Hotspot Mitigation Research Articles

Techno-economic strategy for mitigating Hot-Spot/Partial shading of photovoltaic systems

Photovoltaic systems (PVSs) are the cornerstone of various hybrid renewable energy systems (HRESs). Although the mitigation of hotspots is one of the most crucial challenges for HRESs planners and operators, despite some of the current research trying to find solutions to the hotspot problem, researchers manage to lower the hotspot temperature but without considering commercialization scale perspectives due to design complexity and high-cost consequences. To tackle these challenges, a modified eco-technical strategy for mitigating the hotspot of PVSs is developed in this article. The proposed strategy is simple, economical, and reliable. Wherein additional IGBT is connected in series with the shaded cells in addition to the presence of the prevailing bypass diode (BPD) to distribute the shading effect on the shaded cells. The effectiveness of the modified mitigation strategy is validated during various scenarios of partial shading at solar irradiation change. In addition, the proposed simplified strategy is designed, implemented, and executed using MATLAB/SIMULINKTM. In addition, a practical experiment is conducted in different scenarios and compared with the traditional strategy. The results of the simulation and the practical experiment validate the efficiency and ability of the modified strategy to reduce the power dissipation in the shaded cells by reducing the voltage across the reverse-biased solar cell. The average dissipated power of the shaded cells is reduced by about 74% at low irradiance levels.The thermographic images are produced to validate that the temperature of the shaded cells attached to the proposed simplified strategy under mismatch conditions, has decreased by up to 5 °C compared to the traditional strategy (bypass diode) under the same operational and environmental conditions. The total output power of the modules equipped with the suggested strategy has increased compared to the traditional strategy (BPD) under the same operational and environmental conditions while preserving the reliability of PVSs-based HRESs.

An Improved Hot Spot Mitigation Approach for Photovoltaic Modules Under Mismatch Conditions

The reduction of photogenerated current in photovoltaic (PV) cells due to various degradation mechanisms leads to hot spot (HS) generation, resulting in serious safety and reliability concerns. This article presents a novel hot spot mitigation circuit (HSMC), which effectively counters HS formation in cells under mismatch situations, improving the reliability of the entire PV arrangement. The effectiveness of the proposed HSMC is compared extensively with the conventional bypass diode (BPD) through experimentation on a 3×1 PV string and a 3×3 series-parallel (SP) PV array in an outdoor setting. Detailed thermographic analysis showed that the maximum temperature of the PV module under low shading levels reached 119.16% and 54.11% higher with the traditional BPD than with the proposed HSMC when tested on a 3×1 PV string and 3×3 SP PV array, respectively. This indicates that the mismatched cell was subjected to elevated temperatures with BPD. Moreover, the experimental results confirm that the proposed HSMC resulted in enhanced power output and a significant reduction of power loss in the bypass path of the shaded module under increased shading levels compared to the traditional BPD. The proposed HSMC reduced mismatch loss by up to 89.82% when tested on a 3×1 PV string.

Bidirectional interlayer cooling of 3D chip stack for temperature uniformity enhancement and hotspot mitigation

In this work, bidirectional interlayer cooling with both enhanced temperature uniformity and low flow resistance is proposed for effective 3D chip stack thermal management. Staggered uniform micropin fins and curved entrance and exit regions are elaborately designed. Experimental and numerical studies are carried out to investigate the cooling characteristics under different Reynolds numbers and heat fluxes. In comparison with common interlayer cooling solutions, counter-flow arrangement is achieved between the layers and vertical heat transfer within the chip stack is utilized. Heat fluxes of 300 W/cm2 and 100 W/cm2 are dissipated in the hotspot and background zones respectively. Total thermal resistance of 0.18 K/W is obtained. The maximum decrease in temperature difference is up to 66.2%. Both thermal resistance and coefficient of performance (COP) are considered and compared with the results from previous studies for comprehensive performance evaluation. The maximum COP of 11,523 is reached. The thermal and hydraulic performance advantages of the present design can be verified. The bidirectional interlayer cooling offers new potential for thermal management of 3D chip stack.

