Billerica, MA, United States
Billerica, MA, United States

Time filter

Source Type

News Article | June 15, 2017
Site: www.sciencedaily.com

Researchers from the University of Luxembourg, in cooperation with the University of Strasbourg, have developed a computational method that could be used to guide surgeons during brain surgery. Surgeons often operate in the dark. They have a limited view of the surface of the organ, and can typically not see what lies hidden inside. Quality images can routinely be taken prior to the surgery, but as soon as the operation begins, the position of the surgeon's target and risky areas he must avoid, continuously change. This forces practitioners to rely on their experience when navigating surgical instruments to, for example, remove a tumor without damaging healthy tissue or cutting through important blood supplies. Stéphane Bordas, Professor in Computational Mechanics at the Faculty of Science, Technology and Communication of the University of Luxembourg, and his team have developed methods to train surgeons, help them rehearse for such complex operations and guide them during surgery. To do this, the team develops mathematical models and numerical algorithms to predict the deformation of the organ during surgery and provide information on the current position of target and vulnerable areas. With such tools, the practioner could virtually rehearse a particular operation to anticipate potential complications. As the brain is a composite material, made up of grey matter, white matter and fluids, the researchers use data from medical imaging, such as MRI to decompose the brain into subvolumes, similar to lego blocks. The colour of each lego block depends on which material it represents: white, grey or fluid. This colour-coded "digital lego brain" consists of thousands of these interacting and deforming blocks which are used to compute the deformation of the organ under the action of the surgeon. The more blocks the researchers use to model the brain, the more accurate is the simulation. However, it becomes slower, as it requires more computing power. For the user, it is therefore important to find the right balance between accuracy and speed when he decides how many blocks to use. The crucial aspect of Prof Bordas' work is that it allows, for the first time, to control both the accuracy and the computational time of the simulations. "We developed a method that can save time and money to the user by telling them the minimum size these lego blocks should have to guarantee a given accuracy level. For instance, we can say with certainty: if you can accept a ten per cent error range then your lego blocks should be maximum 1mm, if you are ok with twenty percent you could use 5mm elements," he explains. "The method has two advantages: You have an estimation of the quality and you can focus the computational effort only on areas where it is needed, thus saving precious computational time." Over time, the researchers' goal is to provide surgeons with a solution that can be used during operations, constantly updating the simulation model in real time with data from the patient. But, according to Prof Bordas, it will take a while before this is realized. "We still need to develop robust methods to estimate the mechanical behavior of each lego block representing the brain. We also must develop a user-friendly platform that surgeons can test and tell us if our tool is helpful," he said.


News Article | June 16, 2017
Site: www.rdmag.com

Researchers from the University of Luxembourg, in cooperation with the University of Strasbourg, have developed a computational method that could be used to guide surgeons during brain surgery. Surgeons often operate in the dark. They have a limited view of the surface of the organ, and can typically not see what lies hidden inside. Quality images can routinely be taken prior to the surgery, but as soon as the operation begins, the position of the surgeon's target and risky areas he must avoid, continuously change. This forces practitioners to rely on their experience when navigating surgical instruments to, for example, remove a tumor without damaging healthy tissue or cutting through important blood supplies. Stéphane Bordas, Professor in Computational Mechanics at the Faculty of Science, Technology and Communication of the University of Luxembourg, and his team have developed methods to train surgeons, help them rehearse for such complex operations and guide them during surgery. To do this, the team develops mathematical models and numerical algorithms to predict the deformation of the organ during surgery and provide information on the current position of target and vulnerable areas. With such tools, the practioner could virtually rehearse a particular operation to anticipate potential complications. As the brain is a composite material, made up of grey matter, white matter and fluids, the researchers use data from medical imaging, such as MRI to decompose the brain into subvolumes, similar to lego blocks. The colour of each lego block depends on which material it represents: white, grey or fluid. This colour-coded "digital lego brain" consists of thousands of these interacting and deforming blocks which are used to compute the deformation of the organ under the action of the surgeon. The more blocks the researchers use to model the brain, the more accurate is the simulation. However, it becomes slower, as it requires more computing power. For the user, it is therefore important to find the right balance between accuracy and speed when he decides how many blocks to use. The crucial aspect of Prof Bordas' work is that it allows, for the first time, to control both the accuracy and the computational time of the simulations. "We developed a method that can save time and money to the user by telling them the minimum size these lego blocks should have to guarantee a given accuracy level. For instance, we can say with certainty: if you can accept a ten per cent error range then your lego blocks should be maximum 1mm, if you are ok with twenty percent you could use 5mm elements," he explains. "The method has two advantages: You have an estimation of the quality and you can focus the computational effort only on areas where it is needed, thus saving precious computational time." Over time, the researchers' goal is to provide surgeons with a solution that can be used during operations, constantly updating the simulation model in real time with data from the patient. But, according to Prof Bordas, it will take a while before this is realized. "We still need to develop robust methods to estimate the mechanical behavior of each lego block representing the brain. We also must develop a user-friendly platform that surgeons can test and tell us if our tool is helpful," he said. The researchers published their findings in IEEE Transactions on Biomedical Engineering


