Learning Associations between Features and Clusters: An Interpretable Deep Clustering Method
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97 MATHEMATICS AND COMPUTING↗
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Results for “Performance Modeling”
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Intraday Outdoor Efficiency Changes in Metal-Halide Perovskite Photovoltaic Modules
Here, we present outdoor observations of metal-halide perovskite modules deployed in the Photovoltaic Accelerator for Commercializing Technologies center, which houses one of the world's broadest efforts to test metal-halide perovskite photovoltaic modules outdoors. As of January 2025, outdoor testing has encompassed over 150 modules from 14 different partners. Our findings illustrate how daily changes in efficiency, driven by exposure to light, affect field performance in real-world conditions. These effects cannot be explained by existing outdoor performance models and frustrate the notion of a traditional temperature coefficient.
14 SOLAR ENERGY↗
Novel Data Driven Noise Emulation Framework using Deep Neural Network for Generating Synthetic PMU Measurements
Sensors play a critical role in supporting day-to-day grid operations and they are essential to operator’s decision-making process. Furthermore, sensors and sensor behaviors need to be emulated with grid simulations to perform modeling studies and to design cutting edge power systems applications. Ensuring the accurate behavior of these applications requires accurate emulation of sensors and pertinent signals. However, most grid simulators and modeling tools assume either zero error scenarios or simplistic noise models that may not always correlate to real-world sensors. To address the above issue, this work presents an initial study on the noise characteristics of phasor measurement units (PMUs), along with models for recreating their unique noise signatures. The proposed methods (both analytical and machine-learning-based) provide a substantial increase in a sensor’s model fidelity, a feature that can be leveraged by an end-user application to yield more accurate system representations. The proposed methods were then applied to micro PMU data from the EPFL microgrid campus to extract sensor noise profiles. This data was used to train a deep learning model, which was tested to emulate the noise characteristics present in actual signals. Based on the observed results and the employed data-driven methodology, the proposed methods may be adapted to replicate the behavior of other grid sensors and power new applications capable of detecting sensor degradation and eventual device failures in near real-time.
PMU, noise emulation, synthetic measurements, deep↗
Exaflops Biomedical Knowledge Graph Analytics
We are motivated by newly proposed methods for mining large-scale corpora of scholarly publications (e.g., full biomedical literature), which consists of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover relationships among concepts. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as an all-pairs shortest paths (APSP) and validate connective paths against curated biomedical knowledge graphs (e.g., Spoke). In this context, we present Coast (Exascale Communication-Optimized All-Pairs Shortest Path) and demonstrate 1.004 EF/s on 9,200 Frontier nodes (73,600 GCDs). We develop hyperbolic performance models (HYPERMOD), which guide optimizations and parametric tuning. The proposed Coast algorithm achieved the memory constant parallel efficiency of 99% in the single-precision tropical semiring. Looking forward, Coast will enable the integration of scholarly corpora like PubMed into the Spoke biomedical knowledge graph.
Kannan, Ramakrishnan {ramki}↗
Ensemble Learning Based Convex Approximation of Three-Phase Power Flow
Though the convex optimization has been widely used in power systems, it still cannot guarantee to yield a tight (accurate) solution to some problems. To mitigate this issue, this paper proposes an ensemble learning based convex approximation for alternating current (AC) power flow equations that differs from the existing convex relaxations. The proposed approach is based on three-phase quadratic power flow equations in rectangular coordinates. To develop this data-driven convex approximation of power flows, the polynomial regression (PR) is first deployed as a basic learner to fit convex relationships between the independent and dependent variables. Then, ensemble learning algorithms such as gradient boosting (GB) and bagging are introduced to combine learners to boost model performance. Based on the learned convex approximation of power flow, optimal power flow (OPF) is formulated as a convex quadratic programming problem. The simulation results on IEEE standard cases of both balanced and unbalanced systems show that, in the context of solving OPF, the proposed data-driven convex approximation outperforms the conventional semi-definite programming (SDP) relaxation in both accuracy and computational efficiency, especially in the cases that the conventional SDP relaxation fails
Convex approximation↗
Joint Estimation of Behind-the-Meter Solar Generation in a Community
Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic (PV) generation and electric load in the system. Most of the small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. This paper presents an unsupervised framework for joint disaggregation of the net load readings of a group of customers into the solar PV generation and electric load. Our algorithm synergistically combines a physical PV system performance model for individual solar PV generation estimation with a statistical model for joint load estimation. The electric loads for a group of customers are estimated jointly by a mixed hidden Markov model (MHMM) which enables modeling the general load consumption behavior present in all customers while acknowledging the individual differences. At the same time, the model can capture the change in load patterns over a time period by the hidden Markov states. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is evaluated using the net load, electric load, and solar PV generation data gathered from residential customers located in Austin, Texas. Testing results show that our proposed method reduces the mean squared error of state-of-the-art net-load disaggregation algorithms by 67%.
