The Experts below are selected from a list of 45 Experts worldwide ranked by ideXlab platform

Ali Hussain Kazim - One of the best experts on this subject based on the ideXlab platform.

  • Thermo-electrochemical generator: energy harvesting & thermoregulation for liquid cooling applications
    Sustainable Energy and Fuels, 2020
    Co-Authors: Ali Hussain Kazim, Sai T Stephens, A. Sina Booeshaghi, Baratunde A Cola
    Abstract:

    Managing big data is a Thermodynamics Problem; decreasing size and increasing performance of electronic devices necessitate the use of liquid cooling to dissipate massive amounts of heat that are generated as a result. In locations such as data centers, CPU cooling is accomplished through the use of air and liquid methods. Currently, the purpose of existing liquid cooling designs is to provide cooling to these high power CPU's. We have tested and validated a modified liquid cooling design system that supplements the current cooling architecture with the ability to harvest energy from the waste-heat rejected from these heat sources. An electrolyte capable of undergoing a reversible redox reaction is pumped through a macro-channel flow thermo-electrochemical cell (fTEC). The heat energy flow is coupled with electrical energy through the thermoelectric effect allowing cooling and power harvesting to occur in parallel. Our current design generated 88 μW of power with a power density of 0.05 W m−2, achieved a heat transfer coefficient of 450 W (m2 K)−1. This technology can be employed in any location where liquid cooling is used, from CPU's in data centers to battery packs in electric vehicles. With a fTEC, heat mapping for the entire data center would be possible. This would improve thermoregulation and in addition the energy harvested will reduce the overhead cost for running a data center.

  • thermo electrochemical generator energy harvesting thermoregulation for liquid cooling applications
    Sustainable Energy and Fuels, 2017
    Co-Authors: Ali Hussain Kazim, Sina A Booeshaghi, Sai T Stephens, Baratunde A Cola
    Abstract:

    Managing big data is a Thermodynamics Problem; decreasing size and increasing performance of electronic devices necessitate the use of liquid cooling to dissipate massive amounts of heat that are generated as a result. In locations such as data centers, CPU cooling is accomplished through the use of air and liquid methods. Currently, the purpose of existing liquid cooling designs is to provide cooling to these high power CPU's. We have tested and validated a modified liquid cooling design system that supplements the current cooling architecture with the ability to harvest energy from the waste-heat rejected from these heat sources. An electrolyte capable of undergoing a reversible redox reaction is pumped through a macro-channel flow thermo-electrochemical cell (fTEC). The heat energy flow is coupled with electrical energy through the thermoelectric effect allowing cooling and power harvesting to occur in parallel. Our current design generated 88 μW of power with a power density of 0.05 W m−2, achieved a heat transfer coefficient of 450 W (m2 K)−1. This technology can be employed in any location where liquid cooling is used, from CPU's in data centers to battery packs in electric vehicles. With a fTEC, heat mapping for the entire data center would be possible. This would improve thermoregulation and in addition the energy harvested will reduce the overhead cost for running a data center.

Baratunde A Cola - One of the best experts on this subject based on the ideXlab platform.

  • Thermo-electrochemical generator: energy harvesting & thermoregulation for liquid cooling applications
    Sustainable Energy and Fuels, 2020
    Co-Authors: Ali Hussain Kazim, Sai T Stephens, A. Sina Booeshaghi, Baratunde A Cola
    Abstract:

    Managing big data is a Thermodynamics Problem; decreasing size and increasing performance of electronic devices necessitate the use of liquid cooling to dissipate massive amounts of heat that are generated as a result. In locations such as data centers, CPU cooling is accomplished through the use of air and liquid methods. Currently, the purpose of existing liquid cooling designs is to provide cooling to these high power CPU's. We have tested and validated a modified liquid cooling design system that supplements the current cooling architecture with the ability to harvest energy from the waste-heat rejected from these heat sources. An electrolyte capable of undergoing a reversible redox reaction is pumped through a macro-channel flow thermo-electrochemical cell (fTEC). The heat energy flow is coupled with electrical energy through the thermoelectric effect allowing cooling and power harvesting to occur in parallel. Our current design generated 88 μW of power with a power density of 0.05 W m−2, achieved a heat transfer coefficient of 450 W (m2 K)−1. This technology can be employed in any location where liquid cooling is used, from CPU's in data centers to battery packs in electric vehicles. With a fTEC, heat mapping for the entire data center would be possible. This would improve thermoregulation and in addition the energy harvested will reduce the overhead cost for running a data center.

