Engineering Papers⌕ Search

Engineering topics

Zhang, Y

Publications and source records attributed to Zhang, Y.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Towards a Quantum Algorithm for the Incompressible Nonlinear Navier-Stokes Equations

In this work, we present novel concepts for quantum algorithms to solve transient, nonlinear partial differential equations (PDEs). The challenge lies in how to effectively represent, encode, process, and evolve the nonlinear system of PDEs on quantum computers. We will discuss the new techniques using the incompressible Navier-Stokes equations as an example, because it represents the fundamental nonlinear feature and yet removes certain complexity in physics, allowing us to focus on the design of quantum algorithms. Previous attempts solving nonlinear PDEs in quantum computation have often involved storing multiple copies of solutions or employing linearizations. Neither is practical due to exponential scaling with evolution time or insufficient solution accuracy. We propose a new framework based on matrix product states (MPSs) and matrix product operators (MPOs), in addition to the Krylov subspace methods. For example, the solution variables of the Navier-Stokes equations are represented by MPSs, and the linear and nonlinear terms are processed by MPOs. The time evolution of the operators is attained by a fast-forwarding algorithm using Krylov subspace methods. Furthermore, we discuss various techniques for efficient encoding of MPSs, measurement reduction for MPOs, and use of tensor operations to treat multi-variate, multi-physics characteristics of Navier-Stokes.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2023

Detection of the significant impact of source clustering on higher order statistics with DES Year 3 weak gravitational lensing data

We measure the impact of source galaxy clustering on higher order summary statistics of weak gravitational lensing data. By comparing simulated data with galaxies that either trace or do not trace the underlying density field, we show that this effect can exceed measurement uncertainties for common higher order statistics for certain analysis choices. We evaluate the impact on different weak lensing observables, finding that third moments and wavelet phase harmonics are more affected than peak count statistics. Using Dark Energy Survey (DES) Year 3 (Y3) data, we construct null tests for the source-clustering-free case, finding a p-value of p = 4 × 10 −3 (2.6σ) using third-order map moments and p = 3 × 10 −11 (6.5σ) using wavelet phase harmonics. The impact of source clustering on cosmological inference can be either included in the model or minimized through ad hoc procedures (e.g. scale cuts). We verify that the procedures adopted in existing DES Y3 cosmological analyses were sufficient to render this effect negligible. Failing to account for source clustering can significantly impact cosmological inference from higher order gravitational lensing statistics, e.g. higher order N-point functions, wavelet-moment observables, and deep learning or field-level summary statistics of weak lensing maps.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Comprehensive Analysis of Real-World Accelerometer Data Quality in a Global Smartphone-based Seismic Network

The proliferation of low-cost sensors in smartphones has facilitated numerous applications; however, large-scale deployments often encounter performance issues. Sensing heterogeneity, which refers to varying data quality due to factors such as device differences and user behaviors, presents a significant challenge. In this research, we perform an extensive analysis of 3-axis accelerometer data from the MyShake system, a global seismic network utilizing smartphones. We systematically evaluate the quality of approximately 22 million 3-axis acceleration waveforms from over 81 thousand smartphone devices worldwide, using metrics that represent sampling rate and noise level. We explore a broad range of factors influencing accelerometer data quality, including smartphone and accelerometer manufacturers, phone specifications (release year, RAM, battery), geolocation, and time. Our findings indicate that multiple factors affect data quality, with accelerometer model and smartphone specifications being the most critical. In addition, we examine the influence of data quality on earthquake parameter estimation and show that removing low-quality accelerometer data enhances the accuracy of earthquake magnitude estimation.

58 GEOSCIENCES↗
DOE OSTI2022

Proton spin structure and generalized polarizabilities in the strong quantum chromodynamics regime

The strong interaction is not well understood at low energies or for interactions with low momentum transfer. Chiral perturbation theory gives testable predictions for the nucleonic generalized polarizabilities, which are fundamental quantities describing the nucleon’s response to an external field. We report a measurement of the proton’s generalized spin polarizabilities extracted with a polarized electron beam and a polarized solid ammonia target in the region where chiral perturbation theory is expected to be valid. The investigated structure function g2 characterizes the internal spin structure of the proton. From its moments, we extract the longitudinal–transverse spin polarizability δLT and twist-3 matrix element and polarizability d2¯. Our results provide discriminating power between existing chiral perturbation theory calculations and will help provide a better understanding of this strong quantum chromodynamics regime.