News Article | June 15, 2017
Site: www.eurekalert.org

Researchers from the University of Luxembourg, in cooperation with the University of Strasbourg, have developed a computational method that could be used to guide surgeons during brain surgery. Surgeons often operate in the dark. They have a limited view of the surface of the organ, and can typically not see what lies hidden inside. Quality images can routinely be taken prior to the surgery, but as soon as the operation begins, the position of the surgeon's target and risky areas he must avoid, continuously change. This forces practitioners to rely on their experience when navigating surgical instruments to, for example, remove a tumor without damaging healthy tissue or cutting through important blood supplies. Stéphane Bordas, Professor in Computational Mechanics at the Faculty of Science, Technology and Communication of the University of Luxembourg, and his team have developed methods to train surgeons, help them rehearse for such complex operations and guide them during surgery. To do this, the team develops mathematical models and numerical algorithms to predict the deformation of the organ during surgery and provide information on the current position of target and vulnerable areas. With such tools, the practioner could virtually rehearse a particular operation to anticipate potential complications. As the brain is a composite material, made up of grey matter, white matter and fluids, the researchers use data from medical imaging, such as MRI to decompose the brain into subvolumes, similar to lego blocks. The colour of each lego block depends on which material it represents: white, grey or fluid. This colour-coded "digital lego brain" consists of thousands of these interacting and deforming blocks which are used to compute the deformation of the organ under the action of the surgeon. The more blocks the researchers use to model the brain, the more accurate is the simulation. However, it becomes slower, as it requires more computing power. For the user, it is therefore important to find the right balance between accuracy and speed when he decides how many blocks to use. The crucial aspect of Prof Bordas' work is that it allows, for the first time, to control both the accuracy and the computational time of the simulations. "We developed a method that can save time and money to the user by telling them the minimum size these lego blocks should have to guarantee a given accuracy level. For instance, we can say with certainty: if you can accept a ten per cent error range then your lego blocks should be maximum 1mm, if you are ok with twenty percent you could use 5mm elements," he explains. "The method has two advantages: You have an estimation of the quality and you can focus the computational effort only on areas where it is needed, thus saving precious computational time." Over time, the researchers' goal is to provide surgeons with a solution that can be used during operations, constantly updating the simulation model in real time with data from the patient. But, according to Prof Bordas, it will take a while before this is realized. "We still need to develop robust methods to estimate the mechanical behavior of each lego block representing the brain. We also must develop a user-friendly platform that surgeons can test and tell us if our tool is helpful," he said. The researchers published their findings in IEEE Transactions on Biomedical Engineering


Takase S.,Computational Mechanics Inc | Kashiyama K.,Chuo University | Tanaka S.,University of Tokyo | Tezduyar T.E.,Rice University
Computational Mechanics | Year: 2011

We show that combination of the Deforming-Spatial-Domain/Stabilized Space-Time and the Streamline-Upwind/Petrov-Galerkin formulations can be used quite effectively for computation of shallow-water flows with moving shorelines. The combined formulation is supplemented with a stabilization parameter that was originally introduced for compressible flows, a compressible-flow shock-capturing parameter adapted for shallow-water flows, and remeshing based on using a background mesh. We present a number of test computations and provide comparisons to theoretical results, experimental data and results computed with nonmoving meshes. © 2011 Springer-Verlag.


