bluuebunny/crossref_metadata_2025 huggingface dataset:
Material Selection Using Large Language Models
Authors: Patawari Jain Yash , Grandi Daniele , Groom Allin , Cramer Brandon , McComb Christopher
Published: November 2024

The task of material selection plays a pivotal role in a large number of industries, from manufacturing to construction. Material selection is usually carried out after several cycles of conceptual design, during which designers iteratively refine the design solution and the intended manufacturing approach. In design research, material selection is typically treated as an optimization problem with one correct answer, and restricted to specific types of objects or design functions. In this work, we instead treat materials selection as a problem with multiple answers with varying feasibility. We collect a dataset of experts’ material preferences for nine material categories across 16 diverse design scenarios, and report some trends in the data.

Recent advancements in the natural language processing field around transformer-based architectures, leading to the development of a multitude of pre-trained Large Language Models (LLMs) capable of understanding human language and exhibiting reasoning behaviors, raise some questions about the potential use of this technology for automating the material selection, especially during early the early conceptual design phase. In this work, we identify the degree to which material recommendations provided by an LLM align with those of expert human designers. The results underscore the potential of LLMs to inform material selection.
Multi-Task Learning for Intention and Trajectory Prediction in Human-Robot Collaborative Disassembly Tasks
Authors: Zhang Xinyao , Tian Sibo , Liang Xiao , Zheng Minghui , Behdad Sara
Published: November 2024

Human-robot collaboration (HRC) has become an integral element of many industries, including manufacturing. A fundamental requirement for safe HRC is to understand and predict human intentions and trajectories, especially when humans and robots operate in close proximity. However, predicting both human intention and trajectory components simultaneously remains a research gap. In this paper, we have developed a multi-task learning (MTL) framework designed for HRC, which processes motion data from both human and robot trajectories. The first task predicts human trajectories, focusing on reconstructing the motion sequences. The second task employs supervised learning, specifically a Support Vector Machine (SVM), to predict human intention based on the latent representation. In addition, an unsupervised learning method, Hidden Markov Model (HMM), is utilized for human intention prediction that offers a different approach to decoding the latent features. The proposed framework uses MTL to understand human behavior in complex manufacturing environments. The novelty of the work includes the use of a latent representation to capture temporal dynamics in human motion sequences and a comparative analysis of various encoder architectures. We validate our framework through a case study focused on a HRC disassembly desktop task. The findings confirm the system’s capability to accurately predict both human intentions and trajectories.
Predictive Modeling for Public Policy Design: The Impact of Artificial Lights at Night (ALAN) on Bird Strikes
Authors: Haynes Joey Paul E. , Bhalerao Mayank J. , Honeycutt Wesley T. , Allen Janet K. , Mistree Farrokh
Published: November 2024

Bird strikes present a growing concern to wildlife preservation efforts and pose safety and economic challenges in aviation and urban design that amalgamates a sociotechnical system. Our review of recent evidence suggests that artificial light at night (ALAN) is a contributing factor to bird strikes, especially during migration seasons, when birds are more likely to be disoriented by bright urban lighting.

In this paper, we explore the relationship between light pollution and bird strikes in aviation, with a focus on the application of predictive modeling to inform public policy. Central to our exploration lies is the foundational paradigm of Cyber-Physical-Social Systems (CPSS) that underscores the interaction between the cyber, physical, and social spaces, ensuring effective model-based system design. Multiple criteria are used to perform exploratory data analysis and train a predictive model, including the FAA wildlife strike database, a light pollution GIS map, and BirdCast migration forecasts.

Through exploratory data analysis and predictive modeling techniques, we identify critical correlations between light pollution levels and bird strike incidents. These findings are used to reveal significant trends and offer a model capable of predicting bird strike occurrences with implications for urban planning, lighting design, and aviation safety. We emphasize the potential of these predictive models in informing public policy decisions, aiming to mitigate bird strikes while considering ecological and industrial factors through model-based systems design. Moreover, we acknowledge our method’s limitations and provide recommendations for future research to refine the model’s accuracy and applicability in a policy-making context.
Spatially-Aware Milling Surface Flatness Prediction Through Physics-Based Graph Neural Network
Authors: Zhou Qianyu , Kim Jeongho , Tang Jiong
Published: November 2024

