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DOE OSTI2019

Computationally Efficient Learning of Quality Controlled Word Embeddings for Natural Language Processing

Deep learning (DL) has been used for many natural language processing (NLP) tasks due to its superior performance as compared to traditional machine learning approaches. In DL models for NLP, words are represented using word embeddings, which capture both semantic and syntactic information in text. However, 90-95% of the DL trainable parameters are associated with the word embeddings, resulting in a large storage or memory footprint. Therefore, reducing the number of word embedding parameters is critical, especially with the increase of vocabulary size. In this work, we propose a novel approximate word embeddings approach for convolutional neural networks (CNNs) used for text classification tasks. The proposed approach significantly reduces the number of model trainable parameters without noticeably sacrificing in computing performance accuracy. Compared to other techniques, our proposed word embeddings technique does not require modifications to the DL model architecture. We evaluate the performance of the the proposed word embeddings on three classification tasks using two datasets, composed of Yelp and Amazon reviews. The results show that the proposed method can reduce the number of word embeddings parameters by 98% and 99% for the Yelp and Amazon datasets respectively, with no drop in computing accuracy.

Alawad, Mohammed↗

DOE OSTI2017

Use of DAGMan in CRAB3 to improve the splitting of CMS user jobs

CRAB3 is a workload management tool used by CMS physicists to analyze data acquired by the Compact Muon Solenoid (CMS) detector at the CERN Large Hadron Collider (LHC). Research in high energy physics often requires the analysis of large collections of files, referred to as datasets. The task is divided into jobs that are distributed among a large collection of worker nodes throughout the Worldwide LHC Computing Grid (WLCG). Splitting a large analysis task into optimally sized jobs is critical to efficient use of distributed computing resources. Jobs that are too big will have excessive runtimes and will not distribute the work across all of the available nodes. However, splitting the project into a large number of very small jobs is also inefficient, as each job creates additional overhead which increases load on infrastructure resources. Currently this splitting is done manually, using parameters provided by the user. However the resources needed for each job are difficult to predict because of frequent variations in the performance of the user code and the content of the input dataset. As a result, dividing a task into jobs by hand is difficult and often suboptimal. In this work we present a new feature called “automatic splitting” which removes the need for users to manually specify job splitting parameters. We discuss how HTCondor DAGMan can be used to build dynamic Directed Acyclic Graphs (DAGs) to optimize the performance of large CMS analysis jobs on the Grid. We use DAGMan to dynamically generate interconnected DAGs that estimate the processing time the user code will require to analyze each event. This is used to calculate an estimate of the total processing time per job, and a set of analysis jobs are run using this estimate as a specified time limit. Some jobs may not finish within the alloted time; they are terminated at the time limit, and the unfinished data is regrouped into smaller jobs and resubmitted.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Multi-fidelity equations of state and transport coefficient datasets for pulsed-power applications

Reliably simulating experiments relevant to the National Nuclear Security Administration (NNSA) requires a detailed description of material properties across a wide range of conditions. Such properties include the equations of state, charged-particle transport coefficients, and optical properties like the opacity. Together, these properties make up the material models used in radiation-magnetohydrodynamic simulations of nuclear fusion experiments. Many of these models do not incorporate uncertainties in the data used to produce them. It is unknown whether these uncertainties significantly impact the interpretation of simulation results and diagnostics. The purpose of this work is to quantify how such uncertainties impact simulations of pulsed-power experiments. We accomplished this task by first assessing discrepancies between approaches used to generate the data. This included bringing together members of the high-energy-density community spanning the three NNSA laboratories and multiple universities. Then, using these data, we developed a general framework that systematically incorporates physical uncertainties within the material models suitable for uncertainty quantification analyses. The framework utilizes machine learning, Bayesian inference, and incorporates multi-fidelity datasets. We demonstrated the framework by quantifying the impact that material model uncertainties have on simulations of pulsed-power experiments underway on Z at Sandia National Laboratories. As a result of this work, we discovered that modest uncertainties in material models (roughly 20%) correspond to significant uncertainties in the outputs from simulations. Our framework has enabled rapid construction of material models through an automated procedure and allows for the generation of material models of interest to the NNSA.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Bichromatic Wave Selection for Validation of the Difference-Frequency Transfer Function for the OC6 Validation Campaign: Preprint

