Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction

In this research, we developed a graph-based framework to represent various aspects of optimal thermal management system design, with the aim of rapidly and efficiently identifying optimal design candidates. Initially, the graph-based framework is utilized to generate diverse thermal management system architectures. The dynamics of these system architectures are modeled under various loading conditions, and an open-loop optimal controller is employed to determine each system's optimal performance. These modeled cases constitute the dataset, with the corresponding optimal performance values serving as the labels for the data. In the subsequent step, a Graph Neural Network (GNN) model is trained on 30% of the labeled data to predict the systems' performance, effectively addressing a regression problem. Utilizing this trained model, we estimate the performance values for the remaining 70% of the data, which serves as the test set. In the third step, the predicted performance values are employed to rank the test data, facilitating prioritized evaluation of the design scenarios. Specifically, a small subset of the test data with the highest estimated ranks undergoes evaluation via the open-loop optimal control solver. This targeted approach concentrates on evaluating higher-ranked designs identified by the GNN, replacing the exhaustive search (enumeration-based) of all design cases. The results demonstrate a significant average reduction of over 92% in the number of system dynamic modeling and optimal control analyses required to identify optimal design scenarios.Comment: 13 pages, 17 figure

Multi-split configuration design for fluid-based thermal management systems

High power density systems require efficient cooling to maintain their thermal performance. Despite this, as systems get larger and more complex, human practice and insight may not suffice to determine the desired thermal management system designs. To this end, a framework for automatic architecture exploration is presented in this article for a class of single-phase, multi-split cooling systems. For this class of systems, heat generation devices are clustered based on their spatial information, and flow-split are added only when required and at the location of heat devices. To generate different architectures, candidate architectures are represented as graphs. From these graphs, dynamic physics models are created automatically using a graph-based thermal modeling framework. Then, an optimal fluid flow distribution problem is solved by addressing temperature constraints in the presence of exogenous heat loads to achieve optimal performance. The focus in this work is on the design of general multi-split heat management systems. The architectures discussed here can be used for various applications in the domain of configuration design. The multi-split algorithm can produce configurations where splitting can occur at any of the vertices. The results presented include 3 categories of cases and are discussed in detail.Comment: 11 pages, 18 figure

Extracting Design Knowledge from Optimization Data: Enhancing Engineering Design in Fluid Based Thermal Management Systems

As mechanical systems become more complex and technological advances accelerate, the traditional reliance on heritage designs for engineering endeavors is being diminished in its effectiveness. Considering the dynamic nature of the design industry where new challenges are continually emerging, alternative sources of knowledge need to be sought to guide future design efforts. One promising avenue lies in the analysis of design optimization data, which has the potential to offer valuable insights and overcome the limitations of heritage designs. This paper presents a step toward extracting knowledge from optimization data in multi-split fluid-based thermal management systems using different classification machine learning methods, so that designers can use it to guide decisions in future design efforts. This approach offers several advantages over traditional design heritage methods, including applicability in cases where there is no design heritage and the ability to derive optimal designs. We showcase our framework through four case studies with varying levels of complexity. These studies demonstrate its effectiveness in enhancing the design of complex thermal management systems. Our results show that the knowledge extracted from the configuration design optimization data provides a good basis for more general design of complex thermal management systems. It is shown that the objective value of the estimated optimal configuration closely approximates the true optimal configuration with less than 1 percent error, achieved using basic features based on the system heat loads without involving the corresponding optimal open loop control (OLOC) features. This eliminates the need to solve the OLOC problem, leading to reduced computation costs.Comment: 13 pages, 20 figure

Investigation on the Regional Loss Factor and Its Anisotropy for Aortic Aneurysms