behind-the-meter solar generation↗
Microbial seasonality promotes soil respiratory carbon emission in natural ecosystems: A modeling study
Seasonality is a key feature of the biosphere and the seasonal dynamics of soil carbon (C) emissions represent a fundamental mechanism regulating the terrestrial–climate interaction. We applied a microbial explicit model—CLM-Microbe—to evaluate the impacts of microbial seasonality on soil C cycling in terrestrial ecosystems. Additionally, the CLM-Microbe model was validated in simulating belowground respiratory fluxes, that is, microbial respiration, root respiration, and soil respiration at the site level. On average, the CLM-Microbe model explained 72% (n = 19, p < 0.0001), 65% (n = 19, p < 0.0001), and 71% (n = 18, p < 0.0001) of the variation in microbial respiration, root respiration, and soil respiration, respectively. We then compared the model simulations of soil respiratory fluxes and soil organic C content in top 1 m between the CLM-Microbe model with (CLM-Microbe) and without (CLM-Microbe_wos) seasonal dynamics of soil microbial biomass in natural biomes. Removing soil microbial seasonality reduced model performance in simulating microbial respiration and soil respiration, but led to slight differences in simulating root respiration. Compared with the CLM-Microbe, the CLM-Microbe_wos underestimated the annual flux of microbial respiration by 0.6%–32% and annual flux of soil respiration by 0.4%–29% in natural biomes. Correspondingly, the CLM-Microbe_wos estimated higher soil organic C content in top 1 m (0.2%–7%) except for the sites in Arctic and boreal regions. Our findings suggest that soil microbial seasonality enhances soil respiratory C emissions, leading to a decline in SOC storage. An explicit representation of soil microbial seasonality represents a critical improvement for projecting soil C decomposition and reducing the uncertainties in global C cycle projection under the changing climate.
59 BASIC BIOLOGICAL SCIENCES↗
Predictive Modeling of Local Film-Cooling Flow on a Turbine Rotor Blade
Abstract In the turbine section of a modern gas turbine engine, components exposed to the main gas path flow rely on cooling air to maintain hardware durability targets. Therefore, monitoring turbine cooling flow is essential to the diagnostic and prognostic efficacy of a condition-based operation and maintenance (CBOM) approach. This study supports CBOM goals by leveraging supervised machine learning to estimate relative changes to local film-cooling flowrate using surface temperature measured on the pressure side of a rotating turbine blade operating at engine-relevant aerothermal conditions. Throughout the lifetime of a film-cooled turbine component, characteristics of the film-cooling flow—such as film trajectory and cooling effectiveness—vary as degradation-driven geometry distortions occur, which ultimately affects the relationship between the model input and the model output—film-cooling flowrate predictions. The present study addresses this complication by testing a data-driven model on multiple turbine blades of the same nominal design, but with each blade exhibiting different localized film-cooling flow characteristics. By testing the model in this manner, strategies for mitigating the detrimental effects of film-cooling flow characteristic variations on model performance were investigated, and the corresponding flowrate prediction accuracy was quantified.