Ruth, D↗
DOE OSTI2020

Robust Medium-Voltage Distribution System State Estimation using Multi-Source Data

Due to the lack of sufficient online measurements for distribution system observability, pseudo-measurements from short-term load or distributed renewable energy resources (DERs) forecasting are used. However, the accuracy of them is low and thus significantly limits the performance of distribution system state estimation (DSSE). In this paper, a robust DSSE that integrates multi-source measurement data is proposed. Specifically, the historical low-voltage (LV) side smart meters are used to forecast load and DERs injections via the support vector machine (SVM) with optimally tuned parameters. By contrast, the online smart meters at LV side are utilized to derive equivalent power injections at the MV/LV transformers, yielding more accurate pseudo-measurements compared to the forecasted injections. Furthermore, to deal with bad data caused by communication loss, instrumental errors and cyber attacks, robust DSSE that relies on generalized maximum-likelihood (GM)-estimation criterion is developed. The projection statistics are developed to adjust the weights of each measurement, leading to better balance between pseudo- and real-time measurements. Numerical results conducted on modified IEEE 33-bus system with DG integration demonstrate the effectiveness and robustness of the proposed method.

distribution system state estimation↗
DOE OSTI2016

SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining all features.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-128: Dosimetric Impact of Esophagus Motion in Spine Stereotactic Body Radiotherapy

Purpose: Acute esophageal toxicity is a common side effect in spine stereotactic body radiotherapy (SBRT). The respiratory motion may alter esophageal position from the planning scan resulting in excessive esophageal dose. Here we assessed the dosimetric impact resulting from the esophageal motion using 4DCT. Methods: Nine patients treated to their thoracic spines in one fraction of 24 Gy were identified for this study. The original plan on a free breathing CT was copied to each phase image of a 4DCT scan, recalculated, scaled, and accumulated to the free breathing CT using deformable image registration. A segment of esophagus was contoured in the vicinity of treatment target. Esophagus dose volume histogram (DVH) was generated for both the original planned dose and the accumulated 4D dose for comparison. In parallel, we performed a chained deformable registration of 4DCT phase images to estimate the motion magnitude of the esophagus in a breathing cycle. We examined the correlation between the motion magnitude and the dosimetric deviation. Results: The esophageal motion mostly exhibited in the superior-inferior direction. The cross-sectional motion was small. Esophagus motion at T1 vertebra level (0.7 mm) is much smaller than that at T11 vertebra level (6.5 mm). The difference of Dmax between the original and 4D dose distributions ranged from 9.1 cGy (esophagus motion: 5.6 mm) to 231.1 cGy (esophagus motion: 3.1 mm). The difference of D(5cc) ranged from 5 cGy (esophagus motion: 3.1 mm) to 85 cGy (esophagus motion: 3.3 mm). There was no correlation between the dosimetric deviation and the motion magnitude. The V(11.9Gy)<5cc constraint was met for each patient when examining the DVH calculated from the 4D dose. Conclusion: Respiratory motion did not result in substantial dose increase to esophagus in spine SBRT. 4DCT simulation may not be necessary with regards to esophageal dose assessment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-196: Hypo-Fractionation with Intensity Modulated Proton Therapy for Unilateral Metallic Prosthesis Prostate Cancer Patients

Purpose: The purpose of this study is to investigate the dosimetric feasibility of hypo-fractionated intensity modulated proton therapy (IMPT) for unilateral metallic prosthesis prostate cancer patients based on proton collaborative group (PCG)-GU002-10 (NCT01230866) protocol criteria. Methods: A total of five unilateral metallic prosthesis prostate cancer cases were included in this retrospective study. For each case, IMPT plans were generated for treatment to be delivered with 7.6 Gy[RBE] per fraction in 5 fractions per week for a total dose of 38 Gy(RBE). Each plan was generated using two anterior-oblique beams and one lateral beam. Treatment plans were optimized with an objective meeting PCG-GU002-10 (NCT01230866) protocol criteria: (i) planning target volume (PTV): D99.5% > 36.1 Gy[RBE], (ii) rectum: V24 < 35%, V33.6 < 10%, (iii) bladder: V39 < 8 cc, and (iv) femoral head: V23 < 1cc. Results: All five cases satisfied PTV D99.5% (average=36.82 Gy[RBE]; range, 36.36–37.13 Gy[RBE]). PTV D95% ranged from 36.66 Gy[RBE] to 38.65 Gy[RBE] and PTV V100 ranged from 95.47% to 97.95%. For the rectum, V24 was less than 35% (average=14.07 Gy[RBE]; range, 6.22–18.42%, whereas V33.6 Gy[RBE] was less than 10% (average=6.83; range, 3.06 – 9.15%). Rectal mean dose ranged from 4.22 Gy[RBE] to 9.97 Gy[RBE]. For the bladder, V39 was found to be less than 8 cc (average=3.69 cc; range, 0.19–7.68 cc). Bladder mean dose ranged from 4.22 Gy[RBE] to 18.83 Gy[RBE]. For the femoral head, V23 was 0 in all five cases. Conclusion: All five unilateral metallic prosthesis prostate cancer IMPT plans generated with one lateral and two anterior-oblique beams satisfied the dosimetric criteria of PCG-GU002-10 (NCT01230866) protocol.

60 APPLIED LIFE SCIENCES↗