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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Crowd Dynamics: Modeling and Control of Multiagent Systems

This review aims to present recent developments in modeling and control of multiagent systems. A particular focus is set on crowd dynamics characterized by complex interactions among agents, also called social interactions, and large-scale systems. Specifically, in a crowd each individual agent interacts with a field generated by the other agents and the environment. These systems can be modeled at the microscopic scale by ordinary differential equations, while an alternative description at the mesoscopic scale is given by a partial differential equation for the propagation of the probability density of the agents. Control actions can be applied at the individual level as well as at the level of the corresponding fields. This article presents and compares different control types, and the specific application to multilane, multiclass traffic is developed in some detail, showing the main tools at work in a hybrid setting with relevant impacts on autonomous driving.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Chromium Isotopic Evidence for Mixing of NC and CC Reservoirs in Polymict Ureilites: Implications for Dynamical Models of the Early Solar System

Nucleosynthetic isotope anomalies show that the first few million years of solar system history were characterized by two distinct cosmochemical reservoirs, CC (carbonaceous chondrites and related differentiated meteorites) and NC (the terrestrial planets and all other groups of chondrites and differentiated meteorites), widely interpreted to correspond to the outer and inner solar system, respectively. At some point, however, bulk CC and NC materials became mixed, and several dynamical models offer explanations for how and when this occurred. We use xenoliths of CC materials in polymict ureilite (NC) breccias to test the applicability of such models. Polymict ureilites represent regolith on ureilitic asteroids but contain carbonaceous chondrite-like xenoliths. We present the first 54 Cr isotope data for such clasts, which, combined with oxygen and hydrogen isotopes, show that they are unique CC materials that became mixed with NC materials in these breccias. It has been suggested that such xenoliths were implanted into ureilites by outer solar system bodies migrating into the inner solar system during the gaseous disk phase ~3–5 Myr after CAI, as in the "Grand Tack" model. However, combined textural, petrologic, and spectroscopic observations suggest that they were added to ureilitic regolith at ~50–60 Myr after CAI, along with ordinary, enstatite, and Rumuruti-type chondrites, as a result of the breakup of multiple parent bodies in the asteroid belt at this time. This is consistent with models for an early instability of the giant planets. The C-type asteroids from which the xenoliths were derived were already present in inner solar system orbits.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A hierarchical gray-box dynamic modeling methodology for direct-expansion cooling systems to support control stability analysis; Méthodologie de modélisation dynamique hiérarchique de type boîte grise pour les systèmes de refroidissement à détente directe afin de soutenir l'analyse de stabilité de la commande

In this paper, a gray-box dynamic modeling approach for direct-expansion cooling systems is presented. The overall approach incorporates a multi-stage training procedure that consists of 1) identification of component sub-models from quasi-steady-state performance data, 2) system model integration with estimation of refrigerant charge and 3) fine tuning of thermal capacitances of the evaporator and condenser to capture the system dynamic responses. Compared to traditional physics-based models, the proposed modeling approach has advantages including reduced engineering efforts in the model development phase, improved computational efficiency and enhanced prediction accuracy. The modeling method was validated using a 3-ton variable-speed heat pump and proved to be capable of accurately predicting the system transient behaviors over a wide range of operating conditions. The established dynamic model was then applied for control stability analysis, with a specific goal of determining a proper control execution time step. The case study results showed that the stable control execution time step could change significantly, from 3 sec to 19 sec, as the operating conditions and control settings vary, and a proper selection of the execution time step is critical to ensure stable and reliable operations.

42 ENGINEERING↗
DOE OSTI2020

A Model-Free Voltage Control Approach to Mitigate Motor Stalling and FIDVR for Smart Grids

Electric power networks are large and highly nonlinear dynamical systems that present unique challenges to control design. Though there is a large number of dynamic models for power system stability and control, many models are only useful with right assumptions and wrong for other tasks. Moreover, the dynamic behavior of the grid is increasingly complex under the banner of smart grids. These lead to the difficulty of developing appropriate dynamic modeling, and thus an efficient control strategy. To avoid such modeling challenges, here we present a novel dynamic voltage control strategy based on a model-free control (MFC) approach, requiring no modeling procedure. In particular, it focuses on fault-induced delayed voltage recovery (FIDVR) events, which require complex and accurate dynamic load models to replicate such events. This work utilizes MFC as an online controller to achieve the desired voltage stability under the FIDVR event. The proposed MFC strategy allows simple implementation and low computational cost for efficient mitigation of FIDVR. For benchmarking, a reasonably accurate dynamic performance model is explored. Simulation results with the IEEE 57 bus test network demonstrate the enhanced dynamic voltage profile for load buses having induction motors with the support of reactive power resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Safety Risk Reliability Model Library

