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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Off-policy deep reinforcement learning with automatic entropy adjustment for adaptive online grid emergency control

Electric overloading conditions and contingencies put modern power systems at risk of voltage collapse and blackouts. Load shedding is crucial to maintain voltage stability for grid emergency control. However, the rule- or model-based schemes rely on accurate dynamic system models and face considerable challenges in adapting to various operating conditions and uncertain event occurrences. Here, to address these issues, this paper proposes a novel deep reinforcement learning (DRL)-based voltage stability control algorithm with automatic entropy adjustment (AEA) for grid emergency control. Various dynamic network components for complex system operations are modeled to construct the DRL environment. An off-policy soft actor-critic architecture is developed to maximize the expected reward and policy entropy simultaneously. The AEA mechanism is proposed to facilitate the policy maximum entropy procedure, and the proposed method can automatically provide effective discrete and continuous actions against various fault scenarios. Our approach accomplishes high sampling efficiency, scalability, and auto-adaptivity of the control policies under high uncertainties. Comparative studies with the existing DRL-based control methods in IEEE benchmarks indicate salient performance improvement of the proposed method for dynamic system emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Adaptive Interface-PINNs (AdaI-PINNs) for transient diffusion: Applications to forward and inverse problems in heterogeneous media

We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs utilize different activation functions with trainable slopes tailored to each material region within the computational domain, allowing for a fully automated and adaptive PINNs approach to model interface problems with strongly and weakly discontinuous solutions. To enhance its performance in highly heterogeneous transient diffusion systems, we prescribe a suite of robust practices, including appropriate non-dimensionalization of equations, a biased sampling method, Glorot initialization, and the hard enforcement of boundary and initial conditions. Here we evaluate the efficacy of the proposed method on several benchmark forward and inverse problems. Comparative studies on one-dimensional and two-dimensional benchmark problems reveal that the modified AdaI-PINNs outperform its unmodified counterpart, achieving root-mean-square errors that are at least two orders of magnitude better in forward problems. For inverse problems, the maximum errors in the approximated diffusion coefficients by modified AdaI-PINNs are four orders of magnitude better than those of the unmodified version. Additionally, modified AdaI-PINNs demonstrate improved stability in problems with large material mismatches.

42 ENGINEERING↗
DOE OSTI2025

Adaptive Interface-PINNs (AdaI-PINNs) for inverse problems: Determining material properties for heterogeneous systems

Here, we determine spatially varying discontinuous material properties using a domain-decomposition based physics-informed neural networks (PINNs) framework named the Adaptive Interface-PINNs or AdaI-PINNs (Roy et al., 2024). We propose the use of distinct neural networks for the field variables and material properties within each material, utilizing adaptive activation functions. While the neural networks across different materials share the same weights and biases, their activation functions are uniquely tailored using a hyperparameter that influences the slope of the activation function. The proposed framework is tested on several one-dimensional and two-dimensional benchmark examples, and its performance is compared with conventional PINNs and existing domain-decomposition PINNs frameworks, namely, the Multi-domain physics-informed neural network (M-PINN), and the eXtended physics-informed neural networks (XPINNs). The results demonstrate that the proposed approach can determine randomly distributed discontinuous material properties with an L 2 error of $\mathscr{O}$ (10 -3 ) for the material property and the root-mean-square error of $\mathscr{O}$ (10 -3 ) for the primary variable while the other approaches yield errors that are approximately two orders of magnitude larger (that is, $\mathscr{O}$ (10 -1 )). Moreover, the spatial distribution of material properties obtained using the proposed framework is in close agreement with the true distribution, whereas the other approaches fare much worse. Additionally, the proposed approach is approximately 40% faster than its competitors, indicating its potential as a robust alternative for solving inverse problems in heterogeneous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Trajectory Shaper: A Solution for Disrupted Cooperative Adaptive Cruise Control

