Computational Studies for Sonic Boom Prediction
Outline: Introduction; Launch Ascent and Vehicle Aerodynamics (LAVA) Framework; 1st AIAA Sonic Boom Prediction Workshop Oblique Shock/Plume Interaction; Summary.
Sonic Boom↗
SEARCH · Engineering Papers
Results for “computational framework”
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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Towards Low-Overhead Resilience for Data Parallel Deep Learning
Data parallel techniques have been widely adopted both in academia and industry as a tool to enable scalable training of deep learning models. At scale, DL training jobs can fail due to software or hardware bugs, may need to be preempted or terminated due to unexpected events, or may perform suboptimally because they were misconfigured. Under such circumstances, there is a need to recover and/or reconfigure data-parallel DL training jobs on-the-fly, while minimizing the impact on the accuracy of the DNN model and the runtime overhead. In this regard, state-of-art techniques adopted by the HPC community mostly rely on checkpoint-restart, which inevitably leads to loss of progress, thus increasing the runtime overhead. In this paper we explore alternative techniques that exploit the properties of modern deep learning frameworks (overlapping of gradient averaging and weight updates with local gradient computations through pipeline parallelism) to reduce the overhead of resilience/elasticity. To this end we introduce a failure simulation framework and two resilience strategies (immediate mini-batch rollback and lossy forward recovery), which we study compared with checkpoint-restart approaches in a variety of settings in order to understand the trade-offs between the accuracy loss of the DNN model and the runtime overhead.
data-parallel training↗
Exploring Flood Predictability in Taiwan through Coupled Atmospheric–Hydrological and High-Performance Hydrodynamic Models
Effective flood simulation capabilities can tremendously support early warning and disaster prevention. To examine the applicability of a fully physics-based and high-performance flood simulation and forecasting modeling framework for a flood-prone region in Taiwan, we conduct a numerical experiment that couples the Weather Research and Forecasting (WRF) Model, WRF-Hydrological modeling system (WRF-Hydro), and the Two-Dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) to perform integrated rainfall, streamflow, and flood simulations. Furthermore, we first use the coupled WRF and WRF-Hydro (WWH) to predict rainfall and streamflow and then drive TRITON with the predicted streamflow hydrographs to simulate flood depth and inundation area. With the refined spatial resolution and parameterization, this framework can better predict rainfall with reasonable spatial patterns. Although WWH could overestimate the amount of rainfall in some areas, the uncertain rainfall–streamflow predictions produce reasonable flood maps able to pinpoint regions at risk of flooding. In terms of model efficiency, the graphics processing unit–based computation can yield a speed-up factor as high as ∼13 compared to the central processing unit–based computation, promoting the efficacy of the coupled modeling framework in practical real-time flood forecasting.
Coupled models↗
Ambient and Initial Temperature Effects on Energy Consumption Rate Modeled in FASTSim
Ambient and initial temperatures significantly impact the energy consumption rate (ECR) of battery electric vehicles (BEVs) due to auxiliary loads and the temperature dependence of battery efficiency. This study introduces a streamlined, physics-based thermal modeling approach within the FASTSim tool that bridges the gap between oversimplified constant-load models and computationally expensive high-fidelity simulations. By employing a lumped thermal mass framework, the model captures fundamental energy balances and critical non-linear energy penalties while maintaining the computational efficiency required for expansive sensitivity studies. The simulations evaluated a compact BEV hatchback with a resistive heater over city (UDDS) and highway (HWFET) test cycles. Compared to a 22 degrees Celsius initial and ambient temperature baseline, a -7 degrees Celsius initial/ambient temperature resulted in a 221% increase in the ECR for the city cycle and a 100% increase for the highway cycle. Conversely, a 45 degrees Celsius initial / 40 degrees Celsius ambient temperature resulted in a 40% increase for UDDS and an 18% increase for HWFET. These results demonstrate that while cold conditions impose the most severe energy penalties due to resistive heating, the impact is consistently more pronounced in city driving where auxiliary loads represent a larger proportion of total energy. This lightweight yet robust framework enables researchers to rapidly quantify BEV thermal sensitivity across diverse climates without the need for high-overhead simulation environments.
