Hasil untuk "Low temperature engineering. Cryogenic engineering. Refrigeration"

A. Inoue

G. Ni, G. Ni, A. Mcleod et al.

Aldeida Aleti, Baishakhi Ray, Rashina Hoda et al.

With the rapid rise of AI coding agents, the fundamental premise of what it means to be a software engineer is in question. In this vision paper, we re-examine what it means for an AI agent to be considered a software engineer and then critically think about what makes such an agent trustworthy. \textit{Grounded} in established definitions of software engineering (SE) and informed by recent research on agentic AI systems, we conceptualise AI software engineers as participants in human-AI SE teams composed of human software engineers and AI models and tools, and we distinguish trustworthiness as a key property of these systems and actors rather than a subjective human attitude. Based on historical perspectives and emerging visions, we identify key dimensions that contribute to the trustworthiness of AI software engineers, spanning technical quality, transparency and accountability, epistemic humility, and societal and ethical alignment. We further discuss how trustworthiness can be evaluated and demonstrated, highlighting a fundamental trust measurement gap: not everything that matters for trust can be easily measured. Finally, we outline implications for the design, evaluation, and governance of AI SE systems, advocating for an ethics-by-design approach to enable appropriate trust in future human-AI SE teams.

陈翀, 刘克函, 史波

In order to solve the problem that it is difficult for existing thermal management schemes to actively and efficiently create low temperature heat sink in limited enclosed space, a composite thermal management system based on flat-plate heat pipes is proposed in this study. The numerical simulation model of the composite system is established, and the experimental platform of the composite thermal management system is set up to verify the accuracy of the model. The results show that the proposed composite thermal management system provides low temperature heat sink for the whole thermal management system in a limited space, and solves the phenomenon of high heat flow heat accumulation at the hot end of the thermoelectric refrigeration unit by coupling the plate heat pipe, thus reducing the temperature at the hot end of the thermoelectric refrigeration unit and the temperature difference between the cold and hot ends, and further improving the cooling capacity and COP. The thermoelectric refrigeration system based on flat-plate heat pipe is much better than the thermoelectric refrigeration system based on aluminum fins in the overall working current, and the cooling capacity and COP of a single cooling plate are effectively increased by 38.35% and 14.81% under the best working condition.

Ilgar Aliyev

The reconstruction, management, and optimization of gas pipelines is of significant importance for solving modern engineering problems. This paper presents innovative methodologies aimed at the effective reconstruction of gas pipelines under unstable conditions. The research encompasses the application of machine learning and optimization algorithms, targeting the enhancement of system reliability and the optimization of interventions during emergencies. The findings of the study present engineering solutions aimed at addressing the challenges in real-world applications by comparing the performance of various algorithms. Consequently, this work contributes to the advancement of cutting-edge approaches in the field of engineering and opens new perspectives for future research. A highly reliable and efficient technological Figure has been proposed for managing emergency processes in gas transportation based on the principles of the reconstruction phase. For complex gas pipeline systems, new approaches have been investigated for the modernization of existing control process monitoring systems. These approaches are based on modern achievements in control theory and information technology, aiming to select emergency and technological modes. One of the pressing issues is to develop a method to minimize the transmission time of measured and controlled data on non-stationary flow parameters of gas networks to dispatcher control centers. Therefore, the reporting Figures obtained for creating a reliable information base for dispatcher centers using modern methods to efficiently manage the gas dynamic processes of non-stationary modes are of particular importance.

Cheng-Long Zhou, Yu-Chen Peng, Yong Zhang et al.

