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High efficiency single and multi-phase direct liquid jet-impingement cooling for heterogeneous packaging
No full textThe rapid development of high-power heterogeneously integrated chiplet packages for high-performance computing and datacenters has increased the demand for an energy-efficient thermal management strategy with a high heat transfer potential. This study presents a comprehensive experimental evaluation of direct-liquid jet-impingement cooling using both single-phase (deionized water) and two-phase (HFE-7100) working fluids on a 2.5D thermal test vehicle (TTV) replicating a realistic chiplet architecture. The cooling module features a 4 x 4 jet array precisely aligned over high-power logic dies, with secondary crossflow used to cool adjacent memory regions, effectively reducing pressure drop. Furthermore, deep reactive ion etched (DRIE) micro pin-fins were fabricated on the silicon surface, allowing distinct heat transfer enhancements for both single and two-phase cooling regimes, with minimal increase in required pumping power. Single-phase water cooling demonstrated superior thermal performance, achieving a minimum junction-to-fluid thermal resistance of 0.032 K/W while maintaining chip temperatures under 70 degrees C at 1 kW heat loads with a low pumping power of only 1.3 W. In contrast, two-phase jet cooling on the micro pin-finned surface at elevated inlet temperatures up to 45 degrees C achieved a 78 % improvement in energy efficiency compared to single-phase water jet cooling, which was quantified with an exergy-destruction-based metric, with only an 8.7 % higher thermal resistance. By directly comparing working fluids, operating conditions, and surface structuring on a realistic 2.5D chiplet package, this work offers guidance for selecting cooling strategies based on thermal performance, energy efficiency, and reliability. The findings highlight the effectiveness of direct-liquid jet-impingement combined with silicon micro pin-fin structuring as an energy efficient thermal management strategy for high-power heterogeneously integrated packages.
Grain size estimation in additively manufactured metal thin-wall using noncontact laser ultrasonic and zero-group-velocity Lamb waves
No full textGrain size is a critical factor influencing the performance of fabricated structures, particularly their elastic modulus and yield strength. In metal additive manufacturing (AM), unique thermal properties such as thermal accumulation, thermal gradients, and cooling rates can lead to grain size variations throughout the structure. This study proposes a grain size estimation technique for additively manufactured metal thin-walled structures. The developed technique employs pulsed and continuous wave (CW) lasers to generate and measure zero-group-velocity (ZGV) Lamb waves. By extracting ZGV resonance frequencies from the measured waves, Poisson's ratio is determined and correlated with grain size using a pre-established model. The developed technique offers several key advantages: (1) Localized ZGV Lamb wave modes enable precise, localized grain size estimation. (2) The advanced and localized nature of the technique allows it to accommodate the complex geometries of compact and lightweight AM designs. (3) As a noncontact and non-destructive measurement method using lasers, it is suitable for both in-situ monitoring and post-processing investigation. The technique was experimentally validated on additively manufactured stainless steel 316L thin-walled samples, demonstrating its feasibility for real-time in-situ grain size monitoring in AM.
Provable wavelet-based neural approximation
No full textIn this paper, we develop a wavelet-based theoretical framework for analyzing the universal approximation capabilities of neural networks over a wide range of activation functions. Leveraging wavelet frame theory on the spaces of homogeneous type, we derive sufficient conditions on activation functions to ensure that the associated neural network approximates any functions in the function space induced by the activation function, along with an error estimate. These sufficient conditions accommodate a variety of smooth activation functions, including those that exhibit oscillatory behavior. Furthermore, by considering the L2-distance between smooth and non-smooth activation functions, we establish a generalized approximation result that is applicable to non-smooth activations, with the error explicitly controlled by this distance. This provides increased flexibility in the design of network architectures.
