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NASA NTRS2021

Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

domain adaptation↗

DOE OSTI2024

Adaptive Narrowband Damping for Improving Harmonic Stability of Modular Multilevel Converter: Preprint

Harmonic instability events between modular multilevel converter (MMC) and ac systems have been widely reported in recent years. To resolve it, this paper proposes an adaptive narrowband damping control that automatically programs, adds and adjusts damping around the oscillation frequency when detect the oscillation. The paper first presents a low-pass filter design for MMC control loops, which pushes all negative damping of MMC impedance down to the medium frequency range (< approximately 1000 Hz). This makes narrowband damping can be more targeted and easier to design because multiple harmonic oscillations are avoided. An adaptive damping control that uses online oscillation detection is then proposed, which can automatically configure the narrowband damper to provide damping to the MMC at the detected oscillation frequency. In contrast to existing narrowband damping methods, the proposed adaptive narrowband damper adjusts the damping gain (e.g., to zero when system resonance disappears) and the width of the damping range automatically based on continuous monitoring of system resonance conditions. Electromagnetic transient (EMT) simulation results validate the efficacy of the proposed method in two typical MMC-based power systems.

active damping↗

DOE OSTI2026

Adaptive Control for Load-Following of Boiling Water Reactors Part I: Linear Systems and Fully-Observable Dynamics

Automation control is a key strategy to improve the economic competitiveness of nuclear power plants. Not only does it help reduce operational costs, but it also extends the value proposition of these plants to nontraditional markets, including unattended operations in remote villages and space. However, the dynamics of the operating environments of nuclear reactors are subject to changes over time, and there are no widely adopted methods to ensure that the automation strategy will remain effective over the extended durations required for these applications. Adaptive control is a discipline that offers the possibility to accommodate such changes online. However, it relies on mathematical assumptions that must be respected to ensure robustness and reliability. In this work, we derive an adaptive control formulation for linear systems in which all states are observable and apply it to an instance of load-follow operation for Boiling Water Reactors. We assumed uncertainty in two factors: the temperature coefficient and the control rod worth, both of which are affected over time by the evolution of the nuclear reactor core environment. With an arbitrary penalty factor of 5, we found that the mean absolute and integral time absolute errors can be reduced by more than 90%, underscoring the strength of adaptive control. To extend the application to more challenges, different uncertainties and load-follow trajectories, as well as new formulations that include non-linearity and partial observability, are currently being developed.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗

DOE OSTI2023

Reciprocal transplants reveal asymmetric local adaptation of Himalayan Rhododendron approaching elevational range limit

Abstract As plant species expand their upper limits of distribution under current warming, some retain both traditional climate space and biotic environment while others encounter novel conditions. The latter is the case for Rhododendron campanulatum , a woody shrub that grows both above and below treeline at our study site in the Eastern Himalayas where a very conspicuous, stable treeline was defined by a nearly contiguous canopy of tall Abies spectabilis trees, many of which are over a century old. Prior work showed that treeline had remained static in this region while R. campanulatum expanded its elevational range limit. We tested local adaptation of R. campanulatum by performing reciprocal transplants between the species' current elevational range limit (4023 m above sea level [asl]) and just above treeline (3876 m asl). Contrary to expectation, the coldest temperatures of late winter and early mid‐spring were experienced by plants at the lower elevation: R. campanulatum at species' limit (upper site) were covered by snow for a longer period (40 more days) and escaped the coldest temperatures suffered by conspecifics at treeline (lower site). The harsher spring conditions at treeline likely explain why leaves were smaller at treeline (15.3 cm 2 ) than at species limit (21.3 cm 2 ). Contrary to results from equivalent studies in other regions, survival was reduced more by downslope than by upslope movement, again potentially due to extreme cold temperatures observed at treeline in spring. Upslope transplantation had no effect on mortality, but mortality of species limit saplings transplanted downslope was three times higher than that of residents at both sites. A general expectation is that locals should survive better than foreign transplants, but survival of locals and immigrants at our species limit site was identical. However, those species limit saplings that survived the transplant to treeline grew faster than both locals at treeline and the transplants at species limit. Overall, we found asymmetric adaptation: Compared with treeline saplings, those at species limit (147 m above treeline) were more tolerant of extremes in the growing season but less tolerant of extremes in winter and early mid‐spring, displaying local adaptation in a more complex manner than simply home advantage, and complicating predictions about impacts of future regional climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Ecophysiology of mesophotic reef‐building corals in Hawai‘i is influenced by symbiont–host associations, photoacclimatization, trophic plasticity, and adaptation