Milling operations are critical in the manufacturing sector, pivotal for achieving desired product specifications and surface quality. However, post-milling deformations, primarily induced by residual stress, can significantly affect the final flatness and surface integrity of workpieces, potentially deviating from manufacturing tolerances and standards. This issue is pronounced in the machining of A2024 aluminum, a material favored for its strength and lightweight properties but susceptible to such stress-related deformations. Finite element Method (FEM) can offer valuable insights but at the cost of extensive computational resources, making them less viable for real-time applications and rapid iteration across large parameter spaces. Addressing this challenge, our study introduces a spatially-aware predictive framework utilizing the physics-based Graph Neural Network (GNN). This framework leverages the spatial relationships between nodes on the milling surface and incorporates physics-guided insights from Jonson-Cook model to forecast post-milling surface flatness with crucial process inputs. A meticulously crafted FEM model drives our automated data generation pipeline, ensuring the creation of high-quality data. The fusion of FEM-derived data with specially tailored GNN modeling represents a paradigm shift in predictive machining results, enabling rapid and reliable flatness prediction to support adaptive manufacturing strategies. This framework is generic, and has the potential of being extended to other manufacturing processes.
Examining the Longitudinal Psychological Safety of Mentored Senior Capstone Design Teams
Authors: Arpini-Lorenzoni Ana , Hart Robert , Polk Todd W. , Murphy Alexander R.
Published: November 2024

The psychological safety of members on a team can impact outcomes during the engineering design process, where psychological safety describes to what extent a team member feels accepted and respected by the team. Engineering capstone courses (or senior design) provide an opportunity for students to interface with industry sponsors in a culminating engineering experience. This study measures team member’s psychological safety at three points during a two-semester senior design sequence (at the start, after the first semester, and after the second semester). Both individual and team psychological safety are considered. In this study, each design team is assigned a project mentor from academic and industry backgrounds to guide them throughout the process. Results show that the psychological safety of individuals does not significantly change over time. This result is consistent when comparing the type of mentor (industry-based or academia-based) assigned to each capstone team. In addition, no significant changes are observed for aggregate team psychological safety during the two-semester capstone design experience. This work contributes to an understanding of the factors that influence team members’ psychological safety during senior design courses to ultimately improve educational outcomes for undergraduate engineering students. In addition, this work provides insight into how engineering educators can promote positive collaboration between undergraduates and industry professionals through team mentorship sourced from both industry and academia.
Design for an Additively Manufactured Hollow Glass Microsphere Lattice Support Structure Using Topology Optimization
Authors: Persia Jude Thaddeus , Lee Soobum , Yun Victor
Published: November 2024

For future military mission using high-quality optics to develop remote sensing instruments, it is critical to design a lightweight substrate structure for reliable support to mirrors. This paper proposes a conceptual design methodology for generating a stable and lightweight supporting structure for a satellite mirror substrate using a 3D topology optimized TPMS lattice structure. This lattice would be constructed using 3D printed glass filled with hollow microspheres for further reduced weight while maintaining high rigidity. An optimal 3D topology is generated in MATLAB for maximum stiffness. Using various boundary conditions, multiple TPMS unit cell patterns are produced for comparison. Each unit cell design is used to construct a full assembly and postprocess is followed to eliminate staircases out of topology optimization and smooth the surface profile. Each of assembly is tested in FEM using ANSYS Workbench for stress analysis from vibration and 3-point bending test. A pareto frontier is produced to compare the designs with respect to their loading responses from two tests. This study identifies Schwarz P and N14 as best designs among others. A more realistic assembly with a larger number of unit cells and the corresponding experimental verification is required for detailed performance evaluation for lightweight mirror substrate application.
Pressure-Correction Based Transient Modeling of Wave Energy Powered Reverse Osmosis (RO) Desalination
Authors: Wu Xian , Li Xiaofan , Zuo Lei
Published: November 2024

Ocean wave-powered reverse osmosis (RO) desalination is an emerging field of study that combines the utilization of ocean energy and RO desalination techniques. However, due to the significant fluctuations in pressure and flow rate within the hydraulic system, an accurate transient model is necessary to estimate its performance accurately and effectively. This paper presents a two-dimensional transient model based on the pressure-correction algorithm to simulate the channel flow with porous walls and time-dependent inlet conditions. The coupled pressure, velocity, and salt concentration problem is solved iteratively by decoupling each term and updating them separately. The model is validated by comparing the results with analytical film theory which estimates the formation of the concentration polarization layer under constant inlet conditions. The performance of the RO systems, especially the concentration polarization phenomenon at the member surface, is investigated using different input conditions, including constant flow condition and sinusoidal flow condition. The salt concentration and permeate flux at the membrane boundary are studied to understand the effect of the dynamic inputs. Results show that the system can reach a higher maximum wall concentration and higher average recovery ratio in sinusoidal signal compared with the constant input. The model’s adaptability to different flow regimes, from steady to sinusoidal, underscores its potential as a valuable tool in optimizing RO desalination powered by ocean wave energy.
Increasing Accessibility of 3D-Printed Customized Prosthetics in Resource-Constrained Communities
Authors: Lee Junghun , Nkama Chukwuemeka , Yusuf Hadiza , Maina Joseph , Ikuzwe Jean , Byiringiro Jean , Busogi Moise , Tucker Conrad
Published: November 2024

