Browse Sponsored Projects

Your search matched 8305 Projects
Jul 2017 - Jun 2028
Industry/University Consortium in Hypoid and Bevel Gear Mesh and Dynamics sponsored by University of Cincinnati
  Industry/University Research Consortium in Hypoid and Bevel Gear Mesh and Dynamics
Jul 2022 - Jun 2027
UTA Summer Undergraduate Research Program to Promote Diversity in Health Related Research sponsored by National Institutes of Health
The overall goal of this R25 application is to provide unique research and education opportunities to current underrepresented minority undergraduate students at the University of Texas at Arlington (UTA).
Specifically, we will expose these students to the disciplines of biomedical, behavioral, and clinical research with the ultimate goal of enriching diversity in individuals who will represent the future in biomedical research in topics areas related to the mission of the National Heart Lung and Blood Institute. UTA is a minority majority institution. that ranks among the top 5 in the nation for student ethnic diversity. As of fall 2020 UTA had a total of ~48,000 full time on-campus students, the majority of which are undergraduate students (~35,000). The undergraduate student body is comprised of 31% Hispanic, 15% African American, 13% Asian, and 5% International. In 2021 the U.S News and World Report ranked UTA fifth in the nation among institutions of higher education for ethnic diversity. Furthermore, UTA is ranked #1 in the nation for “Best for Vets: Colleges” by the “Military Times”, thus is ideally suited to accomplish the objective of increasing diversity in health-related research. We will enroll two separate cohorts of underrepresented minority students: First, our selection committee will identify 10 outstanding sophomore, junior, and senior level undergraduate students from UTA (each year) to participate in a “laboratory-based” summer research education experience. Second, our selection committee will identify up to 40 underrepresented minority sophomore, junior, and senior level undergraduate students to participate in a “classroom only” summer research education experience. Each cohort will participate in a 10-week summer research / educational program, beginning the first week of June each year. Students in the “laboratory-based” cohort will be assigned a primary faculty mentor who will supervise them in conducting fulltime research related activities Monday through Thursday. On Friday’s, both cohorts come together for a joint “classroom-based” research education experience, covering a wide range of topics related to biomedical research ranging from Professional Development to the Responsible Conduct of Research. Students will also engage with URM faculty and graduate students every Friday through a series of research seminars. The primary outcomes / metrics of success will include: 1) Returning to the program for more than one summer experience (particularly for the classroom-based cohort who will be strongly encouraged to reapply the following year for the laboratory-based experience). 2) Successful completion of an undergraduate degree in a STEM field. 3) Applying for competitive fellowships for graduate school or other advanced degrees. 4) Enrollment in an advanced degree program in a STEM field. And 5) Subsequent participation in research or employment in a STEM field.
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Apr 2022 - Mar 2027
Collaborative Research: Practices and Research on Student Pathways in Education from Community College and Transfer Students in STEM (PROSPECT S-STEM) sponsored by National Science Foundation (NSF)
Collaborative Research: Practices and Research on Student Pathways in Education from Community College and Transfer Students to STEM  (PROSPECT S-STEM), an S-STEM Research Hub proposal, is organized thematically around the development of a network of S-STEM projects to empower low-income STEM students who transfer from two-year colleges (2YC) to four-year colleges (4YC).
With a collaborative PI team from universities and community colleges representing 10 current S-STEM projects, researchers in this hub are united by their goal to support domestic low-income STEM undergraduates who are navigating the transfer process from 2YC to 4YC. PROSPECT S-STEM will conduct and disseminate rigorous mixed methods research addressing the following: Student Success: PROSPECT S-STEM will actively involve S-STEM Scholars in telling their own navigational success stories through photovoice case studies, in addition to working with the involved S-STEM projects to collect common quantitative data from scholars to better understand the impacts of S-STEM programs on Scholars entering the STEM workforce.  Program Impact: Investigate the nature of the 2YCs’ and 4YCs’ S-STEM programs and other university interventions to support scholars before and after the transfer process.  Partnership Efficacy: InvestigatePROSPECT S-STEM will interview participating S-STEM PI team members, and collect local project and partnership documentation to identify the successful practices and design principles of S-STEM projects and partnership models. Faculty Learning Communities: Involve local S-STEM faculty mentors in a PROSPECT S-STEM Faculty Learning Communities. Participants will learn from each other to explore 2YC and 4YC partnerships and study recent literature on supporting low-income STEM transfer students to design additional supports for local scholars, and will test out those new ideas--adapted to different local contexts--using principles of improvement science. This community will also produce and help disseminate resources on lessons learned and best practices in mentoring and broadening participation in STEM through 2YC and 4YC partnerships.   The overarching goal of PROSPECT S-STEM is to connect research and practice to better support low-income STEM transfer students through focusing on (1) students’ lived experiences; (2) faculty and staff supports of students; (3) programmatic supports for students; and (4) two-year and four-year institutional partnerships to support transfer students. These four dimensions are interrelated, and will be studied through the lens of 10 current S-STEM projects.   
