Papers for

energy system designers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Cova-PINN improves heat transfer accuracy in complex heat exchangers

Cova-PINN: Cross-Domain Conservation Physics-Informed Neural Network for Fluid-Solid Conjugate Heat Transfer in Complex Geometries

Abstract: Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT). However, standard multi-domain PINNs enforce governing equations and interface conditions on separately sampled domain supports, which can yield plausible temperature fields but inaccurate end-to-end energy transfer and outlet temperatures. We propose Cova-PINN, a multi-domain PINN framework that aligns conservation support with thermal interaction paths in complex geometries. Cova-PINN jointly optimizes cross-domain composite control-volume balances at the local scale and paired-wall closure at the global exchanger scale. We evaluate Cova-PINN on four triply periodic minimal surface (TPMS) heat exchangers and a geometrically distinct DualMS design against CHT-specific, optimization-oriented, and complex-geometry PINN baselines under a common protocol. Relative to the closest baseline, MUSA-PINN-CHT, Cova-PINN reduces average outlet-temperature and device-level closure errors across the four TPMS topologies by $37.7\%$ and $60.2\%$, respectively, while also improving full-field and heat-duty accuracy, with consistent gains on DualMS.

Thu 8 OctMachine Learning
The gist
Heating and cooling devices rely on accurately predicting temperature changes where fluids and solids meet. The authors identify that existing methods sometimes guess temperatures well but get the total heat transfer wrong. They introduce Cova-PINN, a new neural network approach that better models how heat moves through these combined fluid-solid spaces. Tested on advanced heat exchanger shapes, it reduces errors significantly, leading to more reliable temperature and energy predictions.
Open → 2610.11108v1

Stochastic hybrid automata model battery power demands with uncertainty

Stochastic Hybrid Automata for Power Profile Modeling in Energy Systems

Abstract: Power profiles are widely used to describe power demand and production in energy systems. Yet, real-world usage often involves uncertainty, making it challenging to determine, e.g., whether a battery can reliably meet a given profile. Formal modeling enables the computation of probabilities that a battery will satisfy such demands. Given the combination of discrete and continuous dynamics, along with uncertain usage patterns, stochastic hybrid automata (SHA) offer a suitable framework for such modeling and analysis. In this paper, we formalize two orthogonal approaches to specify the (uncertain) power profiles that a specific battery can serve as well as a storage model. For both types of power profiles, we provide formal construction rules for SHA reflecting the behavior of the storage model given a specific power profile. We analyze how design choices, such as the power profile, storage model, and representation of uncertainty, affect the complexity of the resulting SHA. Finally, we demonstrate how these models quantify feasibility using the tools Modest and realyst, and assess how the analysis scales with profile size and the number of stochastic components.

Thu 10 SeptFormal Languages and Automata Theory
The gist
Power systems often have uncertain demand and supply, making it hard to know if a battery can meet energy needs. The authors use a special kind of model called stochastic hybrid automata to describe both the continuous changes in power and random events affecting usage. They create formal rules to build these models for different battery power profiles and storage types, helping to calculate the likelihood that a battery can meet those demands. They also explore how different model choices affect complexity and show how their approach works using specific software tools.
Open → 2609.12209v1