Customized prosthetics are often inaccessible in resource-constrained communities. The authors of this work identify two significant barriers. First, obtaining a digital representation of the residual body is inaccessible. The proposed methods overcome this issue by achieving digital representation via smartphones. Second, determining the design and 3D printing process is challenging. Although additive manufacturing technology is widely adopted to fabricate customized prosthetics because of its high accessibility, it has a drawback in that the printing conditions significantly influence mechanical functionality. This issue can be magnified in resource-constrained communities with limited access to high-end 3D-printing technology and filaments. To overcome this issue, we propose a method to predict the mechanical functionality of the prosthetic printed in various printing processes. Finally, a case study demonstrating the implementation of the proposed methods is presented. The case study represents designing customized prosthetic sockets for patients with below-knee amputation on the right leg with a low to mid-end 3D printer. The authors aim to (1) increase accessibility in achieving an accurate digital model of the residual body and (2) reduce the effort of professionals in the 3D printing process, encouraging the implementation of 3D printing, especially in fused filament fabrication/Fused deposition modeling.
Designing Fictional Worlds for Play Through Large Language Models
Authors: Short Ada-Rhodes
Published: November 2024

This paper explores the application of Large Language Models (LLMs) in addressing the challenges of procedural generation in video game design, specifically tackling the Oatmeal Problem and the Bach Faucet Problem. These issues refer to the generation of endlessly similar, unengaging content and the devaluation of content due to its infinite producibility, respectively. By integrating design thinking, computational-cognitive heuristics, and prompt engineering with modern machine intelligence tools, we propose a novel approach termed Procedural Woodworking (PWw). This methodology aims to generate unique worlds and narratives through LLMs while enhancing player engagement by embedding a player’s character into the game’s narrative in a meaningful way. We utilize OpenAI’s ChatGPT-4 to create custom GPTs for world-building and narrative generation, evaluating their novelty and logical consistency through comparative rating scales. Our results indicate that both Top-Down and Bottom-Up world-building methods can produce logically consistent and structurally coherent outputs. The Procedural Woodworking approach, in particular, shows promise in generating novel narrative content that aligns with pre-made human-designed fictional worlds, potentially mitigating the identified procedural generation problems. This study lays the groundwork for further investigation into how LLMs can enrich game design and player experience by offering a more personalized and value-driven narrative generation process.
Fuel Consumption Reductions in Heavy-Duty Vehicles With Crank Pulley Decoupler Mounted Engines
Authors: Pakštys Saulius , Hegde Shailesh , Bonfitto Angelo
Published: November 2024

An increasing amount of effort has been directed to ensure the minimization of carbon dioxide emissions within the transport industry, particularly in the light passenger vehicle segments. This has been achieved through many advancements such as mass and aerodynamic drag reduction as well as the electrification of powertrains. The heavy-duty vehicle segment contributes significantly to the total emissions of the transport sector, however fuel consumption through electrification poses some limitations. Fuel efficiency has been achieved by adopting higher fuel injection pressures generating an increase of the crankshaft nose oscillation making Front-End Accessory Drive (FEAD) design more challenging. This paper provides a solution, with the incorporation of a crank pulley decoupler that isolates the irregularities from the FEAD that arise at the crank of the internal combustion engine. The analysis of a 12.7 L engine tested with a steel pulley and with the decoupler is presented. The brake-specific fuel consumption map is obtained for the engine mounted with the steel pulley and with the decoupler through a dynamometer test. The engine maps are incorporated into a tractor trailer with a 4 × 2 configuration in the Vehicle Energy consumption Tool (VECTO), evaluated over three driving cycles: long-haul delivery, regional delivery, and urban delivery. The outcome indicates a maximum relative fuel saving of 0.63% for a vehicle mounted with a decoupler compared to one mounted with a solid steel pulley.
Data-Driven Modeling Adaptive Aerostructures
Authors: Roetzer James , Boik Hunter , Janke Ben , Li Xingjie , Hall John
Published: November 2024

In the growing domain of wind energy, morphing aerostructures provide an avenue of research to improve aerodynamic performance and turbine control. Branching off previous work by the authors, several data-driven models which relate the actuation of a linearly segmented active morphing blade to an arbitrary performance parameter, aerodynamic thrust load in the case examined, are developed, presented, and compared to evaluate accuracy. Models are developed for the Danish Technical University (DTU) 10 MW and National Renewable Laboratory (NERL) 15 MW reference turbines to ensure generalizability. The models are trained using OpenFAST simulations to generate training and test data. State vector machine (SVM), neural network (NN), decision tree, and gaussian process regression (GPR) models are developed. Comparing root-mean-square error (RMSE), the GPR method produces the least error, in most cases by an order of magnitude or more. The developed model is then used in a demonstrative application, an optimization of the target parameter. Particle swarm optimization is used to find the global minimum input space of the GPR model at discrete wind speeds. The references turbines show a respective maximum improvement in the target performance parameter of 3.15% and 2.66%. Optimization results indicate that refinement of the developed model may improve results.
Surface Roughness Repeatability Analysis for Multi-Build Overhang Parts in Powder Bed Fusion Additive Manufacturing
Authors: Yang Zhuo , Hong Gi Suk , Lu Yan , Hu Chun-Chun , Yang Haw-Ching , Lane Brandon
Published: November 2024