The focus of the Offshore Code Comparison Collaboration, Continuation, with Correlation and unCertainity (OC6) project, which operates under the International Energy Agency (IEA) Wind Task 30, is to refine the accuracy of engineering tools used to design offshore wind turbines. In support of this work, a new validation campaign is being developed that seeks to better understand the nonlinear wave loading that excites floating wind systems at their low-frequency, rigid-body modes in surge and pitch. The validation data will be used in a three-way validation between simplified engineering tools and higher-fidelity tools, such as computational fluid dynamics (CFD). Irregular wave spectrums, which are traditionally used to examine the nonlinear wave interaction with offshore structures, are too computationally expensive to be simulated in CFD tools, and so bichromatic wave cases will be used instead. This paper reviews the process used to choose the bichromatic wave pairs to be used in the campaign to validate the second-order difference-frequency quadratic and potential loads at the surge and pitch natural frequencies of a floating semisubmersible.

17 WIND ENERGY↗

DOE OSTI2013

Task P: Experimental Particle Physics (Final Report)

The UCSD Elementary Particle Physics group investigates physics at the High Energy Frontier by working exclusively on the CMS experiment at the Large Hadron Collider at CERN. The goal of all four PIs is to discover new facets of the as-yet unwritten laws of nature as a new high-energy regime is opened at the LHC. To that end, we are primarily working on the search for the Higgs boson(s), on the search for supersymmetry, and the search for other new particles and interactions. Further, our goal is to have a very significant intellectual impact on the experiment, as we have in the past. We have played a leading role in the Higgs search in the past and particularly in the observation of a new boson that is consistent with a Higgs-like particle. All four PIs are very involved in the Higgs search. Vivek Sharma was the CMS Higgs co-coordinator during 2010-11. One of our key goals is to measure the properties of this particle as accurately as possible during the upcoming proposal period. The most important modes for this measurement are H 0 → γγ, H 0 → ZZ, and H 0 → WW. We have played leading roles in the measurement of these channels up to now and our goal is to continue work in these channels, measuring the production modes of the “Higgs”, the spin and parity, and the decay branching fractions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Well-Controlled Hydro-thermo-chemical Stimulation of a Coso Geothermal Well to Enhance Productivity

The goal of this project was to demonstrate controlled fracturing and permeability increase to enhance resource production and power generation at Coso. It was planned to stimulate at least two intervals in the Well 73-18RD with zonal isolation using advanced high-temperature packer technology. The anticipation was that a hydraulic fracture would form in one zone and in the second zone natural fractures would reopen, shear and extend, thereby increasing fluid communication in the field. In both cases, the intent was to carefully design the stimulation to take advantage of the relative roles of hydraulic pressure, thermal stress and chemical (“hydro-thermo-chemical”) dissolution. The project had planned to use the Welltec Annular Barrier (WAB) system developed as part of another DOE-funded project which was on schedule to be ready for field deployment. If successful, the Coso Operating Company would incorporate this approach into their ongoing well maintenance in other wells increasing the total MWs produced. The project had multiple tasks and the objective of Task 1 was to develop a workover plan, to build a geologic model, to design a stimulation program, and to work with the operating and services companies to develop a detailed budget and obtain the necessary permits and sundry notices. In Phase 2 the workover plan would be implemented, the designed stimulation would be carried out, and subsequently the well would logged and tested to measure the impact of stimulation. Per sponsor request, a Pre-Task 1 activity with a Go/No Go decision point was scheduled in Task 1.

02 PETROLEUM↗

DOE OSTI2013

High performance graphics processor based computed tomography reconstruction algorithms for nuclear and other large scale applications.

The goal of this work is to develop a fast computed tomography (CT) reconstruction algorithm based on graphics processing units (GPU) that achieves significant improvement over traditional central processing unit (CPU) based implementations. The main challenge in developing a CT algorithm that is capable of handling very large datasets is parallelizing the algorithm in such a way that data transfer does not hinder performance of the reconstruction algorithm. General Purpose Graphics Processing (GPGPU) is a new technology that the Science and Technology (S&T) community is starting to adopt in many fields where CPU-based computing is the norm. GPGPU programming requires a new approach to algorithm development that utilizes massively multi-threaded environments. Multi-threaded algorithms in general are difficult to optimize since performance bottlenecks occur that are non-existent in single-threaded algorithms such as memory latencies. If an efficient GPU-based CT reconstruction algorithm can be developed; computational times could be improved by a factor of 20. Additionally, cost benefits will be realized as commodity graphics hardware could potentially replace expensive supercomputers and high-end workstations. This project will take advantage of the CUDA programming environment and attempt to parallelize the task in such a way that multiple slices of the reconstruction volume are computed simultaneously. This work will also take advantage of the GPU memory by utilizing asynchronous memory transfers, GPU texture memory, and (when possible) pinned host memory so that the memory transfer bottleneck inherent to GPGPU is amortized. Additionally, this work will take advantage of GPU-specific hardware (i.e. fast texture memory, pixel-pipelines, hardware interpolators, and varying memory hierarchy) that will allow for additional performance improvements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Distributive Distillation Enabled by Microchannel Process Technology

