搜索结果：engine

Accurate prediction of engine-out NOx is essential for meeting stringent emissions regulations and optimizing engine performance. Traditional approaches rely on models trained on data from a small number of engines, which can be insufficient in generalizing across an entire population of engines due to sensor biases and variations in input conditions. In real world applications, these models require tuning or calibration to maintain acceptable error tolerance when applied to other engines. This highlights the need for models that can adapt with minimal adjustments to accommodate engine-to-engine variability and sensor discrepancies. While previous studies have explored machine learning methods for predicting engine-out NOx, these approaches often fail to generalize reliably across different engines and operating environments. To address these issues, we propose a Bayesian calibration framework that combines Gaussian processes (GP) with approximate Bayesian computation to infer and correct sensor biases. Starting with a pre-trained model developed using nominal engine data, our method identifies engine specific sensor biases and recalibrates predictions accordingly. By incorporating

A famous paper [Am. J. Phys. 43, 22 (1975)] unveiled the efficiency at maximum power (EMP) of the endo-reversible Carnot heat engine, now commonly referred to as the Curzon-Ahlborn (CA) engine, pioneering finite-time thermodynamics. Historically, despite the significance of the CA engine, similar findings had emerged at an earlier time, such as the Yvon engine proposed by J. Yvon in 1955 sharing the exact same EMP. However, the special setup of the Yvon engine has circumscribed its broader influence. This paper extends the Yvon engine model to achieve a level of generality comparable to that of the CA engine. A rigorous comparison reveals that the extended Yvon engine and CA engine represent the steady-state and cyclic forms of the endo-reversible Carnot heat engine, respectively, and are equivalent. Our work provides a pedagogical example for the teaching of thermodynamics and engineering thermodynamics, given that the simple and lucid derivation of the extended Yvon engine helps students initiate their understanding of non-equilibrium thermodynamics.

We report on the study of student difficulties regarding heat engine in the context of Stirling cycle within upper-division undergraduate thermal physics course. An in-class test about a Stirling engine with a regenerator was taken by three classes, and the students were asked to perform one of the most basic activities---calculate the efficiency of the heat engine. Our data suggest that quite a few students have not developed a robust conceptual understanding of basic engineering knowledge of the heat engine, including the function of the regenerator and the influence of piston movements on the heat and work involved in the engine. Most notably, although the science error ratios of the three classes were similar ($\sim$10\%), the engineering error ratios of the three classes were high (above 50\%), and the class that was given a simple tutorial of engineering knowledge of heat engine exhibited significantly smaller engineering error ratio by about 20\% than the other two classes. In addition, both the written answers and post-test interviews show that most of the students can only associate Carnot's theorem with Carnot cycle, but not with other reversible cycles working between tw

The Stirling engine is a type of heat engine known as its high efficiency. It is applied in solar thermal power, cogeneration, space nuclear power, and other fields. Although there are many different types of Stirling engines, their airflow paths are always linear. This article designs two types of Stirling engines with loop airflow path: the O-type engines without regenerator and the 8-type engines with regenerator. The modeling and simulation of the O-type engines show its extremely excellent performance compared with the conventional Stirling engine. Because the regenerator is the main loss and power limitation in Stirling engines, O-type engines do not have this limitation. At the same time, its design without regenerator makes it more practical and has greater potential in terms of power. The 8-type engines use its unique 8-type airflow path to allow gas to enter the regenerator in advance, eliminating the almost useless four heat exchanges, resulting in higher thermal efficiency and better robustness.

For heat engines working between two heat baths, functionality is often conditioned on a set of fixed constraints such as given internal structure of the engine and given temperatures for the baths. It is, however, important to devise heat engines which can function adaptively, in particular when the engine is a quantum system and the baths are subject to fluctuations. Here we study a model for an adaptive quantum heat engine whose heat baths can have variable temperatures. We obtain conditions under which such an engine can still operate. Moreover, we propose an enhancement of the heat engine by coupling it with an appropriate controller which changes the internal structure of the engine. Interestingly, we prove that this enhanced engine can always operate and we also obtain conditions for maximum power extraction from this engine for all temperatures of the heat baths.

