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The Cassini spacecraft, NASA's mission to investigate the Saturn system, has undergone a system-level thermal balance test program to permit verification of the science instrument thermal designs in the simulated worst-case environments.
The Cassini spacecraft, NASA's mission to investigate the Saturn system, has undergone a system level themal balance test program to permit verification of the engineering subsystem themal designs in the simulated worst-case environments.
A series of viscous CFD simulations depicting Space Launch System (SLS) post Mode-1abort scenarios are conducted and analyzed. The primary purpose of these simulations is to study the effect of launch abort vehicle attitude control motor (ACM) and abort motor(AM) plume modeling fidelity on the drag of the aborted core stage. The simulations are first conducted with a fully-coupled, multi-species and chemically reacting model for the freestream gases and the solid rocket abort motor combustion products. Following this, equivalent species are created for the ACM and AM exhaust gases. These equivalent species aim to provide near the same results for core stage drag as the chemically reacting model at a significantly lower computational cost. Finally, the exhaust gases are modeled as calorically perfect air. It was concluded that under most scenarios, both the equivalent species and the perfect air models predict the drag of the aborted core stage sufficiently close to that predicted by the chemically reacting simulations. In these cases, the flow and geometry conditions did not induce significant changes in the composition of the ‘true’ exhaust gas via afterburning of the combustion products. For cases where this was not the case, the properties of the essentially frozen equivalent species and perfect air models deviated from the true mixture, yielding unsatisfactory results. Finally, the accuracy improvements by using the chemically reacting model are weighed against the significant increase in associated computational costs. It was found that the simplified exhaust gas models are at least 4 times less expensive than the chemically reacting model. This may justify the judicious use of these simple models in future work.
A series of viscous CFD simulations depicting Space Launch System (SLS) post Mode-1abort scenarios are conducted and analyzed. The primary purpose of these simulations is to study the effect of launch abort vehicle attitude control motor (ACM) and abort motor(AM) plume modeling fidelity on the drag of the aborted core stage. The simulations are first conducted with a fully-coupled, multi-species and chemically reacting model for the freestream gases and the solid rocket abort motor combustion products. Following this, equivalent species are created for the ACM and AM exhaust gases. These equivalent species aim to provide near the same results for core stage drag as the chemically reacting model at a significantly lower computational cost. Finally, the exhaust gases are modeled as calorically perfect air. It was concluded that under most scenarios, both the equivalent species and the perfect air models predict the drag of the aborted core stage sufficiently close to that predicted by the chemically reacting simulations. In these cases, the flow and geometry conditions did not induce significant changes in the composition of the ‘true’ exhaust gas via afterburning of the combustion products. For cases where this was not the case, the properties of the essentially frozen equivalent species and perfect air models deviated from the true mixture, yielding unsatisfactory results. Finally, the accuracy improvements by using the chemically reacting model are weighed against the significant increase in associated computational costs. It was found that the simplified exhaust gas models are at least 4 times less expensive than the chemically reacting model. This may justify the judicious use of these simple models in future work.
We present results from a novel extension of the boundary layer parameterization in the NASA Goddard Earth Observing System (GEOS) model. An Eddy Diffusivity-Mass Flux (EDMF) multi-plume updraft scheme was previously implemented in GEOS. Here we replace the K-profile eddy diffusivity scheme of Lock et. al. (2000) with the Simplified Higher Order Closure (SHOC). The scheme prognoses turbulent kinetic energy, and derives buoyancy flux, cloud fraction and condensate from a trivariate double-gaussian joint PDF. The original SHOC scheme is modified in two significant respects: (i) contributions from the EDMF updrafts are included in estimates of all higher order moments used to determine the PDF; and (ii) the second and third moments of total water are made prognostic variables, with source terms associated with EDMF updrafts. The predicted higher order moments as well as updraft and environmental properties are compared with large eddy simulations in several case studies.