The application of microchannel technology for distributive distillation was studied to achieve the Grand Challenge goals of 25% energy savings and 10% return on investment. In Task 1, a detailed study was conducted and two distillation systems were identified that would meet the Grand Challenge goals if the microchannel distillation technology was used. Material and heat balance calculations were performed to develop process flow sheet designs for the two distillation systems in Task 2. The process designs were focused on two methods of integrating the microchannel technology 1) Integrating microchannel distillation to an existing conventional column, 2) Microchannel distillation for new plants. A design concept for a modular microchannel distillation unit was developed in Task 3. In Task 4, Ultrasonic Additive Machining (UAM) was evaluated as a manufacturing method for microchannel distillation units. However, it was found that a significant development work would be required to develop process parameters to use UAM for commercial distillation manufacturing. Two alternate manufacturing methods were explored. Both manufacturing approaches were experimentally tested to confirm their validity. The conceptual design of the microchannel distillation unit (Task 3) was combined with the manufacturing methods developed in Task 4 and flowsheet designs in Task 2 to estimate the cost of the microchannel distillation unit and this was compared to a conventional distillation column. The best results were for a methanol-water separation unit for the use in a biodiesel facility. For this application microchannel distillation was found to be more cost effective than conventional system and capable of meeting the DOE Grand Challenge performance requirements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

Budget Period 2 Summary Report Part 2: Hywind Maine Project

This project was performed under the Work for Others—Funds in Agreement FIA-14-1793 between Statoil and the Alliance for Sustainable Energy, manager and operator of the National Renewable Energy Laboratory (NREL). To support the development of a 6-MW spar-mounted offshore wind turbine, Statoil funded NREL to perform tasks in the following three categories: 1. Design and analysis 2. Wake modeling 3. Concept resource assessment. This study expands upon the work conducted in Budget Period 1 (BP1) to investigate the influence of the wake generated from an upstream turbine on a downstream turbine using Computational Fluid Dynamics (CFD) high-fidelity modeling tool. Simulator fOr Wind Farms Application (SOWFA) is an NREL high fidelity modeling tool that couples OpenFOAM CFD and NREL’s Aero-Elastic code Fatigue, Aerodynamics, Structures, and Turbulence (FAST). In BP1 the configuration was based on Hywind-3MW at 140 m water depth in the Gulf of Maine; however this study for Budget Period 2 (BP2) the configuration investigated is based on Hywind-6MW at 220 m water depth off the coast of Boston. The objectives were to perform two-turbines One-Way Coupling (OWC), three-turbines Two-Way Coupling (TWC), and to investigate wind power plant optimization.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Image feature extraction and galaxy classification: a novel and efficient approach with automated machine learning

ABSTRACT In this work, we explore the possibility of applying machine learning methods designed for 1D problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly steps, such as the point spread function deconvolution and the training and application of complex Convolutional Neural Networks of thousands or even millions of parameters. In our approach, we extract features from the galaxy images by analysing the elliptical isophotes in their light distribution and collect the information in a sequence. The sequences obtained with this method present definite features allowing a direct distinction between galaxy types. Then, we train and classify the sequences with machine learning algorithms, designed through the platform Modulos AutoML. As a demonstration of this method, we use the second public release of the Dark Energy Survey (DES DR2). We show that we are able to successfully distinguish between early-type and late-type galaxies, for images with signal-to-noise ratio greater than 300. This yields an accuracy of $86{{\ \rm per\ cent}}$ for the early-type galaxies and $93{{\ \rm per\ cent}}$ for the late-type galaxies, which is on par with most contemporary automated image classification approaches. The data dimensionality reduction of our novel method implies a significant lowering in computational cost of classification. In the perspective of future data sets obtained with e.g. Euclid and the Vera Rubin Observatory, this work represents a path towards using a well-tested and widely used platform from industry in efficiently tackling galaxy classification problems at the peta-byte scale.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Fire compartment partitioning approaches for fire probabilistic safety analysis of a high-temperature gas-cooled reactor - 18545