33 ADVANCED PROPULSION SYSTEMS↗
PLUM: Parallel Load Balancing for Unstructured Adaptive Meshes
Dynamic mesh adaption on unstructured grids is a powerful tool for computing large-scale problems that require grid modifications to efficiently resolve solution features. Unfortunately, an efficient parallel implementation is difficult to achieve, primarily due to the load imbalance created by the dynamically-changing nonuniform grid. To address this problem, we have developed PLUM, an automatic portable framework for performing adaptive large-scale numerical computations in a message-passing environment. First, we present an efficient parallel implementation of a tetrahedral mesh adaption scheme. Extremely promising parallel performance is achieved for various refinement and coarsening strategies on a realistic-sized domain. Next we describe PLUM, a novel method for dynamically balancing the processor workloads in adaptive grid computations. This research includes interfacing the parallel mesh adaption procedure based on actual flow solutions to a data remapping module, and incorporating an efficient parallel mesh repartitioner. A significant runtime improvement is achieved by observing that data movement for a refinement step should be performed after the edge-marking phase but before the actual subdivision. We also present optimal and heuristic remapping cost metrics that can accurately predict the total overhead for data redistribution. Several experiments are performed to verify the effectiveness of PLUM on sequences of dynamically adapted unstructured grids. Portability is demonstrated by presenting results on the two vastly different architectures of the SP2 and the Origin2OOO. Additionally, we evaluate the performance of five state-of-the-art partitioning algorithms that can be used within PLUM. It is shown that for certain classes of unsteady adaption, globally repartitioning the computational mesh produces higher quality results than diffusive repartitioning schemes. We also demonstrate that a coarse starting mesh produces high quality load balancing, at a fraction of the cost required a fine initial mesh. Results indicate that our parallel load balancing strategy will remain viable on large numbers of processors.
Oliker, Leonid↗
Analyses of requirements for computer control and data processing experiment subsystems. Volume 2: ATM experiment S-056 image data processing system software development
The IDAPS (Image Data Processing System) is a user-oriented, computer-based, language and control system, which provides a framework or standard for implementing image data processing applications, simplifies set-up of image processing runs so that the system may be used without a working knowledge of computer programming or operation, streamlines operation of the image processing facility, and allows multiple applications to be run in sequence without operator interaction. The control system loads the operators, interprets the input, constructs the necessary parameters for each application, and cells the application. The overlay feature of the IBSYS loader (IBLDR) provides the means of running multiple operators which would otherwise overflow core storage.
Source record↗
A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis
The global nature of the ITER project along with its projected ~ petabyte per day data generation presents a unique challenge, but also an opportunity for the fusion community to rethink, optimize, and enhance our scientific discovery process. Recognizing this, collaborative research with computational scientists was undertaken over the past several years to create a framework for large-scale data movement across wide-area networks (WANs), to enable global near-real time analysis of fusion data. This would broaden the available computational resources for analysis/simulation, and increase the number of researchers actively participating in experiments. An official demonstration of this framework for fast, large data transfer and real-time analysis was carried out between the KSTAR tokamak in Daejeon, Korea and PPPL in Princeton, USA. Streaming large data transfer, with near real-time movie creation and analysis of the KSTAR Electron Cyclotron Emission imaging (ECEI) data, was performed using the I/O framework ADIOS, and comparisons made at PPPL with simulation results from the XGC1 code. These demonstrations were made possible utilizing an optimized network configuration at PPPL, which achieved over 8.8 Gbps (88% utilization) in throughput tests from NFRI to PPPL. This demonstration showed the feasibility for large-scale data analysis of KSTAR data, and provides a nascent framework to enable use of globally distributed computational and personnel resources in pursuit of scientific knowledge from the ITER experiment.
