High-performance cooling is often necessary for thermal management of high power density systems. However, human intuition and experience may not be adequate to identify optimal thermal management designs as systems increase in size and complexity. This article presents an architecture exploration framework for a class of single-phase cooling systems. This class is specified as architectures with multiple cold plates in series or parallel and a single fluid split and junction. Candidate architectures are represented using labeled rooted tree graphs. Dynamic models are automatically generated from these trees using a graph-based thermal modeling framework. Optimal performance is determined by solving an appropriate fluid flow distribution problem, handling temperature constraints in the presence of exogenous heat loads. Rigorous case studies are performed in simulation, with components subject to heterogeneous heat loads and temperature constraints. Results include optimization of thermal endurance for an enumerated set of 4051 architectures. The framework is also applied to identify cooling system architectures capable of steady-state operation under a given loading.
Increasing the efficiency and density of power electronic systems (PESs) is an important objective for many high-impact applications, such as electric vehicle charging and aircraft electrification. Due to compactness and high heat dissipation, careful thermal monitoring of such PESs is required. Strategic placement of temperature sensors can improve the accuracy of real-time temperature distribution estimates. Enhanced temperature estimation supports increased power throughput and density because PESs can be operated in a less conservative manner while still preventing thermal failure. This article presents new methods for temperature sensor placement for 2- and 3-dimensional PESs that (1) improve computational efficiency (by orders of magnitude in at least one case), (2) support the use of more accurate evaluation metrics, and (3) are scalable to high-dimension sensor placement problems. These methods are tested via sensor placement studies based on a single-phase flying capacitor multi-level (FCML) prototype inverter. Information-based metrics are derived from a resistance-capacitance (RC) lumped parameter thermal model. Other more general metrics and system models are possible through the application of a new continuous relaxation strategy introduced here for placement representation. A new linear programming (LP) formulation is presented that is compatible with a particular type of information-based metric. This LP strategy is demonstrated to support an efficient solution of finely discretized large-scale placement problems. The optimal sensor locations obtained from these methods were tested via physical experiments. The new methods and results presented here may aid the development of thermally aware PESs with significantly enhanced capabilities.
A new method for optimizing the layout of device-routing systems is presented. Gradient-based topology optimization techniques are used to simultaneously optimize both device locations and routing paths of device interconnects. In addition to geometric considerations, this method supports optimization based on system behavior by including physics-based objectives and constraints. Multiple physics domains are modeled using lumped parameter and finite element models. A geometric projection for devices of arbitrary polygonal shape is developed along with sensitivity analysis. Two thermal-fluid systems are optimized to demonstrate the use of this method.
Three-dimensional spatial packaging of interconnected systems with physical interactions (SPI2) design plays a vital role in the functionality, operation, energy usage, and lifecycle of practically all engineered systems, from chips to ships. SPI2 design problems are highly-nonlinear, involving tightly constrained component placement, governed by coupled physical phenomena (thermal, hydraulic, electromagnetic, etc.), and involve energy and material transfer through intricate geometric interconnects. While many aspects of engineering system design have advanced rapidly in the last few decades through breakthroughs in computational support, SPI2 design has largely resisted automation, and in practice requires at least some human-executed design steps. SPI2 system reasoning and design decisions can quickly exceed human cognitive abilities at even moderate complexity levels, thwarting efforts to accelerate design cycles and tackle increasingly complex systems. Existing design methods treat pieces of the SPI2 problem separately without a fundamental systems approach, are sometimes inefficient to evaluate various possible designs and present barriers to effective adoption in practice. This article explores a vision of a holistic SPI2 design approach needed to develop next generation automated design methods capable of rapidly producing viable SPI2 design candidates. We review several technical domains related to holistic SPI2 design, discuss existing knowledge gaps and practical challenges, examine exciting opportunities at the intersection of multiple domains that can enable comprehensive exploration of SPI2 design spaces, and present one viable two-stage SPI2 design automation framework. Holistic SPI2 design opens up a new direction of high industrial and societal relevance for the design research community.
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