Fire compartment partitioning is the foundational task in Fire Probabilistic Safety Analysis (FPSA), that requires approaches able to balance analytical accuracy and computational burden. In this work, two fire compartment partitioning approaches are illustrated and applied to an electric building of a High-Temperature Gas-Cooled Reactor (HTGR, e.g., HTR-PM), and the quantified risks of the compartments obtained with the two approaches are compared. The results show that risk is insensitive to the utilized fire compartment partitioning approach, making the FPSA tasks easier to be performed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

ChatPORT: Fine-Tuned LLM for Easy Code {PORT}ing

Fine-tuning existing LLMs for specialized tasks has become a very attractive alternative due to its low cost and quick development cycle. With many pre-trained LLMs available, it is an increasingly complex task to choose the correct model as the starting point or base model. In this work we discuss ChatPORT - a specialized fine-tuned LLM geared towards providing correctly translated codes from one programming model to another. We evaluate a number of base models and compare and contrast their features and characteristics that make them a viable starting point. In this paper, we focus on the OpenMP offload porting capabilities of ChatPORT. We build our training data using kernels from the Heterogeneous Computing Benchmarks (HeCBench) [12] and the OpenMP Validation and Verification suite [5] to fine-tune the base models. We then test the model using unseen kernels extracted from the HeCBench benchmark suite. Our results show that: (1) not all open LLMs geared towards HPC are aware of programming models like OpenMP, (2) although all base models benefit from fine-tuning they learn differently and produce different correctness rates, (3) depending on the memory size and compute resource available, different base models can be used for fine-tuning without significantly affecting the quality of transpiled code they generate, (4) fine-tuning improved the correctness rate of the LLM by an average of 43.2%, and (5) feedback-based training data further increased the correctness rate by an average of 6% over the LLMs tested.

Pophale, Swaroop [ORNL] (ORCID:0000000185446367)↗

DOE OSTI2024

Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm

Designing polymer membranes with high gas permeability and selectivity is a difficult multi-task constrained problem due to the trade-off between these two properties. In this work, we present a machine learning (ML) driven genetic algorithm to tackle the design problem of polymer membranes for CO 2 separation from N 2 and O 2 . Using literature data of permeability for three gases, we constructed multiple ML models with different fingerprinting featurization schemes to predict gas permeabilities. Then, we employed a genetic algorithm to design new polymers and evaluated their performance using our ML models. We were able to identify new polymer membranes that are promising for both CO 2 /N 2 and CO 2 /O 2 separations. Further, the top discovered polymers are predicted to have high glass transition temperatures. Similarly, the pyridine functionality was found in ≈20% of the predicted polymers. This framework can be used to design polymers for any application involving constrained optimization. Finally, we outlined the challenges and opportunities with using ML guided data-driven inverse design of polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Review on robot-assisted polishing: Status and future trends

Nowadays, precision components have been extensively used in various industries such as biomedicine, photonics, and optics. To achieve nanometric surface roughness, high-precision polishing is mandatory. Compared with the conventional polishing techniques, the robot-assisted polishing has the advantages of flexible working ranges and low cost. Therefore, it has been more widely deployed in demanding polishing tasks. However, few review articles were found on this topic to provide a general progress on the development of the robot-assisted polishing technology. Here in this paper reviews the recent advancement of the robot-assisted polishing. Firstly, the integration of robots with various polishing techniques is overviewed, followed by the introduction of the research status of constant force control in robot-assisted polishing. Then, the errors in typical robot-assisted polishing systems are analyzed, together with the corresponding compensation methods. At last, the future trends in the development of robot-assisted polishing are discussed. This review is intended as a roadmap for researchers or engineers who are interested in robot-assisted polishing or machining.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Physical control oriented model of large scale refrigerators to synthesize advanced control schemes. Design, validation, and first control results