Nano-engineered permeation barriers for composite laminates: Polyamide nanofiber interlayers for cryogenic cycling durability and gas-barrier performance
No full textComposite-enhanced pressure vessels significantly reduce structural mass by leveraging the high specific strength of fiber-reinforced polymers. Yet, their use in cryogenic environments remains limited by microcrack formation caused by mismatches in the coefficients of thermal expansion between reinforcing fibers and polymer matrices, which compromises gas-barrier performance and necessitates a polymer liner. In addition, conventional oven-based processes for composite-overwrapped pressure vessels (COPVs) often result in high void content due to insufficient consolidation pressure, degrading mechanical properties and offsetting weight savings. This study introduces nano-engineered permeation barriers (nano-barriers): ultra-thin electrospun polyamide nanofiber layers interleaved between laminate plies to simultaneously suppress microcracks, reduce voids, and enhance gas impermeability-without the need for autoclave processing. Oven-cured laminates with nano-barriers exhibited void contents comparable to autoclave-cured laminates and achieved a 21% reduction in gas permeability relative to conventional oven-cured laminates. Notably, this performance was achieved using only similar to 6% of polymer mass required for a bulk liner. After 300 cryogenic cycles, nano-barrier-integrated laminates showed no microcracking, whereas controls experienced extensive microcracking and a 15% increase in permeability. Mechanical tests further confirmed that nano-barrier integration enabled improved double edge-notched tensile strength and short-beam strength on par with autoclave-cured composites, demonstrating that high mechanical performance can be achieved without high-pressure processing. Importantly, this approach is fully compatible with existing composite systems and offers a practical, scalable solution to mitigating cryogenic microcracking without modifying conventional manufacturing routes. These results highlight nanofiber-based barriers as a lightweight and robust strategy for enabling truly linerless, cryogenically durable all-composite pressure vessels.
A novel adaptive quality-based multi-fidelity surrogate framework for multiple low-fidelity data sources
No full textIn this paper, a novel adaptive quality-based multi-fidelity (AQBMF) surrogate framework is introduced to maximize the utilization of low-fidelity (LF) data from various domains. The main goal of the proposed method is to adaptively select and combine LF data, by assessing its quality, to create the most accurate surrogate. The core idea lies in interpreting the quality levels of LF data sources as the relative importance of LF surrogates that serve as basis functions in a multi-fidelity (MF) surrogate. Based on this approach, the proposed AQBMF surrogate framework comprises four main stages. In the first stage, a newly defined augmented MF formulation is constructed, initially assuming equal importance for all LF data sources. In the second stage, LF surrogates are ranked by importance through the proposed MF basis screening method. In the third stage, promising candidate surrogates are systematically constructed based on the importance ranking of the LF surrogates. During this stage, both the selection and filtering of LF data, as well as the hierarchical and ensemble combination-based MF methods are considered. In the last stage, the best surrogate is selected from the candidates using the proposed algorithm. Various benchmark test results demonstrate the superior performance of the proposed framework. Finally, engineering application results show that the proposed AQBMF surrogate achieves higher accuracy than existing ones within the same computational budget.
How trading barriers in underlying markets impact ETF trading and characteristics
No full textWe analyze the impact of the underlying market's minimum trading unit (MTU) on the corresponding exchange-traded fund (ETF) market. We provide novel evidence, based on unique handcollected information, that establishes a correlation between market accessibility and the ETF market. MTUs represent the minimum number of shares required for a transaction and act as trading barriers for investors. Our findings indicate that institutional investors exhibit decreased involvement in ETFs when the underlying markets are less accessible. The accessibility of underlying markets is positively correlated with arbitrage activity, tracking ability, and the probability of informed trading in ETFs. Following a decrease in the underlying markets' MTUs, institutional trading activity in ETFs gradually increases. In addition, we observe significant changes in arbitrage activity, tracking ability, and the probability of informed trading in ETFs.
A one-domain ghost-point immersed boundary method for conjugate fluid and porous medium flows
No full textWe develop a one-domain ghost-point immersed boundary (IB) method for conjugate fluid-porous medium flows. The solver is based on the Darcy-Forchheimer-Brinkman equations and employs a compact six-point stencil to enforce both velocity and stress continuity across fluid-porous interfaces on Cartesian grids, achieving second-order accuracy. The numerical implementation uses a fully implicit fractional-step scheme with approximate LU factorization, which decouples the velocity and pressure fields and produces linear systems compatible with efficient line-by-line solvers, enabling a compact and robust pressure Poisson solution. Compared with two-domain IB approaches, the proposed one-domain ghost-point framework shortens the interface-normal stencil, removes the need for body-fitted meshes, and eliminates outer iterations between the fluid and porous sub-solvers. Grid-convergence tests confirm second-order accuracy for both velocity and pressure. The method is validated against benchmark cases with straight and curved interfaces, low Darcy numbers, complex interface conditions, and thin geometries, showing accurate results with suppressed numerical oscillations. The one-domain ghost-point IB framework offers a flexible and efficient approach for conjugate fluid-porous medium simulations.