Additive manufacturing (AM) has been evolving from rapid prototyping towards applications in high-volume production and mass manufacturing. However, achieving consistent quality and meeting industry standards remains a challenge that hinders AM industrialization. While most traditional manufacturing processes have established process control for part quality management, the complete understanding of part quality repeatability across builds, machines and processes is still missing. Hence, quality-related features such as surface roughness cannot be consistently reproduced for mass production. This study initiates the process of investigating laser powder bed fusion part repeatability in terms of surface roughness, based on the measurements conducted at the National Institute of Standards and Technology (NIST). The research compares the roughness of different surfaces of a single AM part, identical parts from a single build, and parts fabricated from two separate builds. The part has designed surfaces varying from up-skin surfaces with different conditions to down-skin surfaces of overhangs. The roughness measurements based on XCT data reveal that average roughness, Ra, of different types of surfaces is distinguishable from each other. It’s also observed that the roughness of up-skin surfaces consistently exhibits lower values compared to down-skin surfaces. However, inherent variations between builds pose challenges to achieving full repeatability. Analysis indicates that the roughness of all four parts from the first build is lower than that of the 16 parts from the second build with the same process settings and feedstock material. Achieving repeatability becomes increasingly difficult when process, machine or material variations between builds are present.
A Vision-Based Health Inspection of Power Line Conductors for the Mobile Damping Robot
Authors: Kang Hyun Myung , Yoon Ranhee , Barry Oumar
Published: November 2024

Ensuring the structural integrity of the overhead power line conductor is crucial for maintaining the safety and reliability of the electrical transmission system. Exposure to environmental hazards like moisture, dust, and Wind-Induced Vibrations (WIV) can lead to defects and corrosion in power line conductors, which are primary contributors to fatigue and shortened lifespan. Thus, this paper presents a vision-based health inspection of power line conductors for a maintenance robot. The method involves image filtering techniques such as Sobel, Scharr, and Gray-scale Variance Normalization (GVN). After filtering the image, row and column analysis is conducted to identify relevant patterns that distinguish healthy and unhealthy conductors, utilizing histograms for data representation. From the histogram data analysis, 10 features were chosen from observation. Subsequently, the collected image data is classified into either healthy or unhealthy categories through supervised machine learning models, including Random Forest (RF), Multi-Layer Perception (MLP), and Gradient Boosting (GB). The best combination of features is extracted to optimize each machine-learning models accordingly. Experimental results validated the effectiveness of our method, which has been specifically fitted for the Mobile Damping Robot (MDR), presenting its potential for enhancing power line maintenance.
Parametric: Empowering In-Situ Parametric Modeling in Augment Reality for Personal Fabrication
Authors: Duan Runlin , Hu Xiyun , Liu Min , Shi Jingyu , Ramani Karthik
Published: November 2024

Parametric modeling software is a powerful design tool for controlling dimensions, accessing high-quality model libraries, and iterating on design concepts. However, its steep learning curve, complex user interfaces, and lack of spatial understanding can hinder novice users from creating reusable and precise solid models. To overcome these barriers, we introduce pARametric, an augmented reality (AR) system that provides an effortless parametric modeling experience in situ. This system features an immersive sketching platform for freehand sketching, a transformer-based parametric sketch construction algorithm, and an AR interface that offers intuitive hand-to-virtual geometry interaction for 3D modeling operations. In pARametric, a novice designer without experience in 3D modeling can create parametric models for personal fabrication. Our system uses environmental information as design references to make decisions about structure, dimension, and shape. The system allows users to express their design intents by sketching in freehand rather than mastering the sophisticated operations of existing modeling software. We also incorporate widely used 3D modeling features such as extrusion, revolution, sweep, and mirror to create a wide variety of models. In a user study with 8 participants, we discovered that novice designers see great potential in pARametric to enhance their capabilities and willingness to engage in parametric modeling for design. We further discuss how our system can be integrated into traditional parametric modeling workflows to benefit a wider range of designers for visualization and collaboration on sophisticated design tasks.
Evaporative Thermal Management of Batteries in Electric Vehicles Using Flexible Structures
Authors: Bouzidi Myriam , Kim Junyoung , Goldman Jonathan , Joshi Yogendra
Published: November 2024

