Office of Naval Research

The growing development of batteries for high-power applications demands thermal management solutions capable of sustaining higher battery charging - discharging rates under constant and dynamic operating conditions. However, this can lead to significant power generation in battery cells, resulting in rapid rise of cell temperatures. This research focuses on the study of a hybrid forced cooling and phase change material-based (FCPCM) battery thermal management system (BTMS) for providing suitable temperature control of a 12 Liion battery (LIB) pack under charging-discharging rates ranging from 12C to 21C. The graphite foam impregnated with mixture of paraffin and encapsulated paraffin microparticles as the composite phase change materials (CPCM) was fabricated to reduce paraffin leakage during phase change while absorbing the heat generated by batteries. The customer-designed cold plates were utilized to remove the heat stored in the phase change material (PCM) and LIBs simultaneously. The results show that the FCPCM system can effectively maintain the temperature of the LIBs within 15-35 degrees C when the constant C-rates are below 15C. However, when the C-rate exceeds 15C, the composite phase change material (CPCM) is rapidly depleted and higher coolant flow rates are needed in order to maintain the batteries operating in ideal temperature range. In addition, the FCPCM system is evaluated with dynamic cycles with discharging-charging rates of 21C-6C to 21C-15C. The results indicate that the CPCM can fully recover when the charging rate of the cycles are below 9C, achieving excellent temperature control of the LIB pack but its energy storage capacity deteriorates under higher charging rates. Further study shows that CPCM with higher latent heat can effectively slow down the depletion of PCM and extend the operational capacity of FCPCM system for high dynamic loading cycles of batteries.

When humans see a bird, they recognize far more than just "bird" – they see a head, wings, and talons, a structured assembly of reusable parts that can be identified across every bird they have ever seen. We ask whether a self-supervised visual model can discover the same compositional structure on its own. To this end, we propose RATS (Register Attention Transformers), which decomposes the classification token into N learnable register tokens that route patch information through an L->N->N->L bottleneck via a three-step compress-communicate-broadcast attention. The N registers are partitioned across the H attention heads, so that registers assigned to different heads do not interact with each other. Without auxiliary losses or part annotations, each register spontaneously specializes into a proto-semantic region whose emerging structure resembles object parts. RATS surpasses all baselines by +12 mIoU on average across five segmentation benchmarks, with consistent gains on ADE20K (+1.11 mIoU) and COCO (+0.2 AP^m). Its register dictionary further exhibits part-level consistency and semantic proximity across related categories. Our results suggest that RATS may provide a useful architectural prior for structured and interpretable visual representation learning.

Phase change materials (PCMs) are attractive for transient thermal management of electronic devices operating either over a limited period or intermittently with occasional thermal dissipation spikes. Since the low thermal conductivity of PCMs presents a significant challenge for heat removal, various ways have been suggested to overcome this issue, which can cause overheating of the electronic devices due to the associated thermal resistance. Commonly, various thermally conductive additives, like extended surfaces, porous structures or particles, have been suggested for this purpose. The present study is an advanced exploration of an alternative approach to mitigating the high thermal resistance of PCM-based systems, based on a concept termed “dynamic PCM”. Dynamic PCMs work by applying an external load to the solid PCM, causing it to move towards the heat source during melting. The melted PCM is squeezed away, and only a thin liquid layer of practically constant thickness separates the heated surface from the solid PCM phase. Following the recent studies where this concept was introduced and confirmed in principle, the objective of the present work is to demonstrate practical implementation and operation of a sealed, cyclic system, based on the same general idea but significantly modified to meet the real-world requirements. This device is based on the “hourglass” concept, introduced and devised in a previous study. Accordingly, in the present work, a fully metallic configuration is developed to demonstrate practical operation at room temperature. The system is simple, consisting of a cylindrical tube and two end caps. The external force, required for dynamic PCM, is created by a weight which, together with the PCM, is located within the system. The heat-generating component itself serves as the driving factor for detachment of solid PCM from the envelope. As a result, the system preserves all positive features of the earlier prototype but allows for stand-alone implementation with a heat-generating component. Following the system design, fabrication and proof-of-concept runs, controlled tests at three power levels, 30 W, 40 W, and 50 W, are conducted to characterize the thermal behavior, reproducibility of results, and effective melting dynamics of the system. The heating and dynamic melting stages are successfully characterized. Then, system recharge (solidification) is explored under the conditions of free and forced convection in air. Numerical simulations, validated using the experimental results, complement the experiments while revealing important details of the underlying processes. A robust and repeatable performance of the system is demonstrated.

Given n independent Bernoulli(p) random variables X_i, i = 1, ..., n, representing the opinions of individuals connected by an underlying random k-regular graph G_n on 1, ..., n, we show that when conditioned on an atypical empirical consensus, which is the normalized sum of X_i X_j over neighboring vertices i, j, the joint distribution of the random variables converges, as n goes to infinity, to an Ising measure on the infinite k-regular tree T^k with a specific external field that depends only on the bias parameter p, and a temperature that depends on both p and the atypical consensus value. In particular, we show that conditional on the empirical consensus being smaller (respy, larger) than typical, the limit is a translation-invariant splitting (TIS) antiferromagnetic (respy, ferromagnetic) Ising measure on T^k. Moreover, if the bias is zero, then there is a phase transition: when the consensus exceeds k/(k-1), the conditional limits could be either the plus or minus boundary condition Ising measures. Furthermore, when X_i, i = 1, ..., n, are i.i.d. on a finite space, we show that when conditioned on an atypical value of the scaled sum of h(X_i, X_j) over neighboring vertices i and j, for any symmetric edge potential h, the limiting joint distribution of X_i lies in the set of (possibly degenerate) TIS Gibbs measures on T^k. The proofs leverage a tractable form of the large deviation rate function for component empirical measures of random regular graphs with i.i.d. marks and Gibbs conditioning principles, and entail careful analyses of associated non-convex constrained optimization problems. As a by-product of our results, we also obtain an (asymptotic) analog of the maximum entropy principle for Gibbs measures on random regular graphs.

Finding the number of meaningful clusters in an unlabeled dataset is important in many applications. Regularized k-means algorithm is a possible approach frequently used to find the correct number of distinct clusters in datasets. The most common formulation of the regularization function is the additive linear term λ k, where k is the number of clusters and λ a positive coefficient. Currently, there are no principled guidelines for setting a value for the critical hyperparameter λ. In this paper, we derive rigorous bounds for λ assuming clusters are ideal. Ideal clusters (defined as d-dimensional spheres with identical radii) are close proxies for k-means clusters (d-dimensional spherically symmetric distributions with identical standard deviations). Experiments show that the k-means algorithm with additive regularizer often yields multiple solutions. Thus, we also analyze k-means algorithm with multiplicative regularizer. The consensus among k-means solutions with additive and multiplicative regularizations reduces the ambiguity of multiple solutions in certain cases. We also present selected experiments that demonstrate performance of the regularized k-means algorithms as clusters deviate from the ideal assumption.