Cooperative adaptive cruise control (CACC) can effectively reduce energy consumption, alleviate traffic congestion, and enhance safety. However, communication-related constraints and uncooperative vehicle users can disrupt CACC during real-world operations, significantly undermining the putative benefits of CACC. To alleviate the negative impacts of disrupted CACC, this study develops the trajectory shaper (TS) methods as backup solutions for two scenarios: (i) communication between vehicles is infeasible, and vehicles execute adaptive cruise control (ACC) using local sensor measurements; (ii) follower vehicles reject forming a cooperative platoon and execute their local distributed controllers using the information attained via communication. When communication is infeasible, a distributed TS is devised on each vehicle to modify the sensor measurements, enabling safe and efficient ACC operations. When communication is available but uncooperative agents are involved, the lead vehicle of the platoon executes a centralized TS to modify the information shared with uncooperative agents, achieving optimal platoon-level performance. The centralized and distributed TSs are implemented based on the model predictive control algorithms to yield optimal modifications on input information. Robustness is also factored to tackle model uncertainties during TS operations to ensure safety and efficiency. Numerical experiments validate the control performance of the proposed TSs.

Zhou, Anye [ORNL] (ORCID:0000000301455579)↗
DOE OSTI2025

Adaptive Algebraic Derivative Estimation for Battery Electric Buses Energy Consumption Forecasting

The limited service life of onboard batteries for EVs is a challenge, underscoring the need for real-time battery usage prediction. This paper proposes an adaptive Algebraic Derivative Estimation (ADE) approach for forecasting the energy consumption of battery electric buses. By dynamically adjusting the sliding window length, the adaptive ADE retains the fixed-length ADE’s key advantage—namely, operating online without reliance on extensive historical datasets—while substantially bolstering forecast accuracy by actively trading estimation bias off estimation variance. Comparative experiments against both the conventional ADE with a fixed length and a representative machine learning algorithm, XGBoost, were conducted, with performance evaluated via root mean square error, mean absolute error, and the coefficient of determination. The results demonstrate that the proposed approach significantly outperforms baseline methods.

Cui, Tianyang [The University of Texas at Dallas]↗
DOE OSTI2016

Clinical Outcomes With Dose-Escalated Adaptive Radiation Therapy for Urinary Bladder Cancer: A Prospective Study

Purpose: The purpose of this study was to assess feasibility, clinical outcomes, and toxicity in patients with bladder cancer treated with adaptive, image guided radiation therapy (IGRT) for bladder preservation as a part of trimodality treatment. The role of dose escalation was also studied. Methods and Materials: Forty-four patients with localized bladder cancer were enrolled in a prospective study. They underwent maximal safe resection of bladder tumor and concurrent platinum-based chemotherapy. Patients with large tumors were offered induction chemotherapy. Radiation therapy planning was done using either 3 (n=34) or 6 (n=10) concentrically grown planning target volumes (PTV). Patients received 64 Gy in 32 fractions to the whole bladder and 55 Gy to the pelvic nodes and, if appropriate, a simultaneous integrated boost to the tumor bed to 68 Gy (equivalent dose for 2-Gy fractions assuming α/β of 10 [EQD2]{sub 10} = 68.7 Gy). Daily megavoltage (MV) imaging helped to choose the most appropriate PTV encompassing bladder for the particular day (using plan-of-the-day approach). Results: Most patients (88%) had T2 disease. Sixteen patients (36%) received neoadjuvant chemotherapy. A majority of the patients (73%) received prophylactic nodal irradiation, whereas 55% of the patients received escalated dose to the tumor bed. With a median follow-up of 30 months, the 3-year locoregional control (LRC), disease-free survival, and overall survival (OS) were 78%, 66%, and 67%, respectively. The bladder preservation rate was 83%. LRC (87% vs 68%, respectively, P=.748) and OS (74% vs 60%, respectively, P=.36) rates were better in patients receiving dose escalation. Instances of acute and late Radiation Therapy Oncology Group (RTOG) grade 3 genitourinary toxicity was seen in 5 (11%) and 2 (4%) patients, respectively. There was no acute or late RTOG grade 3 or higher gastrointestinal toxicity. Conclusions: Adaptive IGRT using plan-of-the-day approach for bladder preservation is clinically feasible, with good oncological outcomes and low rates of acute and late toxicities. Dose escalation is safe and possibly improves outcomes in bladder preservation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Prospective Study Delivering Simultaneous Integrated High-dose Tumor Boost (≤70 Gy) With Image Guided Adaptive Radiation Therapy for Radical Treatment of Localized Muscle-Invasive Bladder Cancer