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Feb 2022 - Jan 2027
Mapping Trajectories of Alzheimer's Progression via Personalized Brain Anchor-nodes sponsored by National Institutes of Health (NIH)
Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disorder, not only in pathophysiology, but alsoat different disease progression stages.
Despite numerous studies that have investigated the clinical utility ofmagnetic resonance imaging (MRI) based biomarkers in characterizing AD stages from asymptomatic to mildlysymptomatic to dementia, making a personalized precision prediction and early diagnosis of AD is stillchallenging. Existing imaging biomarkers are limited in representing significant heterogeneity across differentindividuals and at different clinical stages. This challenge originates from the lack of reliable brain landmarks thatcan simultaneously characterize and represent robust population correspondences and individual variationduring normal aging and AD progression. In response, this project aims to: 1) Identify a set of brain anchornodesas population landmarks based on both group-wise consistent patterns and individualized anatomical andconnectivity properties during normal aging and AD progression among massive, publicly available neuroimagingdata sources; 2) Develop an efficient individualized shape transformation approach based on deep learning tomap population anchor-nodes to individual brains by flexibly leveraging multimodal individual features; and 3)Construct a progression tree using anchor-nodes derived brain measures to unveil and represent the widespectrum of AD development. Individual subjects can thus be projected to the tree structure to effectively andconveniently access their clinical status and predict the trend of AD progression. We will test our new frameworkson four large independent aging/AD cohorts including HCP-Aging, UK Biobank, ADNI and the latest stage ofOpen Access Series of Imaging Studies (OASIS-3), and freely release our computational tools and processeddata to the public.
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Feb 2022 - Jan 2027
CAREER: Prevalence, magnitude, and contribution of gut microbial detoxification services to insect herbivory sponsored by National Science Foundation (NSF)
Many insects are specifically adapted to eat plants; the fundamental interaction serves as a basal link in both natural and human food chains.
Plants defend
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Feb 2022 - Jan 2027
CAREER: Realizing Alternative Cements with Chemical Kinetics: Tuned Mechanical–Chemical Properties of Cementitious Magnesium Silicate Hydrates by Multi-Scale Synthetic Control sponsored by National Science Foundation (NSF)
Discovering new cement chemistries with low processing energies and effective pathways to enhance cement performance will ultimately result in the easing of the energy and environmental burden of the construction industry.
