scispace - formally typeset
Search or ask a question

What is the heat transfer coefficient of liquid nitrogen? 


Best insight from top research papers

The heat transfer coefficient of liquid nitrogen varies depending on the specific conditions and applications. Research has shown that the convective heat transfer coefficient of liquid nitrogen during cryogenic cooling can be determined through a combination of temperature simulation and experiments . Additionally, studies on liquid nitrogen spray cooling have indicated that the maximum heat transfer coefficient achieved was 16.67 W·cm^-2·K^-1, with a critical heat flux of 191.90 W·cm^-2 . Furthermore, in the context of cooling cryovials filled with cryoprotective agent solution in liquid nitrogen, different heat transfer coefficients were observed, ranging from 5 W/(m^2·K) to 245 W/(m^2·K) depending on the heat transfer regime, such as film boiling and nucleate boiling .

Answers from top 5 papers

More filters
Papers (5)Insight
The heat transfer coefficient of liquid nitrogen at the outer surface of a cryovial was determined to be 5 W/(m(2)·K) during film boiling and 245 W/(m(2)·K) during nucleate boiling.
The heat transfer coefficient of liquid nitrogen ranges from 90 to 200 K, with values obtained experimentally for low-velocity flow conditions in a heat exchanger.
The heat transfer coefficient of liquid nitrogen was found to be 16.67 W·cm−2·K−1 in the study on liquid nitrogen spray cooling with specific spray height and heat sink surface diameter.
The paper determines the convection heat transfer coefficient of Ti-5553 in liquid nitrogen cryogenic cutting conditions through temperature simulation and experiments.
The paper does not directly address the heat transfer coefficient of liquid nitrogen.

Related Questions

How does machine learning helps in heat transfer applications?5 answersMachine learning (ML) plays a crucial role in enhancing heat transfer applications by providing efficient analysis and prediction capabilities. ML techniques are utilized to analyze vast amounts of data collected from experiments, field observations, and simulations in the heat transfer field. These techniques offer a more computationally efficient approach compared to traditional methods like Finite Element Analysis (FEA) for thermal response prediction in structural fire engineering applications. Moreover, ML algorithms are employed to develop data-driven surrogate models for predicting the two-phase heat transfer coefficient (HTC) in compact heat exchangers, outperforming traditional empirical correlations. ML also aids in building reduced-order models, optimizing thermal systems, and predicting heat transfer coefficients in various heat transfer applications, showcasing its versatility and effectiveness in this domain.
How is the heat transfer coefficient experimentally modelled or determined?5 answersThe heat transfer coefficient is determined experimentally using various methods. One common approach is to measure the temperature distribution on the surface of the object and use it to calculate the heat transfer coefficient. This can be done by placing thermocouples on the surface and measuring the temperatures. Another method involves heating the object to a known temperature and then inducing a thermal transient by injecting ambient air into its internal cooling system. The external surface temperature is recorded using an infrared camera, and this data is used in a numerical procedure to update the heat flux on the internal surface and calculate the convective heat transfer coefficient. In some cases, the heat transfer coefficient cannot be directly measured, so inverse methods are used. These methods involve assuming an unknown boundary condition and using additional information from sensors located at a certain distance from the surface to calculate the heat transfer coefficient. Additionally, correlations for the heat transfer coefficient can be determined by simultaneously solving for the coefficients in Nusselt number correlations using experimental data.
What is the formula for specific heat?4 answersThe formula for specific heat is given by the equation C = lim(dQ/dT), where C is the specific heat, dQ is the heat input, and dT is the change in temperature. Another formula for specific heat is derived for the non-equilibrium steady state, where it can be calculated by analyzing fluctuations in the steady state or by relating it to the transient response observed when an ensemble of equilibrium systems is perturbed. In the context of high pressure measurements, the specific heat is obtained using the formula cp = C(T,P)U I / m ė, where cp is the specific heat, C(T,P) is a function determined experimentally, U is the voltage drop, I is the current, m is the mass of the sample, and ė is the time derivative of the bridge output voltage. For a free particle in a cubic box with reflecting walls, the specific heat is a function of variable η and approaches different values depending on the value of η. In the study of a one-dimensional phonon system, the specific heat is calculated using the path integral method applied to the expression for the partition function.
How fat affect beef thermal conductivity during cooling?3 answersFat content has a significant effect on beef thermal conductivity during cooling. The abstracts indicate that the thermal conductivity of beef decreases with increasing fat content. In the study by Baghe-Khandan et al., it was found that there is a correlation between beef thermal conductivity and fat content. Additionally, the abstract by Sun and McDonald states that vacuum cooled beef samples, which had higher moisture loss, had lower thermal conductivity compared to other cooling methods. Therefore, it can be concluded that higher fat content in beef leads to lower thermal conductivity during cooling.
What is the heat transfer coefficient of li-po battery?5 answersThe heat transfer coefficient of a lithium polymer (Li-po) battery can be determined using various methods. One approach is to use an entropic coefficient based on the inverse heat transfer problem. Another method involves using nanofluids, such as tetrahydrofuran-graphene nanofluid, to improve the thermal conductivity and reduce thermal resistance. Additionally, the use of phase change materials (PCMs), such as expanded graphite PCM, can enhance the heat transfer rate in Li-po batteries. Experimental data and simulations have shown that the temperature and heat produced by Li-ion batteries increase with higher C-rates, with the highest temperatures observed near the tabs and in the internal space of the battery. However, the specific heat transfer coefficient of a Li-po battery is not explicitly mentioned in the provided abstracts.
How does the heat transfer coefficient affect the thermal performance ratio?5 answersThe heat transfer coefficient has a significant impact on the thermal performance ratio. Experimental research conducted by Sundar et al.showed that using hybrid nanofluids with higher heat transfer coefficients in a heat pipe resulted in lower wall temperatures at the evaporator and condenser sections, leading to improved thermal performance. Additionally, the study found that the heat transfer coefficients of the evaporator and condenser increased when using hybrid nanoparticles based nanorefrigerants compared to the base fluid. Another study by Houanalyzed the effects of heat transfer on the net work output and indicated thermal efficiency of an air standard Dual cycle. The results showed that higher heat transfer to the combustion chamber walls reduced the peak temperature and pressure, resulting in lower work per cycle and efficiency. Therefore, a higher heat transfer coefficient generally leads to improved thermal performance, while a lower heat transfer coefficient can negatively impact the performance.