Developing a hybrid data-driven and informed model for prediction and mitigation of agricultural nitrous oxide flux hotspots

Nitrous oxide (N2O) is one of the most significant contributors to greenhouse forcing and is the biggest contributor to ozone depletion in the 21st century, and roughly 70% of anthropogenic nitrous oxide emissions are from agriculture and soil management. Agricultural nitrous oxide emissions are shown to spike during hotspot events, and according to the data used in this study, over 78% of nitrous oxide flux occurred during just 15% of the recorded data points. Due to the complex biogeochemical processes governing nitrous oxide formation, machine learning and process-based models often fail to predict agricultural nitrous oxide flux. A novel informed neural network was developed that combined the trainability of neural networks with the rigorous differential equation-based framework of process-based models. Differential equations that explained the variability of various nitrogen-containing compounds in soil were derived, and integrated into the network loss. The informed model explained ∼85% of variation in the data and had an F1 score of 0.75, a marked improvement over the classical model explaining ∼30% of variation and having a score of 0.53. The informed network was also able to perform exceptionally well with only small subsets of the training data, having an F1 score of 0.41 with only 25% of training data. The model not only shows great promise in the remarkably accurate prediction of these hotspots but also serves as a potential new paradigm for physics-informed machine learning techniques in environmental and agricultural sciences.

A novel microchannel-twisted pinfin hybrid heat sink for hotspot mitigation

Thermal hotspots cause excessive localized temperature rise, leading to significant temperature gradients across the microprocessor, which is the primary reason for its inadequate performance and early failure. This study proposes microchannel-pinfin hybrid heat sinks incorporating twisted and non-twisted pinfins of various cross-sectional shapes (hexagonal, pentagonal, square, and triangular) to demonstrate energy-efficient hotspot mitigation in a microprocessor with highly non-uniform power distribution. Microchannels and pinfins are positioned at low- and high-heat-flux zones, respectively, to reduce the temperature variations utilizing their unequal heat transfer capabilities. Here, high- and low-heat-flux zones represent the microprocessor-core area (hotspot) and remaining chip area (background zone), characterized by heat fluxes of 300 W/cm2 and 50 W/cm2, respectively. The pinfin twisting angle varies from 0° to 360° at a step of 45°. Conjugate heat transfer analyses are conducted through numerical solutions of the continuity, Navier-Stokes, and energy equations. The performance of the proposed hybrid heat sinks is compared with the non-hybrid (NH) and hybrid circular pinfins (HCP) heat sinks at Re = 120–440. The hybrid heat sink featuring triangular pinfins twisted at 225° angle (HTP-225) exhibits a remarkable reduction of 48.2 % in the total thermal resistance (Rth) and 58.4 % in the temperature non-uniformity (δT,bs) as compared to the NH heat sinks at Re = 440. Compared to the HCP design, the HTP-225 design shows 26.9 % and 35.8 % lower Rth and δT,bs, respectively. Moreover, the HTP-225 heat sink outperforms the NH heat sink with 46.0 % and 57.0 % lower Rth and δT,bs, respectively, at an equal pumping power of 18.6 mW. Furthermore, the HTP-225 heat sink demonstrates a 96.4 % and 38.5 % higher critical hotspot heat flux dissipating capacity than the NH and HCP heat sinks, respectively, making it a viable and effective solution for alleviating hotspots in high-power-density devices.

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm.

