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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Accelerated CO2 Storage Optimization Using Multi-Resolution Fourier Neural Operator at the Illinois Basin Decatur Project (IBDP)

This paper presents a deep learning-based approach for optimizing CO2 injection in carbon capture and storage (CCS) operations. We developed a multi-resolution machine learning model to significantly reduce data generation costs. Utilizing this proxy model, we implemented a multi-objective genetic algorithm to optimize well control during the CO2 injection process. The proposed approach was applied to the Illinois Basin Decatur Project (IBDP), successfully optimizing the CO2 injection schedule based on three key objectives: maximizing the amount of CO2 stored, maximizing sweep efficiency, and minimizing pressure increase. The use of the proxy model accelerated the optimization workflow by two orders of magnitude, while the cost of data generation for the proxy model was reduced by 90% by utilizing a coarse-scale model.

accelerated CO2 storage optimization↗
DOE OSTI2024

Accurate and efficient open-source implementation of domain-based local pair natural orbital (DLPNO) coupled-cluster theory using a t1-transformed Hamiltonian

We present an efficient, open-source formulation for coupled-cluster theory through perturbative triples with domain-based local pair natural orbitals [DLPNO-CCSD(T)]. Similar to the implementation of the DLPNO-CCSD(T) method found in the ORCA package, the most expensive integral generation and contraction steps associated with the CCSD(T) method are linear-scaling. In this work, we show that the t1-transformed Hamiltonian allows for a less complex algorithm when evaluating the local CCSD(T) energy without compromising efficiency or accuracy. Our algorithm yields sub-kJ mol−1 deviations for relative energies when compared with canonical CCSD(T), with typical errors being on the order of 0.1 kcal mol−1, using our TightPNO parameters. We extensively tested and optimized our algorithm and parameters for non-covalent interactions, which have been the most difficult interaction to model for orbital (PNO)-based methods historically. To highlight the capabilities of our code, we tested it on large water clusters, as well as insulin (787 atoms).

Chemistry↗
DOE OSTI2021

Optimal dynamic control for a maglev vehicle moving on multi-span guideway girders

Abstract An optimal control algorithm using a virtual tuned mass damper called virtual TMD to control the levitation force of a magnetic system is developed for resonance suppression of a maglev vehicle moving on multi-span guideway girders. Since the optimal dynamic parameters of a TMD in vibration control are well developed, the optimal tuning gains required to control the magnetic oscillations of the maglev bogie can be directly used and fed back to the maglev control system. To address the dynamic interaction analysis from the maglev vehicle to the guideway girders and vice versa, the entire coupling system is decomposed into two subsystems, one is the moving vehicle subsystem and another the stationary guideway subsystem. Then, an incremental–iterative procedure associated with the Newmark method is presented to solve the two sets of subsystem equations. Finally, the control effectiveness and parametric studies of the optimal virtual TMD scheme on resonance reduction of the moving maglev vehicle are demonstrated.

