Statistical Model Calibration for Energy Harvesting Skin Analysis and Design

Virtual testing is a new engineering development trend to design, evaluate, and test new engineered products. This research proposes a virtual testing framework for new product development using three successive steps: (i) statistical model calibration, (ii) hypothesis test for validity check and (iii) virtual qualification. Statistical model calibration first improves the predictive capability of a computational model over a calibration domain. Next, the hypothesis test is performed under limited observed data to see if a calibrated model is sufficiently predictive for virtual testing of a new design. A u-pooling metric is employed for the hypothesis test to measure the degree of mismatch between predicted and observed results while considering uncertainty in the u-pooling metric due to the lack of experimental data. The calibrated model can be rejected only when the measured metric of the calibrated model strongly suggest that the null hypothesis—the calibrated model is valid—is false. If the null hypothesis is accepted, the virtual qualification process can be executed with a qualified model for new product developments. The qualification process builds a design decision matrix to aid in rational decision-making on the product developments. A computational model of a tire tread block was used to demonstrate the effectiveness of the proposed framework.

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Energy Harvesting With Piezoelectric Nanobrushes: Analysis and Design Principles

ASME/STLE 2009 International Joint Tribology Conference ◽

10.1115/ijtc2009-15210 ◽

2009 ◽

Author(s):

Carmel Majidi ◽

Mikko Haataja ◽

David J. Srolovitz

Keyword(s):

Energy Harvesting ◽

Design Space ◽

Electronic Devices ◽

Wearable Electronics ◽

Nanostructured Surface ◽

Elastic Rod ◽

Analysis And Design ◽

Rod Theory ◽

Piezoelectric Energy ◽

Self Powered

The development of self-powered electronic devices is essential for emerging technologies such as wireless sensor networks, wearable electronics, and microrobotics. Of particular interest is the rapidly growing field of piezoelectric energy harvesting (PEH), in which mechanical strains are converted to electricity. Recently, PEH has been demonstrated by brushing an array of piezoelectric nanowires against a nanostructured surface. The piezoelectric nanobrush generator can be limited to sub-micron dimensions and thus allows for a vast reduction in the size of self-powered devices. Moreover, energy harvesting is controlled through contact between the nanowire tips and nanostructured surface, which broadens the design space to a wealth of innovations in tribology. Here we propose design criteria based on principles of contact mechanics, elastic rod theory, and continuum piezoelasticity.

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Analysis and Design of the Battery Initial Energy Level with Task Scheduling for Energy-Harvesting Embedded Systems

Complexity ◽

10.1155/2021/5580631 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Xingyu Miao ◽

Jiayuan Wei ◽

Yongqi Ge

Keyword(s):

Energy Harvesting ◽

Energy Level ◽

Initial Energy ◽

Success Rates ◽

Worst Case ◽

Task Set ◽

Analysis And Design ◽

Available Energy ◽

Battery Capacity ◽

Task Sets

When the energy-harvesting embedded system (EHES) is running, its available energy (harvesting energy and battery storage energy) seems to be sufficient overall. However, in the process of EHES task execution, an energy shortage may occur in the busy period such that system tasks cannot be scheduled. We call this issue the energy deception (ED) of the EHES. Aiming to address the ED issue, we design an appropriate initial energy level of the battery. In this paper, we propose three algorithms to judge the feasibility of the task set and calculate the appropriate initial energy level of the battery. The holistic energy evaluation (HEE) algorithm makes a preliminary judgment of the task set feasibility according to available energy and consumption energy. A worst-case response time-based initial energy level of the battery (WCRT-IELB) algorithm and an accurate cycle-initial energy level of the battery (AC-IELB) algorithm can calculate the proper initial battery capacity. We use the YARTISS tool to simulate the above three algorithms. We conducted 250 experiments on As Late As Possible (ALAP) and As Soon As Possible (ASAP) scheduling with the maximum battery capacities of 50, 100, 200, 300, and 400. The experimental results show that setting a reasonable initial energy level of the battery can effectively improve the feasibility of the task set. Among the 250 task sets, the HEE algorithm filtered 2.8% of them as infeasible task sets. When the battery capacity is set to 400, the WCRT-BIEL algorithm increases the success rates of the ALAP and ASAP by 17.2% and 26.8%, respectively. The AC-BIEL algorithm increases the success rates of the ALAP and ASAP by 18% and 26.8%, respectively.

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