaction times of this bending system are significantly - at least two orders of magnitude - faster than heliotropic biological systems, with a response time up to one second. The programmability of the artificial muscles is further demonstrated for selective, reversible, and sustained actuation when integrated in butterfly-shaped textiles, along with the capacity to autonomously orient toward a light source. This functionality is maintained even on a rotating platform, with angular velocities of 6°/s, independent of the rotation direction. These attributes collectively represent a breakthrough in the field of artificial muscles, intended to adaptive shape-changing soft systems and biomimetic technologies.

Joint synergy and muscle activity in the motion of the ankle-foot complex.

The movement of the ankle-foot complex joints is coupled as a result of various physiological and physical constraints. This study introduces a novel approach to the analysis of joint synergies and their physiological basis by focusing on joint rotational directions and the types of muscle contractions. We developed a biomimetic model of the ankle-foot complex with seven degrees of freedom, considering the skeletal configuration and physiological axis directions. Motion capture experiments were conducted with eight participants performing dorsiflexion and plantarflexion in open-chain states, as well as various walking tasks in closed-chain states, across different ground inclinations (±10, ±5, 0deg) and walking speeds (3 and 4 km h-1). Hierarchical cluster analysis identified joint synergy clusters and motion primitives, revealing that in open-chain movements, plantarflexion of the ankle, tarsometatarsal and metatarsophalangeal joints exhibited synergy with the inversion of the remaining joints in the complex; meanwhile, dorsiflexion was aligned with eversion. During closed-chain movements, the synergies grouping was exchanged in the subtalar, talonavicular and metatarsophalangeal joints. Further analysis showed that in open-chain movements, synergy patterns influenced by multi-joint muscles crossing oblique joint axes contribute to foot motion. In closed-chain movements, these changes in synergistic patterns enhance the propulsion of the center of mass towards the contralateral leg and improve foot arch compliance, facilitating human motion. Our work enhances the understanding of the physiological mechanisms underlying synergistic motion within the ankle-foot complex.

Numerical Study of the Ultra-High-Speed Aerodynamically Alleviated Marine Vehicle Motion Stability in Winds and Waves

The ultra-high-speed aerodynamically alleviated marine vehicle (AAMV) is a high-performance vessel that combines a hydrodynamic configuration and an aerodynamic wing to reduce wave-making resistance during the high-speed planing phase. The forces of the AAMV exhibit strong nonlinear and water–air coupling characteristics, resulting in particularly complex motion characteristics. This paper presents a longitudinal and lateral stability model of the AAMV, which considers the effects of aerodynamic alleviation. Additionally, a numerical model of wind and wave turbulence forces is established, which considers viscous correction based on the potential theory. Finally, the effect of wind and wave turbulence forces on the motion stability of the AAMV under regular and irregular waves is analyzed by numerical solution. The simulation results demonstrate the influence of these disturbance forces on the stability of the AAMV under different sea states. The motion parameters of the AAMV exhibit a pronounced response to changes in sea state level. The aerodynamically alleviated effect is enhanced as speed increases, and the influence of winds and waves on the AAMV is greatly weakened, reducing the possibility of instability. During the cruising phase under class V sea state, the pitch, roll, and heave response are 0.210°, 0.0229°, and 0.0734 m, respectively. This effect can effectively improve the motion stability of the AAMV in winds and waves.

Ultrastable piezoelectric biomaterial nanofibers and fabrics as an implantable and conformal electromechanical sensor patch.

Poly(l-lactic acid) (PLLA) is a widely used U.S. Food and Drug Administration-approved implantable biomaterial that also possesses strong piezoelectricity. However, the intrinsically low stability of its high-energy piezoelectric β phase and random domain orientations associated with current synthesis approaches remain a critical roadblock to practical applications. Here, we report an interfacial anchoring strategy for fabricating core/shell PLLA/glycine (Gly) nanofibers (NFs) by electrospinning, which show a high ratio of piezoelectric β phase and excellent orientation alignment. The self-assembled core/shell structure offers strong intermolecular interactions between the -OH groups on Gly and C=O groups on PLLA, which promotes the crystallization of oriented PLLA polymer chains and stabilizes the β phase structure. As-received core/shell NFs exhibit substantially enhanced piezoelectric performance and excellent stability. An all NF-based nonwoven fabric is fabricated and assembled as a flexible nanogenerator. The device offers excellent conformality to heavily wrinkled surfaces and thus can precisely detect complex physiological motions often found from biological organs.