Abstract Mesophotic reef corals remain largely unexplored in terms of the genetic adaptations and physiological mechanisms to acquire, allocate, and use energy for survival and reproduction. In the Hawaiian Archipelago, the Leptoseris species complex form the most spatially extensive mesophotic coral ecosystem known and provide habitat for a unique community. To study how the ecophysiology of Leptoseris species relates to symbiont–host specialization and understand the mechanisms responsible for coral energy acquisition in extreme low light environments, we examined Symbiodinium (endosymbiotic dinoflagellate) photobiological characteristics and the lipids and isotopic signatures from Symbiodinium and coral hosts over a depth‐dependent light gradient (55–7 μ mol photons m −2 s −1 , 60–132 m). Clear performance differences demonstrate different photoadaptation and photoacclimatization across this genus. Our results also show that flexibility in photoacclimatization depends primarily on Symbiodinium type. Colonies harboring Symbiodinium sp. COI‐2 showed significant increases in photosynthetic pigment content with increasing depth, whereas colonies harboring Symbiodinium spp. COI‐1 and COI‐3 showed variability in pigment composition, yield measurements for photosystem II, as well as size and density of Symbiodinium cells. Despite remarkable differences in photosynthetic adaptive strategies, there were no significant differences among lipids of Leptoseris species with depth. Finally, isotopic signatures of both host and Symbiodinium changed with depth, indicating that coral colonies acquired energy from different sources depending on depth. This study highlights the complexity in physiological adaptations within this symbiosis and the different strategies used by closely related mesophotic species to diversify energy acquisition and to successfully establish and compete in extreme light‐limited environments.

Padilla‐Gamiño, Jacqueline L.↗

DOE OSTI2020

Feature‐shared adaptive‐boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images

Purpose In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). The automatic determination of a nodule's invasiveness based on chest CT scans can guide treatment planning. However, it is challenging, owing to the insufficiency of training data and their interclass similarity and intraclass variation. To address these challenges, we propose a two‐stage deep learning strategy for this task: prior‐feature learning followed by adaptive‐boost deep learning. Methods The adaptive‐boost deep learning is proposed to train a strong classifier for invasiveness classification of subsolid nodules in chest CT images, using multiple 3D convolutional neural network (CNN)‐based weak classifiers. Because ensembles of multiple deep 3D CNN models have a huge number of parameters and require large computing resources along with more training and testing time, the prior‐feature learning is proposed to reduce the computations by sharing the CNN layers between all weak classifiers. Using this strategy, all weak classifiers can be integrated into a single network. Results Tenfold cross validation of binary classification was conducted on a total of 1357 nodules, including 765 noninvasive (AAH and AIS) and 592 invasive nodules (MIA and IAC). Ablation experimental results indicated that the proposed binary classifier achieved an accuracy of with an AUC of 81.3 . These results are superior compared to those achieved by three experienced chest imaging specialists who achieved an accuracy of , , and , respectively. About 200 additional nodules were also collected. These nodules covered 50 cases for each category (AAH, AIS, MIA, and IAC, respectively). Both binary and multiple classifications were performed on these data and the results demonstrated that the proposed method definitely achieves better performance than the performance achieved by nonensemble deep learning methods. Conclusions It can be concluded that the proposed adaptive‐boost deep learning can significantly improve the performance of invasiveness classification of pulmonary subsolid nodules in CT images, while the prior‐feature learning can significantly reduce the total size of deep models. The promising results on clinical data show that the trained models can be used as an effective lung cancer screening tool in hospitals. Moreover, the proposed strategy can be easily extended to other similar classification tasks in 3D medical images.

