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DOE OSTI2018

Solution of the Problem of Free Vibrations of a Nonthin Orthotropic Shallow Shell of Variable Thickness in the Refined Statement

We consider the problem of investigation of the spectrum of natural vibrations of a nonthin orthotropic shallow shell variable in two coordinate directions of thickness in the nonclassical statement. The approach to the solution of the obtained two-dimensional boundary-value problem is based on its reduction (by the method of spline-approximation of the unknown functions along one coordinate direction) to the one-dimensional problem with its subsequent solution. We study different cases of boundary conditions imposed on the contours of the shell. We also perform the comparison and analysis of the natural frequencies and modes of vibrations of orthotropic shells of constant and variable thickness.

42 ENGINEERING↗

DOE OSTI2016

Assessment of metal artifact reduction methods in pelvic CT

Purpose: Metal artifact reduction (MAR) produces images with improved quality potentially leading to confident and reliable clinical diagnosis and therapy planning. In this work, the authors evaluate the performance of five MAR techniques for the assessment of computed tomography images of patients with hip prostheses. Methods: Five MAR algorithms were evaluated using simulation and clinical studies. The algorithms included one-dimensional linear interpolation (LI) of the corrupted projection bins in the sinogram, two-dimensional interpolation (2D), a normalized metal artifact reduction (NMAR) technique, a metal deletion technique, and a maximum a posteriori completion (MAPC) approach. The algorithms were applied to ten simulated datasets as well as 30 clinical studies of patients with metallic hip implants. Qualitative evaluations were performed by two blinded experienced radiologists who ranked overall artifact severity and pelvic organ recognition for each algorithm by assigning scores from zero to five (zero indicating totally obscured organs with no structures identifiable and five indicating recognition with high confidence). Results: Simulation studies revealed that 2D, NMAR, and MAPC techniques performed almost equally well in all regions. LI falls behind the other approaches in terms of reducing dark streaking artifacts as well as preserving unaffected regions (p < 0.05). Visual assessment of clinical datasets revealed the superiority of NMAR and MAPC in the evaluated pelvic organs and in terms of overall image quality. Conclusions: Overall, all methods, except LI, performed equally well in artifact-free regions. Considering both clinical and simulation studies, 2D, NMAR, and MAPC seem to outperform the other techniques.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Type IIB at eight derivatives: insights from Superstrings, Superfields and Superparticles

We study the non-linear structure of Type IIB eight-derivative couplings involving the metric and the complexified three-form G 3 . We show that, at the level of five-point string amplitudes, the kinematics in the maximally R-symmetry-violating sector is fully matched by standard superspace integrals and by superparticle amplitudes in M-theory on a two-torus. The latter approach is used to determine the complete effective action in this sector and to verify its invariance under SL(2, Z) duality. We further comment on the general structure of the higher-point kinematics. Compactifications to lower dimensions provide both tests for our results and the arena for their applications. We verify that K 3 reductions are fully consistent with the constraints of six-dimensional supersymmetry, and derive the four-dimensional flux scalar potential and axion kinetic terms at order (α') 3 in Calabi-Yau threefold reductions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1974

Aerodynamic investigations into various low speed L/D improvement devices on the 140A/B space shuttle orbiter configuration in the Rockwell International low speed wind tunnel (OA86)

Tests were conducted to investigate various base drag reduction techniques in an attempt to improve Orbiter lift-to-drag ratios and to calculate sting interference effects on the Orbiter aerodynamic characteristics. Test conditions and facilites, and model dimensional data are presented along with the data reduction guidelines and data set/run number collation used for the studies. Aerodynamic force and moment data and the results of stability and control tests are also given.

Mennell, R. C.↗

DOE OSTI2025

Heterogeneous Mixtures of Dictionary Functions to Approximate Subspace Invariance in Koopman Operators: Why Deep Koopman Operators Work

