Fundamental Limits to Nonlinear Energy Harvesting

Linear and nonlinear vibration energy harvesting has been the focus of considerable research in recent years. However, fundamental limits on the harvestable energy of a harvester subjected to an arbitrary excitation force and different constraints is not yet fully understood. Understanding these limits is not only essential for an assessment of the technology potential, but it also provides a broader perspective on the current harvesting mechanisms and guidance in their improvement. Here, we derive the fundamental limits on the output power of an ideal energy harvester for arbitrary excitation waveforms and build on the current analysis framework for the simple computation of this limit for more sophisticated setups. We show that the optimal harvester maximizes the harvested energy through a mechanical analog of a buy-low-sell-high strategy. We also propose a nonresonant passive latch-assisted harvester to realize this strategy for an effective harvesting. It is shown that the proposed harvester harvests energy more effectively than its linear and bistable counterparts over a wider range of excitation frequencies and amplitudes. The buy-low-sell-high strategy also reveals why the conventional bistable harvester works well at low-frequency excitation

Optimization of vibratory energy harvesters with stochastic parametric uncertainty: a new perspective

Vibration energy harvesting has been shown as a promising power source for many small-scale applications mainly because of the considerable reduction in the energy consumption of the electronics and scalability issues of the conventional batteries. However, energy harvesters may not be as robust as the conventional batteries and their performance could drastically deteriorate in the presence of uncertainty in their parameters. Hence, study of uncertainty propagation and optimization under uncertainty is essential for proper and robust performance of harvesters in practice. While all studies have focused on expectation optimization, we propose a new and more practical optimization perspective; optimization for the worst-case (minimum) power. We formulate the problem in a generic fashion and as a simple example apply it to a linear piezoelectric energy harvester. We study the effect of parametric uncertainty in its natural frequency, load resistance, and electromechanical coupling coefficient on its worst-case power and then optimize for it under different confidence levels. The results show that there is a significant improvement in the worst-case power of thus designed harvester compared to that of a naively-optimized (deterministically-optimized) harvester. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Data-driven control of COVID-19 in buildings: a reinforcement-learning approach

In addition to its public health crisis, COVID-19 pandemic has led to the shutdown and closure of workplaces with an estimated total cost of more than $16 trillion. Given the long hours an average person spends in buildings and indoor environments, this research article proposes data-driven control strategies to design optimal indoor airflow to minimize the exposure of occupants to viral pathogens in built environments. A general control framework is put forward for designing an optimal velocity field and proximal policy optimization, a reinforcement learning algorithm is employed to solve the control problem in a data-driven fashion. The same framework is used for optimal placement of disinfectants to neutralize the viral pathogens as an alternative to the airflow design when the latter is practically infeasible or hard to implement. We show, via simulation experiments, that the control agent learns the optimal policy in both scenarios within a reasonable time. The proposed data-driven control framework in this study will have significant societal and economic benefits by setting the foundation for an improved methodology in designing case-specific infection control guidelines that can be realized by affordable ventilation devices and disinfectants

APPRAISAL OF TAKAGI–SUGENO TYPE NEURO-FUZZY NETWORK SYSTEM WITH A MODIFIED DIFFERENTIAL EVOLUTION METHOD TO PREDICT NONLINEAR WHEEL DYNAMICS CAUSED BY ROAD IRREGULARITIES

Wheel dynamics play a substantial role in traversing and controlling the vehicle, braking, ride comfort, steering, and maneuvering. The transient wheel dynamics are difficult to be ascertained in tire–obstacle contact condition. To this end, a single-wheel testing rig was utilized in a soil bin facility for provision of a controlled experimental medium. Differently manufactured obstacles (triangular and Gaussian shaped geometries) were employed at different obstacle heights, wheel loads, tire slippages and forward speeds to measure the forces induced at vertical and horizontal directions at tire–obstacle contact interface. A new Takagi–Sugeno type neuro-fuzzy network system with a modified Differential Evolution (DE) method was used to model wheel dynamics caused by road irregularities. DE is a robust optimization technique for complex and stochastic algorithms with ever expanding applications in real-world problems. It was revealed that the new proposed model can be served as a functional alternative to classical modeling tools for the prediction of nonlinear wheel dynamics

Data-driven control of micro-climate in buildings: an event-triggered reinforcement learning approach

Smart buildings have great potential for shaping an energy-efficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. Future of the smart buildings lies in using sensory data for adaptive decision making and control that is currently gloomed by the key challenge of learning a good control policy in a short period of time in an online and continuing fashion. To tackle this challenge, an event-triggered -- as opposed to classic time-triggered -- paradigm, is proposed in which learning and control decisions are made when events occur and enough information is collected. Events are characterized by certain design conditions and they occur when the conditions are met, for instance, when a certain state threshold is reached. By systematically adjusting the time of learning and control decisions, the proposed framework can potentially reduce the variance in learning, and consequently, improve the control process. We formulate the micro-climate control problem based on semi-Markov decision processes that allow for variable-time state transitions and decision making. Using extended policy gradient theorems and temporal difference methods in a reinforcement learning set-up, we propose two learning algorithms for event-triggered control of micro-climate in buildings. We show the efficacy of our proposed approach via designing a smart learning thermostat that simultaneously optimizes energy consumption and occupants' comfort in a test building