Deng D.,Chongqing University | Deng D.,Harbin Institute of Technology | Kiyoshima S.,Computational Mechanics Inc
Jinshu Xuebao/Acta Metallurgica Sinica | Year: 2014

Austenite stainless steels such as SUS304, owing to their good combination of mechanical properties, corrosion resistance and weldability, are widely used in a variety of industries. In the simulation of welding residual stress of an austenite stainless steel joint, because of the high strain hardening rate and the heating-cooling thermal cycles, both the work hardening phenomenon and the annealing effect have to be taken into account in the material constitutive relations. Though a number of numerical models have included the work hardening by using isotropic rule, kinematic rule or mixed rule, limited models have dealt with the annealing effect. For the steels or alloys with high strain hardening coefficient, neglecting the annealing effect will overestimate the welding residual stresses to a large extent. In this study, the thermal elastic plastic finite element method (T-E-P FEM) was used to simulate welding temperature and residual stresses in a SUS304 steel bead-on joint. In the computational approach based on the T-E-P FEM, a moving heat source with uniform density distribution was used to model the heat input, and a simple model was proposed to consider the annealing effect. Using the developed computational approach, the influences of work hardening and annealing effect on the welding residual stress were clarified. In addition, the effect of annealing temperature on the distribution and magnitude of welding residual stress in the weld zone and its vicinity was examined. The simulated results show that annealing effect has a significant influence on the longitudinal residual stress, and the peak value of longitudinal tensile stress increases with annealing temperature. The longitudinal tensile stresses in the fusion zone and its vicinity also increase with annealing temperature. It seems that the annealing temperature has insignificant influence on the transverse residual stresses. Comparing the simulated results and the measured data, it was found that when the annealing temperature was assumed to be 1000°C for SUS304 steel, the longitudinal residual stresses predicted by the T-E-P FEM generally match the measurements. The present work is helpful for developing more advanced materials model to calculate welding residual stress with high accuracy. © Copyright.


Deng D.,Chongqing University | Kiyoshima S.,Computational Mechanics Inc
Nuclear Engineering and Design | Year: 2010

With the development of computer hardware and software, numerical simulation technology has been widely used to predict welding temperature field, residual stresses and distortion. However, till now the influences of initial stresses induced by the manufacturing process before welding on the welding-induced residual stresses are rarely investigated experimentally and numerically. In the present work, we have developed a computational approach based on thermal elastic plastic FEM to clarify how the initial stresses due to heat treatment affect the welding-induced residual stresses in an austenitic stainless steel pipe. A heat treatment process, which is similar to solution heat treatment, is employed to produce initial stresses in the pipe before welding. After the heat treatment, the laser beam welding is used to perform a girth weld in the middle of the pipe. Through comparing the residual stress distributions after heat treatment and laser beam welding, we have investigated the influence of the initial residual stresses on the welding-induced residual stresses. The numerical results suggest that the initial residual stresses prior to welding have significant effects on the residual stresses after welding in the pipe model. © 2009 Elsevier B.V. All rights reserved.


Deng D.,Chongqing University | Kiyoshima S.,Computational Mechanics Inc
Computational Materials Science | Year: 2010

A finite element approach based on Quick Welder software is developed to simulate welding temperature field and welding residual stress distribution in a 3D multi-pass girth-welded pipe model. The characteristics of welding residual stress distributions in a SUS304 stainless steel pipe induced by heating with a tungsten inert gas arc welding torch are investigated numerically. Meanwhile, an emphasis is focused on examining the welding residual stress distributions in and near the weld start/end location. Moreover, the residual stresses predicted by the present computational approach are compared with the measured data; and the comparison suggests that the numerical simulation method has basically captured the feature of welding residual stress distribution near the weld start/end region. The numerical simulation results show that both the hoop and the axial residual stresses near the weld start/end region have sharp gradients and are significantly different from those in the steady range. © 2010 Elsevier B.V. All rights reserved.