Liu, Linya↗
DOE OSTI2019

Big Data Framework with Machine Learning for D and D Applications - 19108

The nuclear industry is experiencing a steady increase in maintenance costs even though plants are maintained under high levels of safety, capability and reliability. Nuclear power plants are expected to run every unit at maximum capacity at all times, efficiently utilizing assets with minimal downtime. Surveillance and maintenance of nuclear decommissioning infrastructure provides lot challenges with respect to maintenance or decommissioning of the buildings. There is a need for a framework to analyze the huge amount of data generated by the sensors on the nuclear reactor components as well as structures, to monitor the conditions of these building over a period of time. Emerging technologies such as big data analytics have become a requirement in the nuclear industry to improve structural health monitoring and diagnostics. FIU will make use of existing mature technologies in the areas of imaging, robotics, big data, and machine learning/deep learning to assess the structural integrity of aging facilities at DOE sites. As these facilities await decommissioning, there is a need to understand the structural health of these structures. Many of these facilities were built over 50 years ago and in some cases these facilities have gone beyond the operational life expectancy. In other cases, the facilities have been placed in a state of 'cold and dark' and they are sitting unused, awaiting decommissioning. Finally, some older facilities are one-of-a-kind operational/production facilities supporting critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may be compromised, so it is imperative that adequate inspections and data collection/analysis be performed on a continuous and ongoing basis. The primary goals of the research include collecting various formats of data such as structured, unstructured and streaming data from the various sensors deployed in buildings, collect videos/pictures from various imaging sources, ingest them using a Hadoop distributed file system and process the data using Spark to perform batch processing and real time analytics. Research and development on various machine learning/deep learning algorithms will also be performed to analyze the heterogeneous data collected from nuclear decommission infrastructure. FIU will design the big data framework to ingest, store and process huge amounts of heterogeneous data collected from many sources and optimize the algorithms to provide insights into the data and predict anomalies observed when compared against baseline conditions. Various modules of this framework will include heterogeneous data sources, message broker, Hadoop distributed file system, Spark for stream and batch processing, machine learning/ deep learning, Cassandra - persistent data store and visualization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Identification of Stellar Flares Using Differential Evolution Template Optimization

In this paper, we explore methods for the identification of stellar flare events in irregularly sampled data of ground-based time domain surveys. In particular, we describe a new technique for identifying flaring stars, which we have implemented in a publicly available Python module called "PyVAN." The approach uses the Differential Evolution algorithm to optimize parameters of empirically derived light curve templates for different types of stars to fit a candidate light curve. The difference of the likelihoods that these best-fit templates produced the observed data is then used to delineate targets that are well-explained by a flare template but simultaneously poorly explained by templates of common contaminants. By testing on light curves of known identity and morphology, we show that our technique is capable of recovering flaring status in 69% of all light curves containing a flare event above thresholds drawn to include <1% of any contaminant population. By applying to Palomar Transient Factory data, we show consistency with prior samples of flaring stars, and identify a small selection of candidate flaring G-type stars for possible follow-up.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Faster than Real-Time Dynamic Simulation for Large-Size Power System with Detailed Dynamic Models using High-Performance Computing Platform

This paper presents a faster-than-real-time dynamic simulation software package that is designed for large-size power system dynamic simulation. It was developed on the GridPACKTM high-performance computing (HPC) framework. The key features of the developed software package include (1) faster-than-real-time dynamic simulation for a WECC system (17,000 buses) with different types of detailed generator, controller, and relay dynamic models, (2) a decoupled parallel dynamic simulation algorithm with optimized computation architecture to better leverage HPC resources and technologies, (3) options for HPC-based linear and iterative solvers, (4) hidden HPC details, such as data communication and distribution, to enable development centered on mathematical models and algorithms rather than on computational details for power system researchers, and (5) easy integration of new dynamic models and related algorithms into the software package.

Huang, Renke↗
DOE OSTI2021

Centralized and Decentralized Optimal Control of Variable Speed Heat Pumps

Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, as a result of naturally occurring and demand-response events, has the potential to damage power distribution equipment. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this paper is the development of an optimal control algorithm for commonly used variable speed heat pumps. By means of selective peer-to-peer communication, our control architecture allows for the regulation of home temperatures while simultaneously minimizing aggregate power consumption, and aggregate load volatility. An optimal centralized controller is also explored and compared against its decentralized counterpart.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2015

