678 research outputs found
Distinguishing f(R) theories from general relativity by gravitational lensing effect
The post-Newtonian formulation of a general class of f(R) theories is set up
to 3rd order approximation. It turns out that the information of a specific
form of f(R) gravity is encoded in the Yukawa potential, which is contained in
the perturbative expansion of the metric components. Although the Yukawa
potential is canceled in the 2nd order expression of the effective refraction
index of light, detailed analysis shows that the difference of the lensing
effect between the f(R) gravity and general relativity does appear at the 3rd
order when is larger than the distance to the
gravitational source. However, the difference between these two kinds of
theories will disappear in the axially symmetric spacetime region. Therefore
only in very rare case the f(R) theories are distinguishable from general
relativity by gravitational lensing effect at the 3rd order post-Newtonian
approximation.Comment: 14 page
A regenerative supercritical-subcritical dual-loop organic Rankine cycle system for energy recovery from the waste heat of internal combustion engines
Organic Rankine cycle (ORC) system is considered as a promising technology for energy recovery from the waste heat rejected by internal combustion (IC) engines. However, such waste heat is normally contained in both coolant and exhaust gases at quite different temperatures. A single ORC system is usually unable to efficiently recover energy from both of these waste heat sources. A dual loop ORC system which essentially has two cascaded ORCs to recover energy from the engine’s exhaust gases and coolant separately has been proposed to address this challenge. In this way, the overall efficiency of energy recovery can be substantially improved. This paper examines a regenerative dual loop ORC system using a pair of environmentally friendly refrigerants, R1233zd and R1234yf, as working fluids, to recover energy from the waste heat of a compressed natural gas (CNG) engine. Unlike most previous studies focusing on the ORC system only, the present research analyses the ORC system and CNG engine together as an integrated system. As such, the ORC system is analysed on the basis of real data of waste heat sources of the CNG engine under various operational conditions. A numerical model is employed to analyse the performances of the proposed dual loop cycle with four pairs of working fluids. The effects of a regenerative heat exchanger and several other key operating parameters are also analysed and discussed in detail. The performance of the integrated engine-ORC system is then analysed under actual engine operating conditions which were measured beforehand. The performance of the proposed system under off-design conditions has also been analysed. The obtained results show that the proposed dual loop ORC system could achieve better performance than other ORC systems for similar applications
Parametric optimization and heat transfer analysis of a dual loop ORC (organic Rankine cycle) system for CNG engine waste heat recovery
In this study, a dual loop ORC (organic Rankine cycle) system is adopted to recover exhaust energy, waste heat from the coolant system, and intercooler heat rejection of a six-cylinder CNG (compressed natural gas) engine. The thermodynamic, heat transfer, and optimization models for the dual loop ORC system are established. On the basis of the waste heat characteristics of the CNG engine over the whole operating range, a GA (genetic algorithm) is used to solve the Pareto solution for the thermodynamic and heat transfer performances to maximize net power output and minimize heat transfer area. Combined with optimization results, the optimal parameter regions of the dual loop ORC system are determined under various operating conditions. Then, the variation in the heat transfer area with the operating conditions of the CNG engine is analyzed. The results show that the optimal evaporation pressure and superheat degree of the HT (high temperature) cycle are mainly influenced by the operating conditions of the CNG engine. The optimal evaporation pressure and superheat degree of the HT cycle over the whole operating range are within 2.5–2.9 MPa and 0.43–12.35 K, respectively. The optimal condensation temperature of the HT cycle, evaporation and condensation temperatures of the LT (low temperature) cycle, and exhaust temperature at the outlet of evaporator 1 are kept nearly constant under various operating conditions of the CNG engine. The thermal efficiency of the dual loop ORC system is within the range of 8.79%–10.17%. The dual loop ORC system achieves the maximum net power output of 23.62 kW under the engine rated condition. In addition, the operating conditions of the CNG engine and the operating parameters of the dual loop ORC system significantly influence the heat transfer areas for each heat exchanger
Throughput capacity of two-hop relay MANETs under finite buffers
Since the seminal work of Grossglauser and Tse [1], the two-hop relay
algorithm and its variants have been attractive for mobile ad hoc networks
(MANETs) due to their simplicity and efficiency. However, most literature
assumed an infinite buffer size for each node, which is obviously not
applicable to a realistic MANET. In this paper, we focus on the exact
throughput capacity study of two-hop relay MANETs under the practical finite
relay buffer scenario. The arrival process and departure process of the relay
queue are fully characterized, and an ergodic Markov chain-based framework is
also provided. With this framework, we obtain the limiting distribution of the
relay queue and derive the throughput capacity under any relay buffer size.