As the capacity of electric vehicle (EV) battery packs increases and recharging times are reduced, effective battery thermal management becomes a key challenge. When exposed to elevated temperatures and non-uniform thermal conditions, insufficient heat dissipation within the battery module can detrimentally impact both its operational lifespan and performance, posing a potential risk of thermal runaway. Therefore, by reducing maximum temperatures and enhancing temperature uniformity through the batteries in order to improve lifespan and performance. Furthermore, depending on the specific battery cell technologies employed in EVs, battery swelling also emerges as a significant challenge as it can increase heat generation and drops in battery life. Thus, this paper develops a two-phase flexible battery thermal management system, enhanced with a copper microstructure, using a dielectric coolant (HFE-7000) to mitigate battery surface temperatures. The heat transfer performances were experimentally studied in a mass flow rate range of 4.5 g.s−1 to 11 g.s−1 with representitive heat dissipation values between 10 W.m−2 and 1.4 W.m−2 and shows the ability of the system to maintain average surface temperatures in a the battery optimal operational range between 25°C and 35°C.
Commissioning and Thermohydraulic Characterization of a Single-Phase Liquid-Cooled High-Density Data Center Rack
Authors: Heydari Ali , Kisitu Deogratius , Ortega Alfonso , Eslami Bahareh , Shahi Pardeep , Tradat Mohammad , Costello James , Margaritondo Benjamin
Published: November 2024

High-density data centers are currently increasing in demand. As data centers grow in power and complexity, traditional air cooling is no longer adequate for handling contemporarily increasing HPC and AI workloads. Liquid cooling is emerging as an efficient solution for high-density data center cooling, with a host of benefits, including reduced costs, higher sustainability, and the ability to handle higher power densities. In the current work, a single-phase liquid-cooled test high-density rack was assembled, experimentally commissioned, and characterized at full design power. The rack has four servers, each with eight (8) high-power graphic processing units (GPU) thermal test vehicles (TTV) and four (4) low-power switch TTVs. Each TTV was instrumented with Type-T thermocouples to measure the case temperatures. Each GPU TTV was powered at 1000 W and each switch TTV was powered at 250 kW. Liquid-cooled cooling loops (each consisting of eight (8) GPU cold plates and four (4) switch cold plates) were attached to each of the server TTVs. The inlet and outlet of each cooling loop were instrumented with calibrated thermistors, pressure sensors, and magnetic-inductive flow meters to measure the cooling loop’s inlet and exit fluid temperatures, pressure drop, and flow rates, respectively. Two of the test servers were reliability servers and the other two were the thermal verification server and hydraulic verification server. The thermal verification server was hugely instrumented with numerous thermistors to verify the inlet and outlet fluid temperatures of each cold plate. The hydraulic verification server had multiple magnetic-inductive flow meters to verify flow distribution in each cold plate on the cooling loop. Results show that at the coolant [25% propylene glycol/water (PG-25)] supply temperature of 32 °C and full design power, the average GPU TTV case temperature per TTV was close to 64.5 °C. The mean switch heater Tcase is generally identical per server TTV and is about 53.5 °C, with a mean standard deviation of less than 0.7 °C. The energy balance on a rack reveals that about 97.1% of the heat dissipated by server TTVs was rejected to primary side/facilities chilled water.
Capillary-Based Two-Phase Cooling for High Power Density Power Electronics
Authors: Lin Yujui , Kwon Heungdong , He Yini , Chen Hao , Gupta Man Prakash , Degner Michael , Asheghi Mehdi , Mantooth H. Alan , Goodson Kenneth E.
Published: November 2024

Effective thermal management is crucial in power module design, significantly influencing the cost, performance, and reliability of the traction inverters. As we strive for greater power density and smaller form factors, innovative thermal solutions become imperative. In this study, we explore a two-phase cooling method enabled by substrate-embedded microchannels for the power module. We utilize surface enhancement features at the substrate microchannels to enable capillary-driven flow for passive liquid supply. We find that the two-phase cooling in our study leads to a high heat flux removal rate of 400 W/cm2 at a low superheat of 10 °C. Moreover, the coolant flow rate is less than 1 g/min, nearly 50 times lower than the pumped flow-based two-phase microcoolers. We also investigate the implementation of the two-phase cooling method into a power module-like package. A systematic study of packages with 1 and 4 chips is conducted and verified with the simulation. The impact of the condenser and vapor recovery features on the cooling performance is also studied. The proposed two-phase cooler can overcome the typical issues encountered in two-phase cooling, such as flow instability and dry-out.
Evaluation of Warpage Deformation in Semiconductor Device Packaging Process
Authors: Yagi Taiju , Yu Qiang , Shimada Haruyuki , Yanaka Yuichi , Masuko Takashi
Published: November 2024

Compression molding is gaining attention as a next-generation method for molding high-density and thinner semiconductors, driven by the increasing demand for products like smartphones and tablets. Unlike traditional methods, it involves uniformly supplying granular resin to the substrate to minimize resin flow, thus enabling batch encapsulation of large substrates without product variation or resin anisotropy. However, warpage during the semiconductor packaging process poses a technical challenge, especially for large carrier substrate molds. This warpage, caused by differences in the Coefficient of Thermal Expansion (CTE) between the substrate and encapsulant, as well as mold shrinkage effects during curing, can lead to defects. To address this, researchers have studied material combinations to minimize warpage, but predicting warpage for large and thin substrates has been difficult. This study aims to develop a Finite Element Method (FEM) simulation-based method to predict warpage during the packaging process, streamlining the selection of encapsulating materials. Through experimentation and simulation, the team aims to identify patterns of warpage, investigate the effects of factors such as resin cure shrinkage from the substrate and resin combination on warpage, and ultimately improve the consistency and predictability of the packaging process.
Effects of Package Position and Constraint Conditions on Fatigue Life of QFP Interconnect Structure Under Random Vibration
Authors: Shimamura Nozomi , Yu Qiang
Published: November 2024