43 PARTICLE ACCELERATORS↗
Multi-physics melt pool modeling and process optimization for laser direct energy deposition of Nb-based refractory C103: Defect formation, geometric precision, and process mapping
Recent developments in additive manufacturing (AM) technology have reignited interest in the fabrication of the Nb-based refractory C103 alloy offering solutions to the challenges posed by traditional manufacturing methods. However, the limited numerical and experimental studies on laser direct energy deposition (DED) of C103 have hindered the understanding of the relationships between process parameters and build quality. This has made it challenging to consistently produce parts with the desired quality and microstructure suitable for critical applications. In this study, we focus on optimizing the laser DED process for C103 by employing a hybrid approach that combines experimental techniques and computational fluid dynamics (CFD). This approach facilitates the development of process maps for defect detection and geometric precision. To achieve this, multi-layer C103 samples were fabricated using laser DED under various process parameters, enabling the creation of a process map for defect detection. Additionally, a multi-physics, multiphase simulation framework was developed within a high-performance computing (HPC) environment to establish process maps for geometric precision. Using these process maps, printability windows were identified for achieving both the desired geometric accuracy and defect-free prints. It was observed that prints with a power-to-velocity (P/V) ratio close to unity resulted in defect-free outcomes. This study provides a foundation for reducing design lead time and rejected parts, ultimately optimizing the laser DED process for C103.
Defect formation and geometric precision↗
Computational Inference of Vibratory System with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains
Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.
K Zhou↗
Envisioning an Optimal Network of Space-Based Lasers for Orbital Debris Remediation
The rapid increase in resident space objects, including satellites and orbital debris, poses a significant threat to the safety and sustainability of space missions. This paper explores orbital debris remediation using a network of collaborative space-based lasers, leveraging laser ablation for momentum transfer on debris. A novel delta-v vector analysis framework quantifies the e↵ects of multiple simultaneous laser-to-debris (L2D) engagements by using vector composition of the imparted delta-v vectors. The paper introduces the Concurrent LocationScheduling Problem (CLSP), which optimizes the placement of laser platforms and the scheduling of L2D engagements to maximize debris remediation capacity. Due to the computational complexity of the CLSP, it is decomposed into two sequential subproblems: (1) optimal laser platform locations are determined using the Maximal Covering Location Problem, and (2) a novel integer linear programming-based approach schedules L2D engagements within the network configuration to maximize remediation capacity. Computational experiments are conducted to evaluate the proposed framework’s e↵ectiveness under various mission scenarios, demonstrating key network functions such as collaborative nudging, deorbiting, and just-in-time collision avoidance. A sensitivity analysis further examines how varying the number and distribution of laser platforms a↵ects debris remediation capacity, providing insights into optimizing the performance of space-based laser networks.
David O Williams Rogers↗
Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios
This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.
De Souza, Reubun [School of Electrical Engineering↗
Massively Parallel Bayesian Model Calibration and Uncertainty Quantification with Applications to Nuclear Fuels and Materials
The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Fuel behavior in a nuclear reactor is governed by a complex network of mechanisms interacting with various other physics aspects in the reactor system. Any model developed to represent the fuel behavior will likely be idealized resulting in uncertainties in their predictions compared to the observed data. As such, this report was motivated by the need to identify the sources of uncertainties and quantify and propagate them through the fuel model outputs. Such quantification of uncertainties will establish a level of model trustworthiness, identify approaches to improve the model trustworthiness, and even guide optimal experiment design for maximal information gain. To accomplish the uncertainty quantification for computational models, this report has relied on the Bayesian framework which provides probabilistic treatment of models their inputs and outputs. The current state-of-the-art on performing Bayesian Uncertainty Quantification (UQ) for nuclear engineering models using High Performance Computing (HPC) resources have been reviewed. Implementation of capabilities for massively parallel Bayesian UQ in Multiphysics Object-Oriented Simulation Environment (MOOSE) is discussed. Several verification cases are discussed to verify the accuracy of the quantified uncertainties using the developed computational capabilities in MOOSE. Then, the problem of quantifying the uncertainties in TRI-Structural isOtropic (TRISO) fuel silver release is addressed. For the first time, the uncertainties arising from the TRISO Fission Gas Release (FGR) model due to model inadequacy and experimental noise are quantified. Also, the Bayesian capabilities are applied to the calibration of the MATPRO creep model, a widely used model in several fuel assessment cases. The impact of the prediction uncertainties in the MATPRO model on the fuel cladding behavior as part of the TRIBULATION assessment case (which is an integral effects case) is investigated. This report concludes with a discussion on the future work for the UQ for computational models.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Algorithmic construction of SSA-compatible extreme rays of the subadditivity cone and the N = 6 solution