Wang, Jun↗

DOE OSTI2026

Bayesian Adaptive Polynomial Chaos Expansions

Polynomial chaos expansions (PCEs) are widely used for uncertainty quantification (UQ) tasks, particularly in the applied mathematics community. However, PCE has received comparatively less attention in the statistics literature, and fully Bayesian formulations remain rare—especially with implementations in R. Motivated by the success of adaptive Bayesian machine learning models such as BART, BASS and BPPR, we develop a new fully Bayesian adaptive PCE method with an efficient and accessible R implementation: khaos. Our approach includes a novel proposal distribution that enables data-driven interaction selection and supports a modified g-prior tailored to PCE structure. Through simulation studies and real-world UQ applications, we demonstrate that the Bayesian adaptive PCE provides competitive performance for surrogate modeling, global sensitivity analysis and ordinal regression tasks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Towards Adaptive Mesh Refinement for the Spectral Element Solver Nek5000

When performing computational fluid dynamics (CFD) simulations of complex flows, the a priori knowledge of the flow physics and the location of the dominant flow features are usually unknown. For this reason, the development of adaptive remeshing techniques is crucial for large-scale computational problems. Some work has been made recently to provide Nek5000 with adaptive mesh refinement (AMR) capabilities in order to facilitate the generation of the grid and push forward the limit in terms of problem size and complexity [10]. Nek5000 is an open-source, highly scalable and portable code based on the spectral element method (SEM) [4], which offers minimal dissipation and dispersion, high accuracy and exponential convergence. It is aimed at solving direct and large-eddy simulations of turbulent incompressible or low Mach-number flows with heat transfer and species transport. The approach chosen for adapting the mesh is the h-refinement method, where elements are split locally, which requires the relaxation of the conforming grid constraint currently imposed by Nek5000. Other challenges include the implementation of an efficient management of the grid as refinement is applied, the development of tools to localize the critical flow regions via error estimators and the extension of the current preconditioning strategy to non-conforming grids. In this paper, we present a new procedure to setup an algebraic multigrid solver used as part of the preconditioner for the pressure equation.

Offermans, N.↗

DOE OSTI2019

Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer

Molecular dynamics (MD) simulations must take very small (femtosecond) integration steps in simulation-time to avoid numerical errors. Efficient use of parallel programming models and accelerators in state-of-the art MD programs now is pushing Moore’s limit for time-per-MD step. As a result, directly simulating timescales beyond milliseconds will not be attainable directly, even at exascale. However, concepts from statistical physics can be used to combine many parallel simulations to provide information about longer timescales and to adequately sample the simulation space, while preserving details about the dynamics of the system. Implementing such an approach requires a workflow program that allows adaptable steering of task assignments based on extensive statistical analysis of intermediate results. Here we report the implementation of such an adaptable workflow program to drive simulations on the Summit IBM Power System AC922, a pre-exascale supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). We compare to experiences on Titan, Summit’s predecessor, report the performance of the workflow and its components, and describe the porting process. We find that using a workflow program managed by a Mongo database can provide the fault tolerance, scalable performance, task dispatch rate, and reconfigurability required for robust and portable implementation of ensemble simulations such as are used in enhanced-sampling molecular dynamics. This type of workflow generator can also be used to provide adaptive steering of ensemble simulations for other applications in addition to MD.

Ossyra, John R.↗

DOE OSTI2025

Robust Implicit Adaptive Low Rank Time-Stepping Methods for Matrix Differential Equations

In this work, we develop implicit rank-adaptive schemes for time-dependent matrix differential equations. The dynamic low rank approximation (DLRA) is a well-known technique to capture the dynamic low rank structure based on Dirac–Frenkel time-dependent variational principle. In recent years, it has attracted a lot of attention due to its wide applicability. Our schemes are inspired by the three-step procedure used in the rank adaptive version of the unconventional robust integrator (the so called BUG integrator) (Ceruti et al. in BIT Numer Math 62(4):1149–1174, 2022) for DLRA. First, a prediction (basis update) step is made computing the approximate column and row spaces at the next time level. Second, a Galerkin evolution step is invoked using an implicit solves for the small core matrix. Finally, a truncation is made according to a prescribed error threshold. Since the DLRA is evolving the differential equation projected on to the tangent space of the low rank manifold, the error estimate of the BUG integrator contains the tangent projection (modeling) error which cannot be easily controlled by mesh refinement. This can cause convergence issue for equations with cross terms. To address this issue, we propose a simple modification, consisting of merging the row and column spaces from the explicit step truncation method together with the BUG spaces in the prediction step. In addition, we propose an adaptive strategy where the BUG spaces are only computed if the residual for the solution obtained from the prediction space by explicit step truncation method, is too large. Here, we prove stability and estimate the local truncation error of the schemes under assumptions. We benchmark the schemes in several tests, such as anisotropic diffusion, solid body rotation and the combination of the two, to show robust convergence properties.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Insight into adaptive remodeling of the rotor ring complex of the bacterial flagellar motor