An aortic aneurysm is a lethal arterial disease that mainly occurs in the thoracic and abdominal regions of the aorta. Thoracic aortic aneurysms are prevalent in the root/ascending parts of the aorta and can lead to aortic rupture resulting in the sudden death of patients. Understanding the biomechanical and histopathological changes associated with ascending thoracic aortic aneurysms (ATAAs), this study investigates the mechanical properties of the aorta during strip-biaxial tensile cycles. The loss factor—defined as the ratio of dissipated energy to the energy absorbed during a tensile cycle—the incremental modulus, and their anisotropy indexes were compared with the media fiber compositions for aneurysmal (n = 26) and control (n = 4) human ascending aortas. The aneurysmal aortas were categorized into the aortas with bicuspid aortic valves (BAV) and tricuspid aortic valves (TAV). The strip-biaxial loss factor correlates well with the diameter of the aortas with BAV and TAV (for the axial direction, respectively, R2 = 0.71, p = 0.0022 and R2 = 0.54, p = 0.0096). The loss factor increases significantly with patients’ age in the BAV group (for the axial direction: R2 = 0.45, p = 0.0164). The loss factor is isotropic for all TAV quadrants, whereas it is on average only isotropic in the anterior and outer curvature regions of the BAV group. The results suggest that loss factor may be a useful surrogate measure to describe the histopathology of aneurysmal tissue and to demonstrate the differences between ATAAs with the BAV and TAV

Theoretical and experimental analyses of the rupture of ascending thoracic aortic aneurysm

Ascending thoracic aortic aneurysms (ATAAs) is an arterial disease which can lead to a dissection or rupture of the aorta, causing the death of the patients. Several studies have investigated the rupture mechanisms of ATAAs, however, underlying reasons behind aortic rupture (failure) have not been fully understood and further investigations are necessary. Surgical interventions used to treat this disease are associated with risks of mortality and morbidity. While size is the main clinical measure for aortic replacement surgery, there are uncertainties about the efficiency of this measure. To better guide surgical decision making, a biomechanical surrogate measure estimating ATAA wall properties is needed to better stratify patients. Understanding the biomechanical and histopathological changes associated with ascending thoracic aortic aneurysms (ATAAs), this research investigates different mechanical properties of the aorta. The rupture of pathological aortic tissue is a local phenomenon resulting from defects or tears in the vessel wall. In this work, the toughness-based rupture properties of human ATAAs have been examined. The toughness, biaxial tensile characteristics, and histological properties of aneurysmal and control ascending thoracic aortas (ATAs) were characterized from four quadrants of surgically excised aortic rings. To further explore the rupture propensity of ATAAs, the inter-correlation of the toughness properties with histological characteristics have been explored. The toughness properties were also investigated for cryopreserved and control porcine tissues.The loss factor, a surrogate measure defined as the ratio of dissipated energy to the energy absorbed during a tensile cycle, the incremental modulus, and their anisotropy index were also determined for the controlled and aneurysmal human ATAs. These parameters were compared with the media fiber compositions, the aortic diameter, and patients' age for the ATAAs associated with bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV). Additionally, the variations of fiber contents through the aortic thickness and the non-linear tensile characteristic of the aortas have been examined.The results obtained suggest tissue remodeling could affect toughness and stiffness properties differently in ascending aortic aneurysms. The results propose the loss factor, which may be measured non-invasively, could be a useful surrogate measure to describe the histopathology of aneurysmal tissue and to demonstrate the differences between ATAAs with bicuspid and tricuspid aortic valve types.L'anévrisme de l'aorte thoracique ascendante (les ATAAs) est une maladie artérielle qui peut conduire à une dissection ou une rupture de l'aorte, pouvant causer la mort. Plusieurs études ont analysé les mécanismes de rupture des ATAAs, cependant, les raisons sous-jacentes à la rupture de l'aorte (échec) n'ont pas été entièrement élucidées et des investigations additionnelles sont nécessaires. Les interventions chirurgicales utilisées pour traiter cette maladie sont associées à des risques de mortalité et de morbidité élevés. Bien que la taille soit la principale mesure clinique pour la chirurgie de remplacement aortique, il existe des incertitudes quant à l'efficacité de cette intervention. Pour mieux orienter la prise de décision chirurgicale, une mesure de substitution biomécanique pour l'estimation des propriétés de la paroi ATAA est nécessaire pour mieux stratifier les patients. Pour comprendre les propriétés mécaniques de l'aorte, cette recherche étudie différents changements biomécaniques et histopathologiques associés aux anévrismes ascendants de l'aorte thoracique (ATAAs). La rupture du tissu aortique pathologique est un phénomène local résultant de défauts ou de déchirures dans la paroi du vaisseau. Dans ce travail, les propriétés de rupture basées sur la ténacité d'ATAAs humains ont été caractérisées. La ténacité, les caractéristiques de traction biaxiale et les propriétés histologiques anévrysmales et de contrôle d'aortes ascendantes thoraciques (ATAs) ont été caractérisées à l'aide de quatre quadrants et anneaux aortiques chirurgicalement excisées. Pour explorer davantage la propension à la rupture des ATAAs, l'inter-corrélation des propriétés de ténacité avec les caractéristiques histologiques a été explorée. Les propriétés de ténacité ont également été étudiées pour les tissus porcins cryoconservés.Un facteur de perte a été défini, c'est une mesure de substitution définie comme étant le rapport entre l'énergie dissipée à l'énergie absorbée pendant un cycle de traction, le module supplémentaire, et leur indice d'anisotropie ont également été déterminés pour les ATAs humains contrôlées et anévrysmles. Ces paramètres ont été comparés avec les compositions de fibres de la média, le diamètre de l'aorte et l'âge des patients avec ATAAs associés à la bicuspidie aortique (BAV) et la valve aortique tricuspide (TAV). En outre, les variations des teneurs en fibres sur l'épaisseur de l'aorte et de la résistance caractéristique non linéaire des aortes ont été examinées.Les résultats obtenus suggèrent que le remodelage tissulaire pourrait affecter les propriétés de ténacité et de rigidité des anévrismes de l'aorte. Les résultats proposent que le facteur de perte, qui peut être mesuré de manière non invasive, pourrait être une mesure de substitution utile pour décrire l'histopathologie du tissu anévrismal et de démontrer les différences entre ATAAs avec types de valves aortiques bicuspides et tricuspides