In this paper, the effects of board restraint conditions and package location on the fatigue life of quad flat packaging (QFP) interconnect structures under random vibration loading for automotive applications are evaluated using the finite element method. Four different restraint conditions of the board model were used to reproduce the natural frequencies of the board, and six different positions of the package were analyzed and evaluated. The results show that the fatigue life depends on the relationship between the first-order natural frequency and the patterns of power spectral density (PSD) of the input. In addition, it was found that the maximum principal strain of printed circuit board (PCB) directly under the center of package had a significant effect on the maximum damage location and its damage value in the lead area. The relationship between the package mounting position and board restraint conditions and the degree of damage was found to be regular.
Comprehensive Analysis of Two-Phase Flow Boiling for IT Equipment Cooling: A System and Component-Level Approach
Authors: Najjari Mohammad Reza , Turner Cam , Mostafavi Kamal
Published: November 2024

As part of CoolIT System Inc.’s ongoing assessment of liquid cooling technologies, the company performed an in-depth study of two-phase direct liquid cooling (2P DLC). The study focused on the technology’s commercial viability from a system design and operation perspective using a 2P DLC system set up in CoolIT’s Liquid Lab™ Innovation Centre coupled with extensive industry research.

This paper outlines the study’s results, supplemented with research, and describes the inherent challenges system developers must address when developing a two-phase system. We examine how pressure drops affect system performance and evaluate the advantages and disadvantages of arranging components in a series configuration. The authors also examine the operational considerations of using refrigerants.

In sum, the higher costs associated with the refrigerant and equipment required for high working pressures in a 2P DLC system, greater health and safety risks as well as legal liability risks, outweigh the perceived performance benefits for industry-wide deployments. This is especially true given the excellent thermal performance of simpler, more cost-effective single-phase direct liquid cooling (1P DLC).

While the authors conclude that 2P DLC is not yet a viable solution for mass market production and adoption, further investigation is warranted to determine whether it is applicable for specific IT equipment applications.
Validation and Translation of Process Monitoring Tools for Commercial Aerosol Jet Printing Systems
Authors: Rurup Jeremy D. , Secor Ethan B.
Published: November 2024

Aerosol jet printing is a promising method for additive electronics packaging, offer digital, non-contact deposition of functional materials with resolution below 20 microns. However, process reliability remains a key challenge in translating this method to production environments. In recent years, we have developed a real-time process monitoring technology for aerosol jet printing using light scattering from the aerosol stream. In this work, we aim to translate this capability to commercial printing systems. Experimental efforts use two different commercial printing systems, namely Sprint and Decathlon systems from Optomec. Moreover, while prior work focused exclusively on ultrasonic atomization, we will discuss results for both ultrasonic and pneumatic methods for droplet generation. The comparison across printing systems and atomization methods provides valuable insight for the generality of the process monitoring approach. Alongside routine testing to validate the process monitoring method over timescales of tens of minutes, we show that variability in the measurement on the seconds timescale can serve as an indicator for nonidealities in the print process. Overall, this technique advances the utility of aerosol jet printing for electronics packaging applications by improving process reliability in prototyping and production, providing insight on fundamental mechanisms, and establishing a potential approach to efficient and more standardized material and process development.
Thermal Performance of Low GWP Dielectric Fluid for Two-Phase Immersion Cooling
Authors: Yuksel Anil , Pottker Gustavo , Van Wassen Abigail , Brandt Drew
Published: November 2024

Two-phase immersion cooling has become one of the most promising technologies for cooling servers and chips in data centers due to its superior cooling performance. In this work, the thermal performance of a new low-global warming potential (GWP) dielectric fluid, Opteon™ 2P50, was evaluated using an Intel Sapphire Rapids thermal test vehicle (TTV) coupled with a commercially available microporous boiler plate with a design power of 200–600W. Tests were carried out at power levels from 300–1000W, in three orientations and with two different thermal interface materials (TIMs) while the fluid was always kept near saturated conditions. For the vertical and 45° orientation, the case-to-fluid thermal resistance reached a minimum of about 0.02 K/W at about 400W or a heat flux of around 16 W/cm2. At the maximum boiler plate design power of 600W (24 W/cm2), the case-to-fluid thermal resistance still ranged from 0.023–0.026 K/W. It was estimated that the critical heat flux was reached between 900 and 1000W, or 36–40 W/cm2, for the vertical orientation. Results also showed the case-to-fluid thermal resistance was lower for the horizontal than the vertical orientation, consistent with previous studies. Finally, additional experiments demonstrated that indium pad yields a much lower case-to-fluid thermal resistances than the thermal grease tested.
Performance and Reliability Comparison of Thermoformed, Screen-Printed Structures on Polycarbonate (PC) and High Impact Polystyrene (HIPS) Substrates Under Sustained High Temperatures for In-Mold Electronics
Authors: Lall Pradeep , Kulkarni Shriram , Miller Scott
Published: November 2024