Highlights: • Adaptive remodeling of the flagellar motor ring is responsible for motor switching. • Helix{sub MC} connecting FliG{sub M} and FliG{sub C} is responsible for the switching. • A CW-locked deletion of FliG suppresses the oligomerization of FliG in solution. • The CW-locked deletion reduces the binding affinity of FliG for FliM. • We provide an insight into structural remodeling of the flagellar motor. The bacterial flagellar motor rotates in both counterclockwise (CCW) and clockwise (CW) directions. FliG, FliM and FliN form the C ring on the cytoplasmic face of the MS ring made of a transmembrane protein, FliF. The C ring acts not only as a rotor but also as a switch of the direction of motor rotation. FliG consists of three domains: FliG{sub N}, FliG{sub M} and FliG{sub C}. FliG{sub N} directly binds to FliF. Intermolecular interactions between FliG{sub M} and FliG{sub C} drive FliG ring formation. FliG{sub M} is responsible for the interaction with FliM. FliG{sub C} is involved in the interaction with the stator protein MotA. Adaptive remodeling of the C ring occurs when the motor switches between the CCW and CW states. However, it remained unknown how. Here, we report the effects of a CW-locked deletion mutation (ΔPEV) in FliG of Thermotaoga maritia (Tm-FliG) on FliG–FliG and FliG–FliM interactions. The PEV deletion stabilized the intramolecular interaction between FliG{sub M} and FliG{sub C}, thereby suppressing the oligomerization of Tm-FliG{sub MC} in solution. This deletion also induced a conformational change of Helix{sub MC} connecting FliG{sub M} and FliG{sub C} to reduce the binding affinity of Tm-FliG{sub MC} for FliM. We will discuss adaptive remodeling of the C ring responsible for flagellar motor switching.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Adaptive simulations enable computational design of electron beam processing of nanomaterials with supersonic micro-jet precursor

Focused Electron Beam Induced Processing (FEBIP) is a powerful tool for the “direct-write” of nanomaterials with the possibility of atomistic control on suspended 2D material substrates. FEBIP capabilities have been significantly expanded by using a localized jet-based delivery of precursors, and especially when a thermally energized supersonic micro-jet enhances the delivery of mass flux along with controlling the far-from-equilibrium thermodynamic state of adsorbed adatoms. The possibilities of growing nanomaterials with “dialed-in” composition with ultra-high growth rates and aspect ratios up to 100:1 have been demonstrated using the supersonic micro-jet-FEBIP. Bringing this scientific discovery to the level of maturity required for practical applications in additive nanomanufacturing requires simulation tools that are capable of capturing the complex flow physics of micro-jet-substrate interactions that bridge a wide range of flow regimes from the high-density gas micro-jet expanding into the vacuum environment of FEBIP. To address this significant computational challenge, a new approach has been developed and described in this work for a multiscale adaptive DSMC (Direct Simulation Monte Carlo) algorithm to predict the micro-jet gas dynamics in an FEBIP environment, spanning the full range of flow regimes from low Knudsen (Kn) number O(0.01) continuum flow to high Kn of O(10) for the molecular flow within a unified computational framework. Here, the fundamental principles of the adaptive DSMC algorithm are described, its viability as a DSMC technique is demonstrated, and computational improvements for the benchmark cases are discussed in the context of advancing 3D nanofabrication with FEBIP. Ultimately, combining the first principle simulations via adaptive DSMC with the complementary experimental data will enable the creation of the powerful CAD tools for in silico design and optimal operation of micro-jet-FEBIP.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Accounting for adaptation and intensity in projecting heat wave-related mortality

Highlights: • We propose novel models for intensity of and adaptation to heat waves in projections of heat wave- mortality. • The modeling used a large national dataset and the projections used a rich set of climate models and scenarios. • Future heat wave-related mortality would be substantially overestimated, if adaptation were ignored. • The time trend of future heat wave-related mortality will vary substantially across climate regions. How adaptation and intensity of heat waves affect heat wave-related mortality is unclear, making health projections difficult.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Limiter-aware adaptive excitation control for hydropower generators with legacy voltage regulation systems