SR2ML is a software package which contains a set of safety and reliability models designed to be interfaced with the INL developed RAVEN code. These models can be employed to perform both static and dynamic system risk analysis and determine risk importance of specific elements of the considered system. Two classes of reliability models have been developed; the first class includes all classical reliability models (Fault-Trees, Event-Trees, Markov models and Reliability Block Diagrams) which have been extended to deal not only with Boolean logic values but also time dependent values. The second class includes several components aging models. Models of these two classes are designed to be included in a RAVEN ensemble model to perform time dependent system reliability analysis (dynamic analysis). Similarly, these models can be interfaced with system analysis codes to determine failure time of systems and evaluate accident progression (static analysis).

Wang, Congjian↗
DOE OSTI2022

Utilizing CellBox to Describe Varied Dynamical Systems

CellBox is a hybrid modeling framework that integrates machine-learning approaches with explicit mathematical modeling to determine the behavior of dynamical systems, intended for determining the complex interactions of molecule and phenotype within a cellular environment using perturbation/response-based data. The original work utilized analysis of a melanoma cell line, SK-Mel-133, which we sought to expand to and evaluate effectiveness in describing other nonlinear systems both within and outside of biology. We evaluated with two models. One model is based upon the free-fall of an object under a velocity-dependent drag force; this produced inherent perturbation/response data. The other model is based upon a form of mass-action molecular kinetics; for this, we ran the simulation to steady state in each species for each perturbation and select the steady state as input for CellBox. We determined that CellBox, outside of its original use case, can still strongly predict even non-biological dynamical systems in the small-perturbation regime, with r = 0.99977 to 0.85123, p < 0.05 for freefall. However, CellBox cannot accurately predict behavior within large-perturbation regimes, p >> 0.05 for freefall. In addition, we performed a naïve comparison of CellBox network structure to known network structure in the mass-action model to assess accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Aggregated DER_A Model Parameterization via Online Moving Horizon Estimation

Here, this paper introduces a methodology for parameterizing the DER_A model using a novel smooth mathematical representation, simplifying the process and preserving accuracy in modeling inverter-based generator (IBG). The methodology employs an online parameterization process that can operate in real-time. The model parameterization process is structured into five sequential steps, each targeting a specific aspect of the DER_A model through moving horizon estimation. This approach adapts to systems with varying voltage and frequency support requirements by selectively applying each step. Simulation results on systems with both known and unknown parameters validate the methodology’s effectiveness. The online moving horizon estimation technique accurately captures the dynamics of the overall system and ensures that the parameterized DER_A model closely mirrors the real system’s voltage, current, and power dynamics. The findings highlight the potential of this methodology to substantially improve and simplify the dynamic modeling of power systems, paving the way for more reliable and robust IBG and grid integration.

42 ENGINEERING↗
DOE OSTI2026

Latent Twins

Over the past decade, scientific machine learning has transformed the development of mathematical and computational frameworks for analyzing, modeling, and predicting complex systems. From inverse problems to numerical partial differential equations (PDEs), dynamical systems, and model reduction, these advances have pushed the boundaries of what can be simulated. Yet they have often progressed in parallel, with representation learning and algorithmic solution methods evolving largely as separate pipelines. With Latent Twins, we propose a unifying mathematical framework that creates a hidden surrogate in latent space for the underlying equations. Whereas digital twins mirror physical systems in the digital world, Latent Twins mirror mathematical systems in a learned latent space governed by operators. Through this lens, classical modeling, inversion, model reduction, and operator approximation all emerge as special cases of a single principle. We establish the fundamental approximation properties of Latent Twins for both ordinary differential equations (ODEs) and PDEs and demonstrate the framework across three representative settings: (i) canonical ODEs, capturing diverse dynamical regimes; (ii) a PDE benchmark using the shallow-water equations, contrasting Latent Twin simulations with deep operator network and forecasts with a four-dimensional variational method baseline; and (iii) a challenging real-data geopotential reanalysis dataset, reconstructing and forecasting from sparse, noisy observations. Latent Twins provide a compact, interpretable surrogate for solution operators that evaluate across arbitrary time gaps in a single-shot, while remaining compatible with scientific pipelines such as assimilation, control, and uncertainty quantification. Looking forward, this framework offers scalable, theory-grounded surrogates that bridge data-driven representation learning and classical scientific modeling across disciplines.

