Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “theoretical models”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Spontaneous imbibition in a square tube with corner films: theoretical model and numerical simulation

Spontaneous imbibition in an angular tube with corner films is a fundamental problem in many scientific and engineering processes. In this paper, a modified interacting capillary bundle model is developed to describe the liquid imbibition dynamics in a square tube with corner films. The square tube is decomposed into several interacting sub-capillaries and the local capillary pressure in each sub-capillary is derived based on the specific shape of its meniscus. The conductance of each sub-capillary is calculated using single-phase lattice Boltzmann simulation. The modified interacting capillary bundle model and color-gradient lattice Boltzmann method are used to simulate the liquid imbibition dynamics in the square tube with different fluid properties. The predictions by the modified interacting capillary bundle model match well with the lattice Boltzmann simulation results for different conditions, demonstrating the accuracy and robustness of the interacting capillary bundle model to describe the imbibition dynamics with corner films. In addition, the interacting capillary bundle model is helpful to investigate the mechanisms during spontaneous imbibition and the influences of fluid viscosity, surface tension, wetting phase contact angle and gravity on imbibition dynamics. Finally, a universal scaling law of imbibition dynamics for the main meniscus is developed and the scaling law for arc meniscus is also analyzed.

42 ENGINEERING↗
DOE OSTI2024

Theoretical modeling of hepatitis C acute infection in liver-humanized mice support pre-clinical assessment of candidate viruses for controlled-human-infection studies

Designing and carrying out a controlled human infection (CHI) model for hepatitis C virus (HCV) is critical for vaccine development. However, key considerations for a CHI model protocol include understanding of the earliest viral-host kinetic events during the acute phase and susceptibility of the viral isolate under consideration for use in the CHI model to antiviral treatment before any infections in human volunteers can take place. Humanized mouse models lack adaptive immune responses but provide a unique opportunity to obtain quantitative understanding of early HCV kinetics and develop mathematical models to further understand viral and innate immune response dynamics during acute HCV infection. We show that the models reproduce the measured HCV kinetics in humanized mice, which are consistent with early acute HCV-host dynamics in immunocompetent chimpanzees. Our findings suggest that humanized mice are well-suited to support development of a CHI model. In-silico and in-vivo modeling estimates provide a starting point to characterize candidate viruses for testing in CHI model studies.

Agent-based modeling↗
DOE OSTI2025

Game theoretic modeling and optimization of competition and collaboration in dual channel electronic waste supply chains

The rapid growth of electronic waste (e-waste) presents critical challenges for sustainable resource recovery and environmental protection. This study develops a dual-channel closed-loop supply chain (CLSC) model formulated as a hierarchical Stackelberg game, that integrates dynamic pricing and cost-sharing mechanisms to optimize both economic and environmental outcomes. The model explicitly captures strategic interactions between manufacturer-led and third-party recycling channels, accounting for consumer behavior, regulatory incentives, and market competition. Numerical simulations conducted (implemented over a four-iteration horizon using a commercial optimization solver) show that, relative to the baseline equilibrium, manufacturer profit increases from 11.6 thousand USD to 37.9 thousand USD (+226.8%), total recycled volume rises from 7,848 to 7,942 units (+1.2%), and collector profit nearly doubles under cost-sharing, enabling more equitable profit distribution. Furthermore, scenario-based simulations across Sub-Saharan Africa, high-income economies, and emerging Asian industrial countries reveal that infrastructure quality, policy intensity, and labor costs critically shape recycling efficiency and profit allocation. These findings demonstrate that subsidies alone are insufficient to ensure system efficiency. Instead, coordinated strategies that integrate internal incentive alignment with context-sensitive policy support are required. Overall, this study offers a robust framework for designing resilient, efficient, and regionally adaptable e-waste management systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Closed-form theoretical model of the secondary drop size in partial coalescence—Capturing pertinent timescales and viscous forces

One of the most important outcomes of partial drop coalescence is the ratio of the secondary drop radius to the primary drop radius, known as the drop ratio, ri. This ratio is thought to be approximately constant and independent of physical parameters of the fluids involved. However, here we reveal that ambient fluid viscosity can alter the size of the secondary drop and the drop ratio consequently. Using scaling analysis, we derive a model that predicts the behavior of the drop ratio as a function of the Ohnesorge number, a dimensionless ratio of viscous to inertial forces. In addition, we present our experimental results of coalescing drops on a planar interface under the influence of surface tension gradients. A high-speed digital camera is used to observe the evolution of drops as they coalesce with a bulk liquid. We show that this process is influenced by the surface tension gradient between the drop and the bulk liquid. The ratio of the secondary drop to the primary drop in partial coalescence is smaller than the reported values for coalescence without a surface tension gradient. The analytical model derived through this study is based on a new modified Ohnesorge number that includes surface tension gradients. Our analytical model is compared against other models and the results illustrate good agreement with our experimental findings and experimental data in the literature.