See what other people are reading

What does Roberta's Tokenizer base on?
5 answers
RoBERTa's tokenizer is based on identifying individual tokens or words within a token string and generating a language model using these tokens. Additionally, the tokenizer may utilize a weighted vocabulary related to entities like persons or companies, where some tokens are given higher weights. This weighted vocabulary is used as input for the language model to generate pseudo sentences, which are then employed to train the tokenizer. Furthermore, the performance of RoBERTa models, along with other models like ALBERT and ELECTRA, has been evaluated for tasks such as classifying token replacements accurately, supporting the development of question and answer systems in Indonesian language models. The study also highlights the importance of specialized monolingual tokenizers in improving downstream performance of multilingual models like RoBERTa for various languages and tasks.
What is the most common configuration of transformer for medium-voltage distribution system?why?delta/star/ygyg?
5 answers
The most common configuration of transformer for medium-voltage distribution systems is the delta-star (DY11) configuration. This configuration allows for voltage transformation between medium and low voltage levels efficiently. The delta-star configuration provides flexibility in adapting to various voltage inputs and outputs, making it suitable for different grid voltage requirements. Additionally, the star connection on the secondary side enables the transformer to achieve two kinds of voltage output, enhancing its versatility in distribution applications. Overall, the delta-star configuration is widely used due to its ability to handle different voltage levels effectively and its applicability to diverse grid voltage standards, making it a common choice for medium-voltage distribution systems.
How is generative AI being utilized in the field of genomics?
5 answers
Generative AI is revolutionizing the field of genomics through a variety of innovative applications, demonstrating its potential to address complex challenges and unlock new opportunities in genetic research. One of the primary uses of generative AI in genomics is the creation of artificial genomes (AGs) that closely mimic the characteristics of real genomes, including population structure, linkage disequilibrium, and selection signals. This is achieved through advanced models like generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs), which can generate high-quality AGs with high single nucleotide polymorphism (SNP) numbers, preserving genetic privacy without apparent data leakage from the training dataset. These AGs can serve as surrogates for real genomic databases, facilitating research within a safe ethical framework. Deep generative models (DGMs) are also employed for dimensionality reduction, mapping complex genomic data to a latent space, which aids in data visualization and analysis, and for predictive tasks in functional and evolutionary genomics. Furthermore, generative models are instrumental in generating synthetic gene expression data, offering solutions to ethical and logistical constraints in data collection, thereby enhancing the diversity and size of gene expression datasets. In addition to data generation and dimensionality reduction, generative AI is applied in knowledge mining from synthetic biology literature, where tools like GPT-4 automate the extraction of information, facilitating machine learning predictions in microbial performance and biomanufacturing. Moreover, generative models are being explored for simulating SNP sequences, addressing privacy concerns, and reducing bias in genomic datasets. Generative AI's role extends to evaluating uncertainty and deriving insights from genomic data, showcasing its versatility in supervised and unsupervised learning tasks, and out-of-sample generation, which is pivotal for designing molecules and understanding transcriptional variability. Collectively, these applications underscore generative AI's transformative impact on genomics, offering novel solutions for data generation, analysis, and privacy preservation.
What is fault analysis in electrical engineering?
5 answers
Fault analysis in electrical engineering is a crucial process that involves identifying and understanding faults within power systems to ensure grid stability and equipment protection. It plays a vital role in maintaining the consistency and reliability of power grids. Building electrical systems, which encompass various subsystems like lighting and elevators, require fault diagnosis technologies to enhance reliability and stability. An analysis system for electrical equipment faults integrates modules for data acquisition, analysis, and monitoring to improve equipment reliability, prevent damage, and enhance safety. For series circuits, fault diagnosis algorithms utilizing simulation and mathematical models help estimate parameters like resistance, inductance, and capacitance, aiding in identifying circuit faults accurately. Additionally, considering environmental factors like earthquakes is essential in designing electrical equipment to prevent short-circuit faults and ensure operational safety.
What is the effectiveness of ChatGPT in improving speaking skills compared to traditional methods?
5 answers
ChatGPT has shown effectiveness in improving speaking skills compared to traditional methods. Studies have highlighted ChatGPT's ability to serve as a speaking partner for language learners, enhancing their language skills. Additionally, user preferences favor ChatGPT-powered conversational interfaces over traditional techniques, with 70% of users choosing ChatGPT for its convenience, efficiency, and personalization. Despite its strengths, ChatGPT is noted to lack the same level of understanding, empathy, and creativity as humans, suggesting that it cannot fully replace human interaction in most situations. Overall, ChatGPT's integration of NLP technologies and its autonomous generation of natural language conversations make it a valuable tool for improving speaking skills when compared to traditional methods.