Latent Twins↗
DOE OSTI2025

Thermo-hydraulic steam pipe models for district heating simulations: Simplifications to balance accuracy and simulation speed

Steam piping networks are essential for optimizing performance in industrial processes and district heating systems. However, dynamic models that balance thermo-hydraulic accuracy with computational efficiency remain limited. In response, this paper presents a new discretized steam pipe model based on the plug flow approach, capturing key thermo-hydraulic behaviors while simplifying steam phase change processes. Implemented in Modelica, the model accurately calculates temperature and pressure distributions along steam pipelines. To improve computational efficiency for district-scale simulations, five model simplifications are introduced: lumped thermo-hydraulic functions, empirical correlations, fluid state approximations, steady-state dynamics and inclusion of flow derivatives. These simplified models achieve 85%-98% accuracy in predicting pressure drop and condensation losses, including dynamic condensate behavior during pipe warm-up—a factor often overlooked in existing models. The models support diverse network configurations, scaling effectively to systems with multiple distribution pipes and connected building loads. Discrete models provide detailed insights but exhibit a cubic increase in simulation time as the network scales by N connected building O(N 2.42 ). In contrast, lumped models simulate 10–28 times faster than discrete, offering quadratic scaling of simulation time O(N 1.73 ). However, they still require 6 times more computation time than a lossless network, highlighting the inherent computational challenges of modeling compressible fluid flow. In conclusion, the steady-state lumped variant, with its near-linear scalability in computational time O(N 1.01 ), emerges as an efficient solution for preliminary design evaluations and extensive parametric studies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization

This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures.

42 ENGINEERING↗
DOE OSTI2025

Prioritizing urban heat adaptation infrastructure based on multiple outcomes: Comfort, health, and energy

Globally, cities face increasing extreme heat, impacting comfort, health, and energy consumption. Infrastructure-based heat adaptation strategies can improve these outcomes, but each strategy has a unique mix of benefits and drawbacks. Here, we apply an urbanized meteorological model (WRF) with the newly integrated multilayer BEP-Tree street tree model to dynamically downscale Earth System Model projections and a 3-D microclimate model (TUF-Pedestrian) to simulate the street-scale radiation environment impacting pedestrians. We evaluate the performance of five heat adaptation strategies (street trees, cool roofs, green roofs, rooftop photovoltaics (PV), and reflective pavements) during extreme heat events in three cities with contrasting background climates (Toronto, Phoenix, and Miami), under contemporary and end-of-century projected climates, based on three metrics: outdoor heat stress, air conditioning (AC) energy use, and ventilation of vehicular air pollution. No single adaptation strategy improves all three outcomes. While street trees inhibit ventilation, they reduce outdoor heat stress four times more effectively than the next best strategy via shade provision, fully offsetting heat stress increases under a high-emissions end-of-century climate scenario in all cities studied. Cool roofs and green roofs moderately reduce heat stress and energy use. Alternatively, rooftop PV with energy storage can generate sufficient power for space cooling but have marginal effects on heat stress. Reflective pavements are the least effective across metrics. Where the ventilation of street-level emissions is of less concern, our results clearly support the combination of street trees and rooftop PV as a highly complementary and effective means of adaptive mitigation across different climates and neighborhood densities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

CANA v1.0.0: efficient quantification of canalization in automata networks

The biomolecular networks underpinning cell function exhibit canalization, or the buffering of fluctuations required to function in a noisy environment. We present a new major release of $\tt{CANA}$, v1.0.0, an open-source Python package for understanding canalization in automata network models, discrete dynamical systems in which activation of biomolecular entities (e.g. transcription of genes) is modeled as the activity of coupled automata. One understudied putative mechanism for canalization is the functional equivalence of biomolecular regulators (e.g. among the transcription factors for a gene). We study this mechanism using the theory of symmetry in discrete functions. We present a new exact method, $\tt{schematodes}$, for finding maximal symmetry groups among the inputs to discrete functions, and integrate it into $\tt{CANA}$. The $\tt{schematodes}$ method substantially outperforms the inexact method of previous $\tt{CANA}$ versions both in speed and accuracy. We apply $\tt{CANA}$ v1.0.0 to study symmetry in 74 experimentally supported automata network models from the Cell Collective (CC) repository. The symmetry distribution is significantly different in the CC than in random automata with the same in-degree (connectivity) and bias (average output) (Kolmogorov–Smirnov test, P ≪ .001). Its spread is much wider than in a null model (IQR 0.31 versus IQR 0.20 with equal medians), demonstrating that the CC is enriched in functions with extreme symmetry or asymmetry.

Boolean networks↗