In this paper, a physical method to obtain control-oriented dynamical models of large scale cryogenic refrigerators is proposed, in order to synthesize model-based advanced control schemes. These schemes aim to replace classical user experience designed approaches usually based on many independent PI controllers. This is particularly useful in the case where cryoplants are submitted to large pulsed thermal loads, expected to take place in the cryogenic cooling systems of future fusion reactors such as the International Thermonuclear Experimental Reactor (ITER) or the Japan Torus-60 Super Advanced Fusion Experiment (JT-60SA). Advanced control schemes lead to a better perturbation immunity and rejection, to offer a safer utilization of cryoplants. The paper gives details on how basic components used in the field of large scale helium refrigeration (especially those present on the 400W @1.8K helium test facility at CEA-Grenoble) are modeled and assembled to obtain the complete dynamic description of controllable subsystems of the refrigerator (controllable subsystems are namely the Joule-Thompson Cycle, the Brayton Cycle, the Liquid Nitrogen Precooling Unit and the Warm Compression Station). The complete 400W @1.8K (in the 400W @4.4K configuration) helium test facility model is then validated against experimental data and the optimal control of both the Joule-Thompson valve and the turbine valve is proposed, to stabilize the plant under highly variable thermals loads. This work is partially supported through the European Fusion Development Agreement (EFDA) Goal Oriented Training Program, task agreement WP10-GOT-GIRO.

42 ENGINEERING↗

DOE OSTI2014

Model based multivariable controller for large scale compression stations. Design and experimental validation on the LHC 18KW cryorefrigerator

In this paper, a multivariable model-based non-linear controller for Warm Compression Stations (WCS) is proposed. The strategy is to replace all the PID loops controlling the WCS with an optimally designed model-based multivariable loop. This new strategy leads to high stability and fast disturbance rejection such as those induced by a turbine or a compressor stop, a key-aspect in the case of large scale cryogenic refrigeration. The proposed control scheme can be used to have precise control of every pressure in normal operation or to stabilize and control the cryoplant under high variation of thermal loads (such as a pulsed heat load expected to take place in future fusion reactors such as those expected in the cryogenic cooling systems of the International Thermonuclear Experimental Reactor ITER or the Japan Torus-60 Super Advanced fusion experiment JT-60SA). The paper details how to set the WCS model up to synthesize the Linear Quadratic Optimal feedback gain and how to use it. After preliminary tuning at CEA-Grenoble on the 400W@1.8K helium test facility, the controller has been implemented on a Schneider PLC and fully tested first on the CERN's real-time simulator. Then, it was experimentally validated on a real CERN cryoplant. The efficiency of the solution is experimentally assessed using a reasonable operating scenario of start and stop of compressors and cryogenic turbines. This work is partially supported through the European Fusion Development Agreement (EFDA) Goal Oriented Training Program, task agreement WP10-GOT-GIRO.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery

Deep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionally, performing these tasks simultaneously in 3D has proven to be computationally expensive. Currently available methodologies thus largely work to segment 3D objects from 2D images (without context from captured 3D volumes). In this work, we present a novel approach to perform fast and accurate localization, detection, and segmentation of volumes containing cells. Specifically, in our method, we modify and tune two state-of-the-art CNNs, namely 2D YOLOv2 and 3D U-Net, and combine them with a new fusion and image processing algorithms. Annotated volumes in this space are limited, and we have created synthetic data that mimics actual structures for training and testing our proposed approach. Promising results on this test data demonstrate the value of the technique and offers a methodology for 3D cell analysis in real microscopy imagery.

Ziabari, Amir↗

DOE OSTI2022

Tackling the Challenges in Scene Graph Generation With Local-to-Global Interactions

In this work, we seek new insights into the underlying challenges of the scene graph generation (SGG) task. Quantitative and qualitative analysis of the visual genome (VG) dataset implies: 1) ambiguity: even if interobject relationship contains the same object (or predicate), they may not be visually or semantically similar; 2) asymmetry: despite the nature of the relationship that embodied the direction, it was not well addressed in previous studies; and 3) higher-order contexts: leveraging the identities of certain graph elements can help generate accurate scene graphs. Motivated by the analysis, we design a novel SGG framework, Local-to-global interaction networks (LOGINs). Locally, interactions extract the essence between three instances of subject, object, and background, while baking direction awareness into the network by explicitly constraining the input order of subject and object. Globally, interactions encode the contexts between every graph component (i.e., nodes and edges). Finally, Attract and Repel loss is utilized to fine-tune the distribution of predicate embeddings. By design, our framework enables predicting the scene graph in a bottom-up manner, leveraging the possible complementariness. To quantify how much LOGIN is aware of relational direction, a new diagnostic task called Bidirectional Relationship Classification (BRC) is also proposed. Overall, experimental results demonstrate that LOGIN can successfully distinguish relational direction than existing methods (in BRC task), while showing state-of-the-art results on the VG benchmark (in SGG task).

97 MATHEMATICS AND COMPUTING↗