Thermodynamic Losses in a Gas Spring: Comparison of Experimental and Numerical Results

Reciprocating-piston devices can be used as high-efficiency compressors and/or expanders. With an optimal valve design and by carefully adjusting valve timing, pressure losses during intake and exhaust can be largely reduced. The main loss mechanism in reciprocating devices is then the thermal irreversibility due to the unsteady heat transfer between the compressed/expanded gas and the surrounding cylinder walls. In this paper, pressure, volume and temperature measurements in a piston-cylinder crankshaft driven gas spring are compared to numerical results. The experimental apparatus experiences mass leakage while the CFD code predicts heat transfer in an ideal closed gas spring. Comparison of experimental and numerical results allows one to better understand the loss mechanisms in play. Heat and mass losses in the experiment are decoupled and the system losses are calculated over a range of frequencies. As expected, compression and expansion approach adiabatic processes for higher frequencies, resulting in higher efficiency. The objective of this study is to observe and explain the discrepancies obtained between the computational and experimental results and to propose further steps to improve the analysis of the loss mechanisms

Data-driven approaches for techno-economic assessment of waste heat recovery and utilisation in the industrial sector

The industrial sector is a critical element in the sustainability transition as it is currently the largest consumer of fossil fuels, and the consumption is forecasted to continue to increase. Approximately one-fifth of the total industrial primary energy consumption is wasted due to the lack of proven attractive schemes for effective recovery. When addressing the opportunities of industrial waste heat recovery (WHR), it is found that the feasibility depends on multiple factors, including the forms and capacities of the heat sources, the potential heat sinks, and the effectiveness, technological maturity, and economic impact of available technologies. Developing systematic approaches to identify optimal WHR options for different applications is key to effectively reduce plant-scale energy consumption. In particular, power consumption accounts for more than half of the industrial energy use, and its share is expected to grow with the expansion of electrification aspirations. In this paper, industrial WHR technologies are investigated, and tools are developed to understand the sustainability and techno-economic impact of integrating these technologies within industrial processes. We specifically propose a data-driven technology-agnostic approach to evaluate the use of heat engines, which can in practice be organic Rankine cycle (ORC) systems, and of thermally- driven (i.e., absorption) heat pumps in the context of industrial WHR for plant-scale power demand reduction. The scope of this work explores three pathways to achieving efficiency improvements in bulk chemicals plants, represented by olefins production facilities, which are: (i) direct onsite power generation; (ii) enhancement of existing power generation processes; and (iii) reduction in power consumption by compressor efficiency improvements through waste-heat-driven cooling. The techno-economic performance of these technologies is assessed, with particular attention to industrial facilities that reside in hot climates, using fine-tuned technology-agnostic thermodynamic and market-based costing models. Finally, decision-aiding performance maps are derived by varying the quantity and the quality of waste-heat sources and heat sinks, offering application- specific guidelines for selecting appropriate waste-heat recovery schemes. These findings reveal valuable factors for selecting such integration schemes for various industries and scenarios

Lumped dynamic analysis and design of a high-performance reciprocating-piston expander

A spatially - lumped dynamic model of a reciprocating - piston expander is presented in this paper. The model accounts for the three main loss mechanisms in realistic piston machines, namely: pressure losses through the intake and exhaust valves, heat transfer between the gas and the surrounding cylinder walls, and the mass leakage between the compression/expansion chamber and the crankcase throu gh the piston rings. The model also accounts for real - gas effects with the fluid properties calculated from t he NIST database using REFPROP. The numerical calculations are first compared with experimental pressure - volume - temperature data obtained on a cust om reciprocating - piston gas spring over a r ange of oscillation frequencies. The comparison between numerical and experimental results shows good agreement. It also allows the most accurate heat transfer correlation to be selected for calculating the gas - to - wall in - cylinder heat transfer. The semi - heuristic modelling tool is then used to design an expander for specific pressure ratio s and mass flowrate, and to predict the thermodynamic performance of the piston device over a range of part - load conditions

Dynamic control strategies for a solar-ORC system using first-law dynamic and data-driven machine learning models

In this study, we developed and assessed the potential of dynamic control strategies for a domestic scale 1-kW solar thermal power system based on a non-recuperated organic Rankine cycle (ORC) engine coupled to a solar energy system. Such solar-driven systems suffer from part-load performance deterioration due to diurnal and inter-seasonal fluctuations in solar irradiance and ambient temperature. Real-time control strategies for adjusting the operating parameters of these systems have shown great potential to optimise their transient response to time-varying conditions, thus allowing significant gains in the power output delivered by the system. Dynamic model predictive control strategies rely on the development of computationally efficient, fast-solving models. In contrast, traditional physics-based dynamic process models are often too complex to be used for real-time controls. Machine learning techniques (MLTs), especially deep learning artificial neural networks (ANN), have been applied successfully for controlling and optimising nonlinear dynamic systems. In this study, the solar system was controlled using a fuzzy logic controller with optimised decision parameters for maximum solar energy absorption. For the sake of obtaining the optimal ORC thermal efficiency at any instantaneous time, particularly during part-load operation, the first-law ORC model was first replaced by a fast-solving feedforward network model, which was then integrated with a multi-objective genetic algorithm, such that the optimal ORC operating parameters can be obtained. Despite the fact that the feedforward network model was trained using steady-state ORC performance data, it showed comparable results compared with the first-principle model in the dynamic context, with a mean absolute error of 3.3 percent for power prediction and 0.186 percentage points for efficiency prediction