We compute the set of all extreme rays of the 6-party subadditivity cone that are compatible with strong subadditivity. In total, we identify 208 new (genuine 6-party) orbits, 52 of which violate at least one known holographic entropy inequality. For the remaining 156 orbits, which do not violate any such inequalities, we construct holographic graph models for 150 of them. For the final 6 orbits, it remains an open question whether they are holographic. Consistent with the strong form of the conjecture in [1], 148 of these graph models are trees. However, 2 of the graphs contain a “bulk cycle”, leaving open the question of whether equivalent models with tree topology exist, or if these extreme rays are counterexamples to the conjecture. The paper includes a detailed description of the algorithm used for the computation, which is presented in a general framework and can be applied to any situation involving a polyhedral cone defined by a set of linear inequalities and a partial order among them to find extreme rays corresponding to down-sets in this poset.
AdS-CFT correspondence↗
Post-Minkowskian effective field theory for conservative binary dynamics
We develop an Effective Field Theory (EFT) formalism to solve for the conservative dynamics of binary systems in gravity via Post-Minkowskian (PM) scattering data. Our framework combines a systematic EFT approach to compute the deflection angle in the PM expansion, together with the ‘Boundary-to-Bound’ (B2B) dictionary introduced. Due to the nature of scattering processes, a remarkable reduction of complexity occurs both in the number of Feynman diagrams and type of integrals, compared to a direct EFT computation of the potential in a PM scheme. We provide two illustrative examples. Firstly, we compute all the conservative gravitational observables for bound orbits to 2PM, which follow from only one topology beyond leading order. The results agree with those, obtained through the ‘impetus formula’ applied to the classical limit of the one loop amplitude. For the sake of comparison we reconstruct the conservative Hamiltonian to 2PM order, which is equivalent to the one derived from a matching calculation. Secondly, we compute the scattering angle due to tidal effects from the electric- and magnetic-type Love numbers at leading PM order. Using the B2B dictionary we then obtain the tidal contribution to the periastron advance. We also construct a Hamiltonian including tidal effects at leading PM order. Although relying on (relativistic) Feynman diagrams, the EFT formalism developed here does not involve taking the classical limit of a quantum amplitude, neither integrals with internal massive fields, nor additional matching calculations, nor spurious (‘super-classical’) infrared singularities. By construction, the EFT approach can be automatized to all PM orders.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Dynamics and lipid membrane coupling of the RAS-RAF complex revealed via multiscale simulations
To gain molecular and mechanistic insights into initiation of the RAS-RAF signaling cascade, we developed and used a combination of multiscale simulation and experimental approaches. The influence and impact of the membrane on RAS and RAF proteins is a factor we are just beginning to understand and appreciate in more detail. Molecular simulation is an ideal methodology to further study this complicated relationship between the membrane and associated proteins. Our previous work using Multiscale Machine-learned Modeling Infrastructure investigated different lipid compositions solely around the KRAS4b protein and the interplay between protein behavior and these membrane environments. Multiscale Machine-learned Modeling Infrastructure uses machine learning to couple adjacent simulation scales and has been efficiently scaled across some of the world’s largest high-performance computers. Recently, we have expanded this multiresolution framework to include the all-atom simulation scale and to incorporate the RAF RBDCRD domains. Here, we present the overall analysis results from this new simulation campaign comprising a mixture of RAS and RAF RBDCRD proteins. Approximately 35,000 coarse-grained and 10,000 all-atom molecular dynamics simulations were completed, sampled from a variety of protein/lipid composition configurations that were generated from a micron-scale continuum simulation containing hundreds of copies of the proteins. Our studies suggest that orientations of the RAS-RBDCRD complex on the membrane occupy distinct configurational states, and the spatial patterns of lipid arrangements around these different protein states are unique to each state. The extent and size of lipid “fingerprints” imposed on the membrane by the RAS-RBDCRD protein complex are significantly larger than observed for just the RAS protein on its own. These protein complexes strongly associate, but we do not observe statistically significant preferred protein-protein orientations. These observations indicate that spatial colocalization of RAS-RBDCRD proteins in the same vicinity may be assisted by specific membrane environments, acting to increase the probability of signaling complex formation.
