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Abstract This article introduces an effective stochastic operation framework for optimal energy management of the shipboard power systems including large, nonlinear and dynamic loads. The proposed framework divides the ship power system into several agents, which coordinate with each other based on their demands/supplies until. The alternating direction method of multipliers (ADMM) is deployed as the multi-agent framework to solve the reformulated distributed energy management problem in the ship. Two types of turbo-generators are considered in the proposed system model, including single-shaft and twin-shaft models, to increase the part-load efficiency in certain times when facing variable speed operation. The proposed distributed framework is equipped with a recursive mechanism, which helps the ship system for running optimal load scheduling when facing insufficient power generation. In order to model the uncertainty effects associated with the forecast error in the interval-ahead load demand, a stochastic framework based on unscented transform is devised which can work in the nonlinear and correlated environments of shipboard power systems. Due to the nonlinear cost function in each agent, a powerful optimization algorithm based on modified θ-firefly algorithm (Mθ-FOA) is proposed. This is a phasor algorithm, which helps for escaping from premature convergence and getting trapped in local optima. The appropriate performance of the proposed stochastic model is examined on the real dataset of a ship power system. The simulation results show the high robustness, guarantied consensus, economic operation and feasible solution when power generation shortage based on load shedding in the system.

In this study, heat transfer and thermal performance of a periodically dimple-protrusion patterned surface have been investigated to enhance energy-efficiency in compact heat exchangers. The local heat transfer coefficients on the dimple/protrusion walls are derived using a transient TLC (Thermochromic Liquid Crystal) technique. The periodically patterned surface is applied to the bottom wall only or both the bottom and top walls in the test duct. The ratio of dimple (or protrusion) depth to duct height is 0.25, and the ratio of duct height to dimple (or protrusion) print diameter is 1.15. The Reynolds number is tested in low range values from 1000 to 10000. On the single-side patterned walls, various secondary flows generated from the dimple/protrusion coexist. The vortices induced from the upstream affect strongly on the downstream pattern. For the double-side patterned wall case, vortex interaction affected by the opposite wall enhances highly the heat transfer. The heat transfer augmentation is higher in the lower Reynolds number due to the effective vortex interactions. Therefore, the performance factor considering both heat transfer enhancement and pressure loss increases with decreasing the Reynolds number.

Abstract Multiple energy generating cycles such as tri-generation cycles, which produce heat and cold in addition to power through burning of a primary fuel, have increasingly been used in recent decades. On the other hand, advanced exergy analysis of thermodynamic systems by splitting exergy destruction into endogenous and exogenous parts identifies internal irreversibilities of each of the components and the effect of these irreversibilities on the performance of other components. Therefore, main sources of exergy destruction in cycles can be highlighted and useful recommendations in order to improve the performance of thermodynamic cycles can be presented. In the present work, a tri-generation cycle with 100 MW power production, 70 MW heat and 9 MW cooling capacity is considered. For this tri-generation cycle, effects of various thermodynamic parameters on the amount of endogenous and exogenous exergy destructions, exergy loss and the amount of fuel consumption, are investigated. The results indicate that, increasing compressor pressure ratio, pre-heater outlet temperature and excess air leads to better combustion and lower exergy loss and fuel consumption. Increasing the mass flow rate of steam generator, while keeping the cycle outlet temperature constant and considering cooling capacity variable, lead to increase the first- and second-law efficiencies of the cycle.

Abstract This paper presents numerical optimization results for the thermal and effective efficiencies of a solar air heater duct as a function of various roughness geometries on the heated plate. A methodology for the prediction of thermal and effective efficiency of the SAH has been developed on the basis of heat transfer and friction factor correlations. Among the roughness geometries considered, the discrete multiple V-shaped rib results in the best thermal and effective efficiencies over the entire range of design conditions. This work also addresses the scope for future research in the area of roughened SAH ducts and will be helpful for researchers investigating new artificial shapes for SAH ducts to enhance the heat transfer.

