They work 24/7 at high speeds and get searingly hot - but data centre computer chips get plenty of pampering. Some of them basically live at the spa. We'll have fluid that comes up and [then] shower down, or trickle down, onto a component, says Jonathan Ballon, chief executive at liquid cooling firm Iceotope. Some things will get sprayed. In other cases, the industrious gizmos recline in circulating baths of fluid, which ferries away the heat they generate, enabling them to function at very high speeds, known as overclocking.

They gathered together on a sunny July evening, between the churchyard's trees and leaning tombstones, to give thanks for the heat pump. Facing the newly installed system, in its large green metal box, they sang hymns and said prayers. "To thank God, really, for being able to work His wonders in mysterious ways," says Karen Crowhurst, who is part of a committee that helps to run St. The previous month, a flatbed truck carrying a hefty new heat pump system had eased itself onto the church grounds. By late July, the device was fully installed, and soon followed an outdoor thanksgiving service .

As current group contribution (GC) methods are mostly proposed for a wide size-range of molecules, applying them to property prediction of small refrigerant molecules could lead to unacceptable errors. In this sense, for the design of novel refrigerants and refrigeration systems, tailoring GC-based models specifically fitted to refrigerant molecules is of great interest. In this work, databases of potential refrigerant molecules are first collected, focusing on five key properties related to the operational efficiency of refrigeration systems, namely normal boiling point, critical temperature, critical pressure, enthalpy of vaporization, and acentric factor. Based on tailored small-molecule groups, the GC method is combined with machine learning (ML) to model these performance-related properties. Following the development of GC-ML models, their performance is analyzed to highlight the potential group-to-property contributions. Additionally, the refrigerant property databases are extended internally and externally, based on which examples are presented to highlight the significance of the developed models.

Developing environmentally sustainable refrigerants is critical for mitigating the impact of anthropogenic greenhouse gases on global warming. This study presents a predictive modeling framework to estimate the 100-year global warming potential (GWP 100) of single-component refrigerants using a fully connected neural network implemented on the Multi-Sigma platform. Molecular descriptors from RDKit, Mordred, and alvaDesc were utilized to capture various chemical features. The RDKit-based model achieved the best performance, with a Root Mean Square Error (RMSE) of 481.9 and an R2 score of 0.918, demonstrating superior predictive accuracy and generalizability. Dimensionality reduction through Principal Component Analysis (PCA) and quantile transformation were applied to address the high-dimensional and skewed nature of the dataset,enhancing model stability and performance. Factor analysis identified vital molecular features, including molecular weight, lipophilicity, and functional groups, such as nitriles and allylic oxides, as significant contributors to GWP values. These insights provide actionable guidance for designing environmentally sustainable refrigerants. Integrating RDKit descriptors with Multi-Sigma's framework, which includes PCA, quantile transformation, and neural networks, provides a scalable solution for the rapid virtual screening of low-GWP refrigerants. This approach can potentially accelerate the identification of eco-friendly alternatives, directly contributing to climate mitigation by enabling the design of next-generation refrigerants aligned with global sustainability objectives.

The optimization of cooling systems is important in many cases, for example for cabin and battery cooling in electric cars. Such an optimization is governed by multiple, conflicting objectives and it is performed across a multi-dimensional parameter space. The extent of the parameter space, the complexity of the non-linear model of the system, as well as the time needed per simulation run and factors that are not modeled in the simulation necessitate an iterative, semi-automatic approach. We present an interactive visual optimization approach, where the user works with a p-h diagram to steer an iterative, guided optimization process. A deep learning (DL) model provides estimates for parameters, given a target characterization of the system, while numerical simulation is used to compute system characteristics for an ensemble of parameter sets. Since the DL model only serves as an approximation of the inverse of the cooling system and since target characteristics can be chosen according to different, competing objectives, an iterative optimization process is realized, developing multiple sets of intermediate solutions, which are visually related to each other. The standard p-h diagram, integrated interactively in this approach, is complemented by a dual, also interactive visual representation of additional expressive measures representing the system characteristics. We show how the known four-points semantic of the p-h diagram meaningfully transfers to the dual data representation. When evaluating this approach in the automotive domain, we found that our solution helped with the overall comprehension of the cooling system and that it lead to a faster convergence during optimization.

Heat pumps are already incredibly efficient. Researchers in Switzerland say they can push efficiencies even further using artificial intelligence. A research team led by Jürg Alexander Schiffmann at the L'Ecole Polytechnique Fédérale de Lausanne (Swiss Federal Institute of Technology Lausanne, or EPFL) is using AI to design compressors that slash heat pumps' electricity consumption by around 25 percent. Unlike conventional furnaces or boilers, which combust fuels to generate heat, heat pumps use electricity to move heat from one place to another. Employing a compressor and refrigerant, heat pumps expel heat from the indoors to the outside during the cooling season, or capture heat outdoors from the ground or air and draw it indoors in winter.

This reliable, feature-packed air conditioner from GE earned our top honors during testing. The GE Profile Series PHC08LY is a window-mounted air conditioner that blends top-notch cooling capabilities with a variety of unique features, with a bit of style and elegance. During testing, this 8,000 BTU (British Thermal Units) AC unit reduced our 340 square foot test area's temperature by 10 F in only 43 minutes and lowered the room's humidity by 14 percent in the same amount of time. On top of this, it ran (for an air conditioner) quietly. While using the GE Profile Series' Quiet Mode it only put out 49.3 dBA of sound -- that's less noise than an average household refrigerator makes.