We explore a novel approach to achieving anisotropic thermal photon tunneling, inspired by the concept of parity-time symmetry in quantum physics. Our method leverages the modulation of constitutive optical parameters, oscillating between loss and gain regimes. This modulation reveals a variety of distinct effects in thermal photon behavior and dispersion. Specifically, we identify complex tunneling modes through gain-loss engineering, which include thermal photonic defect states and Fermi-arc-like phenomena, which surpass those achievable through traditional polariton engineering. Our research also elucidates the laws governing the evolution of radiative energy in the presence of gain and loss interactions, and highlights the unexpected inefficacy of gain in enhancing thermal photon energy transport compared to systems characterized solely by loss. This study not only broadens our understanding of thermal photon tunneling but also establishes a versatile platform for manipulating photon energy transport, with potential applications in thermal management, heat science, and the development of advanced energy devices.

F. Shahzad, W. Jamshed, S. Devi. S et al.

It is well known that solar energy is the main source of thermal energy coming from the sun responsible for huge operations in engineering studies. It can be seen in the technology of photovoltaic cells, solar streetlights, solar energy plates, and solar water pumping. This study is for investigating solar radiation as well as a method to improve the performance of the solar water pump (SWP) with the use of solar radiation along with nanotechnology. The thermal transfer performance of the pump is checked for the case of many impacts including heat radiations and variable thermal conductivity. An Oldroyd‐B nanofluid with entropy production analysis has been scrutinized as a working coolant liquid in the system. Graphene oxide (GO) and Cu nanoparticles have been employed in engine oil (EO) as the base liquid. It is noticed that the heat transfer performance of SWP increases in the case of amplification in thermal radiation and temperature‐dependent thermal conductivity characteristics. In comparison to low thermal conductivity nanofluid, high thermal conductivity nanofluid provides the best capability for heat transmission. The thermal efficiency of the used (GO/EG) nanofluid has been enhanced by between 7.70% and 26.68% than (Cu/EO) nanofluid.

Nagarjuna Maguluri, G. Suresh, S. R. Guntur

Fused deposition modeling (FDM) is an extensively used method for additive manufacturing of thermoplastic materials. It is growing in a variety of engineering applications because of its ability to produce complicated structural designs with low manufacturing time. However, the mechanical characteristics of the 3D printed components are extremely dependent on the proper selection of the printing conditions. In this present work, the impact of three foremost printing parameters, including fill density, extrusion temperature, and printing speed, is examined on the hardness of poly-lactic acid (PLA) parts. Taguchi design of experiment (DOE) methodology is used to minimize the total experimental runs and evaluate the optimal printing parameters for the maximum hardness of the printed part. Analysis of S/N ratios is utilized for establishing the optimal printing parameters, and the corresponding percentage contribution of control factors is measured using ANOVA. The study results have shown that extrusion temperature profoundly influences the hardness of the 3D printed PLA specimens, while printing speeds have a much smaller impact on it.

Guang Hao Low, Yuan Su, Yu Tong et al.

Quantum dynamics can be simulated on a quantum computer by exponentiating elementary terms from the Hamiltonian in a sequential manner. However, such an implementation of Trotter steps has gate complexity depending on the total Hamiltonian term number, comparing unfavorably to algorithms using more advanced techniques. We develop methods to perform faster Trotter steps with complexity sublinear in the number of terms. We achieve this for a class of Hamiltonians whose interaction strength decays with distance according to power law. Our methods include one based on a recursive block encoding and one based on an average-cost simulation, overcoming the normalization-factor barrier of these advanced quantum simulation techniques. We also realize faster Trotter steps when certain blocks of Hamiltonian coefficients have low rank. Combining with a tighter error analysis, we show that it suffices to use $\left(η^{1/3}n^{1/3}+\frac{n^{2/3}}{η^{2/3}}\right)n^{1+o(1)}$ gates to simulate uniform electron gas with $n$ spin orbitals and $η$ electrons in second quantization in real space, asymptotically improving over the best previous work. We obtain an analogous result when the external potential of nuclei is introduced under the Born-Oppenheimer approximation. We prove a circuit lower bound when the Hamiltonian coefficients take a continuum range of values, showing that generic $n$-qubit $2$-local Hamiltonians with commuting terms require at least $Ω(n^2)$ gates to evolve with accuracy $ε=Ω(1/poly(n))$ for time $t=Ω(ε)$. Our proof is based on a gate-efficient reduction from the approximate synthesis of diagonal unitaries within the Hamming weight-$2$ subspace, which may be of independent interest. Our result thus suggests the use of Hamiltonian structural properties as both necessary and sufficient to implement Trotter steps with lower gate complexity.