Scalable Parallel Methods for Analyzing Metagenomics Data at Extreme Scale

The field of bioinformatics and computational biology is currently experiencing a data revolution. The exciting prospect of making fundamental biological discoveries is fueling the rapid development and deployment of numerous cost-effective, high-throughput next-generation sequencing technologies. The result is that the DNA and protein sequence repositories are being bombarded with new sequence information. Databases are continuing to report a Moore’s law-like growth trajectory in their database sizes, roughly doubling every 18 months. In what seems to be a paradigm-shift, individual projects are now capable of generating billions of raw sequence data that need to be analyzed in the presence of already annotated sequence information. While it is clear that data-driven methods, such as sequencing homology detection, are becoming the mainstay in the field of computational life sciences, the algorithmic advancements essential for implementing complex data analytics at scale have mostly lagged behind. Sequence homology detection is central to a number of bioinformatics applications including genome sequencing and protein family characterization. Given millions of sequences, the goal is to identify all pairs of sequences that are highly similar (or “homologous”) on the basis of alignment criteria. While there are optimal alignment algorithms to compute pairwise homology, their deployment for large-scale is currently not feasible; instead, heuristic methods are used at the expense of quality. In this dissertation, we present the design and evaluation of a parallel implementation for conducting optimal homology detection on distributed memory supercomputers. Our approach uses a combination of techniques from asynchronous load balancing (viz. work stealing, dynamic task counters), data replication, and exact-matching filters to achieve homology detection at scale. Results for a collection of 2.56M sequences show parallel efficiencies of ~75-100% on up to 8K cores, representing a time-to-solution of 33 seconds. We extend this work with a detailed analysis of single-node sequence alignment performance using the latest CPU vector instruction set extensions. Preliminary results reveal that current sequence alignment algorithms are unable to fully utilize widening vector registers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Optimal Operation of Grid-Tied Energy Storage Systems Considering Detailed Device-Level Battery Models

The current optimization-based algorithms to operate grid-tied battery energy storage systems (BESS) typically do not look much under the hood of the BESS, i.e., the device-level characteristics of the batteries. This is often due to modeling as well as optimization complexities. However, simplified models may significantly degrade the performance of BESS operation in practice. Therefore, in this article, we propose a new BESS scheduling optimization framework that accounts for features such as cell-to-cell variations in maximum capacity, charge level balance, and internal resistance. The proposed framework is in the form of tractable mixed integer linear programs. Our approach is to estimate and update the device-level battery model parameters continuously, without the need to interrupt BESS normal operation. We validate the performance compared to an offline approach, which is based on dedicated model testing and calibration. To assure accurate performance evaluation, this article also includes developing a power hardware-in-the-loop testbed that allows for flexible operation and detailed monitoring of BESS under different design scenarios.

25 ENERGY STORAGE↗
DOE OSTI2016

Computing Maximum Cardinality Matchings in Parallel on Bipartite Graphs via Tree-Grafting

It is difficult to obtain high performance when computing matchings on parallel processors because matching algorithms explicitly or implicitly search for paths in the graph, and when these paths become long, there is little concurrency. In spite of this limitation, we present a new algorithm and its shared-memory parallelization that achieves good performance and scalability in computing maximum cardinality matchings in bipartite graphs. This algorithm searches for augmenting paths via specialized breadth-first searches (BFS) from multiple source vertices, hence creating more parallelism than single source algorithms. Algorithms that employ multiple-source searches cannot discard a search tree once no augmenting path is discovered from the tree, unlike algorithms that rely on single-source searches. We describe a novel tree-grafting method that eliminates most of the redundant edge traversals resulting from this property of multiple-source searches. We also employ the recent direction-optimizing BFS algorithm as a subroutine to discover augmenting paths faster. Our algorithm compares favorably with the current best algorithms in terms of the number of edges traversed, the average augmenting path length, and the number of iterations. Here, we provide a proof of correctness for our algorithm. Our NUMA-aware implementation is scalable to 80 threads of an Intel multiprocessor and to 240 threads on an Intel Knights Corner coprocessor. On average, our parallel algorithm runs an order of magnitude faster than the fastest algorithms available. The performance improvement is more significant on graphs with small matching number.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

FASTERp: A Feature Array Search Tool for Estimating Resemblance of Protein Sequences