Purpose: Image guided adaptive radiation therapy offers individualized solutions to improve target coverage and reduce normal tissue irradiation, allowing the opportunity to increase the radiation tumor dose and spare normal bladder tissue. Methods and Materials: A library of 3 intensity modulated radiation therapy plans were created (small, medium, and large) from planning computed tomography (CT) scans performed at 30 and 60 minutes; treating the whole bladder to 52 Gy and the tumor to 70 Gy in 32 fractions. A “plan of the day” approach was used for treatment delivery. A post-treatment cone beam CT (CBCT) scan was acquired weekly to assess intrafraction filling and coverage. Results: A total of 18 patients completed treatment to 70 Gy. The plan and treatment for 1 patient was to 68 Gy. Also, 1 patient's plan was to 70 Gy but the patient was treated to a total dose of 65.6 Gy because dose-limiting toxicity occurred before dose escalation. A total of 734 CBCT scans were evaluated. Small, medium, and large plans were used in 36%, 48%, and 16% of cases, respectively. The mean ± standard deviation rate of intrafraction filling at the start of treatment (ie, week 1) was 4.0 ± 4.8 mL/min (range 0.1-19.4) and at end of radiation therapy (ie, week 5 or 6) was 1.1 ± 1.6 mL/min (range 0.01-7.5; P=.002). The mean D{sub 98} (dose received by 98% volume) of the tumor boost and bladder as assessed on the post-treatment CBCT scan was 97.07% ± 2.10% (range 89.0%-104%) and 99.97% ± 2.62% (range 96.4%-112.0%). At a median follow-up period of 19 months (range 4-33), no muscle-invasive recurrences had developed. Two patients experienced late toxicity (both grade 3 cystitis) at 5.3 months (now resolved) and 18 months after radiation therapy. Conclusions: Image guided adaptive radiation therapy using intensity modulated radiation therapy to deliver a simultaneous integrated tumor boost to 70 Gy is feasible, with acceptable toxicity, and will be evaluated in a randomized trial.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

A Novel Respiratory Motion Perturbation Model Adaptable to Patient Breathing Irregularities

Purpose: To develop a physical, adaptive motion perturbation model to predict tumor motion using feedback from dynamic measurement of breathing conditions to compensate for breathing irregularities. Methods and Materials: A novel respiratory motion perturbation (RMP) model was developed to predict tumor motion variations caused by breathing irregularities. This model contained 2 terms: the initial tumor motion trajectory, measured from 4-dimensional computed tomography (4DCT) images, and motion perturbation, calculated from breathing variations in tidal volume (TV) and breathing pattern (BP). The motion perturbation was derived from the patient-specific anatomy, tumor-specific location, and time-dependent breathing variations. Ten patients were studied, and 2 amplitude-binned 4DCT images for each patient were acquired within 2 weeks. The motion trajectories of 40 corresponding bifurcation points in both 4DCT images of each patient were obtained using deformable image registration. An in-house 4D data processing toolbox was developed to calculate the TV and BP as functions of the breathing phase. The motion was predicted from the simulation 4DCT scan to the treatment 4DCT scan, and vice versa, resulting in 800 predictions. For comparison, noncorrected motion differences and the predictions from a published 5-dimensional model were used. Results: The average motion range in the superoinferior direction was 9.4 ± 4.4 mm, the average ΔTV ranged from 10 to 248 mm{sup 3} (−26% to 61%), and the ΔBP ranged from 0 to 0.2 (−71% to 333%) between the 2 4DCT scans. The mean noncorrected motion difference was 2.0 ± 2.8 mm between 2 4DCT motion trajectories. After applying the RMP model, the mean motion difference was reduced significantly to 1.2 ± 1.8 mm (P=.0018), a 40% improvement, similar to the 1.2 ± 1.8 mm (P=.72) predicted with the 5-dimensional model. Conclusions: A novel physical RMP model was developed with an average accuracy of 1.2 ± 1.8 mm for interfraction motion prediction, similar to that of a published lung motion model. This physical RMP was analytically derived and is able to adapt to breathing irregularities. Further improvement of this RMP model is under investigation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Online Adaptive Radiation Therapy