Although binders based on sulfoaluminates, alkali-activated formulations, and supersulfated phases have shown promise, there remain challenges including the metastability of the phases leading to unpredictable performance in the long-term, the large amounts of caustic activators used that are energy intensive to produce, and their poor durability particularly in the presence of carbon dioxide (CO2). In addition, a major impediment to the implementation of ordinary Portland cement (OPC) substitutes is the lack of raw materials that are available in large enough quantities to support current and future cement production. Here, special focus is placed on magnesium silicate hydrates (MSH) which can be synthesized from widely abundant magnesium-rich solids and brines. Although the structure and thermodynamic properties of MSH endmembers have been studied, the cementitious nature of the phase remains obscure. Nonetheless, the intrinsically lower pH of MSH compared to calcium silicate hydrate (CSH) cements would allow applications at sub-alkaline pH (e.g., nuclear waste disposal) and CO2-rich environments, e.g., in situ geologic CO2mineralization, while the greater barrier for water exchange around Mg2+ than Ca2+ suggests that MSH is more resistant to dissolution and chemical alteration relative to CSH. In this study, we will employ dynamic high-resolution experimental methods to probe, drive, and manipulate MSH synthesis processes, structures, and properties during its nucleation to bulk growth with a focus on the phenomena that occur at the mineral–fluid interface, while emphasizing an integrated approach involving in situmanipulation and observation of disequilibrium structures and metastable states. This will be achieved by the following steps. First, experiments will be performed to derive the kinetic parameters that describe the precipitation processes in aqueous systems containing Mg, Si, and impurities. Second, focus will be placed on MSH nanocrystal and mesocrystal morphologies and the means of manipulating them. Third, we will understand how MSH structures lead to distinguishable mechanical and chemical behavior. We will apply previous learnings on the relatively well-studied CSH system to help develop approaches and interpret results in the MSH system. The central hypothesis of this work is that the rates of MSH precipitation are controlled by ligand exchange around the Mg2+ ions, and that the electric double layer structure of the growth sites controls nanocrystalline assembly into mesocrystals and the consequent mechanical–chemical property development. It is within this framework that bulk MSH growth will be understood and manipulated through its selective synthesis at the nanoscale to mesoscale to obtain phase compositions and morphologies that result in superior mechanical and durability properties. The knowledge acquired from this study can be extended to less-studied magnesium-based cements (e.g., magnesium carbonate) and to analogous systems such as CSH and inform future models of microstructural evolution. These investigations will enable the project’s ultimate goal, which is to evaluate the use of MSH as a viable binder material for construction purposes and an alternative to OPC by revealing the kinetics of its formation, and the processing–structure–property relationships in the system. The fundamental science and discovery gained from this project will expand our understanding of low-temperature mineral crystallization processes and the subsequent property development in cementitious materials across spatial and temporal scales.
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Sep 2021 - Aug 2026
CAREER: System Design of Crowd Logistics via Participatory Agent-based Modeling sponsored by National Science Foundation (NSF)
To improve the resilience of urban food systems in the face of large-scale disruptions like the COVID-19 crisis, the Food and Agriculture Organization of the United Nations has emphasized the importance of short supply chains and stronger rural-urban linkages (FAO 2020).
  Regionalized food supply chains (RFSCs), in which food production and consumption are co-located within a geographic region, can help to fulfill these aims.  The decentralized structure of RFSCs allows risk to be spread among a large number of diverse food producers, thereby increasing regional self-reliance and mitigating the negative effects of a food supply disruption (Dahlberg 2008).  However, innovative transportation and distribution strategies, particularly for last-mile urban logistics, are needed for RFSCs to be scaled up sufficiently to meet consumer demand (FAO 2020).  Transporting food from rural and geographically dispersed farm locations to distant urban demand centers is often cost-prohibitive (Miller & Blanton 2016), and most small and mid-sized regional producers lack the logistical infrastructure and expertise needed to support efficient distribution (Jensen 2010). Crowd logistics (i.e., the crowdsourced transport and delivery of goods and freight) offers a potential solution. A crowd logistics platform for RFSCs would allow food producers to source logistics services from a carrier crowd for delivery to urban consumers, potentially at lower cost, with greater flexibility and shorter lead times than traditional logistics providers.  Crowd logistics can also facilitate home delivery, which has been critical during the COVID-19 emergency.  However, to be successful, a crowd logistics platform must acquire a critical mass of participants, with an appropriate balance between senders and carriers. Critical mass facilitates the emergence of network effects, which are necessary for the platform to grow and thrive: as sender participation increases, carriers’ opportunities and benefits increase, thereby encouraging greater carrier participation, which yields increasing benefits for senders, and so on. These network effects perpetuate a feedback loop in which the value of the platform for all stakeholders increases over time (Kontio, 2016). Therefore, achieving a critical mass of participants is a crowd-shipping platform’s most important goal (Rougès and Montreuil, 2014): If there are too few participants, senders and recipients will be dissatisfied by unfilled service requests, carriers will have insufficient opportunities, and the initiative may never get off the ground.  