Computational Modeling to Predict Mechanical Function of Joints: Application to the Lower Leg With Simulation of Two Cadaver Studies

2007 ◽  
Vol 129 (6) ◽  
pp. 811-817 ◽  
Author(s):  
Peter C. Liacouras ◽  
Jennifer S. Wayne
Keyword(s):  
Experimental Data ◽  
Computational Modeling ◽  
Computational Models ◽  
Three Dimensional ◽  
Lower Leg ◽  
Joint Kinematics ◽  
Syndesmotic Injury ◽  
Joint Function ◽  
Calcaneofibular Ligament ◽  
Cadaver Studies

Computational models of musculoskeletal joints and limbs can provide useful information about joint mechanics. Validated models can be used as predictive devices for understanding joint function and serve as clinical tools for predicting the outcome of surgical procedures. A new computational modeling approach was developed for simulating joint kinematics that are dictated by bone/joint anatomy, ligamentous constraints, and applied loading. Three-dimensional computational models of the lower leg were created to illustrate the application of this new approach. Model development began with generating three-dimensional surfaces of each bone from CT images and then importing into the three-dimensional solid modeling software SOLIDWORKS and motion simulation package COSMOSMOTION. Through SOLIDWORKS and COSMOSMOTION, each bone surface file was filled to create a solid object and positioned necessary components added, and simulations executed. Three-dimensional contacts were added to inhibit intersection of the bones during motion. Ligaments were represented as linear springs. Model predictions were then validated by comparison to two different cadaver studies, syndesmotic injury and repair and ankle inversion following ligament transection. The syndesmotic injury model was able to predict tibial rotation, fibular rotation, and anterior/posterior displacement. In the inversion simulation, calcaneofibular ligament extension and angles of inversion compared well. Some experimental data proved harder to simulate accurately, due to certain software limitations and lack of complete experimental data. Other parameters that could not be easily obtained experimentally can be predicted and analyzed by the computational simulations. In the syndesmotic injury study, the force generated in the tibionavicular and calcaneofibular ligaments reduced with the insertion of the staple, indicating how this repair technique changes joint function. After transection of the calcaneofibular ligament in the inversion stability study, a major increase in force was seen in several of the ligaments on the lateral aspect of the foot and ankle, indicating the recruitment of other structures to permit function after injury. Overall, the computational models were able to predict joint kinematics of the lower leg with particular focus on the ankle complex. This same approach can be taken to create models of other limb segments such as the elbow and wrist. Additional parameters can be calculated in the models that are not easily obtained experimentally such as ligament forces, force transmission across joints, and three-dimensional movement of all bones. Muscle activation can be incorporated in the model through the action of applied forces within the software for future studies.

scholarly journals A Review of Cell-Based Computational Modeling in Cancer Biology

2019 ◽  
pp. 1-13 ◽  
Author(s):  
John Metzcar ◽  
Yafei Wang ◽  
Randy Heiland ◽  
Paul Macklin
Keyword(s):  
Stem Cells ◽  
Computational Modeling ◽  
Single Cell ◽  
Stromal Cells ◽  
Cancer Biology ◽  
Large Scale ◽  
Computational Models ◽  
Three Dimensional ◽  
Discrete Models ◽  
Simulation Studies

Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer’s ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.

Three-Dimensional Modeling of Reduced Material Properties in Ceramics With Added Porosity

2019 ◽  
Author(s):  
Stephanie A. Wimmer ◽  
Virginia G. DeGiorgi ◽  
Edward P. Gorzkowski ◽  
Heonjune Ryou
Keyword(s):  
Mechanical Properties ◽  
Thermal Conductivity ◽  
Finite Element ◽  
Computational Modeling ◽  
Computational Models ◽  
Thermal Protection ◽  
Turbine Blades ◽  
Three Dimensional ◽  
Ceramic Coatings ◽  
Grain Structure

Abstract Manufacturing methods to create ceramic coatings with tailored thermal conductivity are crucial to the development of thermal protection systems for many components including turbine blades in high temperature engines. A designed microstructure of grains, pores, and other defects can reduce the thermal conductivity of the ceramic. However, the same microstructure characteristics can reduce mechanical properties to the point of failure. This work is part of a larger program with the goal of optimizing ceramic coating microstructure for thermal protection while retaining sufficient mechanical strength for the intended application. Processing parameters have been examined to identify methods designed to maintain a nano-sized grain structure of yttria-stabilized zirconia while controlling the added porosity with a specific shape and size. In this paper computational modeling is used to evaluate the effects of porosity on coating performance, both thermal and structural. Coating porosity is incorporated in the computational models by randomly placing empty spaces or defects in the shape of spherical voids, oblate pores, or penny cracks. In addition to computational modeling, prototype coatings are developed in the laboratory with specific porosity. The size and orientation of defects in the computational modeling effort are statistically generated to match experiments. The locations of the defects are totally random. Finite element models are created which include various levels of porosity to calculate effective thermal and mechanical properties. Comparisons are made between three-dimensional finite-element simulations and measured data. The influences of pore size as well as three dimensional computational modeling artifacts are examined.