Abstract Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and is usually approximated numerically by a superposition of functions drawn from a dictionary . In a widely used algorithm, extended dynamic mode decomposition (EDMD), the dictionary functions are drawn from a fixed class of functions. Deep learning combined with EDMD has been used to learn novel dictionary functions in an algorithm called deep dynamic mode decomposition (deepDMD). The learned representation both (1) accurately models and (2) scales well with the dimension of the original nonlinear system. In this paper, we analyze the learned dictionaries from deepDMD and explore the theoretical basis for their strong performance. We explore State-Inclusive Logistic Lifting (SILL) dictionary functions to approximate Koopman observables. Error analysis of these dictionary functions show they satisfy a property of subspace approximation, which we define as uniform finite approximate closure. Typically, a Koopman dictionary’s nonlinear functions are homogeneous. In this paper, we discover that structured mixing of heterogeneous dictionary functions drawn from different classes of nonlinear functions achieve the same accuracy and dimensional scaling as the deep-learning-based deepDMD algorithm Yeung et al. ( In: 2019 American Control Conference (ACC), 2019). We specifically show this by building a heterogeneous dictionary comprised of SILL functions and conjunctive radial basis functions (RBFs). This mixed dictionary achieves similar accuracy and dimensional scaling to deepDMD with an order of magnitude reduction in parameters, while maintaining geometric interpretability. These results strengthen the viability of dictionary-based Koopman models to solving high-dimensional nonlinear learning problems.

Johnson, Charles A.↗

NASA NTRS1989

Three-dimensional compressible boundary layer calculations to fourth order accuracy on wings and fuselages

Laminar flow control and drag reduction research requires accurate boundary layer solutions as input to the three-dimensional stability analysis procedures currently under development. In support of these major programs, a fourth-order accurate finite difference scheme for solving the three-dimensional, compressible boundary layer equations has been developed and is presented in this paper. The method employs a two-point scheme in the wall normal direction and second order zigzag scheme in the cross flow direction. Accurate procedures to interface with the inviscid results are also presented. The results of applying the procedure to laminar flow on wings and fuselages are presented.

Iyer, Venkit↗

DOE OSTI2014

The Effect of Thermal Reduction on the Photoluminescence and Electronic Structures of Graphene Oxides

Electronic structures of graphene oxide (GO) and hydro-thermally reduced graphene oxides (rGOs) processed at low temperatures (120–1806C) were studied using X-ray absorption near-edge structure (XANES), X-ray emission spectroscopy (XES) and resonant inelastic X-ray scattering (RIXS). C K-edge XANES spectra of rGOs reveal that thermal reduction restores C 5 C sp2 bonds and removes some of the oxygen and hydroxyl groups of GO, which initiates the evolution of carbonaceous species. The combination of C K-edge XANES and Ka XES spectra shows that the overlapping p and p* orbitals in rGOs and GO are similar to that of highly ordered pyrolytic graphite (HOPG), which has no band-gap. C Ka RIXS spectra provide evidence that thermal reduction changes the density of states (DOSs) that is generated in the p-region and/or in the gap between the p and p* levels of the GO and rGOs. Two-dimensional C Ka RIXS mapping of the heavy reduction of rGOs further confirms that the residual oxygen and/or oxygen-containing functional groups modify the p and s features, which are dispersed by the photon excitation energy. The dispersion behavior near the K point is approximately linear and differs from the parabolic-like dispersion observed in HOPG.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗

DOE OSTI2016

Oxidation and Reduction under Cover: Chemistry at the Confined Space between Ultrathin Nanoporous Silicates and Ru(0001)

N/A

Zhong, Jian-Qiang↗

NASA NTRS2008

Analysis of Plume Effects on Sonic Boom Signature for Isolated Nozzle Configurations

Computational fluid dynamics (CFD) analysis has been performed to study the plume effects on sonic boom signature for isolated nozzle configurations. The objectives of these analyses were to provide comparison to past work using modern CFD analysis tools, to investigate the differences of high aspect ratio nozzles to circular (axisymmetric) nozzles, and to report the effects of underexpanded nozzle operation on boom signature. CFD analysis was used to address the plume effects on sonic boom signature from a baseline exhaust nozzle. Near-field pressure signatures were collected for nozzle pressure ratios (NPRs) between 6 and 10. A computer code was used to extrapolate these signatures to a ground-observed sonic boom N-wave. Trends show that there is a reduction in sonic boom N-wave signature as NPR is increased from 6 to 10. The performance curve for this supersonic nozzle is flat, so there is not a significant loss in thrust coefficient as the NPR is increased. As a result, this benefit could be realized without significant loss of performance. Analyses were also collected for a high aspect ratio nozzle based on the baseline design for comparison. Pressure signatures were collected for nozzle pressure ratios from 8 to 12. Signatures were nearly twice as strong for the two-dimensional case, and trends also show a reduction in sonic boom signature as NPR is increased from 8 to 12. As low boom designs are developed and improved, there will be a need for understanding the interaction between the aircraft boat tail shocks and the exhaust nozzle plume. These CFD analyses will provide a baseline study for future analysis efforts.