When designing and managing a crowd logistics system, it is critical to be able to understand and predict network effects that will lead to long-term system growth and resilience.  However, the carrier-sender participation feedback loop yields complex and dynamic system behavior that is difficult to predict (Mittal et al. 2019), particularly because RFSC actors have heterogeneous motivations for participating, including personal values and social factors, as well as economic reasons (Krejci et al. 2016). operates through technological platforms The proposed research will collect human behavior and geospatial data from an existing regional food distribution network in Central Texas (the area surrounding the city of Austin).  Central Texas has a small but thriving regional food supply system, which has grown significantly in the past decade as the population of Austin has exploded and demand for local and sustainably-produced food has rapidly increased. The data will be used to create an agent-based model (ABM) that can determine how a crowd logistics platform should be designed and managed to rapidly achieve a critical mass of senders and carriers, benefit from network effects, and grow the platform to become self-sustaining and capable of fulfilling its intended mission: delivering food from rural farms and ranches to urban consumers in both normal and disrupted scenarios, thereby supporting resilient urban food systems.  The model will serve as a virtual RFSC, with food producers and carrier crowd members represented as autonomous agents that make decisions about whether/how to participate in a crowd logistics system.  System behavior and outcomes will be explored by experimentally varying key affordances of a virtual crowd logistics platform, as well as structural characteristics of the physical distribution system.  The ABM will be co-created and validated via participatory modeling sessions with key RFSC stakeholders, which will strengthen the credibility and predictive capabilities of the model. The proposed research will leverage the data and knowledge acquired through my previous USDA-funded research on collaborative RFSC logistics for Iowa farmers and food hubs, my current USDA-funded grant to study regional food transportation in Texas, and a current collaborative NSF INFEWS project that is using empirical ABM to model farmer and consumer decisions to participate in a regionalized food system.  The partnerships that I have established with farmers, ranchers, regional food distributors, and food systems researchers through this work will facilitate access to the empirical human behavior data that is needed to co-create and validate the ABM for this CAREER proposal.    The knowledge gained from the proposed project will improve our understanding of how decentralized distribution systems can improve the resilience of cities, as well as yielding more general insights into the design of crowd logistics platforms, urban last-mile logistics, and regionalized food systems.  Moreover, this new knowledge will contribute to my overarching research goal by 1) providing an improved understanding of how crowd logistics can support decentralized supply networks and 2) establishing the value of integrating empirically-valid human behavior data into models of supply networks. To improve the resilience of urban food systems in the face of large-scale disruptions like the COVID-19 crisis, the Food and Agriculture Organization of the United Nations has emphasized the importance of short supply chains and stronger rural-urban linkages (FAO 2020).  Regionalized food supply chains (RFSCs), in which food production and consumption are co-located within a geographic region, can help to fulfill these aims.  The decentralized structure of RFSCs allows risk to be spread among a large number of diverse food producers, thereby increasing regional self-reliance and mitigating the negative effects of a food supply disruption (Dahlberg 2008).  However, innovative transportation and distribution strategies, particularly for last-mile urban logistics, are needed for RFSCs to be scaled up sufficiently to meet consumer demand (FAO 2020).  Transporting food from rural and geographically dispersed farm locations to distant urban demand centers is often cost-prohibitive (Miller & Blanton 2016), and most small and mid-sized regional producers lack the logistical infrastructure and expertise needed to support efficient distribution (Jensen 2010). Crowd logistics (i.e., the crowdsourced transport and delivery of goods and freight) offers a potential solution. A crowd logistics platform for RFSCs would allow food producers to source logistics services from a carrier crowd for delivery to urban consumers, potentially at lower cost, with greater flexibility and shorter lead times than traditional logistics providers.  Crowd logistics can also facilitate home delivery, which has been critical during the COVID-19 emergency.  However, to be successful, a crowd logistics platform must acquire a critical mass of participants, with an appropriate balance between senders and carriers. Critical mass facilitates the emergence of network effects, which are necessary for the platform to grow and thrive: as sender participation increases, carriers’ opportunities and benefits increase, thereby encouraging greater carrier participation, which yields increasing benefits for senders, and so on. These network effects perpetuate a feedback loop in which the value of the platform for all stakeholders increases over time (Kontio, 2016). Therefore, achieving a critical mass of participants is a crowd-shipping platform’s most important goal (Rougès and Montreuil, 2014): If there are too few participants, senders and recipients will be dissatisfied by unfilled service requests, carriers will have insufficient opportunities, and the initiative may never get off the ground.  When designing and managing a crowd logistics system, it is critical to be able to understand and predict network effects that will lead to long-term system growth and resilience.  