Nonlinear piezoelectricity in electroelastic energy harvesters: Modeling and experimental identification

We propose and experimentally validate a first-principles based model for the nonlinear piezoelectric response of an electroelastic energy harvester. The analysis herein highlights the importance of modeling inherent piezoelectric nonlinearities that are not limited to higher order elastic effects but also include nonlinear coupling to a power harvesting circuit. Furthermore, a nonlinear damping mechanism is shown to accurately restrict the amplitude and bandwidth of the frequency response. The linear piezoelectric modeling framework widely accepted for theoretical investigations is demonstrated to be a weak presumption for near-resonant excitation amplitudes as low as 0.5 g in a prefabricated bimorph whose oscillation amplitudes remain geometrically linear for the full range of experimental tests performed (never exceeding 0.25% of the cantilever overhang length). Nonlinear coefficients are identified via a nonlinear least-squares optimization algorithm that utilizes an approximate analytic solution obtained by the method of harmonic balance. For lead zirconate titanate (PZT-5H), we obtained a fourth order elastic tensor component of c1111p =-3.6673× 1017 N/m2 and a fourth order electroelastic tensor value of e3111 =1.7212× 108 m/V. © 2010 American Institute of Physics.

Dual-energy computed tomography with advanced postimage acquisition data processing: improved determination of urinary stone composition.

INTRODUCTION: The characterization of urinary calculi using noninvasive methods has the potential to affect clinical management. CT remains the gold standard for diagnosis of urinary calculi, but has not reliably differentiated varying stone compositions. Dual-energy CT (DECT) has emerged as a technology to improve CT characterization of anatomic structures. This study aims to assess the ability of DECT to accurately discriminate between different types of urinary calculi in an in vitro model using novel postimage acquisition data processing techniques. METHODS: Fifty urinary calculi were assessed, of which 44 had >or=60% composition of one component. DECT was performed utilizing 64-slice multidetector CT. The attenuation profiles of the lower-energy (DECT-Low) and higher-energy (DECT-High) datasets were used to investigate whether differences could be seen between different stone compositions. RESULTS: Postimage acquisition processing allowed for identification of the main different chemical compositions of urinary calculi: brushite, calcium oxalate-calcium phosphate, struvite, cystine, and uric acid. Statistical analysis demonstrated that this processing identified all stone compositions without obvious graphical overlap. CONCLUSION: Dual-energy multidetector CT with postprocessing techniques allows for accurate discrimination among the main different subtypes of urinary calculi in an in vitro model. The ability to better detect stone composition may have implications in determining the optimum clinical treatment modality for urinary calculi from noninvasive, preprocedure radiological assessment.

Accelerating self-consistent field convergence with the augmented Roothaan-Hall energy function.

Based on Pulay's direct inversion iterative subspace (DIIS) approach, we present a method to accelerate self-consistent field (SCF) convergence. In this method, the quadratic augmented Roothaan-Hall (ARH) energy function, proposed recently by Høst and co-workers [J. Chem. Phys. 129, 124106 (2008)], is used as the object of minimization for obtaining the linear coefficients of Fock matrices within DIIS. This differs from the traditional DIIS of Pulay, which uses an object function derived from the commutator of the density and Fock matrices. Our results show that the present algorithm, abbreviated ADIIS, is more robust and efficient than the energy-DIIS (EDIIS) approach. In particular, several examples demonstrate that the combination of ADIIS and DIIS ("ADIIS+DIIS") is highly reliable and efficient in accelerating SCF convergence.

Cost of wind energy: comparing distant wind resources to local resources in the midwestern United States.

The best wind sites in the United States are often located far from electricity demand centers and lack transmission access. Local sites that have lower quality wind resources but do not require as much power transmission capacity are an alternative to distant wind resources. In this paper, we explore the trade-offs between developing new wind generation at local sites and installing wind farms at remote sites. We first examine the general relationship between the high capital costs required for local wind development and the relatively lower capital costs required to install a wind farm capable of generating the same electrical output at a remote site,with the results representing the maximum amount an investor should be willing to pay for transmission access. We suggest that this analysis can be used as a first step in comparing potential wind resources to meet a state renewable portfolio standard (RPS). To illustrate, we compare the cost of local wind (∼50 km from the load) to the cost of distant wind requiring new transmission (∼550-750 km from the load) to meet the Illinois RPS. We find that local, lower capacity factor wind sites are the lowest cost option for meeting the Illinois RPS if new long distance transmission is required to access distant, higher capacity factor wind resources. If higher capacity wind sites can be connected to the existing grid at minimal cost, in many cases they will have lower costs.