Extraction of energy from bi-directional flows of tidal currents and waves using a horizontal-axis Wells turbine based on the Blade Element Momentum theory
No full textThis study analytically and quantitatively investigates the extraction of energy from bi-directional flows of tidal currents using a horizontal-axis symmetric Wells turbine with zero-twist blades based on the Blade Element Momentum (BEM) theory. Exemplary tidal-current data measured at Kyuingin seaport, a west side of Korean peninsula, are used. During the year 2021, the extracted monthly averaged power and the associated power coefficient are calculated. The average tidal power is 0.93x10(5)similar to 1.42x10(5) W, the average BEM power is 0.34x10(5)similar to 0.58x10(5) W, and the power coefficient (=BEM power/Tidal power) is 0.36 similar to 0.41. This study also analytically and quantitatively investigates the simultaneous extraction of both tidal-current energy and wave energy using the same turbine. During a wave period, the ratio between extracted time-averaged BEM power from both tidal currents and waves (P-tide(+)wave, BEM) and BEM power from tidal currents only (P-tide,P- BEM) are calculated, which turns out to be comparable to the open-sea cases (P-tide(+)wave, open sea, P-tide,P- open sea). For example, for a tidal current speed of 1.5 m/s and waves of amplitude of 1.5 m and the average wave-induced velocity of 0.48 m/s, P-tide(+)wave, BEM/P-tide,P- BEM=1.16, P-tide(+)wave, open sea/P-tide,P- open sea=1.2. Also, power coefficients are comparable to each other (C-p,tide(+)wave=P-tide(+)wave, BEM/P-tide(+)wave, open sea=0.49, C-p,C-tide= P-tide,P- BEM/P-tide,P- open sea= 0.51).
Machine learning-optimized synthesis of freestanding conductive graphitized sheets from polybenzoxazine via self-peeling laser-induced processing
No full textLaser-induced graphene has emerged as a promising method for producing conductive carbon materials, but current approaches suffer from substrate adhesion issues and inefficient parameter optimization. Here, we report a machine learning-optimized synthesis of freestanding conductive graphitized sheets from polybenzoxazine (PBZ) via a novel self-peeling mechanism. The key innovation exploits the coefficient of thermal expansion mismatch between the partially cured PBZ precursor and the laser-formed graphitized layer. This significant thermal mismatch drives spontaneous delamination during processing, eliminating post-processing transfer steps. We implemented a Bayesian optimization framework with Gaussian process regression to efficiently navigate the three-dimensional parameter space of laser power, scanning speed, and working distance. The algorithm achieved optimal conditions (P = 30 W, S = 3 mm/s, WD = 16 mm) in just 33 experiments versus >200 for traditional methods-an 85 % reduction in experimental effort. The optimized process yields freestanding carbon black/reduced graphene oxide (CB/rGO) nanocomposite sheets with exceptional properties: sheet resistance of 0.72-1.2 Omega/square, consistent thickness of 150 +/- 11 mu m, hierarchical porosity with 154.9 m(2)/g BET surface area, and superior Joule heating performance reaching 330 degrees C at 4V. Comprehensive characterization revealed a microcrystalline graphitic structure with 96.81 at% C and 3.19 at% O, sp(2) carbon dominance (>80 % from XPS), and bimodal morphology combining nanostructured carbon black particles with porous rGO foam architecture. Multiple sheets can be produced from a single precursor, enhancing material efficiency. This integration of intelligent process optimization with innovative materials design opens new pathways for scalable production of high-performance graphene-based materials for electronics, energy storage, and thermal management applications.
Extremal density for subdivisions with length or sparsity constraints
No full textGiven a graph H, a balanced subdivision of His obtained by replacing all edges of H with internally disjoint paths of the same length. In this paper, we prove that for any graph H, a linear-in-e(H) bound on average degree guarantees a balanced H-subdivision. This strengthens an old result of Bollob & aacute;s and Thomason, and resolves a question of Gil-Fern & aacute;ndez, Hyde, Liu, Pikhurko and Wu. We observe that this linear bound on average degree is best possible whenever His logarithmically dense. We further show that this logarithmic density is the critical threshold: for many graphs H below this density, its subdivisions are forcible by a sublinear-in-e(H) bound on average degree. We provide such examples by proving that the subdivisions of any almost bipartite graph H with sublogarithmic density are forcible by a sublinear-in-e(H) bound on average degree, provided that H satisfies some additional separability condition.