Przemyslaw Wojciech Swatek, Xudong Hang, Yihong Fan et al.

In the course of searching for promising topological materials for applications in future topological electronics, we evaluated spin-orbit torques (SOTs) in high-quality sputtered $δ-$TaN/Co20Fe60B20 devices through spin-torque ferromagnetic resonance ST-FMR and spin pumping measurements. From the ST-FMR characterization we observed a significant linewidth modulation in the magnetic Co20Fe60B20 layer attributed to the charge-to-spin conversion generated from the $δ-$TaN layer. Remarkably, the spin-torque efficiency determined from ST-FMR and spin pumping measurements is as large as $Θ =$ 0.034 and 0.031, respectively. These values are over two times larger than for $α-$Ta, but almost five times lower than for $β-$Ta, which can be attributed to the low room temperature electrical resistivity $\sim 74μΩ$ cm in $δ-$TaN. A large spin diffusion length of at least $\sim8$ nm is estimated, which is comparable to the spin diffusion length in pure Ta. Comprehensive experimental analysis, together with density functional theory calculations, indicates that the origin of the pronounced SOT effect in $δ-$TaN can be mostly related to a significant contribution from the Berry curvature associated with the presence of a topically nontrivial electronic band structure in the vicinity of the Fermi level (EF). Through additional detailed theoretical analysis, we also found that an isostructural allotrope of the superconducting $δ-$TaN phase, the simple hexagonal structure, $θ-$TaN, has larger Berry curvature, and that, together with expected reasonable charge conductivity, it can also be a promising candidate for exploring a generation of spin-orbit torque magnetic random access memory as cheap, temperature stable, and highly efficient spin current sources.

Chenyang Yang, Rachel Brower-Sinning, Grace A. Lewis et al.

In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences. We envision a capability-based framework, which uses fine-grained specifications for ML model behaviors to unite existing efforts towards better ML engineering. We use concrete scenarios (model design, debugging, and maintenance) to articulate capabilities' broad applications across various different dimensions, and their impact on building safer, more generalizable and more trustworthy models that reflect human needs. Through preliminary experiments, we show capabilities' potential for reflecting model generalizability, which can provide guidance for ML engineering process. We discuss challenges and opportunities for capabilities' integration into ML engineering.

Guangxin Liao, Zhixiang Li, Congcong Luan et al.

C. Hoang, K. Chowdhary, Kookjin Lee et al.

This paper considers the creation of parametric surrogate models for applications in science and engineering where the goal is to predict high-dimensional spatiotemporal output quantities of interest, such as pressure, temperature and displacement fields. The proposed methodology develops a low-dimensional parametrization of these quantities of interest using space-time bases combining with machine learning methods. In particular, the space-time solutions are sought in a low-dimensional space-time linear trial subspace that can be obtained by computing tensor decompositions of usual state-snapshots data. The mapping between the input parameters and the basis expansion coefficients (or generalized coordinates) is approximated using four different machine learning techniques: multivariate polynomial regression, k-nearest-neighbors, random forest and neural network. The relative costs and effectiveness of the four machine learning techniques are explored through three engineering problems: steady heat conduction, unsteady heat conduction and unsteady advective-diffusive-reactive system. Numerical results demonstrate that the proposed method performs well in terms of both accuracy and computational cost, and highlight the important point that the amount of model training data available in an engineering setting is often much less than it is in other machine learning applications, making it essential to incorporate knowledge from physical models. In addition, simpler machine learning techniques are seen to perform better than more elaborate ones.

Yongping Huang, Qing Sun, F. Yao et al.