Semantic segmentation algorithm for video from UAV based on adaptive keyframe scheduling via similarity measurement

Abstract Unmanned aerial vehicle (UAV) videos exhibit complex object motion features and significant differences between frame features. To solve the problems of feature information loss and drastic accuracy decline in applying the video semantic segmentation method based on the fixed-period update strategy of keyframes, a keyframe recognition method based on similarity measurement is proposed, forming a video semantic segmentation algorithm based on adaptive keyframe scheduling. one keyframe recognition method based on pixel similarity measurement is established by modeling the similarity relationship between low-level pixels in adjacent frames. Meanwhile, the other keyframe recognition method based on feature similarity measurement is established by constructing a shallow Siamese network to measure the similarity relationship between features of frames. Then a discriminative network is constructed based on the obtained similarity of inter frames, and the segmentation process is accelerated by reusing features of keyframes through the optical flow network. Thereby a video semantic segmentation method for UAV based on adaptive keyframe strategy is established. The effectiveness of the proposed video semantic segmentation algorithm is verified on UAVid dataset. The results demonstrated that the speed of the proposed algorithm reaches 53.2 FPS and 54.5 FPS on the premise that the mean intersection over union is higher than 40% (this value is compared with the segmentation accuracy in the balanced mode when the similarity threshold is 0.76 and 0.88, respectively), which is 18.5 FPS and 19.8 FPS higher than the PSPNet image semantic segmentation algorithm. In addition, analysis of experiment results shows that the pixel-similarity-based keyframe recognition is suitable for high-precision video semantic segmentation scenes that need to improve segmentation efficiency, and the feature-similarity-based keyframe recognition is more suitable for high real-time video semantic segmentation tasks that require a small decrease in overall algorithm accuracy. 
In a word, the proposed video semantic segmentation algorithm based on adaptive keyframe scheduling via similarity measurement can improve segmentation speed while ensuring segmentation accuracy and stability.

MotionTrack: Learning motion predictor for multiple object tracking

Significant progress has been achieved in multi-object tracking (MOT) through the evolution of detection and re-identification (ReID) techniques. Despite these advancements, accurately tracking objects in scenarios with homogeneous appearance and heterogeneous motion remains a challenge. This challenge arises from two main factors: the insufficient discriminability of ReID features and the predominant utilization of linear motion models in MOT. In this context, we introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor that relies solely on object trajectory information. This predictor comprehensively integrates two levels of granularity in motion features to enhance the modeling of temporal dynamics and facilitate precise future motion prediction for individual objects. Specifically, the proposed approach adopts a self-attention mechanism to capture token-level information and a Dynamic MLP layer to model channel-level features. MotionTrack is a simple, online tracking approach. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT, characterized by highly complex object motion.

Forecasting chaotic behavior of the drill bit in real-time using a DNN model integrated with rock surface morphology

The multiple regenerative effects during the drilling process cause the drilling system to exhibit complex motions, even leading to chaotic behavior. Additionally, due to the inability to obtain real-time rock parameters, traditional drilling dynamics models and time-series forecasting models cannot accurately forecast the real-time operational status of the drilling system. In this paper, a deep neural network (DNN) model integrated with rock surface morphology model is proposed to forecast the real-time operational status of the drill bit. This new model can permanently record the changes in rock surface morphology and multiple regenerative effects during the drilling process, enhancing the perception of rock parameters and improving the forecasting accuracy of the drill bit’s operational status. Experimental results show that the new model forecasts the drill bit’s operational status with higher accuracy compared to other typical time-series forecasting models without integrating surface morphology model. Furthermore, this new model demonstrates superior convergence speed, stability, and generalization ability compared to other models. In addition, the influence of hyperparameters on the forecast accuracy of the new model has also been studied. These results play an important role in forecasting and preventing adverse vibrations such as stick slip, impact, reversal, and rebound of the drill bit.