Correlation Between 3D Computational Prediction of Mechanical Function and Simulated Physical Weightbearing in the Cadaveric Lower Leg

2008 ◽  
Author(s):  
Joseph M. Iaquinto ◽  
Jennifer S. Wayne
Keyword(s):  
Computational Modeling ◽  
Three Dimensional ◽  
Computational Prediction ◽  
Lower Leg ◽  
Recent Model ◽  
Mechanical Function ◽  
Clinical Tests ◽  
Diarthrodial Joints ◽  
Insight Into

The foot is an intricate three dimensional complex that requires insight into both superficial and deep structures to understand mechanical function. Palpation and clinical tests assist with superficial treatment while radiography is invaluable for interior features. Computational modeling has obtained the capacity to predict mechanical function of diarthrodial joints, with a recent model developed of the lower leg [1]. The aim of this work was to extend the predictive capabilities of the lower leg model to simulate stance weightbearing. Comparisons were made to measurements taken from weightbearing loads applied to the cadaveric limb.

scholarly journals Towards Accurate Boundary Conditions for CFD Models of Synthetic Jets in Quiescent Flow

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6514
Author(s):  
Andrea Matiz-Chicacausa ◽  
Omar D. Lopez Mejia
Keyword(s):  
Experimental Data ◽  
Boundary Condition ◽  
Computational Modeling ◽  
Flow Control ◽  
Computational Models ◽  
Active Flow Control ◽  
Synthetic Jets ◽  
Complex Flow ◽  
Main Technique ◽  
Lumped Element Model

In this paper, an accurate model to simulate the dynamics of the flow of synthetic jets (SJ) in quiescent flow is proposed. Computational modeling is an effective approach to understand the physics involved in these devices, commonly used in active flow control for several reasons. For example, SJ actuators are small; hence, it is difficult to experimentally measure pressure changes within the cavity. Although computational modeling is an advantageous approach, experiments are still the main technique employed in the study of SJs due to the lack of accurate computational models. The same aspect that represents an advantage over other techniques also represents a challenge for the computational simulations, such as capturing the unsteady phenomena, localized compressible effects, and boundary layer formation characteristic of this complex flow. One of the main challenges in the simulation of SJs is related to the fact that the spatial and temporal scales of the actuator and the corresponding flow control application differed in several orders of magnitude. Hence, in this study we focus on the use of Computational Fluid Dynamics (CFD) and Reduced Order Models (ROM) to develop an accurate yet low-cost model to capture the complexities of the flow of a SJ in quiescent flow. Numerical results show two possible paths for SJ modeling; (1) to obtain a boundary condition to predict velocity profile and jet formation from experimental data of diaphragm’s deformation; and, (2) to predict peak velocity at the jet’s outlet with a ROM approach and to use the physical details of the actuator to develop an accurate boundary condition for CFD. Both approaches are validated through experimental data available in the literature; good agreement between results from CFD, Lumped Element Model (LEM), and experimental data are achieved. Finally, it was concluded that the coupling between LEM and CFD is a novel and accurate approach, which improves CFD due to the advantages of LEM closing the gap between LEM’s lack of flow detail and CFD’s lack of geometrical/physical information of the actuator.

Cadaveric Evaluation of Knee Joint Kinematics Using the Kansas Knee Simulator

2013 ◽  
Author(s):  
Sami Shalhoub ◽  
Fallon Fitzwater ◽  
Lorin Maletsky
Keyword(s):  
Soft Tissue ◽  
Knee Joint ◽  
Computational Models ◽  
Surgical Techniques ◽  
Joint Kinematics ◽  
Joint Function ◽  
Knee Simulator ◽  
Knee Joint Kinematics

Computational models of the knee are useful for evaluating changes in kinematics, soft tissue loadings, new prosthetic geometries, and surgical techniques [1, 2]. These models are advantageous in their ability to quickly and efficiently evaluate the effect of changes in these parameters on knee joint function. The limitation of modeling is that the results are greatly influenced by the constraints and parameters used to create the model.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

Three-dimensional asymmetric maximum weight lifting prediction considering dynamic joint strength

2021 ◽  
pp. 095441192098703
Author(s):  
Rahid Zaman ◽  
Yujiang Xiang ◽  
Jazmin Cruz ◽  
James Yang
Keyword(s):  
Experimental Data ◽  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Three Dimensional ◽  
Optimization Method ◽  
Weight Lifting ◽  
Joint Torque ◽  
Maximum Weight ◽  
Angular Velocities ◽  
Asymmetric Lifting

In this study, the three-dimensional (3D) asymmetric maximum weight lifting is predicted using an inverse-dynamics-based optimization method considering dynamic joint torque limits. The dynamic joint torque limits are functions of joint angles and angular velocities, and imposed on the hip, knee, ankle, wrist, elbow, shoulder, and lumbar spine joints. The 3D model has 40 degrees of freedom (DOFs) including 34 physical revolute joints and 6 global joints. A multi-objective optimization (MOO) problem is solved by simultaneously maximizing box weight and minimizing the sum of joint torque squares. A total of 12 male subjects were recruited to conduct maximum weight box lifting using squat-lifting strategy. Finally, the predicted lifting motion, ground reaction forces, and maximum lifting weight are validated with the experimental data. The prediction results agree well with the experimental data and the model’s predictive capability is demonstrated. This is the first study that uses MOO to predict maximum lifting weight and 3D asymmetric lifting motion while considering dynamic joint torque limits. The proposed method has the potential to prevent individuals’ risk of injury for lifting.

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