Wireless Sensor Networks (WSNs) consist of small, multifunctional nodes distributed across various locations to monitor and record parameters. These nodes store data and transmit signals for further processing, forming a crucial topic of study. Monitoring the network's status in WSN applications using clustering systems is essential. Collaboration among sensors from various domains enhances the precision of localised information reporting. However, nodes closer to the data sink consume more energy, leading to hotspot challenges. To address these challenges, this research employs clustering and optimised routing techniques. The aggregation of information involves creating clusters, further divided into sub-clusters. Each cluster includes a Cluster Head (CH) or Sensor Nodes (SN) without a CH. Clustering inherently optimises CHs' capabilities, enhances network activity, and establishes a systematic network topology. This model accommodates both multi-hop and single-hop systems. This research focuses on selecting CHs using a Genetic Algorithm (GA), considering various factors. While GA possesses strong exploration capabilities, it requires effective management. This research uses Prairie Dog Optimization (PDO) to overcome this challenge. The proposed Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) significantly improves WSN performance, particularly in hotspot avoidance. With HM-PGA, it achieves a network lifetime of 20913 milliseconds and 310 joules of remaining energy. Comparative analysis with existing techniques demonstrates the superiority of the proposed approach.

Integrating Fiber Sensing for Spatially Resolved Temperature Measurement in Fuel Cells

Fiber optic sensors integrated into fuel cell stacks have the potential to significantly enhance the temperature control and health monitoring of fuel cells. Inhomogeneous loading, both within individual cells and across different cells in a stack, leads to the formation of local hotspots that accelerate aging and degrade performance. This study investigates the behavior and feasibility of incorporating polyimide-coated optical fiber sensors into bipolar plates for precise and spatially resolved temperature monitoring. The sensor is successfully integrated into a single cell of a fuel cell stack, positioned on the bipolar plate in direct contact with the membrane. Pre-tests are conducted to thoroughly evaluate the technical properties of the fiber in relation to specific cell requirements. Additionally, a physical prototype featuring the sensor is developed and employed to validate its effectiveness under realistic operating conditions. The temperature measurement obtained via the fiber exhibits a continuous profile throughout the entire length, covering both the active area and distributor region of the cell. Throughout the entire 60 min test period, the measuring system provided continuous and uninterrupted temperature measurements, encompassing the start of the stack, the heating phase, the subsequent stable operating point, and the cooling phase. However, some technical challenges are identified, as mechanical pressure exerted on the fiber influences the measured temperature. While this work demonstrates promising results, further advancements are necessary to address inhomogeneous loading within fuel cells and hotspot mitigation. The precise monitoring of temperature distribution enables early detection of potential damage, facilitating timely interventions to improve the service life and overall performance of fuel cells.

URBAN GREEN SPACE ESTIMATION FOR HEAT HOTSPOT MITIGATION IN A SMALL CITY, BURIRAM MUNICIPALITY, THAILAND

Heat hotspots cause environmental and health problems to residents in urban areas. Green space has been used to reduce the temperature in urban areas. However, the size and location of the green space that can reduce the temperature in those areas are challenging. This study aims to identify the heat hotspot of an urban area and estimate the green space proportion to reduce the heat hotspot. Therefore, the Split Window method (SW) was initially employed to calculate the Land Surface Temperature (LST) data from the Landsat series in the summertime of 2014, 2016, and 2018. The LST data 2018 were used in heat hotspot investigation using Moran's I and Getis-Ord Gi*. The results show the clustering patterns of LST occurring in barren lands, racetracks, and built-up areas in Buriram Municipality. Then, the monthly regression modeling between the green space proportions and LST was analyzed and applied to the hotspot areas. The green space proportions were represented by estimating in regression models showing the ratio of green space and decreasing temperature in hotspot areas. As a result, the green space proportion around 45% of the area is suggested to mitigate the heat hotspot. The explored green space proportion was applied to the 2014 and 2016 data to assess the feasibility of hotspot mitigation. This research presents a simplified technic that will enable urban planners to estimate the green space proportion to reduce the heat hotspots effectively.