Deng D.,Chongqing University | Kiyoshima S.,Computational Mechanics Inc
Computational Materials Science | Year: 2012

To increase productivity, welding process with large heat input such as electro slag welding (ESW) process has been used to connect the joints between the diaphragm and the column plate in high-rise steel building. However, the heat input of ESW is much higher than those of the other welding processes, and the high heat input not only largely alters the properties of steel but also results in large residual stresses. Consequently, the changes of steel properties and residual stresses induced by ESW have significantly effects on the safety of a structure. In this study, a three dimension (3-D) finite element model with considering moving heat source was developed to simulate the welding temperature field, Δt 8/5 time, welding residual stress and distortion in a typical thick plate joint performed by ESW. The thermal cycles computed by finite element model were compared with experimental measurements. Meanwhile, the features of welding residual stress and distortion distributions in the ESW joint were investigated numerically. In addition, the influences of heat input on the size of heat affected-zone (HAZ), Δt 8/5 time welding residual stress and distortion were examined. The thermal cycle curve and simulated by FEM model can be used to deduce the micro-structure as well as toughness of weld zone and HAZ, while the welding residual stress distribution estimated by numerical model can be helpful to assess the structural integrity. © 2012 Elsevier B.V. All rights reserved.


Deng D.,Chongqing University | Kiyoshima S.,Computational Mechanics Inc
Computational Materials Science | Year: 2011

Residual stresses in a welded structure often reached to or even over the yield strengths of base metal and weld metal. Tensile residual stress is a main factor resulting in stress corrosion cracking, fatigue damage and brittle fracture. Generally, welding cracking and flaw frequently occurs at the weld start/end location. Therefore, it is necessary to investigate the feature of welding residual stress distribution near the weld start/end location. In the present work, both numerical simulation technology and experimental method were used to study welding residual stress distribution in a thick plate joint with a special groove. Meanwhile, an emphasis was played on examining the characteristics of welding residual stress distribution near the weld start/end location. Moreover, the influence of deposition sequence and direction on the welding residual stress distribution was clarified by means of numerical simulation method. © 2011 Elsevier B.V. All rights reserved.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 78.63K | Year: 2011

Computational Mechanics Inc. (CMI) will develop a paint coating condition or"health"monitoring system that can signal without tank entry by an inspector when a coating actually needs possible repair. It will also provide diagnostic assistance in assessing with a high degree of confidence whether or not a coating needs to be replaced through the use of a service life prediction model. The coating sensor can be considered as an enhanced CP system as it will be developed based on the typical components used in a cathodic protection system combined with software simulation technology to provide a"smart"system to identify the current state of the coating system and to forward predict its condition over the life of the vessel. A key innovation will be the integration with the simulation model as this will enable the system to automatically extract and interpret information about the condition of the coating. The simulation model will be based upon similar technology to that used in the BEASY Corrosion and CP software which has been widely used to model Galvanic Corrosion and Cathodic Protection systems. The models are based on Boundary and Finite Element Technology to model the IR drop and electric fields in the electrolyte, models of the electrode kinetics on the metal surfaces and coating degradation models. A key feature of the proposed technology is that results suggest that good predictions of the overall coating condition can be achieved even if the open circuit corrosion potential is unknown, the polarization curve is non-linear and unknown and there are unknown reference electrodes offsets. Therefore the usual detailed inputs into a model are not required. The coating degradation model will provide data on the performance of the coating at a particular location at the time the measurements are made. It will also use historical data to predict how the degradation is expected to change over the service life to provide data for maintenance planning.

Loading Computational Mechanics Inc collaborators
Loading Computational Mechanics Inc collaborators