  • thermo electrochemical generator energy harvesting thermoregulation for liquid cooling applications
    Sustainable Energy and Fuels, 2017
    Co-Authors: Ali Hussain Kazim, Sina A Booeshaghi, Sai T Stephens, Baratunde A Cola
    Abstract:

    Managing big data is a Thermodynamics Problem; decreasing size and increasing performance of electronic devices necessitate the use of liquid cooling to dissipate massive amounts of heat that are generated as a result. In locations such as data centers, CPU cooling is accomplished through the use of air and liquid methods. Currently, the purpose of existing liquid cooling designs is to provide cooling to these high power CPU's. We have tested and validated a modified liquid cooling design system that supplements the current cooling architecture with the ability to harvest energy from the waste-heat rejected from these heat sources. An electrolyte capable of undergoing a reversible redox reaction is pumped through a macro-channel flow thermo-electrochemical cell (fTEC). The heat energy flow is coupled with electrical energy through the thermoelectric effect allowing cooling and power harvesting to occur in parallel. Our current design generated 88 μW of power with a power density of 0.05 W m−2, achieved a heat transfer coefficient of 450 W (m2 K)−1. This technology can be employed in any location where liquid cooling is used, from CPU's in data centers to battery packs in electric vehicles. With a fTEC, heat mapping for the entire data center would be possible. This would improve thermoregulation and in addition the energy harvested will reduce the overhead cost for running a data center.

Sai T Stephens - One of the best experts on this subject based on the ideXlab platform.

  • Thermo-electrochemical generator: energy harvesting & thermoregulation for liquid cooling applications
    Sustainable Energy and Fuels, 2020
    Co-Authors: Ali Hussain Kazim, Sai T Stephens, A. Sina Booeshaghi, Baratunde A Cola
    Abstract:

    Managing big data is a Thermodynamics Problem; decreasing size and increasing performance of electronic devices necessitate the use of liquid cooling to dissipate massive amounts of heat that are generated as a result. In locations such as data centers, CPU cooling is accomplished through the use of air and liquid methods. Currently, the purpose of existing liquid cooling designs is to provide cooling to these high power CPU's. We have tested and validated a modified liquid cooling design system that supplements the current cooling architecture with the ability to harvest energy from the waste-heat rejected from these heat sources. An electrolyte capable of undergoing a reversible redox reaction is pumped through a macro-channel flow thermo-electrochemical cell (fTEC). The heat energy flow is coupled with electrical energy through the thermoelectric effect allowing cooling and power harvesting to occur in parallel. Our current design generated 88 μW of power with a power density of 0.05 W m−2, achieved a heat transfer coefficient of 450 W (m2 K)−1. This technology can be employed in any location where liquid cooling is used, from CPU's in data centers to battery packs in electric vehicles. With a fTEC, heat mapping for the entire data center would be possible. This would improve thermoregulation and in addition the energy harvested will reduce the overhead cost for running a data center.

  • thermo electrochemical generator energy harvesting thermoregulation for liquid cooling applications
    Sustainable Energy and Fuels, 2017
    Co-Authors: Ali Hussain Kazim, Sina A Booeshaghi, Sai T Stephens, Baratunde A Cola
    Abstract:

    Managing big data is a Thermodynamics Problem; decreasing size and increasing performance of electronic devices necessitate the use of liquid cooling to dissipate massive amounts of heat that are generated as a result. In locations such as data centers, CPU cooling is accomplished through the use of air and liquid methods. Currently, the purpose of existing liquid cooling designs is to provide cooling to these high power CPU's. We have tested and validated a modified liquid cooling design system that supplements the current cooling architecture with the ability to harvest energy from the waste-heat rejected from these heat sources. An electrolyte capable of undergoing a reversible redox reaction is pumped through a macro-channel flow thermo-electrochemical cell (fTEC). The heat energy flow is coupled with electrical energy through the thermoelectric effect allowing cooling and power harvesting to occur in parallel. Our current design generated 88 μW of power with a power density of 0.05 W m−2, achieved a heat transfer coefficient of 450 W (m2 K)−1. This technology can be employed in any location where liquid cooling is used, from CPU's in data centers to battery packs in electric vehicles. With a fTEC, heat mapping for the entire data center would be possible. This would improve thermoregulation and in addition the energy harvested will reduce the overhead cost for running a data center.