Performance of elastic ball screen-cleaning device based on DEM-MBD coupling simulation

In seed breeding, the vibrating screen used for pre-sowing grading parental seeds often has the problem of poor self-purification rate. Using elastic balls to clean the plugging seeds and ensure the self-purification of the screen is a viable technical solution. To clarify and improve the operating performance of the elastic ball particles, a DEM-MBD coupling simulation model of the elastic ball screen-cleaning device was established in this study, and a numerical simulation analysis of the complex motion process was carried out. The mechanism of action of the elastic ball was explained more completely from the aspects of excitation force and energy transfer. Through the Plackett-Burman test, it was determined that the vibration frequency has the most significant effect on the excitation force. Multiple linear regression analysis was performed on each factor and the mathematical relationship equation. It was found that when the maximum excitation force was greater than 7 N and the average excitation force was greater than 2 N, the self-purification rate was greater than 95%. The self-purification rate was then increased to 99.81% by variable frequency tests. The variable frequency achieved better self-purification effect.

Performance Evaluation and Comparative Analyses of Tuned Inerter Damper for Stochastic Base Excited Structures Based on Hybrid H2/H∞ Optimization

Compared with tuned mass damper, tuned inerter damper (TID) has higher damping efficiency and lightweight characteristic if appropriate optimal methods are selected. Generally, [Formula: see text] or [Formula: see text] optimal methods are adopted to determine the optimal parameters of TID individually. But the vibration mitigation performance of the TID under near resonance frequency band based on [Formula: see text] optimization is less than [Formula: see text] optimization, but the peak frequency response based on [Formula: see text] optimization will be greater than [Formula: see text] optimization at the same time. Dual-characteristic-based vibration control may not be achievable based on [Formula: see text] or [Formula: see text] criteria, respectively. For this reason, hybrid [Formula: see text] optimization can be adopted. In this work, closed-form expressions are derived for the control of structural displacement responses based on hybrid [Formula: see text] optimal method. The vibration mitigation performance of the different optimized TIDs is evaluated considering the single-degree-of-freedom and multi-degrees-of-freedom systems are subjected to typical ground motions excitation. Results illustrate that because of the use of the optimal stiffness ratio of [Formula: see text] optimization and very close value of [Formula: see text] optimizations’ nominal damping ratio in [Formula: see text] optimization, [Formula: see text] optimization shows ‘dual characteristics’ in different excitation situations. Specifically, the peak response and structural fundamental frequencies’ response in displacement frequency response function using hybrid [Formula: see text] optimal methods are between [Formula: see text] and [Formula: see text] optimization, which indicates an excellent and balanced control performance combining the advantages of [Formula: see text] and [Formula: see text] optimal solutions. Hence, this hybrid method is more suitable for more complex ground motions excitation to control critical structural responses instead of single characteristic excitation. And the comprehensive vibration control in dynamic time histories analyses can be achieved by this dual characteristic-based optimal strategy. When designing the specific TID, hybrid [Formula: see text] optimization can be considered as the best choice for its compatibility and high adaptability for complex practical engineering scenarios with random and diverse excitations.

ArborSim: Articulated, branching, OpenSim routing for constructing models of multi-jointed appendages with complex muscle-tendon architecture.

Computational models of musculoskeletal systems are essential tools for understanding how muscles, tendons, bones, and actuation signals generate motion. In particular, the OpenSim family of models has facilitated a wide range of studies on diverse human motions, clinical studies of gait, and even non-human locomotion. However, biological structures with many joints, such as fingers, necks, tails, and spines, have been a longstanding challenge to the OpenSim modeling community, especially because these structures comprise numerous bones and are frequently actuated by extrinsic muscles that span multiple joints-often more than three-and act through a complex network of branching tendons. Existing model building software, typically optimized for limb structures, makes it difficult to build OpenSim models that accurately reflect these intricacies. Here, we introduce ArborSim, customized software that efficiently creates musculoskeletal models of highly jointed structures and can build branched muscle-tendon architectures. We used ArborSim to construct toy models of articulated structures to determine which morphological features make a structure most sensitive to branching. By comparing the joint kinematics of models constructed with branched and parallel muscle-tendon units, we found that among various parameters-the number of tendon branches, the number of joints between branches, and the ratio of muscle fiber length to muscle tendon unit length-the number of tendon branches and the number of joints between branches are most sensitive to branching modeling method. Notably, the differences between these models showed no predictable pattern with increased complexity. As the proportion of muscle increased, the kinematic differences between branched and parallel models units also increased. Our findings suggest that stress and strain interactions between distal tendon branches and proximal tendon and muscle greatly affect the overall kinematics of a musculoskeletal system. By incorporating complex muscle-tendon branching into OpenSim models using ArborSim, we can gain deeper insight into the interactions between the axial and appendicular skeleton, model the evolution and function of diverse animal tails, and understand the mechanics of more complex motions and tasks.