This paper investigates the composition of search engine results pages. We define what elements the most popular web search engines use on their results pages (e.g., organic results, advertisements, shortcuts) and to which degree they are used for popular vs. rare queries. Therefore, we send 500 queries of both types to the major search engines Google, Yahoo, Live.com and Ask. We count how often the different elements are used by the individual engines. In total, our study is based on 42,758 elements. Findings include that search engines use quite different approaches to results pages composition and therefore, the user gets to see quite different results sets depending on the search engine and search query used. Organic results still play the major role in the results pages, but different shortcuts are of some importance, too. Regarding the frequency of certain host within the results sets, we find that all search engines show Wikipedia results quite often, while other hosts shown depend on the search engine used. Both Google and Yahoo prefer results from their own offerings (such as YouTube or Yahoo Answers). Since we used the .com interfaces of the search engines, results may no

On 4 January 2007 the author published the article Wireless Transfer of Electricity in Outer Space in http://arxiv.org wherein he offered and researched a new revolutionary method of transferring electric energy in space. In that same article, he offered a new engine which produces a large thrust without throwing away large amounts of reaction mass (unlike the conventional rocket engine). In the current article, the author develops the theory of this kind of impulse engine and computes a sample project which shows the big possibilities opened by this new AB-Space Engine. The AB-Space Engine gets the energy from ground-mounted power; a planet electric station can transfer electricity up to 1000 millions (and more) of kilometers by plasma wires. Author shows that AB-Space Engine can produce thrust of 10 tons (and more). That can accelerate a space ship to some thousands of kilometers/second. AB-Space Engine has a staggering specific impulse owing to the very small mass expended. The AB-Space Engine reacts not by expulsion of its own mass (unlike rocket engine) but against the mass of its planet of origin (located perhaps a thousand of millions of kilometers away) through the magnetic

The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness

This paper presents an application of Genetic Algorithm (GA) metaheuristics to optimise the design of two-spool turbofan engines based on exergy and energy theories. The GA is used to seek the optimal values of eight parameters that define the turbofan engine. These parameters are encoded as chromosomes (a metaphor) in GA. A computer program called TurboJet-Engine Optimizer v1.0 (TJEO-1.0) was developed by the authors to perform calculations of thermodynamic properties of the engine and implement the optimisations. The caloric properties of the working fluids are obtained by using an 8th-order polynomial fluid model taken from an existing literature. The turbo-mechanical components in the turbofan engine are as well assumed to exhibit polytropic efficiencies of today's technology level. The TJEO-1.0 is integrated with Pyevolve, an open source GA optimisation framework built for used with Python programming language. The optimal design created by TJEO-1.0 was evaluated with the following criteria: (1.) energy efficiency, (2.) exergy efficiency, and (3.) combination of both of them. The results suggest that the design of turbofan engine optimised based on the third criterion is able

Computational engine sound modeling is central to the automotive audio industry, particularly for active sound design, virtual prototyping, and emerging data-driven engine sound synthesis methods. These applications require large volumes of standardized, clean audio recordings with precisely time-aligned operating-state annotations: data that is difficult to obtain due to high costs, specialized measurement equipment requirements, and inevitable noise contamination. We present an analysis-driven framework for generating engine audio with sample-accurate control annotations. The method extracts harmonic structures from real recordings through pitch-adaptive spectral analysis, which then drive an extended parametric harmonic-plus-noise synthesizer. With this framework, we generate the Procedural Engine Sounds Dataset (19 hours, 5,935 files), a set of engine audio signals with sample-accurate RPM and torque annotations, spanning a wide range of operating conditions, signal complexities, and harmonic profiles. Comparison against real recordings validates that the synthesized data preserves characteristic harmonic structures, and baseline experiments confirm its suitability for learning

While the radiation mechanism of fast radio bursts (FRBs) is unknown, coherent curvature radiation and synchrotron maser are promising candidates. We find that both radiation mechanisms work for a neutron star (NS) central engine with $B\gtrsim 10^{12}$ G, while for the synchrotron maser, the central engine can also be an accreting black hole (BH) with $B\gtrsim 10^{12}$ G and a white dwarf (WD) with $B\sim 10^8-10^9$ G. We study the electromagnetic counterparts associated with such central engines, i.e., nebulae for repeating FRBs and afterglows for non-repeating FRBs. In general, the energy spectrum and flux density of the counterpart depend strongly on its size and total injected energy. We apply the calculation to the nebula of FRB 121102 and find that the persistent radio counterpart requires the average energy injection rate into the nebula to be between $2.7\times10^{39}~{\rm erg/s}$ and $1.5\times10^{44}~{\rm erg/s}$, and the minimum injected energy be $6.0\times10^{47}~{\rm erg}$ in around $7$ yr. Consequently, we find that for FRB 121102 and its nebula: (1) WD and accretion BH central engines are disfavored; (2) a rotation-powered NS central engine works when $1.2\times10

It is shown that in theories of gravity where the cosmological constant is considered a thermodynamic variable, it is natural to use black holes as heat engines. Two examples are presented in detail using AdS charged black holes as the working substance. We notice that for static black holes, the maximally efficient traditional Carnot engine is also a Stirling engine. The case of negative cosmological constant supplies a natural realization of these engines in terms of the field theory description of the fluids to which they are holographically dual. We first propose a precise picture of how the traditional thermodynamic dictionary of holography is extended when the cosmological constant is dynamical and then conjecture that the engine cycles can be performed by using renormalization group flow. We speculate about the existence of a natural dual field theory counterpart to the gravitational thermodynamic volume.

