Cavatappi are lightweight, fluidic, muscle-like actuators made from drawing, twisting, and coiling inexpensive polymer tubes.

The fundamental limits of cantilevered piezoelectric energy harvesters have not been well established. As with any other power generation technology, it is critical to establish the limits of power output and efficiency. Mathematical models for piezoelectric energy harvester power output have seen continued refinement, but these models have mainly been used and compared to individual harvester designs. Moreover, existing models all assume power scales with acceleration input, and take no account for the upper limit of the acceleration due to the ultimate strength of the piezoelectric material. Additionally, models for efficiency have been developed, but the limits have not been thoroughly explored. In this paper, we present the upper limits of input acceleration and output power for a piezoelectric harvester device. We then use these expressions, along with a previously developed ideal design method, to explore the upper limits of power production across a range of system masses and excitation frequencies. We also investigate general efficiency limits of these devices. We show the upper limit using an existing model and develop an alternate model that is applicable to excitation sources that are not capable of energy recovery.

Remote marine sensing systems, such as autonomous underwater vehicles or telemetry tags, can be limited in data collection and deployment duration due to the finite energy of the onboard battery. With these technologies migrating vast distances over long-term deployments, maintaining high data resolution is difficult and often yields nonideal data sets. Furthermore, electronics systems used in telemetry tags are often potted in epoxy, which makes replacing or recharging the battery impractical. The implementation of solar energy harvesting on these technologies could increase the fidelity of collected data and/or tag longevity. This paper presents an assessment model that estimates the energy output of a stationary or migrating solar cell above or below the ocean’s surface. The theory and assumptions behind the model are explained, including a review of established concepts for the purpose of consolidation and variable consistency. The model is then compared to benchmark data for verification. Finally, a preliminary analysis is conducted on the previously collected time, depth, and global location data from a northern elephant seal deployment to demonstrate the resulting model outputs and how they can be used for analysis in future works.

Soft actuators show much promise for use in bioinspired and biomimetic robotics as they share many similarities with actuation systems found in nature. Twisted and coiled actuators are soft actuators that have been shown to outperform many metrics of biological muscles, leading researchers to derive actuation models for future control and implementation. Although models have been proposed for twisted and coiled carbon nanotubes and polymer fibers, cavatappi artificial muscles—a novel twisted and coiled fluidic soft actuator—have not been modeled yet. This work establishes a framework for modeling cavatappi using the thick-wall pressure vessel stress analysis and the spring theory. The presented model uses the mechanical properties of the precursor drawn material used for fabrication, initial twist (internal fiber angle), muscle geometry, and internal pressure to predict the artificial muscles contraction under different external loads. The model predictions agree with the experimental results for cavatappi of different internal fiber angles and load conditions. Given their potential implementation in bioinspired applications, our model can help better design, optimize, and control the actuation response of cavatappi.

In a range of IoT applications, edge nodes require minimal size, mass, and power/energy use. When node complexity is severely constrained, the burden of detection falls to the receiving system. In this paper we present a method of detection for RF signals motivated by wildlife radio telemetry, where miniature beacons emit VHF pulses that are used to detect and localize animals in the field. We address the challenges of limited transmit power and path losses due to terrain that lead to low received SNR. In addition, the pulse train structure requires joint frequency acquisition, time synchronization, and detection; our approach addresses the key challenge of large uncertainty in signal parameters. Our algorithm explicitly integrates frequency acquisition and time synchronization, using nearly phase-coherent matched filtering followed by non-coherent pulse combining and a multiple hypotheses test to provide near-optimal detection performance in low SNR scenarios. We provide quantitative results showing performance as a function of SNR and signal uncertainty. When compared to the single pulse detection case as used in existing systems, our multipulse combining approach pulses improves detection performance by 4 dB, even under the conditions of temporal and frequency/phase uncertainty resulting from beacon design and manufacturing. We also describe an efficient real-time signal processing implementation of the algorithm, including signal tracking that supports localization after initial signal detection.