Castner, Raymond S.↗

NASA NTRS2008

Analysis of Plume Effects on Sonic Boom Signature for Isolated Nozzle Configurations

Castner, Raymond S.↗

DOE OSTI2013

A Randomized Controlled Trial of Lorazepam to Reduce Liver Motion in Patients Receiving Upper Abdominal Radiation Therapy

Purpose: Reduction of respiratory motion is desirable to reduce the volume of normal tissues irradiated, to improve concordance of planned and delivered doses, and to improve image guided radiation therapy (IGRT). We hypothesized that pretreatment lorazepam would lead to a measurable reduction of liver motion. Methods and Materials: Thirty-three patients receiving upper abdominal IGRT were recruited to a double-blinded randomized controlled crossover trial. Patients were randomized to 1 of 2 study arms: arm 1 received lorazepam 2 mg by mouth on day 1, followed by placebo 4 to 8 days later; arm 2 received placebo on day 1, followed by lorazepam 4 to 8 days later. After tablet ingestion and daily radiation therapy, amplitude of liver motion was measured on both study days. The primary outcomes were reduction in craniocaudal (CC) liver motion using 4-dimensional kV cone beam computed tomography (CBCT) and the proportion of patients with liver motion ≤5 mm. Secondary endpoints included motion measured with cine magnetic resonance imaging and kV fluoroscopy. Results: Mean relative and absolute reduction in CC amplitude with lorazepam was 21% and 2.5 mm respectively (95% confidence interval [CI] 1.1-3.9, P=.001), as assessed with CBCT. Reduction in CC amplitude to ≤5 mm residual liver motion was seen in 13% (95% CI 1%-25%) of patients receiving lorazepam (vs 10% receiving placebo, P=NS); 65% (95% CI 48%-81%) had reduction in residual CC liver motion to ≤10 mm (vs 52% with placebo, P=NS). Patients with large respiratory movement and patients who took lorazepam ≥60 minutes before imaging had greater reductions in liver CC motion. Mean reductions in liver CC amplitude on magnetic resonance imaging and fluoroscopy were nonsignificant. Conclusions: Lorazepam reduces liver motion in the CC direction; however, average magnitude of reduction is small, and most patients have residual motion >5 mm.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

TH-CD-207A-07: Prediction of High Dimensional State Subject to Respiratory Motion: A Manifold Learning Approach

Purpose: The development of high dimensional imaging systems (e.g. volumetric MRI, CBCT, photogrammetry systems) in image-guided radiotherapy provides important pathways to the ultimate goal of real-time volumetric/surface motion monitoring. This study aims to develop a prediction method for the high dimensional state subject to respiratory motion. Compared to conventional linear dimension reduction based approaches, our method utilizes manifold learning to construct a descriptive feature submanifold, where more efficient and accurate prediction can be performed. Methods: We developed a prediction framework for high-dimensional state subject to respiratory motion. The proposed method performs dimension reduction in a nonlinear setting to permit more descriptive features compared to its linear counterparts (e.g., classic PCA). Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where low-dimensional prediction is performed. A fixed-point iterative pre-image estimation method is applied subsequently to recover the predicted value in the original state space. We evaluated and compared the proposed method with PCA-based method on 200 level-set surfaces reconstructed from surface point clouds captured by the VisionRT system. The prediction accuracy was evaluated with respect to root-mean-squared-error (RMSE) for both 200ms and 600ms lookahead lengths. Results: The proposed method outperformed PCA-based approach with statistically higher prediction accuracy. In one-dimensional feature subspace, our method achieved mean prediction accuracy of 0.86mm and 0.89mm for 200ms and 600ms lookahead lengths respectively, compared to 0.95mm and 1.04mm from PCA-based method. The paired t-tests further demonstrated the statistical significance of the superiority of our method, with p-values of 6.33e-3 and 5.78e-5, respectively. Conclusion: The proposed approach benefits from the descriptiveness of a nonlinear manifold and the prediction reliability in such low dimensional manifold. The fixed-point iterative approach turns out to work well practically for the pre-image recovery. Our approach is particularly suitable to facilitate managing respiratory motion in image-guide radiotherapy. This work is supported in part by NIH grant R01 CA169102-02.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Three dimensional electromagnetic wavepackets in a plasma: Spatiotemporal modulational instability