Fatigue exhaustion of the mitral valve tissue

Sudden failure and rupture of the tissue is a rare but serious short-term complication after the mitral valve surgical repair. Excessive cyclic loading on the suture line of the repair can progressively damage the surrounding tissue and finally cause tissue rupture. Moreover, mechanical over-tension, which occurs in a diseased mitral valve, gradually leads to tissue floppiness, mitral annular dilation, and leaflet rupture. In this work, the rupture mechanics of mitral valve is studied by characterizing the fracture toughness exhaustion of healthy tissue. Results of this study show that fracture toughness of the posterior mitral valve is lower than its anterior counterpart, indicating that posterior tissue is more prone to failure. Moreover, the decrease in fracture toughness by increasing the number of fatigue cycles shows that excessive mechanical loading leads to progressive failure and rupture of mitral valve tissue within a damage accumulative process

Investigation on the Regional Loss Factor and Its Anisotropy for Aortic Aneurysms

An aortic aneurysm is a lethal arterial disease that mainly occurs in the thoracic and abdominal regions of the aorta. Thoracic aortic aneurysms are prevalent in the root/ascending parts of the aorta and can lead to aortic rupture resulting in the sudden death of patients. Understanding the biomechanical and histopathological changes associated with ascending thoracic aortic aneurysms (ATAAs), this study investigates the mechanical properties of the aorta during strip-biaxial tensile cycles. The loss factor—defined as the ratio of dissipated energy to the energy absorbed during a tensile cycle—the incremental modulus, and their anisotropy indexes were compared with the media fiber compositions for aneurysmal (n = 26) and control (n = 4) human ascending aortas. The aneurysmal aortas were categorized into the aortas with bicuspid aortic valves (BAV) and tricuspid aortic valves (TAV). The strip-biaxial loss factor correlates well with the diameter of the aortas with BAV and TAV (for the axial direction, respectively, R2 = 0.71, p = 0.0022 and R2 = 0.54, p = 0.0096). The loss factor increases significantly with patients’ age in the BAV group (for the axial direction: R2 = 0.45, p = 0.0164). The loss factor is isotropic for all TAV quadrants, whereas it is on average only isotropic in the anterior and outer curvature regions of the BAV group. The results suggest that loss factor may be a useful surrogate measure to describe the histopathology of aneurysmal tissue and to demonstrate the differences between ATAAs with the BAV and TAV