We present results from a novel extension of the boundary layer parameterization in the NASA Goddard Earth Observing System (GEOS) model. An Eddy Diffusivity-Mass Flux (EDMF) multi-plume updraft scheme was previously implemented in GEOS. Here we replace the K-profile eddy diffusivity scheme of Lock et. al. (2000) with the Simplified Higher Order Closure (SHOC; Bogenschutz and Krueger, 2013). The scheme prognoses turbulent kinetic energy, and derives buoyancy flux, cloud fraction and condensate from a trivariate double-gaussian joint PDF. The original SHOC scheme is modified in two significant respects: (i) contributions from the EDMF updrafts are included in estimates of all higher order moments used to determine the PDF; and (ii) the second and third moments of total water are made prognostic variables, with source terms associated with EDMF updrafts. The predicted higher order moments as well as updraft and environmental properties are compared with large eddy simulations in several case studies. Results indicate that many higher order moments are underestimated when the mass flux contribution is neglected.
We present results from a novel extension of the boundary layer parameterization in the NASA Goddard Earth Observing System (GEOS) model. An Eddy Diffusivity-Mass Flux (EDMF) multi-plume updraft scheme was previously implemented in GEOS. Here we replace the K-profile eddy diffusivity scheme of Lock et. al. (2000) with the Simplified Higher Order Closure (SHOC; Bogenschutz and Krueger, 2013). The scheme prognoses turbulent kinetic energy, and derives buoyancy flux, cloud fraction and condensate from a trivariate double-gaussian joint PDF. The original SHOC scheme is modified in two significant respects: (i) contributions from the EDMF updrafts are included in estimates of all higher order moments used to determine the PDF; and (ii) the second and third moments of total water are made prognostic variables, with source terms associated with EDMF updrafts. The predicted higher order moments as well as updraft and environmental properties are compared with large eddy simulations in several case studies. Results indicate that many higher order moments are underestimated when the mass flux contribution is neglected.
InSAR is a key technique used to constrain contributions of diverse processes to coastal subsidence, also known as vertical land motion (VLM). However, coastal environments can pose major challenges for InSAR due to natural disturbances that degrade interferogram quality. We describe a new multi-primary pairing strategy for persistent scatterer InSAR (PS-InSAR) to estimate subsidence in challenging coastal environments. Our method retains only consistent PS candidates across multiprimary substacks and solves for redundant velocity observations using SVD-based inversion, similar to the conventional small baseline subset (SBAS) method. Through simulations and a case study comparing with single-primary PS-InSAR and conventional SBAS techniques, we show that our pairing strategy reduces temporal and spatial uncertainty in subsidence estimates in the presence of strong but temporary decorrelation loss, even with increased distance from the reference point. Moreover, our method visibly dampens time-series variation and decreases standard error in our time series fit by nearly 2x in our case study. Thus, we find that implementing a multi-primary PS-InSAR configuration is a simple method of increasing the robustness of VLM estimates in challenging coastal environments.
Fine, electrically charged, glass like dust particles caused significant damage to the Apollo EMU9 during lunar EVAs, identified as one of the greatest challenges to future exploration. Passive Lunar Dust Mitigation Devices (LDMD) were developed, within a SBIR Phase II, to prohibit this dust from interrupting venting space suit component operation. A Computational Fluid-Dynamics and Discrete Element Method (CFD-DEM) Simulation Tool was developed at the University of Colorado, Boulder to predict venting gas flow ability to self-clean adhered dust particles from LDMD surfaces. Lunar dust properties (i.e., adhesion and cohesion strengths) required to complete Simulation Tool analysis are relatively unknown due to considerable differences between the Earth and the Moon (i.e., gravity, humidity) and due to an absence of dust particles in their native state. Analytically determining gas flow velocity, density and direction within the fluid Boundary Layer, microns from LDMD surfaces presented a second challenge. Dusty Plasma Chamber testing is being performed at Auburn University, Auburn to observe electrostatically charged dust behavior as it adheres to LDMD prototypes and specific geometry and is then blown away by metered gas flow. Test articles were developed to offer insight into the impact of different flow geometries, surface roughness and dust removal within the gas Boundary-Layer. Observed dust behavior is currently being developed to support the CFD-DEM analysis. Many Simulation Tool analytical cases have been processed to support the intention of completing sensitivity studies to assess how different dust adherence values and Boundary Layer fluid properties impact LDMD self-cleaning effectivity.