In-mold electronics offers a path toward reducing the carbon footprint in automotive platforms through weight reduction. This technology enables the integration of human-machine interfaces directly onto automotive surfaces using additive printing processes. Among these processes, screen printing is a promising method due to its suitability for large surface areas and high throughput efficiency. This paper sets out to evaluate and compare the electrical and mechanical performance of additively screen-printed, thermoformed circuits on Polycarbonate (PC) and High Impact Polystyrene (HIPS) substrates under prolonged high temperatures. The research aims to characterize the long-term reliability and stability of these circuits under significant thermal stress and to analyze their failure modes and mechanisms. The methodology involves screen printing two types of test structures — Traces with Pads and Circuits — on both PC and HIPS substrates. In this paper, results are presented on SMD attachment of components on IME Circuits. The interaction of high-temperature processing with circuit performance has been studied.
Evaluation of Printed Logic Circuits With Additively Packaged GaN Bare Die Devices at High Temperatures
Authors: Lall Pradeep , Soni Ved , Karakitie Daniel , Miller Scott
Published: November 2024

Current electronics packaging is not designed to sustain and operate at high temperatures above 150°C. However, in specialized applications, operation at extreme high and extreme low temperatures can be a requirement. Since the current electronics substrates are not high-temperature qualified, low-temperature co-fired ceramic (LTCC) can be used as a substrate for extreme-high-temp capable electronic circuits. Also, since traditional silicon carbide dies and package architectures would not sustain high temperatures, bare gallium nitride dies can be used. In this study, additively printed electronic circuits capable of operation at high temperatures have been attempted to be designed. Copper-clad LTCC has been used as the substrate of choice for fabricating these circuits. The circuit for this study was fabricated using the LPKF U4 Protolaser machine for laser patterning. Components are then attached to the patterned circuit using a transient liquid phase sintering (TLPS) paste, which sustains up to 600°C. The candidate circuit chosen for this study is a full-wave rectifier circuit. The fabricated circuits have been tested in a high-temperature kiln, and their performance is measured using an oscilloscope.
Enabling Reduced Footprint SMT Components in IC Packages: A Simulation-Based Approach for Solder Paste Volume Optimization
Authors: Puliyath Nidheesh , Xie Huxiao , Alcira Cecille , Kothari Nakul
Published: November 2024

The Integrated Circuit (IC) packaging sector is experiencing heightened demand for compact, and efficient packages, driven by advancements in AI and 5G technologies. This necessitates smaller package component footprints and increased component density. For surface mount technology (SMT), solder volume optimization is crucial for component height control and mechanical performance, to ensure device integrity and longevity. Research indicates that solder volume during SMT integration affects joint quality, standoff height and reliability. For the SMT integration, some of the key current challenges in the industry are reducing the size and footprint of SMT components and simultaneously optimizing the solder volume in relation to the reduced size. The use of SMTs on both the die side and the BGA side of the package are also increasingly common. However, existing studies have not sufficiently tackled these complex issues. Additionally, there is a significant research gap concerning the optimization of solder volume for intricate SMT components, like deep trench capacitors (DTC). These gaps provide a potential area for further exploration and advancement in this domain. This paper presents a simulation-based approach for solder paste optimization that can support IC package design and development. Thus, aiming to cut development costs and shorten the time from development to deployment by using the model for proof-of-concept validation. Authors present an energy-based simulation approach for integrating SMT in IC packages, to address current industry design challenges. This approach was validated using two types of Multilayer ceramic capacitor (MLCC) capacitors and a deep trench capacitor (DTC), promising substantial cost and time reductions in package development.
A nearly terrestrial D/H for comet 67P/Churyumov-Gerasimenko
Authors: Mandt Kathleen E. , Lustig-Yaeger Jacob , Luspay-Kuti Adrienn , Wurz Peter , Bodewits Dennis , Fuselier Stephen A. , Mousis Olivier , Petrinec Steven M. , Trattner Karlheinz J.
Published: November 2024