The current paradigm of radiation therapy has the treatment planned on a snapshot dataset of the patient's anatomy taken at the time of simulation. Throughout the course of treatment, this snapshot may vary from initial simulation. Although there is the ability to image patients within the treatment room with technologies such as cone beam computed tomography, the current state of the art is largely limited to rigid-body matching and not accounting for any geometric deformations in the patient's anatomy. A plan that was once attuned to the initial simulation can become suboptimal as the treatment progresses unless improved technologies are brought to bear. Adaptive radiation therapy (ART) is an evolving paradigm that seeks to address this deficiency by accounting for ongoing changes in the patient's anatomy and/or physiology during the course of treatment, affording an increasingly more accurate targeting of disease. ART relies on several components working in concert, namely in-room treatment image guidance, deformable image registration, automatic recontouring, plan evaluation and reoptimization, dose calculation, and quality assurance. Various studies have explored how a putative ART solution would improve the current state of the art of radiation therapy—some centers have even clinically implemented online adaptation. These explorations are reviewed here for a variety of sites.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Dosimetric Impact of Intrafraction Motion in Online-Adaptive Intensity Modulated Proton Therapy for Cervical Cancer

A method was recently developed for online-adaptive intensity modulated proton therapy (IMPT) in patients with cervical cancer. The advantage of this approach, relying on the use of tight margins, is challenged by the intrafraction target motion. The purpose of this study was to evaluate the dosimetric effect of intrafraction motion on the target owing to changes in bladder filling in patients with cervical cancer treated with online-adaptive IMPT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Initial Feasibility and Clinical Implementation of Daily MR-Guided Adaptive Head and Neck Cancer Radiation Therapy on a 1.5T MR-Linac System: Prospective R-IDEAL 2a/2b Systematic Clinical Evaluation of Technical Innovation

This prospective study is, to our knowledge, the first report of daily adaptive radiation therapy (ART) for head and neck cancer (HNC) using a 1.5T magnetic resonance imaging-linear accelerator (MR-linac) with particular focus on safety and feasibility and dosimetric results of an online rigid registration-based adapt to position (ATP) workflow.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Adaptive relay autotuning under static and non-static disturbances with application to friction stir welding

When a traditional relay feedback test is biased, asymmetry is induced, erroneous system identification occurs, and the test may stop oscillating. Adaptive methods which restore symmetry to the relay test are developed, and additional methods are presented to detect and recover from a non-oscillation condition. This is demonstrated both in simulation and in a process with significant transients and disturbances - friction stir welding (FSW). In testing, it is shown that the adaptive method is able to restore symmetry even in the presence of severe and changing disturbances, including after the weld plunge where temperature gradients change rapidly. Finally, these improvements enable system identification of systems such as FSW in a more robust and accurate manner.

42 ENGINEERING↗
DOE OSTI2016

Stability of a modified Peaceman–Rachford method for the paraxial Helmholtz equation on adaptive grids

This study concerns the asymptotic stability of an eikonal, or ray, transformation based Peaceman–Rachford splitting method for solving the paraxial Helmholtz equation with high wave numbers. Arbitrary nonuniform grids are considered in transverse and beam propagation directions. The differential equation targeted has been used for modeling propagations of high intensity laser pulses over a long distance without diffractions. Self-focusing of high intensity beams may be balanced with the de-focusing effect of created ionized plasma channel in the situation, and applications of grid adaptations are frequently essential. It is shown rigorously that the fully discretized oscillation-free decomposition method on arbitrary adaptive grids is asymptotically stable with a stability index one. Simulation experiments are carried out to illustrate our concern and conclusions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A two-stage adaptive stochastic collocation method on nested sparse grids for multiphase flow in randomly heterogeneous porous media

A new computational method is proposed for efficient uncertainty quantification of multiphase flow in porous media with stochastic permeability. For pressure estimation, it combines the dimension-adaptive stochastic collocation method on Smolyak sparse grids and the Kronrod–Patterson–Hermite nested quadrature formulas. For saturation estimation, an additional stage is developed, in which the pressure and velocity samples are first generated by the sparse grid interpolation and then substituted into the transport equation to solve for the saturation samples, to address the low regularity problem of the saturation. Numerical examples are presented for multiphase flow with stochastic permeability fields to demonstrate accuracy and efficiency of the proposed two-stage adaptive stochastic collocation method on nested sparse grids.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Adaptive enriched Galerkin methods for miscible displacement problems with entropy residual stabilization