An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.
The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation. The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, security, and architectural implications of introducing network slicing into traditionally static OT infrastructures such as Industrial Control Systems (ICS) and SCADA. Through simulated deployments and case studies, the research demonstrates how slicing enables better isolation between critical and non-critical services, thereby improving response time, throughput, and security in sensitive environments. The second question considers: How to dynamically implement network slicing and take advantage of network resources towards integrating decentralized machine learning? In response, this thesis proposes a framework that combines Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Federated Learning (FL) to enable real-time analytics while maintaining data locality. The proposed approach reduces the burden on centralized infrastructure and minimizes privacy risks by supporting on-site training of models across distributed OT nodes, coordinated through dynamically allocated network slices. The third focus explores: How slicing helps to increase the resiliency of OT networks through the orchestration of a dynamic DMZ? To answer this, the thesis presents a method for creating and managing Dynamic Demilitarized Zones (DMZs) using network slicing. This enables flexible and automated isolation of sensitive subsystems during threat scenarios or high-risk operations. Coupled with intelligent orchestration and containerized security services, the dynamic DMZ significantly enhances the system's ability to respond to cyber incidents without halting production. Ultimately, this thesis contributes a comprehensive architecture that blends network slicing with machine learning, secure segmentation, and automation, paving the way for resilient, adaptive, and intelligent OT environments. Performance evaluations across multiple scenarios show improvements in system reliability, threat response time, model accuracy, and resource utilization, providing a strong foundation for future industrial automation systems.
Data set including 17 fields of information for 141,506,665 U.S. buildings. Advantages of this dataset include: Resolution - actionable data and models for individual U.S. buildings; Flexibility - ability to modify data, models, and analysis for any simulation-informed use case; Scalability - dynamic archetypes can be used as representative models for any geography of interest; Accuracy - error rates for total energy use at city-scale of 1-5%
Real clouds and clouds systems are inherently three dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D). A few 3D CRMs have been used to study the response of clouds to large-scale forcing. In these 3D simulations, the model domain was small, and the integration time was 6 hours. Only recently have 3D experiments been performed for multi-day periods for tropical cloud system with large horizontal domains at the National Center for Atmospheric Research. The results indicate that surface precipitation and latent heating profiles are very similar between the 2D and 3D simulations of these same cases. The reason for the strong similarity between the 2D and 3D CRM simulations is that the observed large-scale advective tendencies of potential temperature, water vapor mixing ratio, and horizontal momentum were used as the main forcing in both the 2D and 3D models. Interestingly, the 2D and 3D versions of the CRM used in CSU and U.K. Met Office showed significant differences in the rainfall and cloud statistics for three ARM cases. The major objectives of this project are to calculate and axamine: (1)the surface energy and water budgets, (2) the precipitation processes in the convective and stratiform regions, (3) the cloud upward and downward mass fluxes in the convective and stratiform regions; (4) cloud characteristics such as size, updraft intensity and lifetime, and (5) the entrainment and detrainment rates associated with clouds and cloud systems that developed in TOGA COARE, GATE, SCSMEX, ARM and KWAJEX. Of special note is that the analyzed (model generated) data sets are all produced by the same current version of the GCE model, i.e. consistent model physics and configurations. Trajectory analyse and inert tracer calculation will be conducted to identify the differences and similarities in the organization of convection between simulated 2D and 3D cloud systems.