Engineering↗
Supplemental Structural Analyses Used in Support of Certification of the Defense Programs Package 3
The Defense Programs Package (DPP)-3 is to be certified by the National Nuclear Security Administration (NNSA) Packaging and Transportation Division (PTD). Certification is based on successful physical testing of the package, and six certification test units (CTUs) have been subjected to the normal conditions of transport (NCT) and hypothetical accident conditions (HAC) outlined in Title 10 of the Code of Federal Regulations Part 71 (10 CFR 71). During regulatory testing of one of the CTUs, the drum lid closure bolt closest to the impact surface failed during the HAC 30-ft side drop. One of the testing goals for this CTU was to minimize the thermal pathway from the drum exterior shell to the containment vessel (CV) flange and O-rings, leading to the maximum damage scenario in preparation for thermal testing at 1475 °F. Therefore, the puncture bar impact proceeded as originally planned by striking the side wall of the package exterior closest to the location of the CV flange. This puncture bar impact did not attempt to exploit the region of the failed drum lid closure bolt to potentially cause more damage to the package. After the conclusion of regulatory testing, the DPP-3 package finite element model was used to numerically evaluate the effects of puncture bar impacts to the drum exterior in the vicinity of the failed drum lid closure bolt to further demonstrate the robustness of the DPP-3 design. Three alternate puncture bar impact locations and orientations were evaluated to demonstrate that these puncture bar impacts would not substantially reduce the effectiveness of the DPP-3 packaging. This paper describes the supplemental finite element models performed and structural analysis results that demonstrate the alternate HAC 40-in. puncture bar impact tests would have no deleterious effect on the performance of the DPP-3.
Sakalaukus Jr., Peter J.↗
A Sparse Tensor Benchmark Suite for CPUs and GPUs
Tensor computations present significant performance chal- lenges that impact a wide spectrum of applications ranging from machine learning, healthcare analytics, social network analysis, data mining to quantum chemistry and signal processing. Efforts to improve the perfor- mance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. This work presents a benchmark suite for arbitrary-order sparse tensor kernels using state- of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO) on CPUs and GPUs. It presents a set of reference tensor kernel implementations that are compatible with real-world tensors and power law tensors extended from synthetic graph generation techniques. We also propose Roofline performance models for these kernels to provide insights of computer platforms from sparse tensor view. This benchmark suite along with the synthetic tensor generator is publicly available.
Li, Jiajia↗
Deffe: A Data-efficient Framework for Performance Characterization in Domain-Specific Computing
Predicting workload performance is a crucial task for many architecture and system research. In this paper we present Dee, a framework to estimate the workload performances under varying architectural configurations. The infrastructure component of Deffe is based on scalable and easy-to-use open-source software components. By casting the performance modeling as transfer learning tasks, the modeling component of Deffe can leverage the learned knowledge on one workload, and “transfer” it to a new workload. Extensive experimental results show that the method can achieve superior testing accuracy with an effective reduction of 32-80x in terms of the amount of required training data.
Liu, Frank↗
DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware
Spiking Neural Networks (SNNs) are an emerging computation model that uses event-driven activation and bio-inspired learning algorithms. SNN-based machine learning programs are typically executed on tile-based neuromorphic hardware platforms, where each tile consists of a computation unit called a crossbar, which maps neurons and synapses of the program. However, synthesizing such programs on an off-the-shelf neuromorphic hardware is challenging. This is because of the inherent resource and latency limitations of the hardware, which impact both model performance, e.g., accuracy, and hardware performance, e.g., throughput. We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware. The proposed framework works in four steps. First, it analyzes a machine learning program and generates SNN workload using representative data. Second, it partitions the SNN workload and generates clusters that fit on crossbars of the target neuromorphic hardware. Third, it exploits the rich semantics of the Synchronous Dataflow Graph (SDFG) to represent a clustered SNN program, allowing for performance analysis in terms of key hardware constraints such as number of crossbars, dimension of each crossbar, buffer space on tiles, and tile communication bandwidth. Finally, it uses a novel scheduling algorithm to execute clusters on crossbars of the hardware, guaranteeing hardware performance. We evaluate DFSynthesizer with 10 commonly used machine learning programs. Our results demonstrate that DFSynthesizer provides a much tighter performance guarantee compared to current mapping approaches.
Computer Science↗
Tournament-Based Pretraining to Accelerate Federated Learning
Advances in hardware, proliferation of compute at the edge, and data creation at unprecedented scales have made federated learning (FL) necessary for the next leap forward in pervasive machine learning. For privacy and network reasons, large volumes of data remain stranded on endpoints located in geographically austere (or at least austere network-wise) locations. However, challenges exist to the effective use of these data. To solve the system and functional level challenges, we present an three novel variants of a serverless federated learning framework. We also present tournament-based pretraining, which we demonstrate significantly improves model performance in some experiments. Overall, these extensions to FL and our novel training method enable greater focus on science rather than ML development.