Computational Engineering based approach on Artificial Intelligence and Machine learning-Driven Robust Data Centre for Safe Management

This research explores the integration of Artificial Intelligence (AI), specifically the Recurrent Neural Network (RNN) model, into the optimization of data center cooling systems through Computational Engineering. Utilizing Computational Fluid Dynamics (CFD) simulations as a foundational data source, the study aimed to enhance operational efficiency and sustainability in data centers through predictive modeling. The findings revealed that the RNN model, trained on CFD datasets, proficiently forecasted key data center conditions, including temperature variations and airflow dynamics. This AI-driven approach demonstrated marked advantages over traditional methods, significantly minimizing energy wastage commonly incurred through overcooling. Additionally, the proactive nature of the model allowed for the timely identification and mitigation of potential equipment challenges or heat hotspots, ensuring uninterrupted operations and equipment longevity. While the research showcased the transformative potential of merging AI with data center operations, it also indicated areas for further refinement, including the model's adaptability to diverse real-world scenarios and its management of long-term dependencies. In conclusion, the study illuminates a promising avenue for enhancing data center operations, highlighting the significant benefits of an AI-driven approach in achieving efficiency, cost reduction, and environmental sustainability.

Dosimetric and Clinical Results of a Volumetric-Based Skin-Sparing Planning Technique for Patients Treated to the Breast and Chest Wall with Pencil Beam Scanning Proton Therapy

Given clinical concerns for more brisk acute skin toxicity with proton compared with photon RT due to differences in the energy deposition properties, our institution implemented a novel volumetric skin-sparing planning technique (SSPT) for intensity modulated proton therapy (IMPT) for patients treated to the breast or chest wall (CW). This study evaluates SSPT dosimetry and the hypothesis that a SSPT will reduce acute dermatitis during IMPT to the breast and CW. Prior to the development of a consensus technique, skin dose evaluation in IMPT plans was limited to mitigation of skin hot spots and cropping off the skin surface by 3mm for CW and 5mm for intact breast targets (except indications for deliberate skin dosing). In January 2022 our center added volumetric-based skin sparing objectives in addition to hot-spot evaluation as a SSPT. A skin evaluation structure (skin-eval) was defined as a skin rind of 3 mm for CW and 5 mm for intact breast within 5mm of the CTV, bound by the patient's surface. The SSPT incorporated an objective to limit the volume of skin-eval receiving 95% of the prescription dose or more (V95%Rx) to ideally <50% (goal<60%) while still prioritizing CTV coverage and robustness. We compared target coverage, robustness, and skin-eval dosimetry as well as acute on-treatment skin toxicity in patients treated with and without incorporation of this SSPT. Patients with skin/dermal lymphatic invasion or inflammatory breast cancer were excluded. 84 patients who were planned to receive breast or CW IMPT were included (43 planned without and 41 with the SSPT). There was no difference in percentages of patients treated to the intact breast/CW/immediate CW reconstruction between the groups (37%/23%/40% without and 34%/27%/39% with SSPT, p>0.05). Mean skin-evalV95%Rx was 72% vs. 30%, p<0.0001, for those treated without vs. with a SSPT. Maximum %Rx to the skin volume of 0.03 cc, 0.3cc, and 1cc, was higher in patients treated without compared to those with a SSPT (103.1% vs. 101.5%; 101.3% vs. 100.4%; and 101.8% vs. 99.7% (all p=<0.0001)), respectively. There was a small statistical difference in the mean CTV V97.5%Rx in patients treated without vs. with the SSPT (97.8% vs. 96.5%, p=0.0003). Patients planned utilizing the SSPT demonstrated reduced rates of Grade 1 breast pain at Week 2 (12% vs. 33%, p=0.0424) and Grade 2 and 3 dermatitis at Week 5 (grade 2 38% vs. 42%; grade 3, 0% vs. 11%, p=0.0016). There were numerically more patients requiring a treatment break or not completing full intended prescription (4 vs. 1) in the pre-SSPT cohort. A volumetric-based SSPT appears to reduce the frequency of brisk onset dermatitis and near-end of RT significant dermatitis while still maintaining acceptable target coverage and robustness in patients receiving IMPT to the breast and CW.