Finite size effects in the presence of a chemical potential: A study in the classical nonlinear O(2) sigma model

In the presence of a chemical potential, the physics of level crossings leads to singularities at zero temperature, even when the spatial volume is finite. These singularities are smoothed out at a finite temperature but leave behind nontrivial finite size effects which must be understood in order to extract thermodynamic quantities using Monte Carlo methods, particularly close to critical points. We illustrate some of these issues using the classical nonlinear O(2) sigma model with a coupling β and chemical potential μ on a 2+1-dimensional Euclidean lattice. In the conventional formulation this model suffers from a sign problem at nonzero chemical potential and hence cannot be studied with the Wolff cluster algorithm. However, when formulated in terms of the worldline of particles, the sign problem is absent, and the model can be studied efficiently with the "worm algorithm." Using this method we study the finite size effects that arise due to the chemical potential and develop an effective quantum mechanical approach to capture the effects. As a side result we obtain energy levels of up to four particles as a function of the box size and uncover a part of the phase diagram in the (β,μ) plane. © 2010 The American Physical Society.

Preservation of beam emittance in the presence of ion motion in future high-energy plasma-wakefield-based colliders.

The preservation of beam quality in a plasma wakefield accelerator driven by ultrahigh intensity and ultralow emittance beams, characteristic of future particle colliders, is a challenge. The electric field of these beams leads to plasma ions motion, resulting in a nonlinear focusing force and emittance growth of the beam. We propose to use an adiabatic matching section consisting of a short plasma section with a decreasing ion mass to allow for the beam to remain matched to the focusing force. We use analytical models and numerical simulations to show that the emittance growth can be significantly reduced.

An energy-efficient data delivery scheme for delay-sensitive traffic in wireless sensor networks

We propose a novel data-delivery method for delay-sensitive traffic that significantly reduces the energy consumption in wireless sensor networks without reducing the number of packets that meet end-to-end real-time deadlines. The proposed method, referred to as SensiQoS, leverages the spatial and temporal correlation between the data generated by events in a sensor network and realizes energy savings through application-specific in-network aggregation of the data. SensiQoS maximizes energy savings by adaptively waiting for packets from upstream nodes to perform in-network processing without missing the real-time deadline for the data packets. SensiQoS is a distributed packet scheduling scheme, where nodes make localized decisions on when to schedule a packet for transmission to meet its end-to-end real-time deadline and to which neighbor they should forward the packet to save energy. We also present a localized algorithm for nodes to adapt to network traffic to maximize energy savings in the network. Simulation results show that SensiQoS improves the energy savings in sensor networks where events are sensed by multiple nodes, and spatial and/or temporal correlation exists among the data packets. Energy savings due to SensiQoS increase with increase in the density of the sensor nodes and the size of the sensed events. © 2010 Harshavardhan Sabbineni and Krishnendu Chakrabarty.

Mechanical constraints enhance electrical energy densities of soft dielectrics

Thesis

Information programs for technology adoption: The case of energy-efficiency audits

We analyze technology adoption decisions of manufacturing plants in response to government-sponsored energy audits. Overall, plants adopt about half of the recommended energy-efficiency projects. Using fixed effects logit estimation, we find that adoption rates are higher for projects with shorter paybacks, lower costs, greater annual savings, higher energy prices, and greater energy conservation. Plants are 40% more responsive to initial costs than annual savings, suggesting that subsidies may be more effective at promoting energy-efficient technologies than energy price increases. Adoption decisions imply hurdle rates of 50-100%, which is consistent with the investment criteria small and medium-size firms state they use. © 2003 Elsevier B.V. All rights reserved.

Where does energy R&D come from? Examining crowding out from energy R&D

Recent efforts to endogenize technological change in climate policy models demonstrate the importance of accounting for the opportunity cost of climate R&D investments. Because the social returns to R&D investments are typically higher than the social returns to other types of investment, any new climate mitigation R&D that comes at the expense of other R&D investment may dampen the overall gains from induced technological change. Unfortunately, there has been little empirical work to guide modelers as to the potential magnitude of such crowding out effects. This paper considers both the private and social opportunity costs of climate R&D. Addressing private costs, we ask whether an increase in climate R&D represents new R&D spending, or whether some (or all) of the additional climate R&D comes at the expense of other R&D. Addressing social costs, we use patent citations to compare the social value of alternative energy research to other types of R&D that may be crowded out. Beginning at the industry level, we find no evidence of crowding out across sectors-that is, increases in energy R&D do not draw R&D resources away from sectors that do not perform R&D. Given this, we proceed with a detailed look at alternative energy R&D. Linking patent data and financial data by firm, we ask whether an increase in alternative energy patents leads to a decrease in other types of patenting activity. While we find that increases in alternative energy patents do result in fewer patents of other types, the evidence suggests that this is due to profit-maximizing changes in research effort, rather than financial constraints that limit the total amount of R&D possible. Finally, we use patent citation data to compare the social value of alternative energy patents to other patents by these firms. Alternative energy patents are cited more frequently, and by a wider range of other technologies, than other patents by these firms, suggesting that their social value is higher. © 2011 Elsevier B.V.

