Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Experimental and FEM Studies on Secondary Co-Curing Reinforcement of Laminates.

ABSTRACT: In this study, a static tensile test of secondary co-cure reinforcement (SCR) of laminates revealed the damage and fracture locations in the respective structure. Test results indicated that adhesive debonding was the primary cause of structural failure. Finite element modeling (FEM) performed on the large opening laminate and strengthening structure consisted of simulations of the axial tension experiment, damage assessment, and the final load estimate. It was observed that the tensile strength of SCR was increased by 10.81% in comparison with the unrepaired structure. The results of FEM indicated that the initiation and propagation of damage, and final failure, were located in the layer of reinforcing section which was bonded to the adhesive layer, proving that the performance of the adhesive layer was the dominating factor with regard to the reinforced structure and that the thickness of the reinforcing section could be reduced to lessen the weight.

SUBMITTER: Wang Y

PROVIDER: S-EPMC6316344 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Experimental and FEM Studies on Secondary Co-Curing Reinforcement of Laminates.

Wang Yi Y Liu Tiejun T Jiang Peiyi P

Materials (Basel, Switzerland) 20181218 12

In this study, a static tensile test of secondary co-cure reinforcement (SCR) of laminates revealed the damage and fracture locations in the respective structure. Test results indicated that adhesive debonding was the primary cause of structural failure. Finite element modeling (FEM) performed on the large opening laminate and strengthening structure consisted of simulations of the axial tension experiment, damage assessment, and the final load estimate. It was observed that the tensile strength ...[more]

PMID: 30567350

Similar Datasets

Curing of Cellulose Hydrogels by UV Radiation for Mechanical Reinforcement.

Project description:The use of biomaterials as a replacement for thermoplastic polymers is an environmentally sound strategy. In this work, hydrogels of cellulose isolated from wheat husk were modified by UV irradiation (353 nm) to improve mechanical performance. The cellulose was dissolved with a solvent system N,N-dimethylacetamide/lithium chloride (DMAc/LiCl). Infrared spectroscopy showed that the peak height at 1016 cm-1, associated with the C-O bonds of the glycosidic ring, increases with irradiation time. It was determined that the increase in this signal is related to photodegradation, the product of a progressive increase in exposure to UV radiation. The viscoelastic behavior, determined by dynamic mechanical analysis and rotational rheometry, was taken as the most important parameter of this research, showing that the best results are recorded with 15 min of UV treatment. Therefore, at this time or less, the chemical crosslinking is predominant over the photodegradation, producing an increase in the modules, while with 20 min the photodegradation is such that the modules suffer a significant reduction.

| S-EPMC8309531 | biostudies-literature

FEM Numerical and Experimental Work on Extrusion Welding of 7021 Aluminum Alloy.

Project description:Extrusion welding of AlZnMg alloys encounters great technological difficulties in practice associated with high shaping forces and the low quality of longitudinal welds. Three different chemical compositions of 7021 aluminum alloy, differing in terms of Zn and Mg contents, were used in the first stage of the research. The laboratory device modelling the behavior of metal in welding chambers of the porthole die was applied to examine the ability of 7021 alloys to produce high-quality joints. The weldability tests were carried out for different welding temperatures-400, 450 and 500 °C-and for a fixed welding pressure of 300 MPa. The microstructural effects in pressure-welds were evaluated with the use of OM and SEM/EDS. The temperature-pressure parameters in the welding chambers were analyzed by using the FEM method for original porthole dies while extruding tubes with dimensions of Ø50 × 2 mm. Finally, the industrial extrusion trials were performed with examination of the structure and strength of the seam welds. It was found that it is possible to produce high-quality high-strength welds in tubes extruded from AlZnMg alloys in industrial conditions (the strength of welds in the range of 96-101% of the strength of the basic non-welded material) through properly matched alloy chemical composition of the alloy, construction of the porthole dies and temperature-speed conditions of deformation.

| S-EPMC10488767 | biostudies-literature

Load-displacement experimental data from axial tensile loading of CFRP-SPCC hybrid laminates.

Project description:The current paper shows a data set of load-displacement output from axial tensile loading of CFRP-SPCC hybrid laminates. The specimen geometries are cut based on standard procedure from ASTM D-3039. At least 3 positions in each specimen, we measured its width and thickness. Data of the load and displacement were repeated at least 3 samples in each combination of hybrid laminates. Tensile test was conducted with a 1 mm/min of loading rate. The data were recorded from unloading until failure of specimens. The data gives information about the highest load and the behavior of load-displacement in axial tensile loading. By using width and thickness, normalized data can be obtained, the load can be calculated into stress (MPa) unit. The data are useful for researchers and structural engineers that deals with CFRP, SPCC, and hybrid CFRP-SPCC laminates.

| S-EPMC7058899 | biostudies-literature

Experimental data on fracture of open-hole composite laminates subjected to tension loading.

Project description:In this data article, we present detailed failure process of big open-hole carbon fibre reinforced polymer (CFRP) composite laminates subjected to tension loading, which are tested by digital image correlation (DIC) system and electrical strain gauge system. In experiments, three different lay-ups are applied tension loading until specimens' failure. Several strain gages are distributed in different locations of specimens to measure strains during loading, at the same time, DIC is adopted to obtain field strains of specimens. The maximum force and failure modes of different lay-ups composite laminates are tested. The failure process is displayed by strain field images, and final failure images are also supplied in this article. Detailed numerical analysis are reported in [1].

| S-EPMC8010642 | biostudies-literature

Experimental semi-autonomous eigensolver using reinforcement learning.