Cometary comae are a mixture of gas and ice-covered dust. Processing on the surface and in the coma change the composition of ice on dust grains relative to that of the nucleus. As the ice on dust grains sublimates, the local coma composition changes. Rosetta observations of 67P/Churyumov-Gerasimenko previously reported one of the highest D/H values for a comet. However, reanalysis of more than 4000 water isotope measurements over the full mission shows that dust markedly increases local D/H. The isotope ratio measured at a distance from the nucleus where the gas is well mixed is close to terrestrial, like that of other Jupiter family comets. This lower D/H has implications for understanding comet formation and the role of comets in delivering water to Earth.
Compact highly sensitive photothermal RT-LAMP chip for simultaneous multidisease detection
Authors: Guo Wenshang , Tao Ye , Yang Ruizhe , Mao Kaihao , Zhou Hongwei , Xu Minghui , Sun Tie , Li Xiao , Shi Changrui , Ge Zhenyou , Xue Rui , Zhou Haizhou , Ren Yukun
Published: November 2024

Developing instant detection systems with disease diagnostic capabilities holds immense importance for remote or resource-limited areas. However, the task of creating these systems—which are simultaneously easy to operate, rapid in detection, and cost-effective—remains a challenge. In this study, we present a compact highly sensitive photothermal reverse transcriptase–loop-mediated isothermal amplification (RT-LAMP) chip (SPRC) designed for the detection of multiple diseases. The nucleic acid (NA) amplification on the chip is achieved through LAMP driven by either LED illumination or simple sunlight focusing. SPRC performs sample addition and amplification within a limited volume and autonomous enrichment of NA during the sample addition process, achieving a limit of detection (LOD) as low as 0.2 copies per microliter. Through 120 clinical samples, we achieved an accuracy of 95%, with a specificity exceeding 97.5%. Overall, SPRC has achieved promising progress in the application of point-of-care testing (POCT) by using light energy to simultaneously detect multiple diseases.
Channel width modulates the permeability of DNA origami–based nuclear pore mimics
Authors: Feng Qingzhou , Saladin Martin , Wu Chunxiang , Cao Eason , Zheng Wei , Zhang Amy , Bhardwaj Pushpanjali , Li Xia , Shen Qi , Kapinos Larisa E. , Kozai Toshiya , Mariappan Malaiyalam , Lusk C. Patrick , Xiong Yong , Lim Roderick Y. H. , Lin Chenxiang
Published: November 2024

Nucleoporins (nups) in the nuclear pore complex (NPC) form a selective barrier that suppresses the diffusion of most macromolecules while enabling rapid transport of nuclear transport receptor (NTR)–bound cargos. Recent studies have shown that the NPC may dilate and constrict, but how altering the NPC diameter affects its selective barrier properties remains unclear. Here, we build DNA nanopores with programmable diameters and nup arrangements to model the constricted and dilated NPCs. We find that Nup62 proteins form a dynamic cross-channel barrier impermeable to hepatitis B virus (HBV) capsids when grafted inside 60-nm-wide nanopores but not in 79-nm pores, where Nup62 cluster locally. Furthermore, importin-β1 substantially changes the dynamics of Nup62 assemblies and facilitates the passage of HBV capsids through the 60-nm NPC mimics containing Nup62 and Nup153. Our study shows that transport channel width is critical to the permeability of nup barriers and underscores NTRs’ role in dynamically remodeling nup assemblies and mediating the nuclear entry of viruses.
Near-infrared spectroscopy–enabled electromechanical systems for fast mapping of biomechanics and subcutaneous diagnosis
Authors: Wang Yihang , Henderson Josh , Hafiz Priyash , Turlapati Pranav , Ramsgard Daniel , Lipman Will , Liu Yihan , Zhang Lin , Zhang Zhibo , Davis Brayden , Guo Ziheng , Wang Shuodao , Seymour Lillian , Xie Wanrong , Bai Wubin
Published: November 2024

Fast and accurate assessment of skin mechanics holds great promise in diagnosing various epidermal diseases, yet substantial challenges remain in developing simple and wearable strategies for continuous monitoring. Here, we present a design concept, named active near-infrared spectroscopy patch (ANIRP) for continuously mapping skin mechanics. ANIRP addresses these challenges by integrating near-infrared (NIR) sensing with mechanical actuators, enabling rapid measurement (<1 s) of Young’s modulus, high spatial sensing density (~1 cm
2
), and high spatial sensitivity (<1 mm). Unlike conventional electromechanical sensors, NIR sensors precisely capture vibrational frequencies propagated from the actuators without needing ultraclose contact, enhancing wearing comfort. Demonstrated examples include ANIRPs for comprehensively moduli mapping of artificial tissues with varied mechanical properties emulating tumorous fibrosis. On-body validation of the ANIRP across skin locations confirms its practical utility for clinical monitoring of epidermal mechanics, promising considerable advancements in real-time, noninvasive skin diagnostics and continuous health monitoring.
Infection-associated chronic conditions: Why Long Covid is our best chance to untangle Osler’s web
Authors: Peluso Michael J. , Hanson Maureen R. , Deeks Steven G.
Published: November 2024

The recognition of Long Covid has renewed efforts to understand other infection-associated chronic conditions (IACCs). Here, we describe how studies of Long Covid and other IACCs might inform one another. We argue for the importance of a coordinated research agenda addressing these debilitating illnesses.