Metagenome sequencing efforts have provided a large pool of billions of genes for identifying enzymes with desirable biochemical traits. However, homology search with billions of genes in a rapidly growing database has become increasingly computationally impractical. Here we present our pilot efforts to develop a novel alignment-free algorithm for homology search. Specifically, we represent individual proteins as feature vectors that denote the presence or absence of short kmers in the protein sequence. Similarity between feature vectors is then computed using the Tanimoto score, a distance metric that can be rapidly computed on bit string representations of feature vectors. Preliminary results indicate good correlation with optimal alignment algorithms (Spearman r of 0.87, ~;;1,000,000 proteins from Pfam), as well as with heuristic algorithms such as BLAST (Spearman r of 0.86, ~;;1,000,000 proteins). Furthermore, a prototype of FASTERp implemented in Python runs approximately four times faster than BLAST on a small scale dataset (~;;1000 proteins). We are optimizing and scaling to improve FASTERp to enable rapid homology searches against billion-protein databases, thereby enabling more comprehensive gene annotation efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Oncentra brachytherapy planning system

In modern cancer management, treatment planning has progressed as a contemporary tool with all the advances in computing power in recent years. One of the advanced planning tools uses 3-dimensional (3D) data sets for accurate dose distributions in patient prescription. Among these planning processes, brachytherapy has been a very important part of a successful cancer management program, offering clinical benefits with specific or combined treatments with external beam therapy. In this chapter, we mainly discussed the Elekta Oncentra planning system, which is the main treatment planning tool for high-dose rate (HDR) modality in our facility and in many other facilities in the United States. HDR is a technically advanced form of brachytherapy; a high-intensity radiation source (3.6 mm in length) is delivered with step motor in submillimeter precision under computer guidance directly into the tumor areas while minimizing injury to surrounding normal healthy tissue. Oncentra planning is the key component to generate a deliverable brachytherapy procedure, which is executed on the microSelectron V3 remote afterloader treatment system. Creating a highly conformal plan can be a time-consuming task. The development of Oncentra software (version 4.5.3) offers a variety of useful tools that facilitate many of the clinical challenging tasks for planning, such as contouring and image reconstruction, as well as rapid planning calculations with dose and dose volume histogram analysis. Oncentra Brachy module creates workflow and optimizes the planning accuracy for wide varieties of clinical HDR treatments, such as skin, gynecologic (GYN), breast, prostate, and many other applications. The treatment file can also be transferred to the afterloader control station for speedy delivery. The design concept, calculation algorithms, and optimization modules presented some key characteristics to plan and treat the patients effectively and accurately. The dose distribution and accuracy of several clinical sample cases were discussed to illustrate the effectiveness and clinical efficacy. The American Association of Physicists in Medicine brachytherapy reports of TG-43 and TG-186 were also described and compared in evaluations of fundamental calculation methodologies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Optimizing Mu2e Spill Regulation System Algorithms

A slow extraction system is being developed for the Fermilab’s Delivery Ring to deliver protons to the Mu2e experiment. During the extraction, the beam on target experiences small intensity variations owing to many factors. Various adaptive learning algorithms will be employed for beam regulation to achieve the required spill quality. We discuss here preliminary results of the slow and fast regulation algorithms validation through the computer simulations before their implementation in the FPGA. Particle tracking with sextupole resonance was used to determine the fine shape of the spill profile. Fast semi-analytical simulation schemes and Machine Learning models were used to optimize the fast regulation loop.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Reverse and Forward Engineering of Local Voltage Control in Distribution Networks

The increasing penetration of renewable and distributed energy resources in distribution networks calls for real-time and distributed voltage control. As such, in this paper we investigate local Volt/VAR control with a general class of control functions, and show that the power system dynamics with non-incremental local voltage control can be seen as a distributed algorithm for solving a well-defined optimization problem (reverse engineering). The reverse engineering further reveals a fundamental limitation of the non-incremental voltage control: the convergence condition is restrictive and prevents better voltage regulation at equilibrium. This motivates us to design two incremental local voltage control schemes based on the subgradient and pseudo-gradient algorithms respectively for solving the same optimization problem (forward engineering). The new control schemes decouple the dynamical property from the equilibrium property, and have much less restrictive convergence conditions. This work presents another step towards developing a new foundation—network dynamics as optimization algorithms—for distributed real-time control and optimization of future power networks.