Extensive simulation results are provided to validate our theoretical framework
and explore the relationship among the throughput capacity, the relay buffer
size and the number of nodes
Thermodynamic analysis of a dual-loop organic Rankine cycle (ORC) for waste heat recovery of a petrol engine
Huge amounts of low-grade heat energy are discharged to the environment by vehicular engines. Considering the large number of vehicles in the world, such waste energy has a great impact on our environment globally. The Organic Rankine Cycle (ORC), which uses an organic fluid with a low boiling point as the working medium, is considered to be the most promising technology to recover energy from low-grade waste heat. In this study, a dual-loop ORC is presented to simultaneously recover energy from both the exhaust gases and the coolant of a petrol engine. A high-temperature (HT) ORC loop is used to recover heat from the exhaust gases, while a low-temperature (LT) ORC loop is used to recover heat from the coolant and the condensation heat of the HT loop. Figure 1 shows the schematic of the dual-loop ORC. Differing from previous research, two more environmentally friendly working fluids are used, and the corresponding optimisation is conducted. First, the system structure and operating principle are described. Then, a mathematical model of the designed dual-loop ORC is established. Next, the performance of the dual-loop cycle is analysed over the entire engine operating region. Furthermore, the states of each point along the cycle and the heat load of each component are compared with the results of previous research. The results show that the dual-loop ORC can effectively recover the waste heat from the petrol engine, and that the effective thermal efficiency can be improved by about 20 ~ 24%, 14~20%, and 30% in the high-speed, medium-speed, and low-speed operation regions, respectively. The designed dual-loop ORC can achieve a higher system efficiency than previous ORCs of this structure. Therefore, it is a good choice for waste heat recovery from vehicle engines
Research on the Quality Management Mechanism of Chinese Government Procurement of Public Services
The government meets the growing surge of social supply and demand through the purchase of public services from social organizations. Since the introduction of concept to China in the 1990s, government procurement of services has become one of the main ideas of public service reform; government procurement plays an increasingly important role in constructing a service-oriented government and improving public services. In recent years, China attaches a growing importance to the government procurement of public services and starts to reform the way, process and scope of government procurement of public services, and constantly improves quality control mechanisms of government procurement of public services. Based on a wide literature review of texts on the purchase of public services at home and abroad, this paper combines with the actual needs of our citizens and studies the quality control mechanisms of government procurement of public services
From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting
This paper investigates the performance of Large Language Models (LLMs) and
Tool-augmented LLMs in tackling complex mathematical reasoning tasks. We
introduce IMP-TIP: Improving Math Reasoning with Tool-augmented Interleaf
Prompting, a framework that combines the strengths of both LLMs and
Tool-augmented LLMs. IMP-TIP follows the ``From Good to Great" concept,
collecting multiple potential solutions from both LLMs and their Tool-Augmented
counterparts for the same math problem, and then selecting or re-generating the
most accurate answer after cross-checking these solutions via tool-augmented
interleaf prompting. The framework incorporates two key aspects: self-prompt
and tool-augmented interleaf prompting (TIP). The former allows LLMs to
autonomously refine and improve an initial prompt related to tool usage, while
the latter enables LLMs to derive the final answer by dynamically analyzing the
problem, cross-checking potential solutions, and revising previous reasoning
hints in an interleaved manner. Experimental analysis shows that IMP-TIP
achieves enhanced mathematical capabilities and outperforms traditional LLMs
and tool-augmented LLMs in accuracy and reasoning diversity on math reasoning
tasks. For instance, IMP-TIP can improve Tool-augmented ChatGPT on GSM8K-Hard
from 56.0% to 65.2%.Comment: 16 page
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