Hydropower units often rely on legacy excitation systems with fixed automatic voltage regulator (AVR) settings, but parameter drift, changing operating conditions, and evolving grid-service demands can degrade terminal-voltage regulation over time. This problem should be addressed because poor voltage regulation affects reactive-power support, system stability, and reliable plant operation, and replacing installed AVR and limiter logic is costly and disruptive in practice. To address this issue, this paper proposes a retrofit-friendly enhancement for hydropower generator excitation systems that combines adaptive tuning of the AVR middle lead–lag branch with a small proportional-integral correction for removing steady bias and slow drift. The adaptive laws are normalized, projection-bounded, and equipped with leakage, while adaptation is enabled only in the middle branch. Thus, the intent of the underexcitation and overexcitation limiters, as well as legacy current and rate limits, remains unchanged. Stability is analyzed under incremental performance assumptions, yielding boundedness and asymptotic tracking in the absence of saturation. The method is evaluated using real operational data from a utility-scale hydropower unit at Rocky Reach Dam, Unit C-8. Compared with the fixed AVR baseline, the proposed method reduces terminal-voltage mean squared error by about 70%, removes long periods of steady-state bias, and improves transient tracking while coexisting with limiter actions. These results show that substantial voltage-control improvement can be achieved on existing hydropower units with minimal integration effort and without replacing the installed AVR structure.

ESST5B model↗

DOE OSTI2018

Dosimetric Evaluation of Incorporating Patient Geometric Variations Into Adaptive Plan Optimization Through Probabilistic Treatment Planning in Head and Neck Cancers

Four-dimensional (4D) adaptive radiation therapy (ART) treatment planning is an alternative to the conventional margin-based treatment planning approach. In 4D ART, interfraction patient geometric variations, gathered from computed tomography (CT) or cone beam CT (CBCT) images acquired during the patient treatment course, are directly incorporated into the adaptive plan optimization using a probabilistic treatment planning method. The goal of the present planning study was to evaluate the dosimetric differences between 4D ART and conventional margin-based adaptive planning strategies for head and neck cancers. In addition, we examined whether the dose differences achieved with 4D ART would translate into clinically relevant toxicity reductions using the existing normal tissue complication probability (NTCP) models.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable for real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

A block-spectral adaptive H-/$p$-refinement strategy for shock-dominated problems

An adaptive H-/p-refinement strategy using a novel sensor is devised and tested in a block-spectral compressible Euler code equipped with adaptive-mesh refinement (AMR) and high-order flux-reconstruction numerics. At each Gauss quadrature point (or solution point) within each spectral block (or mesh element) the discrete velocity jump ΔU = ∂U/∂y 1 Δy 1 + ∂V/∂y 2 Δy 2 + ∂W/∂y 3 Δy 3 is calculated and normalized by the local speed of sound, a. Here, the grid spacing, Δx i , is calculated in each direction as the distance between auxiliary Gauss-Lobatto points, staggered relative to the solution points. The polynomial order is increased from p = 0 to p = p max in regions of weak compression, (ΔU/a) crit < ΔU/a < 0 and kept at p = p max in regions of flow expansion ΔU/a ≥ 0, while staying at the H = 0 base mesh level. Regions experiencing strong compressions, i.e. ΔU/a < (ΔU/a) crit , are H-refined up to H = H max where H max is applied at the location of maximum compression, ΔU/a = min(ΔU/a) in the domain, while keeping p = 0 to guarantee robustness and monotonicity of the solution in the H refined region. The critical value of (ΔU/a) crit = -0.06 is found to effectively separate smooth and non-smooth solution regions, supported by a 1D detonation initiation test case in ideal gas and a shock-to-detonation transition in high explosives. Using this value, the Sod shock tube, Shu-Osher problem, double Mach reflection and a 2D detonation in a high-explosive are simulated with the proposed adaptive H-/p-refinement. In the Sod shock tube case, p-refinement resolves the (weak) contact discontinuity while H-refinement enhances the grid resolution in the shock exploiting the monotonicity of the p = 0 reconstruction. For the Shu-Osher problem, p-refinement captures the small-scale oscillations trailing the shock that would be otherwise attenuated, while H-refinement triggered by the ΔU-sensor appropriately tracks the shock. In the double Mach reflection problem, H-refinement confines the numerical diffusion around the reflected shock while p-refinement recaptures many physical features trailing the shock. Finally, in the 2D high-explosive detonation case, H-refinement follows the leading shock and resolves the curvature of the detonation wave, while p-refinement adds resolution to the trailing reaction zone. Finally, the proposed methodology is tested in a detonation-wave propagation test case in high-explosives with numerical predictions comparing favorably against experiments.

97 MATHEMATICS AND COMPUTING↗