A Computational Approach on Mitigation of Hotspots in a Microprocessor by Employing CNT Nanofluid in Bifurcated Microchannel

A Computational Approach on Mitigation of Hotspots in a Microprocessor by Employing CNT Nanofluid in Bifurcated Microchannel

A Modified Bypass Circuit for Improved Reliability of PV Module Validated With Real-Time Data

Solar Power is a promising way of electricity generation but it comes with a drawback of low conversion efficiency which is caused by several factors, the most prominent being partial shading. Partial shading leads to mismatch of PV cells, resulting in formation of hot spot. This leads to power dissipation and physical damage to PV cells. Consequently, panel’s credibility is called into question. A bypass diode reduces the amount of power dissipated by a shaded cell, but it doesn’t entirely prevent hot spot formation. Moreover, bypass diode doesn’t bypass every small shade as it has a shaded area threshold at and above which it begins to bypass the shaded module. Whereas, the rise in temperature may also be triggered even by a small shaded area, like the area of falling leaves, which further creates reliability issues. A few hot spot mitigation strategies have been devised; however, it still has room for improvement. This paper presents a modified bypass circuit arrangement for reliability enhancement of PV systems and it is evaluated for a 3×1 PV string. A comparative analysis with bypass diode is carried out in an experimental study. The efficacy of the proposed methodology is verified based on four performance metrics, namely reverse voltage, power loss under partial shading, the sensitivity of the bypass circuit, and the temperature of the hot spotted module. The proposed technique ensures sensitivity enhancement of the bypass circuit towards shade effect; thus, enhancing the reliability of PV system.

Efficient Thermoelectric Cooler for Localized Cooling in Electronic Devices

Abstract Modern electronic devices operate at high power density due to increased processing speeds and the miniaturization of electronic chips. Conventional fan cooling alone is not effective. The thermoelectric cooler (TEC) is one of the most viable substitutes, providing site-specific, rapid, and precise cooling. In the present work, we propose an efficient thermoelectric cooler design for mitigating the cooling demand of high-end electronic components such as microprocessors, semiconductor lasers, etc. A 3D numerical model is developed using the finite element method (FEM)-based commercial software COMSOL Multiphysics to investigate the effect of various geometric and operating parameters on the cooling performance of the thermoelectric cooler. The parameters such as fill factor, leg dimensions, heat sink size, and phase change material (PCM) filling pattern in the inter-fin spacings/gaps are optimized. Two heat sink PCM designs, M1 (alternate fin gaps filled) and M2 (all fin gaps filled), are investigated for hotspot mitigation. For no-load conditions, the thermoelectric cooler module with a 20% fill factor produces a cooling of 20.5 °C with an average cooling per unit input power of 37.5 °CW−1. When a heating load of 625 W/cm2 is applied, its cold-side temperature reaches 91 °C. TEC module with n-eicosane PCM (M2 design) provides an effective cooling of 37 °C and an average cooling per unit input power of 42.3 °CW−1.

Mitigation of hot-spot effect via back side cooling techniques: A potential for electrical and thermal performance improvement

Hot-spot mitigation is an ever-present issue in photovoltaic system and it significantly affects the performance of photovoltaic (PV) panels. Most of the hot-spots are actually partial hot-spots which cover only a part of PV cell, or part of PV panel. In this work an influence of cooling mechanism on hot-spot mitigation is investigated by using previously developed numerical model. Specific parameters such as insolation, hot-spot intensity, convection intensity, cooling medium temperature and number of affected cells were varied for single panel. By varying parameters, it was found that partial hot-spots with less than 15% shade percentage on up to 4 cells (corresponds with 1% of total shade on PV panel) can be mitigated with adequate cooling technique applied on the PV panel. The hot-spot cases with larger shade can be mitigated, but only with high cooling intensity which makes the feasibility of the mitigation highly questionable. Hot-spot cases with more than 15% shade percentage on more than 10 affected cells (corresponds with 2.5% of total shade on PV panel) practically cannot be completely mitigated with any back side cooling technique. Nevertheless, cooling technique still provides additional electrical energy gain even with intense hot-spot cases. Results gained in this investigation can also be used to estimate potential in higher electricity output with specific cooling technique, and to estimate thermal energy potential such as house or hot water heating. A simplified case study is presented, where result application is explained step-by-step. These results are of particular interest for PV panels installed in urban areas with lots of obstructions and where regular cleaning is not practical. Such applications are rooftop PV systems, facade PV and photovoltaic-thermal (PVT) systems.