42 ENGINEERING↗
DOE OSTI2025

A theoretical model for sheath dynamics in the two-plasma mode of a cylindrical filament discharge

We present a refined understanding of cylindrical plasma-facing emitter cathodes by examining the limitations of the conventional space charge limited model, which overlooks ion trapping dynamics within the virtual cathode. While conventional theory distinguishes between temperature-limited and space charge limited regimes based on electron current constraints, our findings reveal that trapped ions can form a quasi-neutral “second plasma,” expanding upstream and interacting with the primary plasma. In cylindrical and other non-planar geometries, an expanding trapped-ions layer produces current enhancement by converting more of the emitted electron flux to passing flux. The current enhancement leads to a complex coupling between the trapped-ions plasma and upstream plasma through ionizing collisions in the upstream region and charge exchange collisions in the virtual cathode. The coupling results in mutual density enhancement and at the same time, antagonistic expansion dynamics between the two plasma layers. The process is understood using a new “aid-compete” model. Numerical solutions, validated against simulations, reveal the model's capacity to predict system evolution and complex discharge behaviors, albeit with qualitative approximations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

HALLUFIELD: DETECTING LLM HALLUCINATIONS VIA FIELD-THEORETIC MODELING

A research-focused Python package that implements our hallucination-detection method for large language models(LLMs). The code computes stability signals from LLM predictions across various hyperparameters sweeps and combines free-energy/entropy–style metrics to flag likely hallucinations, with tunable thresholds for batch scoring. The repo includes evaluation scripts, config files, and example notebooks to reproduce benchmark results and ablations; it depends on standard open-source libraries (PyTorch, Hugging Face) and runs on CPU/GPU. The repository examples only use public models/datasets only.

Bhattarai, Manish [Los Alamos National Labs]↗
DOE OSTI2025

Theoretical Modeling of Reactor Relevant Conditions for Plasma Jet Driven Magneto-Inertial Fusion

The Charger Advanced Power and Propulsion Laboratory (CAPP), a laboratory within the Propulsion Research Center (PRC) at the University of Alabama in Huntsville (UAH) is working with Los Alamos National Laboratory (LANL). to develop models and inform on promising paths for high gain magneto-inertial fusion (MIF) conditions. This report provides a framework for identifying promising conditions for achieving ignition in plasma-jet-driven magneto-inertial fusion (PJMIF)[1]. As proposed, for the first part of the contract, UAH proposes to develop a gain over unity set of stagnation conditions to provide a state of plasma conditions to achieve to set long terms goals for the PJMIF program. Specifically, UAH will model PJMIF stagnation conditions to include radiation, heat transfer, two temperature energy equations, fusion reactivity and nonlocal fusion product deposition, but no hydrodynamics for these purposes. These calculations will use a stationary plasma model to reduce simulation complexity—focusing on a DT target at 10 keV. Subsequent work will include a DD plasma layer acting as an afterburner. UAH will assume an initial magnetic field without any consideration of the topology, just assume a field strength, most likely scaled with consideration of the local hall parameter. This effort will inform the team on the tradeoff between mass, peak target field, etc and the achievable gain.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Bayesian model-data comparison incorporating theoretical uncertainties

Accurate comparisons between theoretical models and experimental data are critical for scientific progress. However, inferred physical model parameters can vary significantly with the chosen physics model, highlighting the importance of properly accounting for theoretical uncertainties. In this Letter, we present a Bayesian framework that explicitly quantifies these uncertainties by statistically modeling theory errors, guided by qualitative knowledge of a theory’s varying reliability across the input domain. We demonstrate the effectiveness of this approach using two systems: a simple ball drop experiment and multi-stage heavy-ion simulations. In both cases incorporating model discrepancy leads to improved parameter estimates, with systematic improvements observed as additional experimental observables are integrated.

Bayesian methods↗
DOE OSTI2022

Learning to simulate high energy particle collisions from unlabeled data

In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often reconstructed from indirect measurements causing the aggregate transformation from theoretical models to experimental data to be poorly-described analytically. Instead, numerical simulations are used at great computational cost. We introduce Optimal-Transport-based Unfolding and Simulation (OTUS), a fast simulator based on unsupervised machine-learning that is capable of predicting experimental data from theoretical models. Without the aid of current simulation information, OTUS trains a probabilistic autoencoder to transform directly between theoretical models and experimental data. Identifying the probabilistic autoencoder’s latent space with the space of theoretical models causes the decoder network to become a fast, predictive simulator with the potential to replace current, computationally-costly simulators. Here, we provide proof-of-principle results on two particle physics examples, Z-boson and top-quark decays, but stress that OTUS can be widely applied to other fields.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Aerosol Hygroscopic Growth, Mixing State, and Cloud Condensation Nuclei Activity during TRACER (Field Campaign Report)