42 ENGINEERING↗
DOE OSTI2021

Application of a machine learning algorithm (XGBoost) to offline RHIC luminosity optimization

The operation parameter optimization in 2020 RHIC low energy run is difficult. First, the RHIC luminosity is affected by many RHIC operation parameters, as well as affected by many Low Energy RHIC electron Cooling (LEReC) operation parameters. Second, the luminosity signal in this run is noisy and not sensitive to these parameter changes, especially when these parameters are very close to their optimized values. It is not easy to distinguish the effects of one parameter from all other operation parameters separately. Therefore, it is difficult to optimize the luminosity by varying these parameters one by one. To find a way for luminosity optimization, we analyze some operation parameters via a machine learning algorithm - XGBoost. After constructing a black-box surrogate model from XGBoost and plotting their partial dependency plots (PDF) and SHAP value plots for different operation parameters, we can find the effects of an individual parameter on the RHIC luminosity and optimize it accordingly.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Degenerate coupled-cluster theory

A size-extensive, converging, black-box, ab initio coupled-cluster (ΔCC) ansatz is introduced that computes the energies and wave functions of states from any degenerate or nondegenerate Slater-determinant references with any numbers of α- and β-spin electrons, any patterns of orbital occupancy, any spin multiplicities, and any spatial symmetries. For a nondegenerate reference, it reduces to the single-reference coupled-cluster ansatz. For a degenerate multireference, it is a natural coupled-cluster extension of degenerate Møller–Plesset perturbation (ΔMP) theory. For ionized and electron-attached references, it is a coupled-cluster Green’s function, although the present theory is convergent toward the full-configuration-interaction limits, while the Feynman–Dyson many-body Green’s function (MBGF) theory generally is not. Its single-excitation instance is a projection Hartree–Fock theory as per the Thouless theorem, which may be useful for core ionizations, high-spin states, and possibly electron affinities. Additionally, a new multireference coupled-cluster theory for a general model space is developed. This quasidegenerate coupled-cluster (QCC) theory is exactly converging, but not black-box, and intended for strong correlation. Determinant-based, general-order algorithms of ΔCC and QCC theories are implemented and compared with configuration-interaction (CI) and equation-of-motion coupled-cluster (EOM-CC) theories through octuple excitations and with ΔMP and MBGF theories up to the nineteenth order. An algebraic, optimal-scaling algorithm of the ΔCC theory is computer-synthesized at the levels of single excitations (ΔCCS) and of single and double excitations (ΔCCSD). As a result, the order of performance is QCC ≈ ΔCC > EOM-CC > CI at the same order or QCC ≈ ΔCC > ΔMP > MBGF at the same cost scaling.

Hirata, So [University of Illinois at Urbana-Champ↗
DOE OSTI2026

Optimized Gear Selection to Maximize Energy Savings in Electric Traction Drives for Medium and Heavy Duty Vehicles

Multi‑gear transmission systems are commonly used in electric traction drives for medium and heavy‑duty vehicles, while most passenger‑vehicle electric drivetrains rely on a single fixed ratio to reduce cost, weight, and complexity. Using multiple gear ratios can enable downsizing of the motor and inverter while still meeting performance requirements. Additionally, appropriately chosen ratios allow the motor to operate more frequently in high‑efficiency regions, improving overall energy usage and reducing operating costs over the drive cycle. This paper presents a systematic approach for selecting optimal gear ratios for electric drive systems. A neural‑network model is first developed to represent motor losses across the full torque–speed range using data generated from finite element analysis. This model enables fast, accurate evaluation of motor efficiency under varying operating conditions. A genetic‑algorithm‑based optimization framework is then applied to identify gear ratios that maximize energy cost savings over the drive cycle, with the resulting optimal ratios stored for real‑time implementation.

Gadiyar, Nishanth [ORNL] (ORCID:0000000348267524)↗