Yusuf Pisan - One of the best experts on this subject based on the ideXlab platform.

  • An integrated architecture for engineering Problem-solving
    1998
    Co-Authors: Yusuf Pisan
    Abstract:

    Abstract : Problem solving is an essential function of human cognition. To build intelligent systems that are capable of assisting engineers and tutoring students, we need to develop an information processing model that captures the skills used in engineering Problem solving. This thesis describes the Integrated Problem Solving Architecture (IPSA) that combines qualitative, quantitative and diagrammatic reasoning skills to produce annotated solutions to engineering Problems. We focus on representing expert knowledge, and examine how control knowledge provides the structure for using domain knowledge. To demonstrate our architecture for engineering Problem solving, we present a Thermodynamics Problem Solver (TPS) that uses the IPSA architecture. TPS solves over 150 Thermodynamics Problems taken from the first four chapters of a common Thermodynamics textbook and produces expert-like solutions.

  • Australian Joint Conference on Artificial Intelligence - Controlling Engineering Problem Solving
    Advanced Topics in Artificial Intelligence, 1997
    Co-Authors: Yusuf Pisan
    Abstract:

    Engineering Problem solving requires both domain knowledge and an understanding of how to apply that knowledge. While much of the recent work in qualitative physics has focused on building reusable domain theories, there has been little attention paid to representing the control knowledge necessary for applying these models. This paper shows how qualitative representations and compositional modeling can be used to create control knowledge for solving engineering Problems. This control knowledge includes modeling assumptions, plans and preferences. We describe an implemented system, called TPS (Thermodynamics Problem Solver) that illustrates the utility of these ideas in the domain of engineering Thermodynamics. To date, TPS has solved over 30 Problems, and its solutions are similar to those of experts. We argue that our control vocabulary can be extended to most engineering Problem solving domains and employed in a variety of Problem solving architectures.

  • Using Qualitative Representations in Controlling Engineering Problem Solving 1
    1996
    Co-Authors: Yusuf Pisan
    Abstract:

    Engineering Problem solving requires both domain knowledge and an understanding of how to apply that knowledge. While much of the recent work in qualitative physics has focused on building reusable domain theories, there has been little attention paid to representing the control knowledge necessary for applying these models. This paper shows how qualitative representations and compositional modeling can be used to create control knowledge for solving engineering Problems. This control knowledge includes modeling assumptions, plans and preferences. We describe an implemented system, called TPS (Thermodynamics Problem Solver) that illustrates the utility of these ideas in the domain of engineering Thermodynamics. TPS to date has solved over 30 Problems, and its solutions are similar to those of experts. We argue that our control vocabulary can be extended to most engineering Problem solving domains, and employed in a wide variety of Problem solving architectures.

Hendrik Huwald - One of the best experts on this subject based on the ideXlab platform.

  • comparison of different numerical approaches to the 1d sea ice Thermodynamics Problem
    Ocean Modelling, 2015
    Co-Authors: Frederic Dupont, Martin Vancoppenolle, Louisbruno Tremblay, Hendrik Huwald
    Abstract:

    The vertical one-dimensional sea-ice thermodynamic Problem using the principle of conservation of enthalpy is revisited here using (1) the Bitz and Lipscomb (1999) finite-difference approach (FD), (2) a reformulation of the sigma-level transformation of Huwald et al. (2005b) (FV) and (3) a Finite Element approach also in sigma coordinates (FE). These three formulations are compared in terms of physics, numerics, and performance, in order to identify the best choice for large-scale climate models. The BL99 formulation sequentially treats the diffusion of heat and the changes in the vertical position of the ice-snow layers. In contrast, the FV sigma-level transformation elegantly treats both simultaneously. The original FV formulation suffers however from slow convergence. The convergence can nonetheless be improved significantly with a few simple modifications to the original code. The three formulations are compared following the experimental protocol of the Sea Ice Model Intercomparison Project for ice Thermodynamics (SIMIP2). It is found that all formulations converge to the same solution. The FD approach, however, suffers from the added cost of the remapping step at large number of ice layers we include in the appendix an optimized version of the FD code–written by one of the reviewer–that resolves this issue. Finally the FE formulation results in a sub-surface temperature over-estimation at low resolution, a Problem which disappears at high resolution. Hence, only FD and FV are found suitable for climate models.