Here we present a novel approach to the simulation of miscible displacement by employing adaptive enriched Galerkin finite element methods (EG) coupled with entropy residual stabilization for transport. In particular, numerical simulations of viscous fingering instabilities in heterogeneous porous media and Hele–Shaw cells are illustrated. EG is formulated by enriching the conforming continuous Galerkin finite element method (CG) with piecewise constant functions. The method provides locally and globally conservative fluxes, which are crucial for coupled flow and transport problems. Moreover, EG has fewer degrees of freedom in comparison with discontinuous Galerkin (DG) and an efficient flow solver has been derived which allows for higher order schemes. Dynamic adaptive mesh refinement is applied in order to reduce computational costs for large-scale three dimensional applications. In addition, entropy residual based stabilization for high order EG transport systems prevents spurious oscillations. Numerical tests are presented to show the capabilities of EG applied to flow and transport.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

An adaptive fast multipole accelerated Poisson solver for complex geometries

Here, we present a fast, direct and adaptive Poisson solver for complex two-dimensional geometries based on potential theory and fast multipole acceleration. More precisely, the solver relies on the standard decomposition of the solution as the sum of a volume integral to account for the source distribution and a layer potential to enforce the desired boundary condition. The volume integral is computed by applying the FMM on a square box that encloses the domain of interest. For the sake of efficiency and convergence acceleration, we first extend the source distribution (the right-hand side in the Poisson equation) to the enclosing box as a $C$ 0 function using a fast, boundary integral-based method. We demonstrate on multiply connected domains with irregular boundaries that this continuous extension leads to high accuracy without excessive adaptive refinement near the boundary and, as a result, to an extremely efficient “black box” fast solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Adaptive sparse polynomial dimensional decomposition for derivative-based sensitivity

In this report applications, such as the plasma-coupled combustion system we consider, derivative-based sensitivity indices (DSI) are known to have several advantages over Sobol's total sensitivity indices, especially for small sample sizes. Several properties of derivative-based sensitivity measures are leveraged to develop a new and efficient numerical approach to estimate them. It is based on computing the DSI measures by effectively cost-free Monte Carlo sampling of an adaptively constructed orthogonal polynomial surrogate with uncertain input parameters that can have arbitrary probability distributions. The adaptivity reduces the number of necessary model evaluations, which is demonstrated both in a constructed example (the Moon function) and in two plasma-combustion systems with up to 55 uncertain parameters. Unimportant parameters are successfully identified and neglected with a low number of model evaluations, which makes it an attractive non-intrusive approach when adjoint solutions are unavailable to provide sensitivity information.

42 ENGINEERING↗
DOE OSTI2019

Adaptive spectral solution method for the Landau and Lenard-Balescu equations

In this paper, we present an adaptive spectral method for solving the Landau/Fokker-Planck equation for electron-ion systems. The heart of the algorithm is an expansion in Laguerre polynomials, which has several advantages, including automatic conservation of both energy and particles without the need for any special discretization or time-stepping schemes. One drawback of such an expansion is the O(N 3 ) memory requirement, where N is the number of polynomials used. This can impose an inconvenient limit in cases of practical interest, such as when two particle species have widely separated temperatures. The algorithm we describe here addresses this problem by periodically re-projecting the solution onto a judicious choice of new basis functions that are still Laguerre polynomials but have arguments adapted to the current physical conditions. This results in a reduction in the number of polynomials needed, at the expense of increased solution time. Because the equations are solved with little difficulty, this added time is not of much concern compared to the savings in memory. To demonstrate the algorithm, we solve several relaxation problems that could not be computed with the spectral method without re-projection. Another major advantage of this method is that it can be used for collision operators more complicated than that of the Landau equation, and we demonstrate this here by using it to solve the non-degenerate quantum Lenard-Balescu (QLB) equation for a hydrogen plasma. We conclude with some comparisons of temperature relaxation problems solved with the latter equation and the Landau equation with a Coulomb logarithm inspired by the properties of the QLB operator. We find that with this choice of Coulomb logarithm, there is little difference between using the two equations for these particular systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