Techno-economic comparison of hydrogen- and electricity-driven technologies for the decarbonisation of domestic heating

Sustainable transition pathways currently being proposed for moving away from the use of natural gas and oil in domestic heating focus on two main energy vectors: electricity and hydrogen. The former transition would most likely be implemented using electric vapour-compression heat pumps, which are currently experiencing market growth in many industrialised countries. Electric heat pumps have proven to be an efficient alternative to gas boilers under certain conditions, but their techno-economic potential is highly dependent on the local climate conditions. Hydrogen-based heating systems, which could potentially utilise existing natural gas infrastructure, are being proposed as providing an attractive opportunity to maximise the use of existing assets to facilitate the energy-system transition. In this case, hydrogen can substitute natural gas in boilers or in thermally driven absorption heat pumps. Both heating system transition pathways may involve either installing new technologies at the household level or producing heat in centralised hubs and distributing it via district-heating systems. Although the potential of hydrogen in the context of heating decarbonisation has been explored in the past, a comprehensive comparison of electricity- and hydrogen-driven domestic heating options is lacking in literature. In this paper, a thermodynamic and economic methodology is developed to assess the competitiveness of a domestic-scale ammonia-water absorption heat pump driven by heat from a hydrogen boiler compared to a standalone hydrogen boiler, a classic vapour-compression heat pump and district heating, all from a homeowner’s perspective. Using a previously developed electric heat pump model, the different systems are compared for various climate conditions and fuel-price scenarios under a unified framework. The coefficient of performance of the absorption heat pump system under design conditions and the total system cost are found to be 1.4 and £5400, respectively. Comparing the annualised total costs of the options under consideration, it is shown that, assuming the future price of hydrogen for domestic end-users can be below 0.12 £/kWh, absorption heat pumps and hydrogen boilers can become competitive domestic heating technologies, and otherwise, electrification and the use of vapour-compression heat pump will be preferred

Investigation of the 3-Level Unified Power Flow Controller (UPFC)

Flexible AC Transmission Systems (FACTS) are systems based on power electronics using GTO or IGCT semiconductors [1] that allow a better use of the transfer capacities of the transmission lines and permit to satisfy the requirements due to the liberalization of the electrical energy market. The Unified Power Flow Controller (UPFC) is one of the most versatile topologies of the FACTS family. The UPFC can be decomposed in two different power circuits: the parallel one maintaining the network bus voltage by consuming or producing reactive power, and the series one controlling the active and reactive power flow with the insertion of a series voltage in the transmission line. The aim of the present paper is to investigate in details the power quality and the dynamic performance of the 3-level UPFC

Bioaccumulation of perfluoroalkyl compounds in midge (Chironomus riparius) larvae exposed to sediment

Midge larvae (Chironomus riparius) were exposed to sediments from a deposition sampled at a site along the Rhône River (France) downstream of an industrial site releasing various perfluorinated chemicals. This sediment is characterized by high concentrations of perfluoroundecanoic acid (PFUnA) and perfluorotridecanoic acid (PFTrDA) and a low perfluorooctane sulfonate (PFOS) concentration. Concentrations of 23 perfluoroalkyl compounds, including C4eC14 carboxylate acids, C4eC10 sulfonates, and seven precursors, were analyzed in overlying and pore water, sediment, and larvae. Midge larvae accumulated carboxylate acids (C11eC14), PFOS, and two precursors (perfluorooctane sulfonamide: FOSA and 6:2 fluorotelomer sulfonic acid, 6:2 FTSA). These substances accumulated mainly during the fourth instar larvae exponential growth phase. Accumulation of 6:2 FTSA, PFUnA, and PFOS occured via trophic and tegumentary routes. Other compounds mainly accumulated from food. Kinetics followed a partition model, from which uptake and elimination constants were derived

Modeling of Multi-winding Phase Shifting Transformers : Application to DC and Multi-level VSI Supplies.

This paper deals with the modeling of multi-winding transformers. Based on the real example of a railway DC - supply, a modeling methodology is presented as the original part of the paper. Afterwards two application examples of multi-winding phase shifting transformers (18 and 24 pulse) are described, these applications have been simulated using the SIMSEN simulation software package [1,2]. Harmonics analysis of simulation results (elimination of low order harmonics 5, 7, 11, 13) has validated the proposed methodology