Cost-effectiveness of electricity energy efficiency programs

We analyze the cost-effectiveness of electric utility ratepayer-funded programs to promote demand-side management (DSM) and energy efficiency (EE) investments. We specify a model that relates electricity demand to previous EE DSM spending, energy prices, income, weather, and other demand factors. In contrast to previous studies, we allow EE DSM spending to have a potential longterm demand effect and explicitly address possible endogeneity in spending. We find that current period EE DSM expenditures reduce electricity demand and that this effect persists for a number of years. Our findings suggest that ratepayer funded DSM expenditures between 1992 and 2006 produced a central estimate of 0.9 percent savings in electricity consumption over that time period and a 1.8 percent savings over all years. These energy savings came at an expected average cost to utilities of roughly 5 cents per kWh saved when future savings are discounted at a 5 percent rate. Copyright © 2012 by the IAEE. All rights reserved.

Carbon mitigation costs for the commercial building sector: Discrete-continuous choice analysis of multifuel energy demand

We estimate a carbon mitigation cost curve for the U.S. commercial sector based on econometric estimation of the responsiveness of fuel demand and equipment choices to energy price changes. The model econometrically estimates fuel demand conditional on fuel choice, which is characterized by a multinomial logit model. Separate estimation of end uses (e.g., heating, cooking) using the U.S. Commercial Buildings Energy Consumption Survey allows for exceptionally detailed estimation of price responsiveness disaggregated by end use and fuel type. We then construct aggregate long-run elasticities, by fuel type, through a series of simulations; own-price elasticities range from -0.9 for district heat services to -2.9 for fuel oil. The simulations form the basis of a marginal cost curve for carbon mitigation, which suggests that a price of $20 per ton of carbon would result in an 8% reduction in commercial carbon emissions, and a price of $100 per ton would result in a 28% reduction. © 2008 Elsevier B.V. All rights reserved.

Energy recovery in individuals with knee osteoarthritis.

OBJECTIVE: Pathological gaits have been shown to limit transfer between potential (PE) and kinetic (KE) energy during walking, which can increase locomotor costs. The purpose of this study was to examine whether energy exchange would be limited in people with knee osteoarthritis (OA). METHODS: Ground reaction forces during walking were collected from 93 subjects with symptomatic knee OA (self-selected and fast speeds) and 13 healthy controls (self-selected speed) and used to calculate their center of mass (COM) movements, PE and KE relationships, and energy recovery during a stride. Correlations and linear regressions examined the impact of energy fluctuation phase and amplitude, walking velocity, body mass, self-reported pain, and radiographic severity on recovery. Paired t-tests were run to compare energy recovery between cohorts. RESULTS: Symptomatic knee OA subjects displayed lower energetic recovery during self-selected walking speeds than healthy controls (P = 0.0018). PE and KE phase relationships explained the majority (66%) of variance in recovery. Recovery had a complex relationship with velocity and its change across speeds was significantly influenced by the self-selected walking speed of each subject. Neither radiographic OA scores nor subject self-reported measures demonstrated any relationship with energy recovery. CONCLUSIONS: Knee OA reduces effective exchange of PE and KE, potentially increasing the muscular work required to control movements of the COM. Gait retraining may return subjects to more normal patterns of energy exchange and allow them to reduce fatigue.

The induced innovation hypothesis and energy-saving technological change

We develop a methodology for testing Hicks's induced innovation hypothesis by estimating a product-characteristics model of energy-using consumer durables, augmenting the hypothesis to allow for the influence of government regulations. For the products we explored, the evidence suggests that (i) the rate of overall innovation was independent of energy prices and regulations; (ii) the direction of innovation was responsive to energy price changes for some products but not for others; (iii) energy price changes induced changes in the subset of technically feasible models that were offered for sale; (iv) this responsiveness increased substantially during the period after energy-efficiency product labeling was required; and (v) nonetheless, a sizable portion of efficiency improvements were autonomous.

Energy efficiency policies: A retrospective examination

We review literature on several types of energy efficiency policies: appliance standards, financial incentive programs, information and voluntary programs, and management of government energy use. For each, we provide a brief synopsis of the relevant programs, along with available existing estimates of energy savings, costs, and cost-effectiveness at a national level. The literature examining these estimates points to potential issues in determining the energy savings and costs, but recent evidence suggests that techniques for measuring both have improved. Taken together, the literature identifies up to four quads of energy savings annually from these programs - at least half of which is attributable to appliance standards and utility-based demand-side management, with possible additional energy savings from the U.S. Department of Energy's (DOE's) ENERGY STAR, Climate Challenge, and Section 1605b voluntary programs to reduce carbon dioxide (CO 2) emissions. Related reductions in CO 2 and criteria air pollutants may contribute an additional 10% to the value of energy savings above the price of energy itself. Copyright © 2006 by Annual Reviews. All rights reserved.