Project description:The characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eigenvectors of an arbitrary Hermitian operator using the IBM quantum computer. To this end, we only use single-shot measurements and pseudo-random changes handled by a feedback loop, reducing the number of measures in the system. Due to the classical feedback loop, this algorithm can be cast into the reinforcement learning paradigm. Using this algorithm, for a single-qubit observable, we obtain both eigenvectors with fidelities over 0.97 with around 200 single-shot measurements. For two-qubits observables, we get fidelities over 0.91 with around 1500 single-shot measurements for the four eigenvectors, which is a comparatively low resource demand, suitable for current devices. This work is useful to the development of quantum devices able to decide with partial information, which helps to implement future technologies in quantum artificial intelligence.

| S-EPMC8192530 | biostudies-literature

Experimental study on tailings cementation by MICP technique with immersion curing.

Project description:The filling mining method is an effective method for controlling ground stress and preventing surface subsidence in the mining field during exploitation of underground resources. Tailings can be utilized as the filling material, so as to realize the reuse of industrial waste. However, utilization of the traditional Portland cement as the cementing material for tailings leads to groundwater pollution. In addition, production of Portland cement results in consumption of a great amount of ore and air pollution. In this paper, a tailings cementation method by using the microbial induced calcite precipitation (MICP) technique with immersion curing is proposed. Tailings are cemented by the MICP technique with aerobic bacteria (Sporosarcina pasteurii) under a soaked curing environment. The variable control method is applied to investigate the factors influencing the cementation effects by the MICP technique with Sporosarcina pasteurii, including the bacterial solution concentration, the cementing solution concentration, the particle size of tailings, and the curing temperature. The results indicate that: when OD600 of the Sporosarcina pasteurii solution is 1.6, the urea concentration in the cementing solution is 0.75 mol/L, the tailings are raw materials without grinding, and the curing temperature is 30°C, the cementation effect is the best. In view of uneven calcification during MICP with Sporosarcina pasteurii, mixed Sporosarcina pasteurii and Castellaniella denitrificans are used for tailings cementation. Higher strength of cemented tailings is achieved. It is proved that the MICP technique with mixed aerobic bacteria and facultative anaerobes is an effective method for tailings cementation.

| S-EPMC9342764 | biostudies-literature

Deep reinforcement learning for optimal experimental design in biology.

Project description:The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence-reinforcement learning-to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty.

| S-EPMC9721483 | biostudies-literature

FEM: feature-enhanced map.

Project description:A method is presented that modifies a 2mFobs - DFmodel ?A-weighted map such that the resulting map can strengthen a weak signal, if present, and can reduce model bias and noise. The method consists of first randomizing the starting map and filling in missing reflections using multiple methods. This is followed by restricting the map to regions with convincing density and the application of sharpening. The final map is then created by combining a series of histogram-equalized intermediate maps. In the test cases shown, the maps produced in this way are found to have increased interpretability and decreased model bias compared with the starting 2mFobs - DFmodel ?A-weighted map.

| S-EPMC4356370 | biostudies-literature

Reinforcement learning accounts for moody conditional cooperation behavior: experimental results.

Project description:In social dilemma games, human participants often show conditional cooperation (CC) behavior or its variant called moody conditional cooperation (MCC), with which they basically tend to cooperate when many other peers have previously cooperated. Recent computational studies showed that CC and MCC behavioral patterns could be explained by reinforcement learning. In the present study, we use a repeated multiplayer prisoner's dilemma game and the repeated public goods game played by human participants to examine whether MCC is observed across different types of game and the possibility that reinforcement learning explains observed behavior. We observed MCC behavior in both games, but the MCC that we observed was different from that observed in the past experiments. In the present study, whether or not a focal participant cooperated previously affected the overall level of cooperation, instead of changing the tendency of cooperation in response to cooperation of other participants in the previous time step. We found that, across different conditions, reinforcement learning models were approximately as accurate as a MCC model in describing the experimental results. Consistent with the previous computational studies, the present results suggest that reinforcement learning may be a major proximate mechanism governing MCC behavior.

| S-EPMC5223288 | biostudies-literature

An industrial demonstration study on CO<sub>2</sub> mineralization curing for concrete.

Project description:A 10,000 ton-CO2/y mineralization curing (CMC) process was demonstrated in Jiaozuo city, China by retrofitting a traditional autoclaved curing plant. An industrial concrete formula with synergistic effects of aggregate gradation, early hydration, and alkali excitation was developed using local solid wastes resources. Approximately 90% of the raw materials, including fly ash, furnace blaster slag, steel slag, and carbide slag, came from coal-based industries. An extraordinary phenomenon of high-temperature accumulation from room temperature to 140°C was first observed in an industrial scale because of the rapid and strong exothermic carbonation reaction. A step pressure-equalizing procedure was developed to achieve a rapid carbonation rate, a high CO2 conversion ratio of >98%, and efficient carbonation exotherm recycling. The global warming potential life cycle analysis revealed that compared with autoclaved curing, CMC showed significantly decreased the emission of 182 kg CO2-Eq/m3-product, with direct CO2 sequestration accounting for ∼65% of the reduction.

| S-EPMC9062350 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data