Îlot urbain

Un îlot urbain, aussi appelé pâté de maisons ou block en anglais, est un ensemble de parcelles, bâties ou non, constituant une unité élémentaire de l'espace urbain, séparé des autres par des rues. Isolé, il évoque un îlot, une petite île. Ce nom est donné de préférence aux parties formant une structure ramassée (carré de maisons). Dans l'époque moderne, un îlot est un ensemble restreint de bâtiments et de parcelles non bâties entourées par des rues ou des avenues en réseau public. L'îlot de verdure est un espace aménagé avec de la végétation. Cela peut être un espace exclusivement jardiné. En s'inspirant de la théorie des îles biogéographiques, des auteurs comme Sukopp et Werner ont en effet proposé de comparer ces espaces verts à des « îles urbaines ». L'îlot insalubre est un îlot dans lequel l'environnement bâti est malsain et rendent nécessaire sa transformation ou destruction. L'îlot haussmannien est un îlot fermé dans lequel les façades des bâtiments sont disposées en conti

Energy performance of a high BHEs density at district scale

Lisa Cassina

It is known that the ground represents a vast heat energy reservoir but the main challenge relies how technically exploit it in a sustainable way. Vertical BHEs represent a non-invasive application to improve the decarbonisation of the heating sector for dense populated urban areas. The conflicts arise when a large number of BHEs is installed. The present project investigated the energy performance of a high BHEs deployment for a district in Chicago (USA). The analytical approach developed for a specific BHEs field design pointed out that 24% and 39.3% of the total heating and respectively cooling demand can be satisfied. Numerical calculations are performed for two identified urban blocks using the finite element software COMSOL Multiphysicsr to consider the energy performance losses due to adjacent geothermal installations. For five smaller adjacent BHEs fields a temperature drop of 1.2 − 2.1!C occurred in the heat carrier fluid due to their mutual operation. The scenario is worse for three larger neighbouring BHEs fields recording a fluid temperature decrease of 3.7 − 5.9!C. Obtained results highlighted that there is a need to better frame the thermal performance losses due to a high BHEs deployment and to integrate them in design requirements. We applied correction factors to adjust the energy performance of isolated BHEs field in order to avoid a potential cooling of the ground and conflicts between neighbours. The thermal e↵ect of heated buildings basements on the BHEs energy performance is also investigated. An increase ranging from 0.3!C to 0.4!C occurred in the mean temperature fluid after 50 years

2021

Dynamically Tuning Processor Resources with Adaptive Processing

Sandhya Dwarkadas

The productivity of modern society has become inextricably linked to its ability to produce energy-efficient computing technology. Increasingly sophisticated mobile computing systems, powered for hours solely by batteries, continue to proliferate rapidly throughout society, while battery technology improves at a much slower pace. In large data centers that handle everything from online orders for a dot-com company to sophisticated Web searches, row upon row of tightly packed computers may be warehoused in a city block. Microprocessor energy wastage in such a facility directly translates into higher electric bills. Simply receiving sufficient electricity from utilities to power such a center is no longer certain. Given this situation, energy efficiency has rapidly moved to the forefront of modern microprocessor design. The adaptive processing approach to improving microprocessor energy efficiency dynamically tunes major microprocessor resources—such as caches and hardware queues—during execution to better match varying application needs.1,2 This tuning usually involves reducing the size of a resource when its full capabilities are not needed, then restoring the disabled portions when they are needed again. Dynamically tailoring processor resources in active use contrasts sharply with techniques that simply turn off entire sections of a processor when they become idle. Presenting the application with the required amount of hardware—and nothing more— throughout its execution can achieve a potentially significant reduction in energy consumption. The challenges facing adaptive processing lie in achieving this greater efficiency with reasonable hardware and software overhead, and doing so without incurring undue performance loss. Unlike reconfigurable computing, which typically uses very different technology such as FPGAs, adaptive processing exploits the dynamic superscalar design approach that developers have used successfully in many generations of general-purpose processors. Whereas reconfigurable processors must demonstrate performance or energy savings large enough to overcome very large clock frequency and circuit density disadvantages, adaptive processors typically have baseline overheads of only a few percent.

2003

Dynamically Tuning Processor Resources with Adaptive Processing

Sandhya Dwarkadas

2003

Energy performance of a high BHEs density at district scale

Natural and Urban, Green and Grey? Studies on specificities of contemporary urban architecture.

Dynamically Tuning Processor Resources with Adaptive Processing

Energy performance of a high BHEs density at district scale

Natural and Urban, Green and Grey? Studies on specificities of contemporary urban architecture.

Dynamically Tuning Processor Resources with Adaptive Processing