Unequal clustering scheme for hotspot mitigation in IoT-enabled wireless sensor networks based on fire hawk optimization

Wireless sensor networks (WSNs) related to the Internet of Things (IoT) have shown incredible growth with technological advancements, offering a greater number of applications all over the world. Sensor nodes are deployed closer to the base station (BS) in some scenarios and held accountable for transmitting data from neighboring and own nodes against the BS and depletes energy. In the network, this problem is referred to as the "hotspot issue" and it may be fixed using procedures that use unequal clustering. In this study, a novel approach for IoT-assisted wireless sensor networks called the Fire Hawk Optimization-based Unequal Clustering Scheme for Hotspot Mitigation (FHOUCS-HSM) is developed. In the presented FHOUCS-HSM model, a first-order radio energy model has been utilized. The FHOUCS-HSM technique follows the foraging characteristics of whistling kites, black kites, and brown falcons. The design of the FHO algorithm for unequal clustering shows the novelty of the work. In addition to this, the FHOUCS-HSM model computes a fitness function for the use of uneven cluster size and the selection of cluster heads (CH). The comparative research highlights the advantages that the FHOUCS-HSM technique has over many models that are already in use.

Hotspot Mitigation for Mobile Edge Computing

Due to the long latency of accessing services from cloud datacenters, mobile edge computing (MEC) has been proposed to provide the capability of cloud computing in close proximity to mobile users. Mobile devices can offload computation-intensive applications to MEC servers to achieve better performance and reduce power consumption. Owing to user mobility, workloads of MEC servers would vary severely to cause imbalanced loads and incur hotspot servers. Previous studies of power saving and hotspot management apply mechanisms for cloud datacenters to MEC servers, but these studies may not be suitable for MEC servers due to the limited computation and communication capacity. We are motivated to propose a scheme, complementary offloading for hotspot mitigation (COHM), for MEC. COHM consists of mechanisms for MEC servers with different states, namely busy, idle, and available. These mechanisms jointly consider the computation tasks for MEC servers with different states to mitigate hotspots. The simulation results show that COHM can decrease the number of hotspot servers without degrading the quality of service for applications.

Effects of Thin Film Heat Spreader on Hot Spots Mitigation in Heat Sinks

Abstract The effects of graphene platelets and diamond-based thin film heat spreaders on maximum temperature of integrated electronic circuits were investigated. A fully three-dimensional conjugate heat transfer analysis was performed to investigate the effects of thin film material and thickness on the temperature of a hot spot and temperature uniformity on the heated surface of the integrated circuit when subjected to forced convective cooling. Two different materials, diamond and graphene, were simulated as materials for thin films. The thin film heat spreaders were applied to the top wall of an array of micro pin fins having circular cross sections. The integrated circuit with a 4 × 3 mm footprint featured a 0.5 × 0.5 mm hot spot located on the top wall, which was also exposed to a uniform background heat flux of 500 W cm−2. A hot spot uniform heat flux of magnitude 2000 W cm−2 was centrally situated on the top surface over a small area of 0.5 × 0.5 mm. Both isotropic and anisotropic properties of the thin film heat spreaders made of graphene platelets and diamond were computationally analyzed. The conjugate heat transfer analysis also incorporated thermal contact resistance between the thin film and the silicon substrate. It was found that isotropic thin film heat spreaders significantly reduce the hot spot temperature and increase temperature uniformity, allowing for increased thermal loads. Furthermore, it was found that thickness of the thin film heat spreader does not have to be greater than a few tens of microns.

Multiobjective Optimization of Graded, Hybrid Micropillar Wicks for Capillary-Fed Evaporation.

