Second-order statistical bootstrap for the uncertainty quantification of time-temperature-superposition analysis

Maiti, Amitesh

doi:10.1007/s00397-019-01138-y

Cited by 10 publications

(12 citation statements)

References 30 publications

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“…Over the years there have been a few suggested algorithms for objective TTS shifting, which include minimizing the sum of square errors in horizontal distances in the overlapping region of neighboring curves 22–26 , minimizing areas in the overlapping regions between two successive curves 27 , and minimizing the arclength of the master curve in the complex (storage, loss) modulus plane 28,29 . The last method, i.e., arclength-minimization, has recently been shown to be statistically unbiased 30 . Moreover, in case of data from only limited specimens, as is true in the present study, the work shows how to obtain more consistent shift factors through averaging over a large number of with-replacement (bootstrap) re-samples 31 .…”

Section: Time-temperature Superposition and Long-term Predictionmentioning

confidence: 99%

“…Following the method outlined in ref. 30 , the bootstrap-mean-minimum-arc algorithm was used to carry out TTS shifting of the Fig. 3 isotherms.…”

Section: Time-temperature Superposition and Long-term Predictionmentioning

confidence: 99%

“…We used the bootstrap-mean-minimum-arclength implementation of TTS as described in ref. 30 . Each property displays nearly the same Arrhenius barrier (in KJ, as indicated).…”

Section: Time-temperature Superposition and Long-term Predictionmentioning

confidence: 99%

“…Finally, as noted in ref. 30 , the bootstrap-mean-minimum-arc method yields a sampling distribution of activation barriers that is normal with a near-zero-bias sampling mean. This allows us to construct confidence interval (i.e., margin curves) for the long-term prediction.…”

Section: Time-temperature Superposition and Long-term Predictionmentioning

confidence: 99%

“…We observe that lower-temperature measurement noise at short times do not influence the margins as much as the noise in latter-time measurements, especially at elevated temperatures.

Figure 590% confidence margins in the long-term prediction of compression set and load retention, using a second-order bootstrap method, as described in ref. 30 . The shift factors are constrained by the Arrhenius model (Eq.…”

Section: Time-temperature Superposition and Long-term Predictionmentioning

confidence: 99%

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Age-aware constitutive materials model for a 3D printed polymeric foam

Maiti

Small

Lewicki

et al. 2019

Sci Rep

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Traditional open or closed-cell stochastic elastomeric foams have wide-ranging applications in numerous industries: from thermal insulation, shock absorbing/gap-filling support cushions, packaging, to light-weight structural and positional components. Recent developments in 3D printing technologies by direct ink-write have opened the possibility of replacing stochastic foam parts by more controlled printed micro-structures with superior stress-distribution and longer functional life. For successful deployment as mechanical support or structural components, it is crucial to characterize the response of such printed materials to long-term external loads in terms of stress-strain behavior evolution and in terms of irreversible structural and load-bearing capacity changes over time. To this end, here we report a thermal-age-aware constitutive model for a 3D printed close-packed foam structure under compression. The model is based on the Ogden hyperfoam strain-energy functional within the framework of Tobolsky two-network scheme. It accurately describes experimentally measured stress-strain response, compression set, and load retention for various aging times and temperatures. Through the technique of time-temperature-superposition the model enables the prediction of long-term changes along with the quantification of uncertainty stemming from sample-to-sample variation and measurement noise. All aging parameters appear to possess the same Arrhenius activation barrier, which suggests a single dominant aging mechanism at the molecular/network level.

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Section: Time-temperature Superposition and Long-term Predictionmentioning

confidence: 99%

“…Following the method outlined in ref. 30 , the bootstrap-mean-minimum-arc algorithm was used to carry out TTS shifting of the Fig. 3 isotherms.…”

Section: Time-temperature Superposition and Long-term Predictionmentioning

confidence: 99%

“…We used the bootstrap-mean-minimum-arclength implementation of TTS as described in ref. 30 . Each property displays nearly the same Arrhenius barrier (in KJ, as indicated).…”

Section: Time-temperature Superposition and Long-term Predictionmentioning

confidence: 99%

Section: Time-temperature Superposition and Long-term Predictionmentioning

confidence: 99%

“…We observe that lower-temperature measurement noise at short times do not influence the margins as much as the noise in latter-time measurements, especially at elevated temperatures.

Section: Time-temperature Superposition and Long-term Predictionmentioning

confidence: 99%

See 3 more Smart Citations

Age-aware constitutive materials model for a 3D printed polymeric foam

Maiti

Small

Lewicki

et al. 2019

Sci Rep

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Effect of thermal conditioning on the initiation threshold of secondary high‐explosives

Maiti,

Shaw,

Clarke

et al. 2023

Propellants Explo Pyrotec

Self Cite

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While most performance metrics of high‐explosive (HE) based devices like detonation velocity, detonation pressure, and energy output are expected to degrade over time, the evolution of initiation threshold appears less clear, with claims of both increasing and decreasing trends in threshold having been made in the literature. This work analyzes D‐optimally designed sequential binary test data for a few thermally conditioned porous‐powder and polymer‐bonded HE initiator systems using a Bayesian likelihood method employing the probit regression model. We find that in most cases the initiation threshold decreases (i. e., sensitivity increases) upon accelerated thermal conditioning. However, such results are nuanced and influenced by factors like the contact area of initiating stimulus, HE characteristics like density and specific surface area, as well as possible thermally induced changes to other materials and interfaces involved.

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Time-temperature-superposition analysis of diverse datasets by the minimum-arclength method: long-term prediction with uncertainty margins

Maiti

2021

Rheol Acta

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Second-order statistical bootstrap for the uncertainty quantification of time-temperature-superposition analysis

Cited by 10 publications

References 30 publications

Age-aware constitutive materials model for a 3D printed polymeric foam

Age-aware constitutive materials model for a 3D printed polymeric foam

Effect of thermal conditioning on the initiation threshold of secondary high‐explosives

Time-temperature-superposition analysis of diverse datasets by the minimum-arclength method: long-term prediction with uncertainty margins

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