However, the carrier-sender participation feedback loop yields complex and dynamic system behavior that is difficult to predict (Mittal et al. 2019), particularly because RFSC actors have heterogeneous motivations for participating, including personal values and social factors, as well as economic reasons (Krejci et al. 2016). The proposed research will collect human behavior and geospatial data from an existing regional food distribution network in Central Texas (the area surrounding the city of Austin).  Central Texas has a small but thriving regional food supply system, which has grown significantly in the past decade as the population of Austin has exploded and demand for local and sustainably-produced food has rapidly increased. The data will be used to create an agent-based model (ABM) that can determine how a crowd logistics platform should be designed and managed to rapidly achieve a critical mass of senders and carriers, benefit from network effects, and grow the platform to become self-sustaining and capable of fulfilling its intended mission: delivering food from rural farms and ranches to urban consumers in both normal and disrupted scenarios, thereby supporting resilient urban food systems.  The model will serve as a virtual RFSC, with food producers and carrier crowd members represented as autonomous agents that make decisions about whether/how to participate in a crowd logistics system.  System behavior and outcomes will be explored by experimentally varying key affordances of a virtual crowd logistics platform, as well as structural characteristics of the physical distribution system.  The ABM will be co-created and validated via participatory modeling sessions with key RFSC stakeholders, which will strengthen the credibility and predictive capabilities of the model. The proposed research will leverage the data and knowledge acquired through my previous USDA-funded research on collaborative RFSC logistics for Iowa farmers and food hubs, my current USDA-funded grant to study regional food transportation in Texas, and a current collaborative NSF INFEWS project that is using empirical ABM to model farmer and consumer decisions to participate in a regionalized food system.  The partnerships that I have established with farmers, ranchers, regional food distributors, and food systems researchers through this work will facilitate access to the empirical human behavior data that is needed to co-create and validate the ABM for this CAREER proposal. The knowledge gained from the proposed project will improve our understanding of how decentralized distribution systems can improve the resilience of cities, as well as yielding more general insights into the design of crowd logistics platforms, urban last-mile logistics, and regionalized food systems.  Moreover, this new knowledge will contribute to my overarching research goal by 1) providing an improved understanding of how crowd logistics can support decentralized supply networks and 2) establishing the value of integrating empirically-valid human behavior data into models of supply networks.
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Sep 2021 - Aug 2026
Longitudinal study of patient reported outcomes, symptom burden, and quality of life among women newly diagnosed epithelial ovarian cancer: “Real-world” experiences of women who proceed to maintenance therapy or routine surveillance upon completion of primary treatment sponsored by UT MD Anderson Cancer Center
The goal of this project is to longitudinally assess the symptom burden and other patient-reported outcomes of women diagnosed with epithelial ovarian cancer and compare between women who receive maintenance therapy or routine surveillance.
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Sep 2021 - Aug 2026
UT Arlington EOC sponsored by US Department of Education
The University of Texas at Arlington (UTA) requests $232,050 to fund an EducationalOpportunity Center (EOC) for five (5) years (September 1, 2021 – August 31, 2026) to serve 850 individuals annually in Tarrant and Dallas Counties (Dallas, Arlington and Fort Worth) target area that meet the federal eligibility criteria at the cost of $273 per Participant.
The UTA EOC Project will help adults further their education by exploring options for Adult Basic Education or High School Equivalency programs, or enrolling in college, university or vocational school and obtain financial aid for enrollment. The program will offer assistance free of charge toeconomically disadvantaged adults who live in Tarrant or Dallas County. The Need for an EOC Project in the target area is apparent due to the high percentage of low incomefamilies, high percentage of individuals with education completion levels below abachelor’s degree and the changing socioeconomic problems faced by the adult residents in the target area. Based on the need, the UTA TS Project will implement five (5) ambitious yet attainable Objectives: (a) attainment of a secondary school diploma, or equivalent; (b) completion of financial aid applications; (c) completion of college admission applications; and (d) college enrollment. The Project will achieve these objectives through a comprehensive delivery of servicesplan outlined in the Plan of Operation. This plan will be implemented by qualified and experienced staff detailed in the Quality of Personnel and funded by a reasonable, cost-effective,and adequate Budget in relation to the objectives. UTA commits its facilities, equipment, supplies,and other in-kind contributions to this EOC Project outlined in the Applicant and Community Support. Finally, the outcomes of all EOC Project objectives will be monitored throughout each year and evaluated by implementing an appropriate formative and summative Evaluation Plan. This application will address the following three (3) Competitive Preference Priorities (CPPs): Competitive Preference Priority 1: Ensuring that Service Members, Veterans, and Their Families Have Access to High-Quality Educational Options. Competitive Preference Priority 2: Fostering Flexible and Affordable Paths to Obtaining Knowledge and Skills. Competitive Preference Priority 3: Applications that Demonstrate a Rationale.