As electronic device power densities continue to increase, vapor chambers and heat pipes have emerged as effective thermal management solutions for hotspot mitigation. A crucial aspect of vapor chamber functionality depends on the properties of the microporous wick that drives heat and mass transport within the device. While many prior studies have focused on the optimization of these porous structures to increase the maximum capillary-limited dryout heat flux, an equally important aspect of porous wick design is the minimization of the thermal resistance above heated areas. Segmented wicks with geometries that vary along the length of the wick are attractive candidates that can potentially be used to fulfill these simultaneous design goals. Previous studies on bisegmented wicks with only two distinct adiabatic and heated region geometries, however, have shown mixed results regarding the degree of performance benefit over homogeneous wicks. In this work, we present a systematic modeling approach to investigate the optimal composition of segmented micropillar wicks comprising multiple, discrete regions of graded geometry. Using a genetic algorithm, we generate Pareto fronts of optimal segmented wick distributions that maximize the dryout heat flux and minimize the thermal resistance for a given heating configuration. We find that optimal, graded segmented wicks are capable of dissipating dryout heat fluxes more than 200% higher than baseline homogeneous wicks with significantly lower thermal resistance. The sensitivity of the wick performance to the total number of geometry segments is found to vary depending on the desired heat flux and thermal resistance operating regimes.

A silicon-diamond microchannel heat sink for die-level hotspot thermal management

• A silicon-diamond microchannel heat sink is introduced for microprocessor cooling. • Heat sinks are evaluated at various flow rates, heating conditions, and hotspot sizes. • The proposed design exhibits an excellent die-level hotspot mitigation capability. • A substantial improvement in performance is obtained at the same pumping power. • An extended diamond zone over the hotspot shows further performance enhancement. A novel microchannel heat sink with an excellent die-level hotspot mitigation capability has been introduced for electronic cooling applications. The basic concept of the proposed composite design is based on the dissimilarity in the thermal conductivities of materials. The microprocessor area was divided into multiple zones of different heat fluxes and the heat sink was developed using dissimilar materials for each zone. Silicon was used for the low-heat-flux zone and diamond with substantially higher thermal conductivity (ten times higher than silicon) was used for the high-heat-flux zone. The non-composite design was completely made of silicon with the same geometric parameters. A comprehensive numerical analysis of the composite heat sink was carried out and the results were compared with that of the non-composite heat sink. Both heat sink designs were assessed at various flow rates, heating schemes, and hotspot sizes. The thermal resistance, temperature non-uniformity, and pumping power were calculated to assess the performance of both heat sinks. The proposed design exhibited a substantial performance enhancement without any increase in the pressure drop or pumping power. Besides, the composite design was analyzed by varying the diamond zone size while keeping the hotspot size fixed which showed further enhancement in its performance.

Measuring the spatiotemporal evolution of accident hot spots

One reality of transportation systems is that vehicular accidents can happen practically anywhere and at any time. An increasing body of research suggests though that spatial and/or temporal dependencies (i.e., clusters or hot spots) among accidents likely exist. Along with understanding where and when such spatiotemporal dependencies may occur, another important facet to consider is the geographic extent or area associated with the hot spots. For example, an accident hot spot may involve a small, isolated portion of the transportation system or a much more expansive geographic area. Better delineation and quantification of the morphological characteristics of accident hotspots can provide valuable decision support for planning for accident hot spot mitigation and prevention. As the size and shape of accident hot spots may evolve over time, the capability to track such dynamics is vital, especially with respect to the identification of processes effecting hot spot occurrence as well as assessments of the efficacy of efforts to mitigate factors underlying hot spot development. To this end, a Geographical Information Systems (GIS) based framework is outlined to facilitate the analysis of the morphological characteristics of hot spots over time. The analysis framework is applied to a case study of vehicular accidents reported over a two-year period to demonstrate its practical utility. The application results indicate that patterns of change in hot spot morphology can be effectively quantified and a variety of informative spatial and temporal patterns can be detected.