Abstract The present research conducts an experimental study on the thermal performance of the finned metal foam (FMF) heat sinks with phase change material. The dynamic temperature response of an FMF heat sink is analyzed and compared with the corresponding finned heat sink. The effects of porosity and pore density on the thermal performance of FMF heat sinks are analyzed. Furthermore, the enhancement ratio and heat exchange capability are evaluated for the optimization of FMF heat sinks. The results indicate that the thermal conduction enhancement caused by the addition of metal foam exceeds the heat transfer loss arising from the suppression of natural convection. A decrease in porosity leads to an increase in heat exchange capability and contributes to a higher enhancement ratio. Considering the tradeoff between the low operating temperature and longer duration of reliability, the porosity of 0.9 is the best choice for FMF heat sinks. Though the porosity is identical, the thermal performance of an FMF heat sink is better with the increase of pore density. Considering the maximum energy charging capacities are identical, the FMF heat sink with larger pore density is recommended for practical engineering applications.

Fuhai Wang, Tuo Huang, Gongfeng Xin et al.

As a new type of pavement material, bioasphalt has received more and more attention. However, the high-temperature behavior of bioasphalt is poor after blending with asphalt binder. In order to solve this problem and facilitate the waste utilization and resource conservation, the corn stalk bioasphalt/PPA composite modified asphalt was proposed. The conventional performance tests and rheological tests were conducted to evaluate high-temperature and low-temperature behavior. Fourier transform infrared reflection (FTIR) test was undertaken to analyze the mechanism of modified asphalt. The results indicated that blended asphalt penetration and ductility gradually decrease with the PPA content increasing. The softening point and viscosity of the modified asphalt increased, which led to an improvement of blended asphalt’s rigidity. The PPA increased the rutting index of corn stalk bioasphalt/PPA composite modified asphalt. However, bioasphalt had a negative effect on its high-temperature performance. The corn stalk bioasphalt/PPA composite modified asphalt could meet the specification requirement at −18°C considering the creep rate and stiffness modulus, indicating it had outstanding crack resistance. When the PPA and bioasphalt respect to the weight of neat asphalt were 6%–8% and 10%–16%, respectively, the corn stalk bioasphalt/PPA composite modified asphalt performance was optimal. However, shear time and shear rate merely affected the proposed modified asphalt performance. The bioasphalt did not affect the chemical structure of asphalt. However, PPA generated new functional groups (P-O single bond, phosphate (RO)3P = O, and P=O double bond) causing a chemical modification in the asphalt binder. This study can provide a basis for applying bioasphalt, making road engineering more economical and environmentally friendly.

V. Nguyen, T. Huynh, T. P. Nguyen et al.

This paper presents practice and application of Design of Experiment techniques and Genetic Algorithm in single and multi-objective optimization with low cost, robustness, and high effectiveness through 3D printing case studies. 3D printing brings many benefits for engineering design, product development, and production process. However, it faces many challenges related to parameters control. The wrong parameter setup can result in excessive time, high production cost, waste material, and low-quality printing. This study is conducted to optimize the parameter sets for 3D Fused Deposition Modelling (FDM) products. The parameter sets, i.e., layer height, infill percentage, printing temperature, printing speed with different levels are experimented and analyzed to build mathematic models. The objectives are to describe the relationship between the inputs (parameter values) and the outputs (printing quality in term of weight, printing time and tensile strength of products). Single-objective and multi-objective models according to user’s desire are constructed and studied to identify the optimal set, optimal trade-off set of parameters. Besides, an integrated method of response surface methodology and Genetic algorithm to deal with multi-objective optimization is discussed in the paper. 3D printer, testing machines, and quality tools are used for doing experiments, measurement and collecting data. Minitab and Matlab software aid for analysis and decision-making. Proposed solutions for handling multi-objective optimization through 3D Fused Deposition Modelling product printing case study are practical and can extend for other case studies.