Carpenter, Timothy S. [Lawrence Livermore National↗
Validation of Phasor-Domain Transmission and Distribution Co-simulation Against Electromagnetic Transient Simulation
The rapid deployment of renewable energy resources has led to the widespread use of power electronics in modern power systems. As these systems transition from being dominated by large synchronous machines to increasingly incorporating inverter-based resources (IBRs), traditional methods are becoming inadequate. Addressing this challenge, this paper introduces a scalable phasor-domain T\&D co-simulation framework based on open-source software. It focuses on the framework's validation against the PSCAD Electromagnetic Transient (EMT) analysis tool. The validation results demonstrate the framework's high-fidelity and a computational time speed-up of 60 to 100 times, marking a pioneering validation effort in T\&D co-simulation research.
Inverter-based resources, co-simulation, Electroma↗
Turbulence-parameter estimation for current-energy converters using surrogate model optimization
Surrogate models maximize information utility by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain simulations to estimate array efficiencies and environmental impacts. Meso-scale models typically represent turbines as actuator discs that act as momentum sinks and sources of turbulence and its dissipation. An OpenFOAM model was developed where actuator disc k-ε turbulence was characterized using an approach developed for flows through vegetative canopies. Turbine-wake data from laboratory flume experiments collected at two influent turbulence intensities were used to calibrate parameters in the turbulence-source terms in the k-ε equations. Additionally, parameter influences on longitudinal wake profiles were estimated using Gaussian process regression with subsequent optimization minimizing the objective function within 3.1% of those obtained using the full model representation, but for 74% of the computational cost (far fewer model runs). This framework facilitates more efficient parameterization of the turbulence-source equations using turbine-wake data.
16 TIDAL AND WAVE POWER↗
Preparation of Neptunyl and Plutonyl Acetates To Access Nonaqueous Transuranium Coordination Chemistry
Uranyl diacetate dihydrate is a useful reagent for the preparation of uranyl (UO 2 2+ ) coordination complexes, as it is a well-defined stoichiometric compound featuring moderately basic acetates that can facilitate protonolysis reactivity, unlike other anions commonly used in synthetic actinide chemistry such as halides or nitrate. Despite these attractive features, analogous neptunium (Np) and plutonium (Pu) compounds are unknown to date. Here, in this study, a modular synthetic route is reported for accessing stoichiometric neptunyl(VI) and plutonyl(VI) diacetate compounds that can serve as starting materials for transuranic coordination chemistry. The new NpO 2 2+ and PuO 2 2+ complexes, as well as a corresponding molecular UO 2 2+ complex, are isomorphous in the solid state, and in solution show similar solubility properties that facilitate their use in synthesis. In both solid and solution state, the +VI oxidation state (O.S.) is maintained, as demonstrated by vibrational and optical spectroscopy, confirming that acetate anions stabilize the oxidizing, high-valent +VI states of Np and Pu as they do for the more stable U(VI). All three acetate salts readily react with a model diprotic ligand, affording incorporation of U(VI), Np(VI), and Pu(VI) cores into molecular coordination compounds that occurs concomitantly with elimination of acetic acid; the new complexes are high-valent, yet overall charge neutral, facilitating entry into nonaqueous chemistry by rational synthesis. Computational studies reveal that the dianionic ligand framework assists in stabilizing the +VI O.S. via donation to the 5f shells of the actinides, highlighting the potential usefulness of protonolysis reactivity toward preparation of stabilized high-valent transuranic species.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