What surveys/reviews talked about MSW-Transformer models for ECG classification ?
4 answers
Surveys and reviews discussing MSW-Transformer models for ECG classification include works by Ren-Wei Cheng et al., Zibin Zhao, and Jorge Adrihan do N de Moraes. These sources highlight the significance of transformer architectures in enhancing ECG analysis, particularly in capturing complex temporal relationships within ECG signals. The MSW-Transformer model, proposed by Ren-Wei Cheng et al., utilizes a multi-window sliding attention mechanism at different scales to extract features in various dimensions, achieving state-of-the-art performance on diverse ECG classification tasks. Zhao and Moraes further emphasize the potential of transformer-based models for real-time and accurate ECG diagnosis, underscoring the need for robust architectures to handle the increasing complexity of ECG data and improve classification accuracy.
What is evaporation?
5 answers
Evaporation is the process of converting liquid water into vapor. It involves the removal of water from a solution by boiling the liquid in an evaporator and withdrawing the vapor, which can lead to the formation of crystals if the solution contains dissolved solids. This method is commonly used for concentrating aqueous solutions and has applications in various industries, including the pharmaceutical sector. Evaporation is a relatively expensive operation primarily used for recovering valuable byproducts from waste liquids and treating certain types of waste where alternative methods are not available. It plays a crucial role in processes like desalination, where heat-induced vaporization of the solvent, usually water, helps in concentrating solutions of nonvolatile solutes.
What is numerical reasoning?
4 answers
Numerical reasoning involves utilizing arithmetic operations like addition, subtraction, sorting, and counting within machine reading comprehension tasks. It is a complex subtask that requires models to perform calculations and statistics to reason out answers effectively. In the financial domain, numerical reasoning plays a crucial role in analyzing quantitative data from financial reports, enhancing business efficiency, and reducing costs significantly. Various model architectures have been developed to tackle numerical reasoning tasks, with some achieving near-human performance on benchmarks like the DROP dataset. These models often involve components like retriever modules, generator modules, and ensemble modules to process and answer numerical reasoning questions accurately.
What is the mindset of homecooks when cooking a family recipe?
5 answers
Homecooks, particularly women, exhibit a diverse mindset when cooking family recipes. Womenpreneurs in the culinary industry showcase elements like passion, self-leadership habits, creativity habits, improvisation habits, and self-efficacy, driven by a love for cooking and a desire to innovate in their businesses. Traditional gender roles in cooking highlight women's historical responsibility for family food preparation, with men increasingly participating but often focusing on more appealing aspects, leading to potential power conflicts in the kitchen. Additionally, the use of technology and social media in cooking practices among low-income domestic cooks in Morocco demonstrates how kitchen appliances and online platforms are utilized to maintain elaborate family meals, emphasizing the importance of culinary connectivity in inter-generational negotiations of cooking knowledge and power dynamics within families.
What is the recommended shelf life for shea nut cake?
5 answers
The recommended shelf life for shea nut cake can vary depending on its processing and storage conditions. Studies have shown that shea nut cake can be successfully composted with caprine manure for six months, resulting in stable and mature compost that can contribute to soil organic matter and provide essential fertilizing elements. On the other hand, the physicochemical characteristics and microbiological quality of bakery shortening formulated with shea stearin and fluted pumpkin seed oil blends remained stable throughout a 90-day storage period at room temperature, indicating good storage stability. These findings suggest that shea nut cake can have a shelf life of at least six months when composted and potentially up to 90 days when used in bakery formulations.
What are the current advancements in the Pointcloud Machine Learning field?
5 answers
Current advancements in Pointcloud Machine Learning include innovative approaches like PointGPT, which extends the GPT concept to point clouds, achieving state-of-the-art performance on various tasks. Additionally, PointNeXt has shown significant improvements by incorporating neighborhood point features and implementing weight averaging strategies, enhancing classification accuracies on real-world datasets. Furthermore, PointStack introduces multi-resolution feature learning and learnable pooling to extract high-semantic point features effectively, enabling the representation of both global and local contexts of point clouds while comprehending their structure and shape details. These advancements address challenges related to disorder properties, low information density, and task gaps, pushing the boundaries of feature learning and classification accuracy in the Pointcloud Machine Learning domain.