Aditya Kuchibhotla, D. Banerjee, V. Dhir

Abstract Molten salts are used in a wide range of engineering platforms, with particular niche involving high-temperature thermo-fluidic applications. Molten salts are employed as industrial working fluids for several high temperature processing applications, including: as a catalytic medium for fuel production, for synthesis of exotic ceramic materials, and pyrolysis of waste products. Molten salts are of particular interest as materials for energy related applications, such as in the fields of Concentrated Solar Power (CSP), enhanced oil recovery, advanced nuclear reactors and nuclear fuel cycles. Industry wide applications include molten salt eutectics as materials for Thermal Energy Storage (TES) and as Heat Transfer Fluids (HTF), typically for enhancing the commercial grid-scale reliability of utilities that utilize CSP. Molten salts offer unique advantages such as low vapor pressure, extended operating temperature ranges, safe operation, minimal environmental footprint and moderate cost (compared to conventional heat transfer oils and ionic liquids). The scope of this literature review is limited to forced convective heat transfer data for a variety of molten salt formulations. The motivation of this study is guided by the promising role of molten salts in energy related applications, since the knowledge of the range of forced-convective heat transfer coefficients accruing from leveraging this class of working fluids are going to be crucial for determining (as well designing) the size and performance of the thermal devices. This review reveals that there is still a need for characterizing the heat transfer performance for various commercial molten salt eutectics and the available heat transfer data can be predicted reasonably well with available standard correlations. In addition, the goal of this review is to analyze the available literature data for the purpose of identifying the future research directions on this topic.

Y. Lim, Shi Lin, Y. Ju et al.

Abstract The purpose of this study was to evaluate the feasibility of lightweight aggregate (LWA) made from dredged harbor sediment and basic oxygen furnace (BOF) slag to increase their reutilization. The effects of preheating, sintering and raw materials proportion on the properties of LWA were discussed in term of particle density, water absorption, and compressive strength. Microscopic structure, water-soluble chloride and heavy metals leachability of the LWA were also examined. Results showed that the mixture of the dredged harbor sediment and BOF slag under the manufacturing process with preheating temperature at 500 °C for 10 min and sintering temperature at 1175 °C for 15 min was conducive to be a low water absorption and high strength LWA, which had as low as 1.73 g cm−3 and 3.45% for dry particle density and water absorption respectively. Its compressive strength reached 23.2 MPa when the addition of BOF slag was up to 27%. The LWA prepared in this study also have very low water-soluble chloride and heavy metals leachability, all compliant with Taiwan regulatory standards, thus would be suitable for further civil engineering application. The results of this study provide useful information on co-treating of wastes and recycled resources as LWA.

Nicholas B. Carrigy, Lu Liang, Hui Wang et al.

Wang Qianlong, Wang Yao, Qian Suxin et al.

Among all cold chain equipment, the energy consumption of an open refrigerated display case is usually high, especially the dual-temperature display cabinet, which provides simultaneous heating and cooling and consumes even more electricity. The condensing heat can be used for heating and therefore will lead to significant energy savings for the dual-temperature display cabinet. This paper simulates a dual-temperature display cabinet with condensing heat recovery. Employing COP and effective heating power as the indices, the optimization direction of each component in the refrigeration system is determined, and its performance is compared with the baseline cabinet with an electric heater. The simulation results of the baseline cabinet are validated by experimental results, and the energy-saving potential of the new system is studied. Simulation results suggest that the dual-temperature display cabinet with condensation heat recovery has significant energy-saving advantages. The key is the air volumetric flowrate of the condenser. It is recommended to install a variable-speed fan, which can produce optimal air volumes of 0.018 m3/s, 0.03 m3/s, 0.045 m3/s, and 0.095 m3/s at ambient temperatures of 15 ℃, 20 ℃, 25 ℃, and 32 °C, respectively. When the condenser air flow rate is kept at 0.06 m3/s, the cabinet has a decent energy-saving potential when the ambient temperature is high. The best energy savings is achieved when the ambient temperature is 25 ℃, with a reduction in power consumption of 52%.