Vibration protection of laptop hard disk drives in harsh environmental conditions

Ultra-portability and compact design of laptop computers have made them more vulnerable to harsh environments. Hard disk drives (HDDs) in particular, are critical components in laptop computers whose read/write performance is severely affected by excessive vibrations. Here we take a system-level approach to design an optimal vibration isolator so as to minimize the transmitted vibration to the HDD while the laptop chassis is confined within an allowable vibration travel. The laptop is modeled as a 3-dof lumped-parameter system and the base excitation is assumed Gaussian random vibration with zero mean and uniform power spectral density over the frequency range [0–2000] Hz. The problem is cast as a constrained optimization problem with two decision variables, namely isolation frequency and damping. A combination of analytical and numerical approaches is utilized to solve the constrained optimization problem. It is shown that the optimized isolation system could reduce the transmitted root-mean-square acceleration to the HDD by a factor of over four compared to a rigidly-mounted laptop. Furthermore, the methodology presented here is not case-specific and could be applied to the isolation system design of a wide range of systems

Nonlinear vibration energy harvesting : fundamental limits, robustness issues, and practical approaches

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 119-130).The problem of a scalable energy supply is one of the biggest issues in miniaturizing electronic devices. Advances in technology have reduced the power consumption of electronic devices such as wireless sensors, data transmitters, and medical implants to the point where harvesting ambient vibration, a universal and widely available source of energy, has become a viable alternative to costly and bulky traditional batteries. However, implementation of vibratory energy harvesters is currently impeded by three main challenges: broadband harvesting, low-frequency harvesting at small (micro) scales, and robust energy harvesting at presence of parametric uncertainties. This thesis investigates two main directions for effective vibration energy harvesting: (i) fundamental limits to nonlinear energy harvesting and techniques to approach them, and (ii) robust energy harvesting under uncertainties. As well as being of fundamental scientific interest, understanding maximal power limits is essential for assessment of the technology potential and it also provides a broader perspective on the current harvesting mechanisms and guidance in their improvement. We begin by developing a general framework and model hierarchy for the derivation of fundamental limits of the nonlinear energy harvesting rate based on Euler-Lagrangian variational approach. The framework allows for an easy incorporation of almost any constraints and arbitrary forcing statistics and represents the maximal harvesting rate as a solution of either a set of DAEs or a standard nonlinear optimization problem. Closed-form expressions are derived for two cases of damping-dominated and displacement-constrained motion. Stemming from the study of fundamental limits, we present an almost-universal strategy termed buy-low-sell-high (BLSH) to maximize the harvested energy for a wide range of set-ups and excitation statistics. We further propose two techniques to realize the non-resonant BLSH strategy, namely latch-assisted harvester and adaptive bistable harvester. To validate the efficacy of the proposed strategy and practical techniques, we perform a simulation experiment by exposing the said harvesters to harmonic and experimental, random walking-motion excitations; it is shown that they outperform their linear and conventional bistable counterparts in a wide range of harmonic excitation and random vibration. Furthermore, we propose to harvest energy by exploiting surface instability or in general instability in layered composites which is, in part, motivated by the BLSH strategy. Instabilities in soft matter and composite structures e.g. wrinkling allow large local strains to take place throughout the entire structure and at regular patterns. Unlike conventional harvesting techniques, this allows to harvest energy from the entire volume of the structure e.g. by attaching piezoelectric patches at large-strain locations throughout the structure. We show that this significantly improves the power to volume ratios of the harvesting devices. In addition, these structural instabilities are non-resonant that consequently enhances robustness of such harvesters with respect to excitation characteristics. The high efficacy of energy harvesting via structural instabilities, in part, is attributed to its ability to approximately follow the BLSH logic. Additionally, we put forth the idea of extending this idea to control the instability; and hence, extend the application of the aforementioned idea from energy harvesting to a whole new level of tunable material/structures with a myriad of applications from electromechanical sensors and amplifiers to fast-motion actuators in soft robotics. And last but not least, to more specifically address the robustness issues of passive harvesters, we propose a new modeling philosophy for optimization under uncertainty; optimization for the worst-case scenario (minimum power) rather than for the ensemble expectation of the power. The proposed optimization philosophy is practically very useful when there is a minimum requirement on the harvested power. We formulate the problems of uncertainty propagation and optimization under uncertainty in a generic and architecture-independent fashion. Furthermore, to resolve the ubiquitous problem of coexisting attractors in nonlinear energy harvesters, we propose a novel robust and adaptive sliding mode controller for active harvesters to move the harvester to any desired attractor by a short entrainment on the desired attractor. The proposed controller is robust to disturbances and unmodeled dynamics and adaptive to the system parameters.by Ashkan Haji Hosseinloo.Ph. D