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May 2019 - Aug 2026
Modeling for Affordable, Sustainable Composites (MASC) sponsored by Wichita State University
The proposed DDM technology is based on Regularized Extended Finite Element Methodology (Rx- FEM) which is currently implemented in the BSAM software including the framework required for geometrically nonlinear analysis.
  Significant advancements of BSAM software efficiency have taken place in the previous work phase. Previous work focused on verification and validation (V&V) of existing Rx-FEM capabilities under static and fatigue loading. In the present work new capabilities of Rx-Fem will be developed. Three technology areas will be addressed. First, we will concentrate on reformulating the Rx-FEM framework to reduce the insertion distance between neighboring cracks up to half of the present gap required. Second, we will extend the methodology to allow for intersection of Rx-FEM cracks. This capability has a wide range of application. Presently we will look at interaction of fiber and matrix failure in laminates including geometrically nonlinear regime.  Finally new methodologies for addressing sub-element and element level structural elements will be developed.                 Present Rx-FEM twinning methodology requires a minimum of 6 element gap between two neighboring crack paths. Such gap is restrictive with respect to crack density and requires high mesh refinements. In the present methodology a neutral zone, called zone=0 is required between domains, where the signed distance function for each crack is defined. It is proposed to eliminate this neutral zone by directly connecting the outside elements in the neighboring crack regions by using penalty method. This approach will be called no gap element twining methodology and will be developed first. We will then address the Rx-FEM crack intersection. This problem requires generation of multiple duplicate elements and not just direct twining. In principle this process requires twining of each twinned element, etc. and in this respect it can be termed hierarchic secondary twining.  The same hierarchic twinning can be algorithmically represented as a list of elements, which are associated with one original element and differ by the step function. This step function would be calculated as a product of two or more step functions of parental elements. Such serial one layer storage of the twins (multiples) of the original element is better aligned with present BSAM architecture and may prove more efficient and easier to implement.                 The intersecting Rx-FEM crack capability has a number of applications in various material forms. In laminated composites it allows to represent both matrix cracking and fiber failure by Rx-FEM. In the case of matrix or transfer cracking the procedure has been applied extensively. In this case the crack orientation is mainly defined by fiber orientation. The direction of the fiber failure crack is less clearly defined. While the maximum principle stress orientation is a good starting point to define the plane of such crack, additional research is needed both in the case of tensile and compression loading. Finally the volumetric scaling of strength and random nature of strength via spatial seeding of the cohesive zone properties for fiber failure will be considered.                 Addressing the next level of the building block approach and performance prediction on sub-element and element level requires both increasing the software efficiency through programming tools as well as new computational approaches. BSAM software efficiency has been significantly advanced recently by utilizing OMP level parallelization and general algorithmic improvements.  While software improvements are critical and these efforts will continue new computational approaches are necessary. Two approaches will be considered. Two way global local analysis and implementation of DDM within shell theory framework will be considered. One way global –local analysis by using a combination of commercial software ABAQUS and BSAM has already been demonstrated. What is proposed below is to explore the back feed of discrete damage in the form of delamination, etc. into a global model when it growth outside the local model region. The second methodology, i.e. implementation of DDM into shell level model is based on the fly local expansion of a shell model into a ply–by-ply model with subsequent insertion of MIC and delamination modeling. Such expansion is facilitated by first setting up a full ply-by-ply model and collapsing it into shell kinematics by using constraints. These constraints will then be removed based on shell level failure criteria locally and the 3D formulation activated. The first stage of this development is removing the double nodes on the ply interfaces at the initial solution stage and exploring the efficiency of the approach.
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