Convective clouds play a critical role in the Earth’s climate system. Recent research has shown that a realistic representation of convective processes is critical to constraining climate sensitivity in global climate models. Theoretical and modeling studies showed that aerosols could have strong dynamic feedback to convection in warm and humid environments through enhancing ice-related processes and condensational growth. A few observation-based studies also suggested the influence of aerosols on convective cloud and precipitation properties. However, robust observational quantification of an aerosol effect on convective clouds isolated from other factors remains elusive. Understanding the impact of aerosol on convective clouds requires knowledge of the cloud condensation nuclei (CCN) spectrum, which represents the number of particles that uptake water and form cloud droplets as a function of supersaturation. The water uptake by aerosol is also of critical importance for the direct interaction of aerosol with radiation (i.e., aerosol direct effect) due to light scattering and absorption by aerosol. While both droplet activation under supersaturated conditions (i.e., relative humidity RH > 100%) and the hygroscopic growth under sub-saturated conditions (i.e., RH < 100%) are strongly influenced by particle hygroscopicity, the thermodynamic regimes and measurement methods are quite different. Aerosol particles, especially organic particles, can exhibit higher hygroscopicity for droplet activation than that for hygroscopic growth. In the subsaturated regime, the hygroscopicity of organic particles can also vary strongly with RH. However, global climate models usually treat organic species in aerosols with a constant hygroscopicity, potentially introducing substantial uncertainties in the quantification of aerosol radiative effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Theoretical Operational Model for Complex Experiments and its Invariance Theorems

We develop and systematize the Theoretical–Operational Model (TOM), a framework that treats preparation and measurement —including their operational residues— as intrinsic structures of physical theory. The central contribution is a principled geometric–algebraic organization of admissible operational deformations, formulated using quantum channels, renormalization-style flows, and information-geometric tools. Within this structure, operational residues and background processes are represented as effective morphisms attached to these operational components, whose invariants yield constraints on how theoretical parameters vary under specified classes of deformations. Illustrations drawn from muon–electron conversion, long-baseline neutrino oscillations, and quark–gluon-plasma phenomenology show how TOM maps operational effects into inferences about theoretical parameters, enables systematic cross-experimental comparisons, and stabilizes parameter estimation against defined deformation families. By embedding the operational layer—together with its residues—within a structured theoretical setting, TOM supports both theory testing and theory development, clarifying the conceptual relation between experimental realization and the physical quantities represented by the theory.

Pronskikh, Vitaly [Fermilab] (ORCID:00000002518174↗
DOE OSTI2022

The Influence of Shear on Deep Convection Initiation. Part II: Simulations

Abstract This study evaluates a hypothesis for the role of vertical wind shear in deep convection initiation (DCI) that was introduced in Part I by examining behavior of a series of numerical simulations. The hypothesis states, “Initial moist updrafts that exceed a width and shear threshold will ‘root’ within a progressively deeper steering current with time, increase their low-level cloud-relative flow and inflow, widen, and subsequently reduce their susceptibility to entrainment-driven dilution, evolving toward a quasi-steady self-sustaining state.” A theoretical model that embodied key elements of the hypothesis was developed in Part I, and the behavior of this model was explored within a multidimensional environmental parameter space. Remarkably similar behavior is evident in the simulations studied here to that of the theoretical model, both in terms of the temporal evolution of DCI and in the sensitivity of DCI to environmental parameters. Notably, both the simulations and theoretical model experience a bifurcation in outcomes, whereby nascent clouds that are narrower than a given initial radius R 0 threshold quickly decay and those above the R 0 threshold undergo DCI. An important assumption in the theoretical model, which states that the cloud-relative flow of the background environment V CR determines cloud radius R , is scrutinized in the simulations. It is shown that storm-induced inflow is small relative to V CR beyond a few kilometers from the updraft edge, and V CR therefore plays a predominant role in transporting conditionally unstable air to the updraft. Thus, the critical role of V CR in determining R is validated.

58 GEOSCIENCES↗
DOE OSTI2021

Transfer-Learnt Energy Models for Predicting Electricity Consumption in Buildings with Limited and Sparse Field Data

Modeling energy consumption is critical for energy-efficient utilization of the electric appliances in a building, smart grid programs (like demand-response), and many other smart home applications. State-of-the-art energy modeling techniques either rely on theoretical models, or extensive instrumentation of the building envelope to gather ``big" data to train a deep neural network. While theoretical models are often limited by their estimation accuracy, it is not always feasible to gather a significant amount of field data. In this paper, we explore transfer learning-based strategies to train much more accurate model for energy estimation when using a sparse field data. We transferred knowledge, in the form of data and parameters, from the simulation framework to the field data. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that transfer learning-based models trained over one month data can perform comparative (and in some cases better) than the state-of-the-art machine learning and deep learning solutions.

Jain, Milan↗