We present a case study of catalytic propane dehydrogenation with our open-source multiphysics solver, Mesoflow. The solver was developed to simulate reactive flow coupled to heterogeneous catalytic reactions and deactivation in the context of complex, mesoscale geometry. The method leverages cartesian block-structured adaptive mesh refinement to capture realistic catalyst microstructural features acquired directly from X-ray computed tomography data. A kinetic model for propane dehydrogenation and catalyst deactivation was developed based on temporal analysis of products (TAP) reactor experiments. The TAP reactor experiments allow for precise characterization of intrinsic kinetic reaction steps which are implemented into Mesoflow simulations to model the spatial and temporal evolution of reactants, products, and catalyst active sites. The short-term and long-term deactivation behavior is studied by using XCT data collected from fresh and aged catalyst pellets, which exhibit different microstructural features. This study employs time-splitting algorithms to connect disparate reaction and flow timescales, enabling the simulations to achieve realistic deactivation timescales on the order of minutes while the flow time-scales for small particles (100 microns) are several milliseconds. We also introduce a flexible automated python script that writes the necessary files to construct a Mesoflow simulation from user-created chemical mechanisms. We will also introduce a few new features that are added to Mesoflow such as higher order schemes, implicit chemistry integrators and the ability to run on AMD and NVIDIA graphics-processing-units.
Significant asymmetries in the fluid dynamics were calculated for some cases in the CFD simulations of the Orion Launch Abort Vehicle through its abort trajectories. The CFD simulations were performed steady state with symmetric boundary conditions and geometries. The trajectory points at issue were in the transonic regime, at 0 and 5 angles of attack with the Abort Motors with and without the Attitude Control Motors (ACM) firing. In some of the cases the asymmetric fluid dynamics resulted in aerodynamic side forces that were large enough that would overcome the control authority of the ACMs. MSFC s Fluid Dynamics Group supported the investigation into the cause of the flow asymmetries with time accurate CFD simulations, utilizing a hybrid RANS-LES turbulence model. The results show that the flow over the vehicle and the subsequent interaction with the AB and ACM motor plumes were unsteady. The resulting instantaneous aerodynamic forces were oscillatory with fairly large magnitudes. Time averaged aerodynamic forces were essentially symmetric.
High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.
Significant asymmetries in the fluid dynamics were calculated for some cases in the CFD simulations of the Orion Launch Abort Vehicle through its abort trajectories. The CFD simulations were performed steady state and in three dimensions with symmetric geometries, no freestream sideslip angle, and motors firing. The trajectory points at issue were in the transonic regime, at 0 and +/- 5 angles of attack with the Abort Motors with and without the Attitude Control Motors (ACM) firing. In some of the cases the asymmetric fluid dynamics resulted in aerodynamic side forces that were large enough that would overcome the control authority of the ACMs. MSFC's Fluid Dynamics Group supported the investigation into the cause of the flow asymmetries with time accurate CFD simulations, utilizing a hybrid RANS-LES turbulence model. The results show that the flow over the vehicle and the subsequent interaction with the AB and ACM motor plumes were unsteady. The resulting instantaneous aerodynamic forces were oscillatory with fairly large magnitudes. Time averaged aerodynamic forces were essentially symmetric.
This paper introduces T&D LIBRA, a HELICS-based dynamic co-simulation framework designed for transmission and distribution (T&D) load and inverter-based resource assessment. This framework is designed to model interactions between single-phase induction motors (i.e., Motor-D), distributed energy resources (DERs) and the transmission network, emphasizing system stability during fault conditions. The primary objective of this research is to study the fault-induced delayed voltage recovery (FIDVR) behavior of the T&D networks using the co-simulation framework and to compare the results with conventional PSS/E only simulations. Simulations are validated using T&D co-simulations with a single distribution node and the IEEE 123-bus distribution test case. The co-simulation results demonstrate the value of using T&D co-simulations to capture motor stalling, DER tripping and other sources of voltage diversity on distribution networks.