Is Ortho-Terphenyl a Rigid Glass Former?

Ortho-terphenyl (OTP) has long been used as a model system to study the glass transition due to its apparent simplicity and a widespread assumption that it is a rigid molecule. Here, we employ terahertz time-domain spectroscopy and low-frequency Raman spectroscopy to investigate the rigidity of OTP by direct observation of the low-frequency vibrational dynamics. These terahertz phonons involve complex large-amplitude atomic motions where intramolecular and intermolecular displacements are often mixed. Comparison of experimental results with density functional theory and ab initio molecular dynamics simulations shows that the assumption of rigidity neglects important implications for the glass transition and must be revisited. These results highlight the significance of terahertz modes on elasticity, which will be even more critical in more complex systems such as biomolecules.

Study on Aerodynamic Performance and Wake Characteristics of a Floating Offshore Wind Turbine in Wind–Wave Coupling Field

Floating offshore wind turbines (FOWTs) exhibit complex motion with multiple degrees of freedom due to the interaction of wind and waves. The aerodynamic performance and wake characteristics of these turbines are highly intricate and challenging to accurately capture. In this study, dynamic fluid body interaction (DFBI) and overset grid technology are employed to investigate the dynamic motion of a 5 MW FOWT. We use the volume of fluid (VOF) method and improved delayed detached eddy simulation (IDDES) model to investigate the aerodynamic performance and wake evolution mechanism for various wave periods and heights. According to the findings, the magnitude of the pitch motion increases with the period and height of the waves, leading to a decrease in both the power output and thrust; the maximum power was reduced by nearly 6.8% compared to a wind turbine without motion. The value of power and thrust reduction varies for different wave periods and heights, and is influenced by the relative speed and pitch angle, which play a crucial role. Wind–wave coupling has a significant impact on the evolution of both wake and vortex structures for FOWT. The wake shape downstream is also dynamically influenced by the waves. In the presence of wind and wave coupling, the interaction between the wind turbine and the wake is heightened, leading to the merger of two unstable vortex rings into a single, larger vortex ring. The research unveils a comprehensive picture of the offshore wind energy dynamics and wake field, which holds immense significance for the design of floating wind turbines and the optimization of wind farm layout.

Self-consistent modelling of the Milky Way structure using live potentials

ABSTRACT To advance our understanding of the evolution of the interstellar medium (ISM) of our Galaxy, numerical models of Milky Way (MW) type galaxies are widely used. However, most models only vaguely resemble the MW (e.g. in total mass), and often use imposed analytic potentials (which cannot evolve dynamically). This poses a problem in asserting their applicability for the interpretation of observations of our own Galaxy. The goal of this work is to identify a numerical model that is not only an MW-type galaxy, but one that can mimic some of the main observed structures of our Galaxy, using dynamically evolving potentials, so that it can be used as a base model to study the ISM cycle in a galaxy like our own. This paper introduces a suite of 15 MW-type galaxy models developed using the arepo numerical code, that are compared to Galactic observations of $^{12}$CO and H i emission via longitude–velocity plots, from where we extract and compare the skeletons of major galactic features and the terminal gas velocities. We found that our best-fitting model to the overall structure, also reproduces some of the more specific observed features of the MW, including a bar with a pattern speed of $30.0 \pm 0.2$ km s$^{-1}$ kpc$^{-1}$, and a bar half-length of $3.2 \pm 0.8$ kpc. Our model shows large streaming motions around spiral arms, and strong radial motions well beyond the inner bar. This model highlights the complex motions of a dynamic MW-type galaxy and has the potential to offer valuable insight into how our Galaxy regulates the ISM and star formation.