Abstract Accurate power forecasting is of great importance to the turbine control and predictive maintenance. However, traditional physics models and statistical models can no longer meet the needs of precision and flexibility when thermal power plants frequently undertake more and more peak and frequency modulation tasks. In this study, the recurrent neural network (RNN) and convolutional neural network (CNN) for power prediction are proposed, and are applied to predict real-time power of turbine based on DCS data (recorded for 719 days) from a power plant. In addition, the performances of two deep learning models and five typical machine learning models are compared, including prediction deviation, variance and time cost. It is found that deep learning models outperform other shallow models and RNN model performs best in balancing the accuracy-efficient trade-off for power prediction (the relative prediction error of 99.76% samples is less than 1% in all load range for test 216 days). Moreover, the influence of training size and input time-steps on the performance of RNN model is also explored. The model can achieve remarkable performance by learning only 30% samples (about 216 days) with 3 input time-steps (about 60 s). Those results of the proposed models based on deep-learning methods indicated that deep learning is of great help to improve the accuracy of turbine power prediction. It is therefore convinced that those models have a high potential for turbine control and predictable maintenance in actual industrial scenarios.

Abstract To improve the conversion efficiency of thermophotovoltaic devices, we designed a thermophotovoltaic system based on an InAs/InGaAsSb/GaSb three-junction tandem cell. The tandem cell can recover photons in the wavelength range of 200–3650 nm and therefore enhance the output power of the system. To further improve system performance, we designed a multilayer circular truncated cone metamaterial emitter matching the tandem cell. Existing TPV systems based on multi-junction tandem PV cells can achieve conversion efficiencies of 33.3%–41%, while the thermophotovoltaic system coupled with the multilayer circular truncated cone metamaterial can recover more photons of 1.44 mol/(m2·s) and achieve a higher conversion efficiency of 52.8% at 1773 K. The thermophotovoltaic system designed here demonstrates an extremely high energy conversion efficiency and has good application prospects.

Abstract In this paper, energy and exergy analyses of the geothermal-based hydrogen production via thermochemical water decomposition using a new, four-step copper–chlorine (Cu–Cl) cycle are conducted, and the respective cycle energy and exergy efficiencies are examined. Also, a parametric study is performed to investigate how each step of the cycle and its overall cycle performance are affected by reference environment temperatures, reaction temperatures, as well as energy efficiency of the geothermal power plant itself. As a result, overall energy and exergy efficiencies of the cycle are found to be 21.67% and 19.35%, respectively, for a reference case.

Abstract In this study, the compatibility and corrosion behavior of carbon steel, stainless steel, aluminum and copper in contact with blends composed of biodiesel (consisting of soybean oil, beef tallow and swine lard) and petrodiesel were evaluated. Static immersion tests (total, partial and crevice) and exposure in vapor phase were carried out for 2160 h at 50 °C. Our main findings indicate that fuel blends influence the corrosion behavior, being observed a less corrosive attack on the materials, except for copper that presented an anomalous corrosion behavior, with a clear trend towards lower corrosion rates. Carbon steel and stainless steel in total, partial and crevice tests presented surface morphology with slightly changes, while carbon steel exposed in vapor phase showed corrosive attack. Copper presented the highest corrosion rates in partial (9.5273 μm/y), total (9.1484 μm/y) and crevice (6.6178 μm/y) tests in the B7 blend, respectively; and the lowest corrosion rates in total (0.0547 μm/y) for the B15 blend; in partial (0.4926 μm/y) and crevice (0.0182 μm/y) tests for the B30 blend. Among the materials evaluated, copper showed higher influence on biodiesel oxidative stability. Aluminum exhibited good compatibility and did not show any compound formed on its surface without presenting any corrosion.

Abstract A generalized modeling framework to value Public Benefit Fund (PBF) programs is developed. The potential economic and environmental impact associated with PBF programs at the state-level is described through a simulation model that values energy savings and emissions reduction and an input–output model that estimates the total economic benefit of the program. The valuation strategy is based on publicly-available data and infer results under a reasonable assumption set. The methodology is illustrated through a case study for a proposed PBF program targeted for Louisiana across the residential and commercial sector.