The nonlinear interaction of an intense electromagnetic beam with relativistic collisionless unmagnetized plasma is investigated by invoking the reductive perturbation technique, resting on the model of three-dimensional nonlinear Schrödinger (NLS) equation with cubic nonlinearity which incorporates the effects of self-focusing, self-phase modulation, and diffraction on wave propagation. Relying on the derived NLS equation, the occurrence of spatiotemporal modulational instability is investigated in detail.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Physics informed machine learning for chemistry tabulation

Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent transport. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evolve, the two sub-systems are typically (re)solved separately. Popular approaches such as the Flamelet Generated Manifolds (FGM) use a two-step strategy where the governing reaction kinetics are pre-computed and mapped to a low-dimensional manifold, characterized by a few reaction progress variables (model reduction) and the manifold is then “looked-up” during the run-time to estimate the high-dimensional system state by the turbulent transport system. While existing works have focused on these two steps independently, in this work we show that joint learning of the progress variables and the look-up model, can yield more accurate results. Here, we build on the base formulation and implementation to include the dynamically generated Thermochemical State Variables (Lower Dimensional Dynamic Source Terms). We discuss the challenges in the implementation of this deep neural network architecture and experimentally demonstrate its superior performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Optimized Tandem Catalyst Patterning for CO 2 Reduction Flow Reactors

Tandem catalysis involves two or more catalysts arranged in proximity within a single reaction vessel, with the aim of synergistically aligning the catalysts’ reaction pathways to maximize overall system performance. This study presents a proof of concept showing the integration of continuum transport modeling with design optimization in a simplified two-dimensional flow reactor setup for electrochemical CO 2 reduction. Ag catalysts provide the CO 2 ⟶ CO reaction capability, and Cu catalysts provide the CO ⟶ high-value products reaction capability. Given a set of input parameters, the optimization algorithm uses adjoint methods to modify the Ag/Cu surface patterning in order to maximize the current density toward high-value products, such as ethylene. The optimized designs yield significant performance enhancement especially at more negative applied voltages (i.e., stronger surface reactions) and for larger numbers of patterning sections. For an applied voltage of −1.7 V vs. SHE, the 12-section optimized design increases the current density toward ethylene by up to 65% compared to the unoptimized 2-section design. For the optimized cases, observed differences in the production and consumption of CO (the key intermediate species) and minimized zones of low CO reactant surface concentration on Cu sections explain the improved reactor performance.

CO2 reduction↗

NASA NTRS1979

Conditional replenishment using motion prediction

Conditional replenishment is an interframe video compression method that uses correlation in time to reduce video transmission rates. This method works by detecting and sending only the changing portions of the image and by having the receiver use the video data from the previous frame for the non-changing portion. The amount of compression that can be achieved through this technique depends to a large extent on the rate of change within the image, and can vary from 10 to 1 to less than 2 to 1. An additional 3 to 1 reduction in rate is obtained by the intraframe coding of data blocks using a 2-dimensional variable rate Hadamard transform coder. A further additional 2 to 1 rate reduction is achieved by using motion prediction. Motion prediction works by measuring the relative displacements of a subpicture from one frame to the next. The subpicture can then be transmitted by sending only the value of the 2-dimensional displacement. Computer simulations have demonstrated that data rates of 2 to 4 Mega-bits/second can be achieved while still retaining good fidelity in the image.

Hein, D. N.↗

DOE OSTI2022

Neural-network learning of SPOD latent dynamics

Here, we aim to reconstruct the latent space dynamics of high dimensional, quasi-stationary systems using model order reduction via the spectral proper orthogonal decomposition (SPOD). The proposed method is based on three fundamental steps: in the first, once that the mean flow field has been subtracted from the realizations (also referred to as snapshots), we compress the data from a high-dimensional representation to a lower dimensional one by constructing the SPOD latent space; in the second, we build the time-dependent coefficients by projecting the snapshots containing the fluctuations onto the SPOD basis and we learn their evolution in time with the aid of recurrent neural networks; in the third, we reconstruct the high-dimensional data from the learnt lower -dimensional representation. The proposed method is demonstrated on two different test cases, namely, a compressible jet flow, and a geophysical problem known as the Madden-Julian Oscillation. An extensive comparison between SPOD and the equivalent POD-based counterpart is provided and differences between the two approaches are highlighted. The numerical results suggest that the proposed model is able to provide low rank predictions of complex statistically stationary data and to provide insights into the evolution of phenomena characterized by specific range of frequencies. The comparison between POD and SPOD surrogate strategies highlights the need for further work on the characterization of the interplay of error between data reduction techniques and neural network forecasts.

97 MATHEMATICS AND COMPUTING↗