Baughman, Matt↗
Carbon Support Microstructure Impact on High Current Density Transport Resistances in PEMFC Cathode
Cell voltages at high current densities (HCD) of an operating proton-exchange membrane fuel cell (PEMFC) cathode suffer from losses due to the local-O 2 and bulk-H + transport resistances in the catalyst layer. Particularly, the microstructure of high surface area carbon (HSC) support upon which both the platinum catalyst and ionomer are dispersed play a pivotal role in controlling the reactant transport to the active site in the catalyst layer. In this study, we perform a systematic analysis of the underlying microstructure of platinum-cobalt catalyst dispersed on various HSC supports in terms of their surface area and pore-size distribution. The carbon microstructure was found to strongly influence the PtCo nanoparticle dispersion, catalyst layer ionomer distribution and transport losses governing the performance at HCD. Catalyst layer electrochemical diagnostics carried out to quantify local-O 2 transport resistance and bulk-H + transport resistance in the cathode were found to be directly correlated to the micropore (<2 nm) and macropore (>8 nm) surface areas of the carbon support, respectively. Finally, a 1D-performance model has been developed to assimilate our understanding of the catalyst layer microstructure and transport resistances at HCD.
25 ENERGY STORAGE↗
Editors’ Choice—Ionomer Side Chain Length and Equivalent Weight Impact on High Current Density Transport Resistances in PEMFC Cathodes
Cell voltage at high current densities (HCD) of an operating proton-exchange membrane fuel cell (PEMFC) suffers from losses due to the local-O 2 and bulk-H + transport resistances in the cathode catalyst layer (CCL). Particularly, the interaction of perfluorosulfonic acid (PFSA) ionomer with the carbon supported platinum catalyst plays a critical role in controlling reactant transport to the active site. In this study, we perform a systematic analysis of the side chain length and equivalent weight (EW) of PFSA ionomers on the CCL transport resistances. Ex situ measurements were carried out to quantify the ionomer characteristics such as the molecular weight, proton conductivity and water uptake. Nanomorphology of ionomers cast as 60–120 nm thin-films is characterized using grazing-incidence X-ray scattering. In situ fuel cell electrochemical diagnostic measurements were carried out to quantify the reactant (H + /O 2 ) transport properties of the CCL. Ionomer EW was found to play a major role with decreasing EW yielding higher proton conductivity and water uptake that led to lower bulk-H + and local-O 2 transport resistances in the CCL. Finally, a 1D-semi-empirical performance model has been developed to quantify the impact of ionomer EW on cell voltage loss factors.
08 HYDROGEN↗
MBMS1.0: An Open-Source Code for Modeling and Simulation of Membrane-Based Dehumidification and Energy Recovery
Membrane-based dehumidification is currently being considered as a promising solution for the building application due to its low cost and very limited energy consumption. Developing a simple and efficient open-source code simulation tool is important for boosting the optimization and evaluation of such device in HVAC community. This paper reports a first-order physics based model which accounts for the fundamental heat and mass transfer of humid-air vapor at feed side to flow stream at permeate side. The current model comprises two membrane mass transfer submodels (i.e. microstructure model and performance map model); and it adopts a segment-by-segment methodology for discretizing heat and mass transfer governing equations. The model is capable of simulating both dehumidifiers and energy recovery ventilators with parallel-flow cross-flow, and counter-flow configurations. The model was validated with the measurements at appropriate device. The practices in dehumidification and energy recovery exchangers are also discussed. The model and open-source codes are expected to become a solid fundament for developing a more comprehensive and accurate membrane-based dehumidification in the future.
Gao, Zhiming↗
LLEPE
LLEPE (liquid-liquid equilibrium parameter estimator) is a tool for estimating thermodynamic parameters like standard enthalpies, standard molar volumes and Pitzer coefficients, by combining Gibbs minimization with regression to multiphase equilibrium isotherm data. It also includes visualization tools to access data quality and prediction model performance.
ILOEJE, CHUKWUNWIKE↗
BUTTER: An Empirical Deep Learning Experimental Framework [SWR-22-42]
BUTTER ( An Empirical Deep Learning Experimental Framework) is a system with which empirical deep learning experiments can be conducted and their results, including training and model performance characteristics, can be accumulated for further analysis.
Tripp, Charles↗