The Fluidyne is a two-part hot-air engine, which has the peculiarity that both its power piston and displacer are liquids. Both parts operate in tandem with the common working gas (air) transferring energy from the displacer to the piston side, from which work is extracted. We describe analytically the thermodynamics of the Fluidyne engine using the approach previously developed for the Stirling engine. We obtain explicit expressions for the amplitude of the power piston movement and for the working gas temperatures and pressure as functions of the engine parameters. We also study numerically the power and efficiency of the engine in terms of the phase shift between the motions of piston and displacer.

A cyclic thermodynamic heat engine runs most efficiently if it is reversible. Carnot constructed such a reversible heat engine by combining adiabatic and isothermal processes for a system containing an ideal gas. Here, we present an example of a cyclic engine based on a single quantum-mechanical particle confined to a potential well. The efficiency of this engine is shown to equal the Carnot efficiency because quantum dynamics is reversible. The quantum heat engine has a cycle consisting of adiabatic and isothermal quantum processes that are close analogues of the corresponding classical processes.

In todays era with the rapid growth of information on the web, makes users turn to search engines as a replacement of traditional media. This makes sorting of particular information through billions of webpages and displaying the relevant data makes the task tough for the search engine. Remedy for this is SEO i.e. having a website optimized in such a way that it will display the relevant webpages based on ranking. This is the main reason that makes search engine optimization a prominent position in online world. This paper present a synonym based data mining approach for SEO that makes the task of improving the ranking of the website much easier way and user will get answer to their query easily through any of search engine available in market.

The Discovery Engine is a general purpose automated system for scientific discovery, which combines machine learning with state-of-the-art ML interpretability to enable rapid and robust scientific insight across diverse datasets. In this paper, we benchmark the Discovery Engine against five recent peer-reviewed scientific publications applying machine learning across medicine, materials science, social science, and environmental science. In each case, the Discovery Engine matches or exceeds prior predictive performance while also generating deeper, more actionable insights through rich interpretability artefacts. These results demonstrate its potential as a new standard for automated, interpretable scientific modelling that enables complex knowledge discovery from data.

The stringent regulatory requirements on nitrogen oxides (NOx) emissions from diesel compression ignition engines require accurate and reliable models for real time monitoring and diagnostics. Although traditional methods such as physical sensors and virtual engine control module (ECM) sensors provide essential data, they are only used for estimation. Ubiquitous literature primarily focuses on deterministic models with little emphasis on capturing the various uncertainties. The lack of probabilistic frameworks restricts the applicability of these models for robust diagnostics. The objective of this paper is to develop and validate a probabilistic model to predict engine-out NOx emissions using Gaussian process regression. Our approach is as follows. We employ three variants of Gaussian process models: the first with a standard radial basis function kernel with input window, the second incorporating a deep kernel using convolutional neural networks to capture temporal dependencies, and the third enriching the deep kernel with a causal graph derived via graph convolutional networks. The causal graph embeds physics knowledge into the learning process. All models are compared against a

Inspired by the available examples of Microcanonical Szilárd Engines and by the original Szilárd Engine, we devise a system with two degrees of freedom whose ensemble average energy, starting with a microcanical ensemble, decreases after a cyclic variation of its external parameters. We use the Ergodic Adiabatic Theorem to motivate our cycle and numerical simulations to check the decrement in the average energy. We then compare our system to the aforementioned Szilárd Engines, Microcanonical or not, and speculate about symmetry breaking being the cause of energy extraction in cyclic processes, even when non-integrability and chaos are present.

Cyber Security is a critical topic for organizations with IT/OT networks as they are always susceptible to attack, whether insider or outsider. Since the cyber landscape is an ever-evolving scenario, one must keep upgrading its security systems to enhance the security of the infrastructure. Tools like Security Information and Event Management (SIEM), Endpoint Detection and Response (EDR), Threat Intelligence Platform (TIP), Information Technology Service Management (ITSM), along with other defensive techniques like Intrusion Detection System (IDS), Intrusion Protection System (IPS), and many others enhance the cyber security posture of the infrastructure. However, the proposed protection mechanisms have their limitations, they are insufficient to ensure security, and the attacker penetrates the network. Deception technology, along with Honeypots, provides a false sense of vulnerability in the target systems to the attackers. The attacker deceived reveals threat intel about their modus operandi. We have developed a Security Orchestration, Automation, and Response (SOAR) Engine that dynamically deploys custom honeypots inside the internal network infrastructure based on the attacker'

I summarize the main characteristics of AGNs in nearby galaxies and present a physical picture of their central engines.